tag:blogger.com,1999:blog-4973932623100581112024-02-18T21:30:14.790-08:00Nemagraptus gracilisA Blog on Paleontology, Phylogeny and Graptolitesdwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.comBlogger30125tag:blogger.com,1999:blog-497393262310058111.post-56869299171764542472016-05-27T15:00:00.000-07:002016-05-27T15:00:02.507-07:00A New (Or Maybe Not?) Metric for Measuring How Much Two Topologies Contradict Each Other<!DOCTYPE html>
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D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlKNcrJfmvyKlbaqps0Oa%2FBSPXk4FJjtWF2wOHfpFY63qA49US3T180HXmbqEjYsSpRFkkOVMCBH1C9JAJTRgjCWNAjl2MSA%2BvZjADldAJGTIoWBOcOEUI4qwvBSI4hMj8m7WHoFosA0M4Q4yS8XtN%2B5emVGJDGqX8D5SVbE%2F0bWETJRyRFYKQ8umFTkY2ZnV9Lz%2BVW9hgHq%2BHYhsrghzcANy%2B02yEIiJuDFngZnEHSFnTnqDAicaKOgtLZkRXBqGS3bgcqBK6B%2BmV5G4%2BxzA1IlYTTtL30wAPCkFZuhlgsViFEnA5Rg4UXmBbXBAW7JuNvZlXTVsYDMV305DdUPX6QyUFMwndSexwLkpBDsIQ4lUnD9QoEF8QmpcQk8FJckIIkVwvETqBSrIvHRim18B7iqFwNCcFoUKmSj9cSfX37WL%2BCT4mAKH%2BS76AYlpBE3eYErTSL68qrzEKiOZo1mbN1ujQapDrmTWb1s3iBTeQMCYcYfVeS5ks0rTlCPWr9AjV%2FbgZ8To6s7qOr3%2BkRUCKT5GAAUzFlHOkmV3fJEImmhCZ4S5ivYOcC3SUx70Is%2BgfKTQfQBsES9NEX4x0WTcQkWV5JKEJNl0UV8pDmBNnC%2F4qRYIkjLTUknCCTbfLBLJzy4rnSGTntJHjdpYIWlqzxK%2BG%2BRBy22IJ8gMQIibkjgh8Y9Q2H5aenHLwEJg2iDmhQybR8G3gfKxBAZwppQkDi9rFMfLSe8Fgalu2z9mKNwe%2BF24HWYNjNI3zoQfXsIck6YiAwCMhAsEDMvxfp9PxAAxF94h42eFp8V8BZxS3g%2FoQhPG05yIyOc5GxA27VgPq%2FN9uxGcdZCP9PAWYlmmiIlMHE%2FRj3IzE8YKgGEh8OmOkN4KuO6GYyNnRhoWNSgmY9%2BVfMfhBm7xIwZPp7ggg%3D%3D%3Bsrc%3Adata%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cb08AABFOAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAjPL%2FpQAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAAxADsAUABTACAAMAAwADEALgAwADAAMQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAHa4ADV2ADV8AC1cEs3pishg2FfJaEtxSn94IlU6ciwvljRcm9wVDgxsadLb0%2FGIypoPBp1Fx0xGTbQdxoAU51YoZ9RXGB0bXNPyK3JLMApRwa%2FIMy1PPoJDx39kimekZX1c%2BDSW41tEZBuFiwdwx1dRoPVA2vWPSlsSDqhNkqYfhrqlVUGD0J3HEAgZavmtLnDC5WBriSpD8Uk02KsUkJ9vCFz2CZXAd5viwGZ2xcVYRPa1bEIai51nMYlbL2ERuB2TCzLAbPWWRZ3%2FsZ%2FKBjLk8%2FgAZzK1OaxNw4oGBbsNhx6Reg5HRFVCrwa15kGmJEy5kX1YypVm%2FHo7TjKP3l%2B%2FnuCTOyiOa6S8QEJbuiGYNlCnM7tHChCHRRQHXQh7yPXASlcvc5KrNKol6orb35kbo%2BiEwl8d230cfWPwTy00bFDRYURYGchbwsoDcaR2AJsFGCrbWCzBdQ0qCobWwLfueqAzSkzaHX3yCjDlGYPV0VZqVXlqbr4poRaG5NNPMDv5MCHkLSFyABMRBhMiGSZiDgABYmwsLWDjUZCUnHwvXt0VAUy4%2FqjwVgjfUqp1evzUqutJB7DW99Cq5aEhGePsa4omKUp7LnSQUz%2BZq51pU72ApMxkaVZE9jF3IGKVnF0GlK1E6iqLJXFzy1BEv6Kr0ngCzIgVANRwCkVHYJj86Sv2dzqgVrjYKlg8W94FbPGQghEbTakZ4AoS9h0u39DOoRJTA2iirs9xa5m9Ir0KwkogoqFNVDbTIGO0%2FWuJYfp61Kq0bhgbcbnvbTDj4HBESsXMdvrvpp7Owf%2FS2EbndoEoglO%2FjigGAuLUTUmG07ZGlRCZ4QZLu81tPEgTtAvF99BxfnxNslgVYH5Jc4AfHwsiktDBO4wjgjizFMsIeBTGmIYrhtrK3tvtBu5tYB0PDMXILUkFYPM%2B5yLLGOU9DtoDLYWLQig%2B8wNRZbJZZWeK5JyKNwumLgP7X6pqgiZMFUvfCTl6Urx1gUOjzhcICumUUsiNESZCmTANkgHoh%2BKU0t1fgQYOBkNfp4MRiJysezS1Vz%2BahE7ohM0iJBn4I%2BT9RJN%2FuQk6SjQGwC%2Bom%2FSD7Zq2RxvcSjtdxHfo7NtktA46mEg5gBkTtMKu7oFb1KgqwqBLzQqZI%2Fs4QSRhYF3ani1URTcMD38kDDqlc3DChmYJTCR9WpaWrYFiXeNi7jYa1Oe%2BSP30gxVyeC10DYOakGpWaU1qkQ9OXlujEbM4A6A%2BQ243TA2QnI8mKzNXNo1HPN1CFawST6uC9dkyQTqq5bOKkkuGjz0nkj35Sr%2BQ%2FkLGg2iQMrTKImSGshnX7%2F0Jyh0JziDbTzxp3IrbosXgpchLcBJpLLUBrFbt%2FU6qXqAaNFJoeBItBoqgQlPZkpTq31s5VZsgIkFupAno5G9oKwCGRqv5294xFqOrEpewcjx%2Be06mxDkN85c0ckmWmMzbkFX9YHAf83p0EsnBywjZbcPxZw91kqg7voxasp3VfDhEDAufUx0MTddLO5kCyKkUBwOm%2FMurQH4bJQgK%2Bp14dkBBhgxpBUkTlSbTE8DDu9qKiDSCtvmPY7FRMn0wuS6XR7J%2BnpjjeaDNwiOgIKUWCP9aLb%2BXpc4toQCryp6gjCVq1Cl1Di%2BtEx4WnBp2hmGZpEksZwTSggXuqAWD4B7WAMcir2wmA%2BI43ECxUSeTDT%2BxgR3O4XFPOGxHh09lxpScampLahUbICao0db7%2F%2F%2B4CBMcCokQ7aOQ02MOzh0NEaJGGOvPR1t4sY1I55lNfQ3IkF04onnAomXxiNg2EbBbPTMBBlv%2BggPSmkb4GNFW6Ehd6nDzJlnIXFfzr1bVWQlfylDodFBbhCDf%2FVB13j441dc2rx3h4hfAWIJuZqbH%2Fwbuafg%2FnTQJ9bnMfR3U6TwYgDQJIOPOtmZW4ZHsR2VifZGaC6Pzv4ZzC2nSAaqSe09F9MxuI4UmUVA2RAUqBUIETEIAhB%2BtBhsxhVMXUmv3RPe1grhYWLM%2BPHwwO5W069qIgDXtETXZQ3741DSk%2BvO6y8k%2F7Mg6GFC5fSK02yXZ1XV0NZe8iknoIbSFnDm%2BbMP%2BBCQhoLKOeU3FVNQ%2BgPEntwkHT950cQYEYQc%2FXuL4nxwcRW2bIN%2ByskPaD5Rz6lN8LW5IDLYfuvhxlLcjWXSADeTshWsJk%2FHCuRbwjqLcx66fc6UlF33HipliQ1cvfKlgRpVcsmIsiYnJjzJIovUyqi%2F2OTQcIwt%2B3WW44BAQh%2F2GymOIscSpIt0YUePiwOCJN%2BQ60lqkSRbWi10lotzgWZsqzEI7OGFGeIoFyqli41%2FNn7LfLQ0qtxkaAwbsKZQ0D9s%2FwhBqoxhAlxCVNVF%2FJ6rsUBQ49MLtR3ICtKPLKZ%2FQPEeIRgWUNLbNR3v3tTaL8uiXO%2BOj3tSJc56KJHol9nGPuclbrQpCn4keT05ETSOQ%2FDwwKjCRLgIzBi7x%2BU7ByTZGUWcGLPY0VtXWC7kQ38C5thIGJx%2FxLM3KsoGyRwC5AFUNWPzU0QBCaVXo6z1NCclLuPVA8%2FwWuNhiAG9CPPSSlIOCuh2AQM1d6MyIEIQArIa9YenQ5ug37h9gPNDHVLImcjE91ewI7pz7HgOEVqTD%2FjREYRtEoDIfgxzDePkVvHWJAtcpCJ%2Bg2o4akZpphRUQ0U92lJcKZGJI9mnpl8UVzpMi8SBGXUAPpgcS2cDAusSE6q0hNibhCgZHLReLsA%2BSv8wF6LiDL3TogB1DsO%2FHeMg0ETucRO2hIOvbp1NVkFMclluNmBQbTy%2Bosr2bJ7oWdUtSHi1s%2B2vWxq8njY1eW4BCVr2EqSyIUuFKS9iwVF5FBGeg0hRcHwCKMtqYLBfvVSYXHdGZK3Ug95C2d%2B3t9ZWEYqq6F4TocgcDS4tI8cvEEXK1%2ByB9RofdKpHoA3eP9%2FB%2FEBG%2BIgvmwZ4Iir1SOYFEtUG%2FoVkECQqoPTdHoNpQKpA9K9k7TisAUBsKPtWgBriyeOxs1o%2B4KCIFumN744dH3Yf7X8F426LGtEhFLULhlKFgXF8Wq2D6kFSBkViLad9D9U%2FP6A%2Fr8SE7jsr9s%2Bsb5ODWI5uS6BkZJI5RQ5QDVZ5%2FKOG6Dzc1RLQKAdGkgwLiZLQdEYQ9yhQLtD5kWr4wGHTv4GqloWePyHYyuy3Ek3iQVQkdkAz0MxcsHzccdMLO8KFh7ywo4jXywraIhIicAI4dWdD5wEXBhsMklzbWc8zi8opC3IlPsLXHCWOqOgq4iPU6xyDS2tdSLnlhSFjsCCWvMRt5TAI30EHR4f16NYyzoG%2FoKgCUsW4182Wvj5gpb0bGGLe8YRIYRGVgF2hwIlEfm7eUwwLZWW%2BmmBZPggPnIKI0b6G%2BlHCncx0wkaVPRQpZKWIIrzh4yC4Ar2vTKzhVKSutNi1dSzmM73AGUXSBJrQH5J4Dfb%2BRU25tYrIVdLr4ogiwTE2i2hl39ffJWHNTSWq3PvVJplNqDTJgJcvCHYoQAuHeOew0gZGfmX3p4D0aSr1BhXbXRA1qipfS6LiggtiT1eDg4FTCWwCdAFC9FCOZ52EdrFetMBxUdC0dfnqpr0mELJE2m5FVMvwplyf0XIKclHa6%2FQrw%2FFOVkVKK71Ab5iAVWm4GA3HnkCJhYkW06XJ3TDGB9YhVW9RuC39AFUcFWwU5eTNzuck7IQty7ZC9IjAGNHuZhQ8yDWZhGUoNyhDcKYQiUDqAC782makzJ1o3XJFIEDXgObv%2BTlS%2FdAr5rn2C%2FCDmkI0WO3cSTJwSLrS5dBdkyfpxAu4XghB3jgGShG8UCorOibUp7NciZgEDLmT6eOxDegpLRk7HomganEaTmRZAAMrhobApDjFWtOxht%2BNQngtKVgGL2yy6Qnsmju6DAg4d%2FRvxHa5mBaEMcBekCMtwaPPGeKFPMsgAJTidusMLUbTAUBJMOmXRwhONkoLPqBhpTuLSYN%2Bf8aeX1NKSxF2OUST9ezObkHETTsr04iWClpoFDE8OdIiDPQJqkHhnUPTcE43OsKBGS%2Fzk6F57sqVmhZKjPxle2S%2BiIE%2BMXOiTOoRKF0Q7mbNRYGPZDp5eIdPOZHboa4CTvwnlZb0zCOW6kZJh6klDDvYghmQ2FCDKB5DD%2B0nImiNcfSzaGT8aSPzEZjMQ2GPpA1Ws4Xeysyv0zSSMVVsiOoiowo2SfYFdapUBsWIoRXMAKYdguyVcwqX%2BzVWYLiNddi8p%2BxMV4dQIoUs6SQ4C1%2FBjtBw0ocOzLmMkEv5D8N4%2FZu4gYA4ryDb5Y3gkIxdb37qMCWYmDGgsyEE00y3BZ4FgyT%2FdJLHxMQwx0gViQjWL0QyBfcufCgRYYGiCHrxez874MrcqldLCEQLkCCK6pWCnULCh%2BVK6FW%2B1nm%2BN668e16QTKWprvRw3mkwitjPefHt5QKrVumkXsvsVrHdAJrodfWckxhhzknXZ8cMVIvKphJVfhvWbhoZbbgo3fygOr8PlKcVEYLTmUbjp8rTFlPRtPrdA8tljZ0REfnhwApCZ65Igtn1gBROJdPCCb9ZIqlQUq3W89ZjSgSnUz8nEBW03JCcQ%2FSxouQtYBE7ichYHbASPB5J1%2FzVfPINn38CqTMws1KKo%2FyvNXpDgBvCI08SBLSwX5Szp6WlpzoPEio8DsNzZQqgUMoq0%2BNMALrygrOlErJohXvIJ%2F0IqziZok6OuApG2eoGAEFsTc6yWf9eRcklYZRO3PkA%2BW8aN5ME8aLNC3Kdwtm64WyqytOEW1WQIg6bOFHAs1ovo3OfCvvZASxBcxg2SRCRTTJDBKajFH0LL1kSBroe6L9xvVxMCIFS7Zo6y4ZOEIkAI9QtGVIKKBlCvUUEq2S8FiR7VkJGbvBFaS6UzQV%2BayV6NKw62tbJbEjkJ7AMC17DK399DZr%2FtE5sUtaAPwaHNj9VCoVB4VAG0iNI9IjDo0mr97FWjYeDRSr3pstZTVZ6nj5Vp3IbssG3g0r9E2eSmHY97LYobIHPnEGkKDJP15WPWtwMgS8HeLd6NEnP4mIFlRJB4zLeDeoBElpUNRIQMzLbRQsGLj3DZhQytDQPLAUE9JWHiLKAJxVTOJNZsObEiutLqwox%2BM51t7vJQhBeQm2i5bTa4D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlK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<h4 class="author"><em>David W. Bapst</em></h4>
<h4 class="date"><em>May 27, 2016</em></h4>
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<br>
<p>Recently, I’ve been working on finishing up a study where my collaborators and I did more than 15 different phylogenetic analyses: using different character data, different sets of taxa, different methods (parsimony versus Bayesian), different settings (different priors in Bayesian analyses), etc. Many of them produced somewhat less-than-well-resolved results. This makes comparing and contrasting all these topologies a real headache. Nevermind the additional need to also know how the topologies differed from previous analyses in this group!</p>
<p>What I needed was a metric that captured how much two trees disagreed in the relationships inferred (for taxa shared between the two analyses). For example, let’s simulate some trees with <code>ape</code>.</p>
<pre class="r"><code># let's simulate two trees
library(ape)
set.seed(1)
treeA<-rtree(30,br=NULL)
treeB<-rtree(30,br=NULL)</code></pre>
<p>We can visually compare these trees with <code>cophylo</code> in package <code>phytools</code>.</p>
<pre class="r"><code>library(phytools)
plot(cophylo(treeA,treeB))</code></pre>
<pre><code>## Rotating nodes to optimize matching...
## Done.</code></pre>
<p><img 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" title alt width="672" /></p>
<p>A mess, as expected. But how different are they? Obviously, my first thought was the symmmetric Robinson-Foulds distance, or some other similar topological distance, as is available in package <code>phangorn</code> in function <code>treedist</code>.</p>
<pre class="r"><code>library(phangorn)</code></pre>
<pre class="r"><code>treedist(treeA,treeB)</code></pre>
<pre><code>## symmetric.difference path.difference
## 54.00000 69.77822</code></pre>
<p>In this case, the <code>symmetric.distance</code> is the symmetric RF distance. However, this doesn’t actually capture what I want to capture: how much do two topologies conflict? RF will say two trees differ even if one tree is just the other tree with less resolution (i.e., more polytomies). What about a metric that just captures the disagreements, the incongruences between two topologies?</p>
<p>To take an extreme example, we’d want the difference between a star tree (a topology with no splits) and a well-resolved topology of the same taxa to be zero. For example…</p>
<pre class="r"><code># simulate a star tree
treeC<-stree(30)
# plot the tanglegram between A and C
plot(cophylo(treeA,treeC))</code></pre>
<pre><code>## Rotating nodes to optimize matching...
## Done.</code></pre>
<p><img 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" title alt width="672" /></p>
<p>(This is an utter mess because <code>phytools</code>’s <code>cophylo</code> doesn’t rotate lineages within polytomies; oh well.)</p>
<p>But as we can test, this isn’t the case with Robinson-Foulds distance.</p>
<pre class="r"><code>treedist(treeA,treeC)</code></pre>
<pre><code>## Warning in treedist(treeA, treeC): Trees are not binary!</code></pre>
<pre><code>## symmetric.difference path.difference
## 27.000 146.806</code></pre>
<p>Notice that, as implied by that warning, that topological distances aren’t even stable when non-binary trees are assessed. Regardless, the distance between the two trees is essentially relative to the number of nodes missing from the star tree. This is problematic: I want to know if somewhere in the 17+ by 17+ table of pair-wise topological distances we’ll end up building whether two tree actively disagree. If one tree agrees with a tree but just has lower resolution, well, I don’t care. There’s no actual contradicting relationships being inferred.</p>
<p>The solution is to invent my own metric for topological comparison, calculated as the total number of conflicting splits across the two topologies, after dropping any tip taxa not shared between the two trees. By dividing this number by the <span class="math inline">\(2 * (Number\ of\ shared\ tips - 2)\)</span>, the maximum number of splits that could conflict between two given topologies of the same size (remember, we dropped all the unshared taxa), we can scale this metric to between 0 (no conflicting relationships) and 1 (two entirely conflicting topologies), similar to the rescaling in Colless’s consensus fork index.</p>
<p>Algorithmically, we can identify those conflicting splits by counting the number of splits (via <code>ape</code>’s <code>prop.part</code>) on one tree that disagree with at least one split on the other tree: for example, split (AB)CD would be contradicted by split (AC)BD. To put it another way, all we need to test for is whether the taxa segregated by that split were found to be more closely related to some other taxa, not so segregated by the considered split. Here’s the code for calculating this metric, essentially a function for testing whether two splits contradict each other, another function which uses the first to count the number of contradictions across the splits from two trees, and a third which takes these counts of contradictions and scales them:</p>
<pre class="r"><code>testContradiction<-function(namesA,namesB){
matchA<-namesA %in% namesB
matchB<-namesB %in% namesA
if(any(matchB)){
res<-!(all(matchA) | all(matchB))
}else{
res<-FALSE
}
return(res)
}</code></pre>
<pre class="r"><code>nContradiction<-function(partA,partB){
partContra<-sapply(partA,function(x)
any(sapply(partB,function(y)
testContradiction(x,y))))
res<-sum(partContra)
return(res)
}
treeContradiction<-function(tree1,tree2,rescale=TRUE){
# checks
if(!inherits(tree1, "phylo")){
stop("tree1 is not of class phylo")
}
if(!inherits(tree2, "phylo")){
stop("tree2 is not of class phylo")
}
#
tree1<-drop.tip(tree1,setdiff(tree1$tip.label,tree2$tip.label))
tree2<-drop.tip(tree2,setdiff(tree2$tip.label,tree1$tip.label))
#
# more checks
if(Ntip(tree1)!=Ntip(tree2)){
stop("Trees do not contain same number of tips after pruning to tips with identical labels (?!)")}
if(Ntip(tree1)<2 | Ntip(tree2)<2){
stop("Trees contain less than one tip after pruning")}
#
# now measure number of contraditioncs
nUnshared<-sum(1==attr(prop.part(tree1,tree2),"number"))
part1<-lapply(prop.part(tree1),function(x) tree1$tip.label[x])
part2<-lapply(prop.part(tree2),function(x) tree2$tip.label[x])
nContra1<-nContradiction(part1,part2)
nContra2<-nContradiction(part2,part1)
res<-nContra1+nContra2
#
# rescale to 0-1 scale?
if(rescale){
#number of possible nodes that could contradict on one unrooted tree
nPossNodes<-Ntip(tree1)-2 # per tree
res<-res/(2*nPossNodes) # per two trees
}
return(res)
}</code></pre>
<p>Now, this ‘contradiction distance’ metric has some unusual but fully-intended mathematical properties; for example, this metric is non-Euclidean, as a number of very different well-resolved trees would be 0 distance from a star tree of the same taxa (but would have non-zero distances between each other). However, as we are using this metric solely as an indicator of the extent of disagreement, such properties are of little concern. If we were using this metric as the basis for an ordination, god-help-us. Instead, this allows us to quantify the difference of whether a poorly resolved tree only slightly or greatly contradicts a well-resolved tree.</p>
<p>We can see this when we try out the contradiction distances between our three trees from before; remember, C is a star tree.</p>
<pre class="r"><code>treeContradiction(treeA,treeB) # should be non-zero distance</code></pre>
<pre><code>## [1] 1</code></pre>
<pre class="r"><code>treeContradiction(treeA,treeC) # should be zero distance</code></pre>
<pre><code>## [1] 0</code></pre>
<pre class="r"><code>treeContradiction(treeB,treeC) # should be zero distance</code></pre>
<pre><code>## [1] 0</code></pre>
<p>And between two identical trees?</p>
<pre class="r"><code>treeContradiction(treeA,treeA) # should be zero distance</code></pre>
<pre><code>## [1] 0</code></pre>
<p>Another perhaps less ideal property is that a two taxa on opposite ends of the tree, moving from side of the topology to the other while holding the rest of the structure constant, would produce two trees with the maximum contradiction distance possible (i.e., <code>= 1</code>).</p>
<pre class="r"><code>treeAA<-read.tree(text="(A,(B,(C,(D,(E,F)))));")
treeBB<-read.tree(text="(E,(B,(C,(D,(A,F)))));")
plot(cophylo(treeAA,treeBB))</code></pre>
<pre><code>## Rotating nodes to optimize matching...
## Done.</code></pre>
<p><img 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" title alt width="672" /></p>
<pre class="r"><code>treeContradiction(treeAA,treeBB)</code></pre>
<pre><code>## [1] 1</code></pre>
<p>But that’s not just a property of this metric, but RF distance too:</p>
<pre class="r"><code>treedist(treeAA,treeBB)</code></pre>
<pre><code>## symmetric.difference path.difference
## 6.000000 6.324555</code></pre>
<p>Basically, RF thinks the two are pretty distant too.</p>
<p>Playing around with this metric has been a lot of fun, and helped me identify all sorts of interesting patterns in my data, like Bayesian analyses being fairly consistent with each other, but small variations in maximum-parsimony producing wildly different topologies. However, its usefulness worries me a great deal.</p>
<p>Now… here’s my problem. I just invented such a metric, but my guess is someone else has already invented it. Phylogenetics is, as any graduate student will tell you, a vast field with a decades-deep history. It is extremely hard to never everything, in every nook and cranny of the field (yes, comprehending phylogenetics is like buttering an English muffin, I guess). People often invent things in papers using terms you could never guess to search for, in papers with ridiculous titles that have nothing to do with the grand thing they just invented. Maybe every field is a little like that; certainly, paleontology is. But that makes someone like me, who worries a lot about priority, very worried when he stumbles on something very useful and very simple. It means that someone probably though of it before. The problem, I can’t find anything like it in the literature.</p>
<p>So help me out here! Have you seen anything like this before? Whose wheel am I reinventing? Or is it really, somehow, a new wheel?</p>
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dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-79184973136793652142016-05-26T10:28:00.001-07:002016-05-26T10:28:43.943-07:00'T151: New Approaches to Phylogenetic Paleobiology' at GSA Annual Meeting 2016. Sept 25-28 in DenverHello all, a little reminder, as the abstract submission date (July 12th) is fast approaching...<br />
<br />
We are pleased to announce a topical session scheduled for the 2016 annual Geological Society of America meeting held from September 25th-28th in Denver, Colorado, USA. Our oral topical session, “New Approaches to Phylogenetic Paleobiology”, is organized by myself (David W. Bapst, South Dakota School of Mines and Technology), Melanie J. Hopkins (American Museum of Natural History), April M. Wright (Iowa State University), and David F. Wright (The Ohio State University), and sponsored by the Paleontological Society and the Paleontological Research Institution.<br />
<br />
We hope to highlight novel methods and analyses for inferring phylogenies of fossil taxa, dating divergences between clades and for using phylogenies to test macroevolutionary hypotheses. We would really love to see both work that combines molecular phylogenies with fossil data or work that uses phylogenies of fossil data. If you're doing anything that sounds like it might fit in, we encourage you to submit an abstract for our session (T151) and give an oral presentation on your work. Here's our more detailed session description:<br />
<br />
<blockquote>
While paleontology has always been strongly linked to phylogenetics, the past decade has seen a major acceleration in the development and deployment of new methodologies for inferring phylogenies containing fossil taxa, using fossil information for dating divergences, and using phylogenies of fossil taxa to address major questions of ecology and evolution in deep time. Notably, these cutting-edge methodological advances have come from both the paleobiology and evolutionary biology communities. This session will provide an opportunity to bridge the gap between disciplines whose members often have little reason to attend the same meeting (i.e. GSA versus Evolution) and promote the unification of fossil and phylogenetic approaches to macroevolution.</blockquote><br />
Abstract submission is now open and closes on July 12th. The abstract fee is $50.00 for professionals and $25.00 for students. When you submit, please select Topical Session and then select our session (T151) from the list:<br />
<br />
<a href="https://gsa.confex.com/gsa/2016AM/cfp.epl">
https://gsa.confex.com/gsa/2016AM/cfp.epl
</a><br />
<br />
Those of you who are classical biologists may be wondering 'why attend a geology meeting?'. GSA is one of the largest annual conferences attended by the paleontological community at large, as it also serves as the annual meeting for the Paleontological Society. The paleontological sessions at GSA include paleontologists working on vertebrates, invertebrates, plants and pretty much anything else that leaves a fossil, and many of the sessions are focused on evolutionary and ecological analyses of deep time.<br />
<br />
Please note there are several other topical sessions that dovetail with our interests in phylogenetic paleobiology: T152: Troubles and Triumphs with Fossil Phylogenies, chaired by Jennifer E. Bauer (University of Tennessee), Adriane R. Lam (University of Massachusetts Amherst), and Sarah L. Sheffield (University of Tennessee); T136: Across Space and through Time: Understanding Evolution and Ecology using Biogeography, chaired by Alexander M. Dunhill (University of Leeds) and Erin E. Saupe (Yale University); and T140: Evolution, Development, and Paleogenomics, chaired by David J. Bottjer (University of Southern California), and Jeffrey R. Thompson (University of Southern California). You can find the full list of topical sessions here:<br />
<br />
<a href="http://www.geosociety.org/meetings/2016/sessions/topical.asp">http://www.geosociety.org/meetings/2016/sessions/topical.asp</a><br />
<br />
In addition to topical sessions on specific topics, there will be a number of general technical sessions on morphometrics, diversification, macroevolutionary dynamics, functional morphology and mass extinctions, dependent on the abstracts submitted. You will likely find interesting paleobiological sessions running from the start of the conference to its end.<br />
<br />
For registration purposes, You can receive all the benefits of a GSA membership by being a Paleontological Society member instead, at a lower rate ($30-55), although if you are a student, it may be more prudent to consider GSA membership as some travel support may be dependent on that. Travel support for students and international researchers attending the GSA meeting can be found at <a href="http://community.geosociety.org/gsa2016/social-business/otf">http://community.geosociety.org/gsa2016/social-business/otf</a> and <a href="http://community.geosociety.org/gsa2016/attendeeinfo/travel">http://community.geosociety.org/gsa2016/attendeeinfo/travel</a><br />
<br />
The Denver conference center is close to a number of hotels that offer special rates for attendees, and room reservation can be done through GSA. Downtown Denver is also home to a number of museums, a zoo and many restaurants. Registration for the conference and lodging reservation will open in May.<br />
<br />
If you have any questions regarding our session, feel free to contact me directly at dwbapst(at)gmail.com!dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-68901819948140422692016-05-09T15:35:00.000-07:002016-05-09T15:35:17.123-07:00Outputting a Data Table to PDF from R<html xmlns="http://www.w3.org/1999/xhtml">
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D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlKNcrJfmvyKlbaqps0Oa%2FBSPXk4FJjtWF2wOHfpFY63qA49US3T180HXmbqEjYsSpRFkkOVMCBH1C9JAJTRgjCWNAjl2MSA%2BvZjADldAJGTIoWBOcOEUI4qwvBSI4hMj8m7WHoFosA0M4Q4yS8XtN%2B5emVGJDGqX8D5SVbE%2F0bWETJRyRFYKQ8umFTkY2ZnV9Lz%2BVW9hgHq%2BHYhsrghzcANy%2B02yEIiJuDFngZnEHSFnTnqDAicaKOgtLZkRXBqGS3bgcqBK6B%2BmV5G4%2BxzA1IlYTTtL30wAPCkFZuhlgsViFEnA5Rg4UXmBbXBAW7JuNvZlXTVsYDMV305DdUPX6QyUFMwndSexwLkpBDsIQ4lUnD9QoEF8QmpcQk8FJckIIkVwvETqBSrIvHRim18B7iqFwNCcFoUKmSj9cSfX37WL%2BCT4mAKH%2BS76AYlpBE3eYErTSL68qrzEKiOZo1mbN1ujQapDrmTWb1s3iBTeQMCYcYfVeS5ks0rTlCPWr9AjV%2FbgZ8To6s7qOr3%2BkRUCKT5GAAUzFlHOkmV3fJEImmhCZ4S5ivYOcC3SUx70Is%2BgfKTQfQBsES9NEX4x0WTcQkWV5JKEJNl0UV8pDmBNnC%2F4qRYIkjLTUknCCTbfLBLJzy4rnSGTntJHjdpYIWlqzxK%2BG%2BRBy22IJ8gMQIibkjgh8Y9Q2H5aenHLwEJg2iDmhQybR8G3gfKxBAZwppQkDi9rFMfLSe8Fgalu2z9mKNwe%2BF24HWYNjNI3zoQfXsIck6YiAwCMhAsEDMvxfp9PxAAxF94h42eFp8V8BZxS3g%2FoQhPG05yIyOc5GxA27VgPq%2FN9uxGcdZCP9PAWYlmmiIlMHE%2FRj3IzE8YKgGEh8OmOkN4KuO6GYyNnRhoWNSgmY9%2BVfMfhBm7xIwZPp7ggg%3D%3D%3Bsrc%3Adata%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cb08AABFOAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAjPL%2FpQAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAAxADsAUABTACAAMAAwADEALgAwADAAMQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAHa4ADV2ADV8AC1cEs3pishg2FfJaEtxSn94IlU6ciwvljRcm9wVDgxsadLb0%2FGIypoPBp1Fx0xGTbQdxoAU51YoZ9RXGB0bXNPyK3JLMApRwa%2FIMy1PPoJDx39kimekZX1c%2BDSW41tEZBuFiwdwx1dRoPVA2vWPSlsSDqhNkqYfhrqlVUGD0J3HEAgZavmtLnDC5WBriSpD8Uk02KsUkJ9vCFz2CZXAd5viwGZ2xcVYRPa1bEIai51nMYlbL2ERuB2TCzLAbPWWRZ3%2FsZ%2FKBjLk8%2FgAZzK1OaxNw4oGBbsNhx6Reg5HRFVCrwa15kGmJEy5kX1YypVm%2FHo7TjKP3l%2B%2FnuCTOyiOa6S8QEJbuiGYNlCnM7tHChCHRRQHXQh7yPXASlcvc5KrNKol6orb35kbo%2BiEwl8d230cfWPwTy00bFDRYURYGchbwsoDcaR2AJsFGCrbWCzBdQ0qCobWwLfueqAzSkzaHX3yCjDlGYPV0VZqVXlqbr4poRaG5NNPMDv5MCHkLSFyABMRBhMiGSZiDgABYmwsLWDjUZCUnHwvXt0VAUy4%2FqjwVgjfUqp1evzUqutJB7DW99Cq5aEhGePsa4omKUp7LnSQUz%2BZq51pU72ApMxkaVZE9jF3IGKVnF0GlK1E6iqLJXFzy1BEv6Kr0ngCzIgVANRwCkVHYJj86Sv2dzqgVrjYKlg8W94FbPGQghEbTakZ4AoS9h0u39DOoRJTA2iirs9xa5m9Ir0KwkogoqFNVDbTIGO0%2FWuJYfp61Kq0bhgbcbnvbTDj4HBESsXMdvrvpp7Owf%2FS2EbndoEoglO%2FjigGAuLUTUmG07ZGlRCZ4QZLu81tPEgTtAvF99BxfnxNslgVYH5Jc4AfHwsiktDBO4wjgjizFMsIeBTGmIYrhtrK3tvtBu5tYB0PDMXILUkFYPM%2B5yLLGOU9DtoDLYWLQig%2B8wNRZbJZZWeK5JyKNwumLgP7X6pqgiZMFUvfCTl6Urx1gUOjzhcICumUUsiNESZCmTANkgHoh%2BKU0t1fgQYOBkNfp4MRiJysezS1Vz%2BahE7ohM0iJBn4I%2BT9RJN%2FuQk6SjQGwC%2Bom%2FSD7Zq2RxvcSjtdxHfo7NtktA46mEg5gBkTtMKu7oFb1KgqwqBLzQqZI%2Fs4QSRhYF3ani1URTcMD38kDDqlc3DChmYJTCR9WpaWrYFiXeNi7jYa1Oe%2BSP30gxVyeC10DYOakGpWaU1qkQ9OXlujEbM4A6A%2BQ243TA2QnI8mKzNXNo1HPN1CFawST6uC9dkyQTqq5bOKkkuGjz0nkj35Sr%2BQ%2FkLGg2iQMrTKImSGshnX7%2F0Jyh0JziDbTzxp3IrbosXgpchLcBJpLLUBrFbt%2FU6qXqAaNFJoeBItBoqgQlPZkpTq31s5VZsgIkFupAno5G9oKwCGRqv5294xFqOrEpewcjx%2Be06mxDkN85c0ckmWmMzbkFX9YHAf83p0EsnBywjZbcPxZw91kqg7voxasp3VfDhEDAufUx0MTddLO5kCyKkUBwOm%2FMurQH4bJQgK%2Bp14dkBBhgxpBUkTlSbTE8DDu9qKiDSCtvmPY7FRMn0wuS6XR7J%2BnpjjeaDNwiOgIKUWCP9aLb%2BXpc4toQCryp6gjCVq1Cl1Di%2BtEx4WnBp2hmGZpEksZwTSggXuqAWD4B7WAMcir2wmA%2BI43ECxUSeTDT%2BxgR3O4XFPOGxHh09lxpScampLahUbICao0db7%2F%2F%2B4CBMcCokQ7aOQ02MOzh0NEaJGGOvPR1t4sY1I55lNfQ3IkF04onnAomXxiNg2EbBbPTMBBlv%2BggPSmkb4GNFW6Ehd6nDzJlnIXFfzr1bVWQlfylDodFBbhCDf%2FVB13j441dc2rx3h4hfAWIJuZqbH%2Fwbuafg%2FnTQJ9bnMfR3U6TwYgDQJIOPOtmZW4ZHsR2VifZGaC6Pzv4ZzC2nSAaqSe09F9MxuI4UmUVA2RAUqBUIETEIAhB%2BtBhsxhVMXUmv3RPe1grhYWLM%2BPHwwO5W069qIgDXtETXZQ3741DSk%2BvO6y8k%2F7Mg6GFC5fSK02yXZ1XV0NZe8iknoIbSFnDm%2BbMP%2BBCQhoLKOeU3FVNQ%2BgPEntwkHT950cQYEYQc%2FXuL4nxwcRW2bIN%2ByskPaD5Rz6lN8LW5IDLYfuvhxlLcjWXSADeTshWsJk%2FHCuRbwjqLcx66fc6UlF33HipliQ1cvfKlgRpVcsmIsiYnJjzJIovUyqi%2F2OTQcIwt%2B3WW44BAQh%2F2GymOIscSpIt0YUePiwOCJN%2BQ60lqkSRbWi10lotzgWZsqzEI7OGFGeIoFyqli41%2FNn7LfLQ0qtxkaAwbsKZQ0D9s%2FwhBqoxhAlxCVNVF%2FJ6rsUBQ49MLtR3ICtKPLKZ%2FQPEeIRgWUNLbNR3v3tTaL8uiXO%2BOj3tSJc56KJHol9nGPuclbrQpCn4keT05ETSOQ%2FDwwKjCRLgIzBi7x%2BU7ByTZGUWcGLPY0VtXWC7kQ38C5thIGJx%2FxLM3KsoGyRwC5AFUNWPzU0QBCaVXo6z1NCclLuPVA8%2FwWuNhiAG9CPPSSlIOCuh2AQM1d6MyIEIQArIa9YenQ5ug37h9gPNDHVLImcjE91ewI7pz7HgOEVqTD%2FjREYRtEoDIfgxzDePkVvHWJAtcpCJ%2Bg2o4akZpphRUQ0U92lJcKZGJI9mnpl8UVzpMi8SBGXUAPpgcS2cDAusSE6q0hNibhCgZHLReLsA%2BSv8wF6LiDL3TogB1DsO%2FHeMg0ETucRO2hIOvbp1NVkFMclluNmBQbTy%2Bosr2bJ7oWdUtSHi1s%2B2vWxq8njY1eW4BCVr2EqSyIUuFKS9iwVF5FBGeg0hRcHwCKMtqYLBfvVSYXHdGZK3Ug95C2d%2B3t9ZWEYqq6F4TocgcDS4tI8cvEEXK1%2ByB9RofdKpHoA3eP9%2FB%2FEBG%2BIgvmwZ4Iir1SOYFEtUG%2FoVkECQqoPTdHoNpQKpA9K9k7TisAUBsKPtWgBriyeOxs1o%2B4KCIFumN744dH3Yf7X8F426LGtEhFLULhlKFgXF8Wq2D6kFSBkViLad9D9U%2FP6A%2Fr8SE7jsr9s%2Bsb5ODWI5uS6BkZJI5RQ5QDVZ5%2FKOG6Dzc1RLQKAdGkgwLiZLQdEYQ9yhQLtD5kWr4wGHTv4GqloWePyHYyuy3Ek3iQVQkdkAz0MxcsHzccdMLO8KFh7ywo4jXywraIhIicAI4dWdD5wEXBhsMklzbWc8zi8opC3IlPsLXHCWOqOgq4iPU6xyDS2tdSLnlhSFjsCCWvMRt5TAI30EHR4f16NYyzoG%2FoKgCUsW4182Wvj5gpb0bGGLe8YRIYRGVgF2hwIlEfm7eUwwLZWW%2BmmBZPggPnIKI0b6G%2BlHCncx0wkaVPRQpZKWIIrzh4yC4Ar2vTKzhVKSutNi1dSzmM73AGUXSBJrQH5J4Dfb%2BRU25tYrIVdLr4ogiwTE2i2hl39ffJWHNTSWq3PvVJplNqDTJgJcvCHYoQAuHeOew0gZGfmX3p4D0aSr1BhXbXRA1qipfS6LiggtiT1eDg4FTCWwCdAFC9FCOZ52EdrFetMBxUdC0dfnqpr0mELJE2m5FVMvwplyf0XIKclHa6%2FQrw%2FFOVkVKK71Ab5iAVWm4GA3HnkCJhYkW06XJ3TDGB9YhVW9RuC39AFUcFWwU5eTNzuck7IQty7ZC9IjAGNHuZhQ8yDWZhGUoNyhDcKYQiUDqAC782makzJ1o3XJFIEDXgObv%2BTlS%2FdAr5rn2C%2FCDmkI0WO3cSTJwSLrS5dBdkyfpxAu4XghB3jgGShG8UCorOibUp7NciZgEDLmT6eOxDegpLRk7HomganEaTmRZAAMrhobApDjFWtOxht%2BNQngtKVgGL2yy6Qnsmju6DAg4d%2FRvxHa5mBaEMcBekCMtwaPPGeKFPMsgAJTidusMLUbTAUBJMOmXRwhONkoLPqBhpTuLSYN%2Bf8aeX1NKSxF2OUST9ezObkHETTsr04iWClpoFDE8OdIiDPQJqkHhnUPTcE43OsKBGS%2Fzk6F57sqVmhZKjPxle2S%2BiIE%2BMXOiTOoRKF0Q7mbNRYGPZDp5eIdPOZHboa4CTvwnlZb0zCOW6kZJh6klDDvYghmQ2FCDKB5DD%2B0nImiNcfSzaGT8aSPzEZjMQ2GPpA1Ws4Xeysyv0zSSMVVsiOoiowo2SfYFdapUBsWIoRXMAKYdguyVcwqX%2BzVWYLiNddi8p%2BxMV4dQIoUs6SQ4C1%2FBjtBw0ocOzLmMkEv5D8N4%2FZu4gYA4ryDb5Y3gkIxdb37qMCWYmDGgsyEE00y3BZ4FgyT%2FdJLHxMQwx0gViQjWL0QyBfcufCgRYYGiCHrxez874MrcqldLCEQLkCCK6pWCnULCh%2BVK6FW%2B1nm%2BN668e16QTKWprvRw3mkwitjPefHt5QKrVumkXsvsVrHdAJrodfWckxhhzknXZ8cMVIvKphJVfhvWbhoZbbgo3fygOr8PlKcVEYLTmUbjp8rTFlPRtPrdA8tljZ0REfnhwApCZ65Igtn1gBROJdPCCb9ZIqlQUq3W89ZjSgSnUz8nEBW03JCcQ%2FSxouQtYBE7ichYHbASPB5J1%2FzVfPINn38CqTMws1KKo%2FyvNXpDgBvCI08SBLSwX5Szp6WlpzoPEio8DsNzZQqgUMoq0%2BNMALrygrOlErJohXvIJ%2F0IqziZok6OuApG2eoGAEFsTc6yWf9eRcklYZRO3PkA%2BW8aN5ME8aLNC3Kdwtm64WyqytOEW1WQIg6bOFHAs1ovo3OfCvvZASxBcxg2SRCRTTJDBKajFH0LL1kSBroe6L9xvVxMCIFS7Zo6y4ZOEIkAI9QtGVIKKBlCvUUEq2S8FiR7VkJGbvBFaS6UzQV%2BayV6NKw62tbJbEjkJ7AMC17DK399DZr%2FtE5sUtaAPwaHNj9VCoVB4VAG0iNI9IjDo0mr97FWjYeDRSr3pstZTVZ6nj5Vp3IbssG3g0r9E2eSmHY97LYobIHPnEGkKDJP15WPWtwMgS8HeLd6NEnP4mIFlRJB4zLeDeoBElpUNRIQMzLbRQsGLj3DZhQytDQPLAUE9JWHiLKAJxVTOJNZsObEiutLqwox%2BM51t7vJQhBeQm2i5bTa4D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlK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<h4 class="author">
<em>David W. Bapst</em></h4>
<h4 class="date">
<em>May 9, 2016</em></h4>
</div>
I’ve recently developed a workflow for writing manuscripts with co-authors lately where I:<br />
<ol style="list-style-type: decimal;">
<li>Perform all possible analyses in R in a single Rmarkdown script in Rstudio, knitted to a PDF. Use the notebook aspect of Rmarkdown extensively, so all my thoughts of what I’m doing and why are in there.</li>
<li>Save the final workspace from the Rmarkdown script (with all the datasets input, transformed and further analyzed) to an external .Rdata file.</li>
<li>Produce all the necessary figures for publication by sourcing a separate R script that contains code for producing those figures, generally as TIFF image files, at the necessary sizes for submission.</li>
<li>Transfer all my miscellanous thoughts on methodology and results to actual submittable-manuscript form in Word as a .docx because many of my collaborators don’t use Markdown or Latex or even Google Docs, and plus tracking revisions is easiest in Word, as opposed to Google Docs or PDF.</li>
</ol>
Steps 2+3 are semi-necessary because the Rmarkdown script may take very long to run (i.e. multiple days on my machine), which sometiems is just lengthy data transformations (<em>cough</em> dating phylogenies in R <em>cough</em>) and so its useful to tweak figures slightly by just sourcing a script in a saved environment.<br />
<br />
But recently I ran into an issue where I wanted R to spit out an auto-generated table as a PDF that looked nice, for the sake of including as a supplemental table to a manuscript.<br />
<br />
So I has a bunch of symmetric pair-wise distances with some long (but necessary) labels, which meant one really ugly distance matrix. Imagine we have such a thing and we asked R to print it out.<br />
<pre><code>## ezjibmlvpy hiefdsurjx dpigheqcnr nphmejaiqg ebijmurcsf
## ezjibmlvpy 0 4 4 3 5
## hiefdsurjx 4 0 2 3 1
## dpigheqcnr 4 2 0 1 NA
## nphmejaiqg 3 3 1 0 5
## ebijmurcsf 5 1 NA 5 0
## csvhynkzjw 1 4 NA 2 NA
## celqhvdntm NA 4 NA 4 5
## vecxfatldq NA 2 3 NA 1
## meavfdgznh 3 NA 4 4 NA
## sjctdwmxki 1 1 NA 5 NA
## csvhynkzjw celqhvdntm vecxfatldq meavfdgznh sjctdwmxki
## ezjibmlvpy 1 NA NA 3 1
## hiefdsurjx 4 4 2 NA 1
## dpigheqcnr NA NA 3 4 NA
## nphmejaiqg 2 4 NA 4 5
## ebijmurcsf NA 5 1 NA NA
## csvhynkzjw 0 4 1 NA 1
## celqhvdntm 4 0 NA 5 1
## vecxfatldq 1 NA 0 1 4
## meavfdgznh NA 5 1 0 1
## sjctdwmxki 1 1 4 1 0</code></pre>
<br />
Truly a very ugly distance matrix. We have a zero diagonal we don’t want to show, and showing both the upper and lower triangles is rather redundant. So, we can clean it up a little, mainly by erasing values we don’t want to look at…<br />
<pre class="r"><code>table[lower.tri(table,diag=TRUE)]<-NA
table[is.na(table)]<-" "
table<-table[-ncol(table),-1]</code></pre>
<pre><code>## hiefdsurjx dpigheqcnr nphmejaiqg ebijmurcsf csvhynkzjw
## ezjibmlvpy "4" "4" "3" "5" "1"
## hiefdsurjx " " "2" "3" "1" "4"
## dpigheqcnr " " " " "1" " " " "
## nphmejaiqg " " " " " " "5" "2"
## ebijmurcsf " " " " " " " " " "
## csvhynkzjw " " " " " " " " " "
## celqhvdntm " " " " " " " " " "
## vecxfatldq " " " " " " " " " "
## meavfdgznh " " " " " " " " " "
## celqhvdntm vecxfatldq meavfdgznh sjctdwmxki
## ezjibmlvpy " " " " "3" "1"
## hiefdsurjx "4" "2" " " "1"
## dpigheqcnr " " "3" "4" " "
## nphmejaiqg "4" " " "4" "5"
## ebijmurcsf "5" "1" " " " "
## csvhynkzjw "4" "1" " " "1"
## celqhvdntm " " " " "5" "1"
## vecxfatldq " " " " "1" "4"
## meavfdgznh " " " " " " "1"</code></pre>
Still, printing it in R is pretty ugly, and copy-pasting it to Excel or Word or something and making it prettier for a manuscript seems rather time-consuming, especially if I’m going to be tweaking things upstream and these values might be changing (or even the number of comparisons might change. It would be nice to automize converting this to a servicable PDF.<br />
<br />
Now, I just wanted to quickly generate a decent-looking PDF of this table from an environment just by sourcing a script, like I do with figures. As someone who only learned Rmarkdown two years ago and still mainly uses it via Rstudio, this was a bit of a challenge. So I put a call out to Twitter, and I got a helpful response from François Michonneau that put me on the right track:<br />
<blockquote class="twitter-tweet" data-lang="en">
<div dir="ltr" lang="en">
<a href="https://twitter.com/dwbapst">@dwbapst</a> probably something like rmarkdown::render(knitr:: kable(my_table), output_format="pdf_document ")</div>
— François Michonneau (@fmic_) <a href="https://twitter.com/fmic_/status/727589631114489857">May 3, 2016</a></blockquote>
<script async="" charset="utf-8" src="https://platform.twitter.com/widgets.js"></script>
Hmm, well, it did turn out to be a little more involved than that. Using packages <code>knitr</code> and <code>rmarkdown</code>, we can convert a table using <code>kable</code>, output the table to a separate .Rmd file and then <code>render</code> that file.<br />
<pre class="r"><code>library(knitr)
library(rmarkdown)
table<-kable(table, format="markdown")
cat(table, sep="\n", file="Table.Rmd")
render("table.Rmd",output_format = "pdf_document")</code></pre>
And here’s a screen shot of what that produces.<br />
<br />
<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg0Y9vX5lQ1EjGDbO72GTKrFTeg9EXvFDv6I4YkjRmxTnrJH3mL3dBMAxZuSOlS1E03-NIKKao-VPhB1kVLTU4T0t8h-XbH1tvKcbbuFyGC2uC0tVEoFWFz5wTsZwVEM14edGvejClmEFwI/s1600/SCREENSHOT1.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg0Y9vX5lQ1EjGDbO72GTKrFTeg9EXvFDv6I4YkjRmxTnrJH3mL3dBMAxZuSOlS1E03-NIKKao-VPhB1kVLTU4T0t8h-XbH1tvKcbbuFyGC2uC0tVEoFWFz5wTsZwVEM14edGvejClmEFwI/s320/SCREENSHOT1.jpg" /></a></div>
<br />
The table is trailing off the page, because its wider than your typical LaTeX page size. Usually we could just rotate the table to avoid this, but in this particular case, we have a symmetric table with long column and row labels. So we need to rotate the page, and then we can plot our table comfortably…<br />
<pre class="r"><code>format<-"---\noutput: pdf_document\npapersize: landscape\n---\n\n"
cat(c(format,table),
sep="\n",file="tableRotate.Rmd")
render("tableRotate.Rmd")</code></pre>
And here is what that looks like:<br />
<br />
<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgHrFi0cQeXGqBYxKsNJvsAsbsQfvM1KzYdydjFYep49Ac96snMdkpvQKXLHYNHvz4vRhnfAWqWGDCwS5l_c2hqwezrVQ1l-eRvdyV8Op0k5Uxen1bac3uBeFAH1mOZVwA78uyTaxvg5Ob4/s1600/SCREENSHOT2.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgHrFi0cQeXGqBYxKsNJvsAsbsQfvM1KzYdydjFYep49Ac96snMdkpvQKXLHYNHvz4vRhnfAWqWGDCwS5l_c2hqwezrVQ1l-eRvdyV8Op0k5Uxen1bac3uBeFAH1mOZVwA78uyTaxvg5Ob4/s320/SCREENSHOT2.jpg" /></a></div>
<br />
The page is a little bigger than I’d like, but close enough! I’ll call it a success.<br />
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dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-23905937978954446382015-10-26T08:43:00.000-07:002015-10-26T08:43:55.591-07:00What is a College Degree? or Why an Engineering Student Takes French and Why a Paleontology Student Takes Calculus<!--[if gte mso 9]><xml>
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="endnote reference"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="endnote text"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="table of authorities"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="macro"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="toa heading"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Bullet 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Bullet 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number 5"/>
<w:LsdException Locked="false" Priority="10" QFormat="true" Name="Title"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Closing"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Signature"/>
<w:LsdException Locked="false" Priority="1" SemiHidden="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text Indent"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Message Header"/>
<w:LsdException Locked="false" Priority="11" QFormat="true" Name="Subtitle"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text First Indent 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text Indent 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="FollowedHyperlink"/>
<w:LsdException Locked="false" Priority="22" QFormat="true" Name="Strong"/>
<w:LsdException Locked="false" Priority="20" QFormat="true" Name="Emphasis"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Address"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Preformatted"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Typewriter"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Columns 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" Priority="39" Name="Table Grid"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" Name="Placeholder Text"/>
<w:LsdException Locked="false" Priority="1" QFormat="true" Name="No Spacing"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading"/>
<w:LsdException Locked="false" Priority="61" Name="Light List"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 1"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 1"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 1"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 1"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 1"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 1"/>
<w:LsdException Locked="false" SemiHidden="true" Name="Revision"/>
<w:LsdException Locked="false" Priority="34" QFormat="true"
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<w:LsdException Locked="false" Priority="29" QFormat="true" Name="Quote"/>
<w:LsdException Locked="false" Priority="30" QFormat="true"
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<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 1"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 1"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 1"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 1"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 1"/>
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<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 1"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 1"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 2"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 2"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 2"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 2"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 2"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 2"/>
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<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 2"/>
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<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 2"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 2"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 2"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 2"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 2"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 3"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 3"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 3"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 3"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 3"/>
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<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 3"/>
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<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 3"/>
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<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 3"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 3"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 4"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 4"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 4"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 4"/>
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<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 4"/>
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<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 4"/>
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<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 4"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 4"/>
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<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 4"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 4"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 5"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 5"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 5"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 5"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 5"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 5"/>
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<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 5"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 5"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 5"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 5"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 5"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 5"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 5"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 6"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 6"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 6"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 6"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 6"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 6"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 6"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 6"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 6"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 6"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 6"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 6"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 6"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 6"/>
<w:LsdException Locked="false" Priority="19" QFormat="true"
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<w:LsdException Locked="false" Priority="21" QFormat="true"
Name="Intense Emphasis"/>
<w:LsdException Locked="false" Priority="31" QFormat="true"
Name="Subtle Reference"/>
<w:LsdException Locked="false" Priority="32" QFormat="true"
Name="Intense Reference"/>
<w:LsdException Locked="false" Priority="33" QFormat="true" Name="Book Title"/>
<w:LsdException Locked="false" Priority="37" SemiHidden="true"
UnhideWhenUsed="true" Name="Bibliography"/>
<w:LsdException Locked="false" Priority="39" SemiHidden="true"
UnhideWhenUsed="true" QFormat="true" Name="TOC Heading"/>
<w:LsdException Locked="false" Priority="41" Name="Plain Table 1"/>
<w:LsdException Locked="false" Priority="42" Name="Plain Table 2"/>
<w:LsdException Locked="false" Priority="43" Name="Plain Table 3"/>
<w:LsdException Locked="false" Priority="44" Name="Plain Table 4"/>
<w:LsdException Locked="false" Priority="45" Name="Plain Table 5"/>
<w:LsdException Locked="false" Priority="40" Name="Grid Table Light"/>
<w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark"/>
<w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful"/>
<w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 1"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 1"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 1"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 1"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 1"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 1"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 1"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 2"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 2"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 2"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 2"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 2"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 2"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 2"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 3"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 3"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 3"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 3"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 3"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 3"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 3"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 4"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 4"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 4"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 4"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 4"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 4"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 4"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 5"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 5"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 5"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 5"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 5"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 5"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 5"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 6"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 6"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 6"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 6"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 6"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 6"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 6"/>
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<span style="font-family: "Times New Roman",serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman";">Recently, I read a conversation online where an individual asked why a typical
‘paleontology’ degree (i.e. geology or, sometimes, biology) requires calculus.
People gave the typical answers, but they rarely give the answer that I think
makes sense. This answer does not even appear to readily exist in people’s
minds. I see similar questions get asked all the time: forget calculus, why
does a paleontologist have to take hard-rock petrology? Why does an engineer
have to take French? Why does an English major have to take biology? These
aren’t likely to be directly ‘useful’, at least not professionally, to these
students. So why take them?<br />
<br />
I’d like to talk about that today. I don’t usually talk about my philosophical
view of teaching, even privately. One of my mentors in academia has actually
complimented me on how practical my teaching statements are written, detailing
precisely what activities and tasks would be involved in particular courses.
But I don’t believe we can do anything well without a strong theoretical
foundation for structuring how we should move forward, so here it is. Here’s my
theory.</span></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<span style="font-family: "Times New Roman",serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman";"><br />
First, I’m going to preface all of this by saying this is entirely just my
viewpoint on the subject. I’m an academic, but I am poorly read on philosophy
of education, or the larger end-goals of education, or the historical purpose
of secondary education. That is probably pretty typical for most scientists.
Also, my perception of college and its apparent goals is strongly colored by my
observation of American universities. So, I might be a little off my rocker
here, and I apologize if anyone with actual background in those areas reads
this and shakes their head sadly. That’s fine, and if so, let me know what you
know that shows maybe there is some other intention designed into our college
system. I’ll also more or less use ‘university’ and ‘college’ interchangeably,
which might annoy some who see a very clear distinction.<br />
<br />
What I’ll say here largely comes from my own thinking, discussion with others
and two sources, both of which I was first exposed to as a graduate student in
an education seminar at the University of Chicago:<br />
<br />
<i style="mso-bidi-font-style: normal;">Booker, H. G. 1963. University Education
and Applied Science. Science 141(3580):486-576.<br />
<br />
Richter, F. 1991. Geology and the university. Geotimes 36(9):5.</i><br />
<br />
They aren’t very long reads; if you can’t get these and want a copy, please
contact me. Another thought-provoking read that I encountered later, which
expresses somewhat opposing arguments, is:<br />
<br />
<i style="mso-bidi-font-style: normal;">Crow, M. M. 2012. Citizen science U: The
best way to teach today's hyperconnected students is to get rid of the
departments of geology and biology. Scientific American 307(4):48-49.</i><br />
<br />
So, let’s cover the ‘typical’ answers to the question of why a paleontologist
needs calculus. Well, the most immediately proximal causation, and least
helpful answer, is that that’s how modern universities work: you have ‘common’
or ‘general’ education requirements you need to fulfill for any major or
specialty. Some are for any student enrolled at all, while some are for certain
degrees, like how geology Bachelor degrees often require calculus in addition
to ‘general’ requirements. Ultimately, students are effectively required to
take classes from nearly every broad area of education in the college. You
don’t have to be extremely shrewd or know much about how financial matters are
solved ‘behind the scenes’ at a university to know that this is very
financially convenient for certain departments, especially those with small
numbers of students actually majoring in that field. It is also very convenient
for graduate students in those departments, as it means there are teaching
assistantships available for graduate students associated with those classes,
and thus they can have financial support while they get their degree.<br />
<br />
But there’s better answers than this. If nothing else, we can reject this
simple answer because if a student was only fulfilling general course requirements
for the university’s financial gain, then (a) the courses required would be
completely random, when generally a very similar set of course requirements
exists across all universities, and (b) whether you actually took these
courses, or your performance in them, wouldn’t be of concern to most graduate
degrees admissions committees, when generally, they are actually quite
important (if sometimes of less consideration than other qualities as an
indicator of future success).<br />
<br />
The most common rationale I see given to this line of questioning is to attempt
to find some specific reason why that specific course is somehow related to the
actual field. And, sure, there are plenty of application of calculus in
paleontology, mainly related to biomechanics issues. There’s also plenty of
engineering literature in French. But these are corner cases: the vast majority
of paleontologists never do any calculus. There’s lots of math in paleontology
today, and I would recommend anyone in the natural sciences (paleontology,
geology, biology) to be familiar with univariate and multivariate statistics at
a bare minimum. Now, I work with math and quantitative analysis much more
often than most paleontologists, and even I handed off the one derivation I’ve
encountered in my work to a statistician colleague, rather than do it myself.
In general, you will be hard pressed to show how anyone found specific uses for
every single general requirement class they took in college. And, to be honest,
I doubt that many non-engineering students retain the knowledge and capability
to derive and integrate for more than a year or two after passing Calculus,
anyway. At this point, I only vaguely remember how to integrate. So, if
the goal is that students should gain and retain specific skills for future
use, the system doesn't seem optimal for that.</span></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<span style="font-family: "Times New Roman",serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman";"><br />
One answer I see given infrequently given is that a student takes such classes
to 'broaden' themselves; that may be infrequently given because it sounds like
a line from a university propaganda pamphlet. But no one seems to know what
that means. I’d argue that the best reason is precisely this ‘broadening’, but
we need to be able to explain what that is. How does ‘broadening’ aid in our
professional development?<br />
<br />
The answer is that secondary education has *nothing* to do with your
professional development: getting a job and getting a college degree are at
cross purposes. Booker (1963) and Richter (1990) argue, collectively, that
secondary education is ultimately about developing yourself as an individual.
You take courses in a subject area to expose yourself to the particular mode of
thinking applied by that subject area. Every subject area in a liberal arts
college is ultimately a different approach to addressing some set of questions.
Why does a student take a basic English class? To learn how comparative
analysis of literature and writing can be used to address issues. Why does a
student take French? To be exposed to how the vocabulary of another language
works, and the basic concept that the language you use can make it easier (or
harder) to communicate and express certain thoughts.<br />
<br />
Why does a student take a basic geology class? To learn how geologists approach
scientific questions about the earth’s history and environment, and that of
other planets. Think about it: one of the most common topics in an introductory
geology class is training students to understand and appreciate the magnitude
of the geological timescale. Dealing with time and spatial scales that are much
larger than those we interact with daily is part of the mental toolbox of a
geologist. Taking an introductory geology course exposes you to this toolbox,
and allows you to add those tools to how *you* go forward and approach
problems. While I imagine that many of those who have taken an introductory
geology class do not recall the names of the geologic periods or their exact
order, hopefully what they do retain is a lasting impression of the immensity
of time in earth history.<br />
<br />
And that’s really what I think college is: building a mental toolbox that helps
you see how to approach problems. They could be problems you encounter in your
work, your personal life, your hobbies, whatever! And the key to getting that
toolbox is being exposed to a diverse array of fields of study and gaining those
important conceptual insights and perspectives unique to those
disciplines. So, why does a paleontologist need calculus? So that they
comprehend concepts like the relationship among successive derivatives of a
given function, the relationship of derivatives to the concept of the area
under a curve defined by a function, etc.<br />
<br />
And, I think, that many students who get through a calculus sequence gain that
conceptual understanding for the long-term. In my opinion, college classes
generally succeed at the unstated goal of broadening the perspective of
students. However, I don’t think this is entirely intentional on the part of
the educators. First, I should state the caveat that many college educators
give considerably more time and effort to the art of education than public
perception gives them due for, and I think many give a great deal of consideration
to understanding what the ultimate goal of education is. I think the stereotype
of the professor who treats teaching as a burden to be avoided at all costs
mainly results from the fact the majority of time spent ‘teaching’ isn’t
contact hours (i.e. time spent lecturing or managing a lab session) but rather the
many hours spent outside of the classroom preparing lectures, assignments,
exams and grading. However, teaching is hard work, and I think it is not
uncommon to lose sight of what the greater goal is of course work. Being
actively cognizant of such a goal, and the need for the course to transmit a
new way of thinking to the students, and actively working toward that is a difficult
mental task to juggle, and so I think many do not actively think about this
when making course materials, because often, just making course materials at
all is a high enough bar. Thus, my perception (flavored mainly by my state
school undergraduate education) is that the end result of this is that many
college educators do not think of their role in terms of exposing students to
different lines of investigation, or any similar goal. Thankfully, the nature
of the college system seems to counteract the need for active recognition of
the end result, because that end-result is hard-wired in the system, a result
of how majors are designed and how a diversity of courses are required from
outside of the discipline. Thus, I think the system generally achieves the goal
of expanding student perspectives even without directed intent. </span></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<br /></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<span style="font-family: "Times New Roman",serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman";">That said, even having a discussion
about the ultimate objectives of higher education could have a great benefit.
Regardless of whether the system works currently (and I think it does), it
could work even better. We should design our courses to embrace the
presentation of new perspectives and the process of investigation in that
field. I think there are many ways of doing this, but I think there are also
many ways that do not work toward this goal. In particular, courses that depend
on rote memorization and a small number of examinations (particularly multiple
choice) incentivize a superficial understanding of the material, focused on
memorization of details rather than an understanding of the larger-scale
patterns and processes. This results in students cramming before exams as their
study strategy, followed by regurgitating that information at every written
answer question in hopes that some phrase in that mess is close enough to score
some more valuable grade points. Little, if anything, is retained long-term in
such classes in my experience, which is ironic as many courses with this design
are intended to play a critical role in various degree programs, providing
background information for later classes. Furthermore, this course structure may
also incentivize cheating, with grades often determined more by exams than any
other form of assessment, and made easier when exams are based on repeating a
litany of facts rather than critical assessment of ideas. </span></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<br /></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<span style="font-family: "Times New Roman",serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman";">Now, you might say ‘but students
have to know an exhaustive amount of knowledge from introductory course for
later courses they are prerequisites of’, and on those grounds, you perhaps
will argue that it is necessary to test all of those areas, to make sure they
know all of it. Well, personally, I don’t see how testing for it one class at
all guarantees, or even necessarily correlates with knowing it for a later
class. My guess is that most students forget most of the factual details, but
do retain the cognitive, investigative tools that I’m arguing is the actual
purposes of college. For example, I’d wager that the majority of students who
take introductory geology probably doesn’t retain information like what time
interval the breakup of Rodinia occurred during, although they might recall
that Rodinia is the name of some paleo-continent; rather, they’ll retain the
ability to read a time-scale, the ability to use stratigraphic relationships to
interpret the order of deposition of rock units, general knowledge of how
continental drift works, the general position of the antecedents of modern
continents over the Phanerozoic, etc. If, for example, a class two years later
gives them a reading assignment that assumes they know the what, when and where
of Rodinia, a student well-prepared by previous courses may not know off the
top of their head, but can go look it up very easily and understand what they
find. As college educators, perhaps we shouldn’t rely on students coming into a
class knowing any particular background details, but rather that they have the
thinking skills and conceptual basis to comprehend new material, and the
ability to go back and fill in holes in their knowledge about old material.</span></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<br /></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<span style="font-family: "Times New Roman",serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman";">If we accept this theory that
university curriculum is intended to diversify a student's ability to handle
novel problems, than that idea disagrees strongly with the belief that college should
encompass any significant element of professional training. <span style="mso-spacerun: yes;"> </span>College isn’t about preparing you for job
skills: in general, you will learn day-to-day job skills on the job. College is
about teaching you how to think, and my sense is that this is also the opinion
of many employers: they want employees who can think and thus learn new skills
quickly, rather than students who have the wrong skillset and have to be
retrained. Do not interpret this as my admonishing courses that teach any
sort of practical ability. Learning how to do certain procedures, such as
applying statistical techniques or writing computer code, brings a student
face-to-face with the investigative approach used by that field. Courses with
such practical material are important components of many programs and
absolutely essential elements for graduate. What I am opposed to are courses
that attempt to teach based on the demands of the mercurial job market,
particularly if done at the expense of demonstrating to the students the
broader aspects of the course's subject matter. Choosing to teach specific
skills for the sole purpose of making a student into an ideal job candidate is
making a risky gamble that the job market will remain the same for any length
of time, or that that targeted industry will still exist in a decade (and has
that ever been a good bet?). It seems like a better bet and more efficient to
leave job training for when graduates become employed, and instead teach them
how to think, which gives them the flexibility to deal with the unpredictable
things that will happen over their lifetime.</span></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<br /></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<span style="font-family: "Times New Roman",serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman";">If there is any particular
demographic that is the source for the belief that college should be job training,
it is current college students, many of whom appear to think that they have
been conned into taking classes that are a waste of time; distracting from
obtaining that desired employment. A university degree provides no special
ingredient essential to professional development, but rather makes the
statement that the bearer has had a series of courses in which they encountered
a diverse set of approaches to handling questions of knowledge, and thus might
show more creativity and comprehension than a job candidate who does not hold
such a degree. But you can certainly have many of the positive attributes of a
college graduate without attending college, and there are many experiences that
trump college in providing raw perspective. Of course, the flip-side of this is
that college degrees shouldn’t be a prerequisite for getting a job. In some
sense, our modern society expects applicants for many positions to hold a
college degree, thus reflecting the cultural saturation of degrees, and further
reinforcing the notion that a college education is job training, and thus
committing a great disservice to our colleges and universities by perpetuating
that erroneous belief.</span></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<br /></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<span style="font-family: "Times New Roman",serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman";">In Pirsig’s Zen and the Art of
Motorcycle Maintenance (a book with both considerable strengths and
weaknesses), an extended discussion of the meaning of higher education ends
with the suggestion that college shouldn’t be seen as a requirement thrust upon
every citizen, but an experience selected as the result of a careful,
thoughtful decision to devote additional years to learning and personal
betterment. I think this describes an ideal world, one I would personally
prefer to live in, rather than our current society which treats college as an
inevitability, considers the choice to actively not go to college (if able) as
a mild insanity, and deems the lack of a college degree as a serious
disadvantage on one’s opportunities. If you are honest about what a college
educations is, you should recognize many individuals already have the
perspective and thinking skills that college would grant them without ever attending,
and unless they want to go even further with their understanding of a subject
matter, it isn’t actually useful for them to attend college. Furthermore, some
individuals may never want what college offers, regardless of whether they
already have it or not. I think in an ideal world, these sets of individuals
would get to opt out of our societal expectations, because we shouldn’t be able
to simply force higher education on them.</span></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<br /></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<br /></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<span style="font-family: "Times New Roman",serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman";">However…we don’t get to live in that
ideal world. The reality is that many jobs arbitrarily require a college
education, and increasingly require more advanced graduate degrees as the
marketplace saturates with bachelor’s degrees. This puts an enormous amount of
pressure for the core mission of colleges and universities to change, to fit
the perceived notion of what ‘college is for’: mainly, to match the perception
of students and their families who mistake college for glorified job training.
Some have suggested that the very concept of college is on the verge of
changing dramatically. It is hard in this modern digital age to argue that any
traditional institution is invulnerable or unchangeable, as we have already
seen many areas of society where technological ‘disruption’ has, well,
disrupted an entire industry. However, university and higher education are
centuries old institutions: and it is equally hubris-incurring to point out any
particular long-lived institution and forecast that it must change to survive,
or to predict that institution as being ripe for extinction. Regardless, it is
unclear how our college system will adapt to changing pressures. My hope is
that we can communicate the value of the current system, where students are
required to sample a diverse array of academic fields, and where courses place
theory and discussion, rather than move toward conveying technical skills for
professional development (after all, we already have tech schools).</span></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<br /></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<span style="font-family: "Times New Roman",serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman";">So let’s go back to where I started
this discussion.</span></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<br /></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<span style="font-family: "Times New Roman",serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman";">We’ve now built this argument that
the structure of college education is to increase one’s exposure to the
diversity of thought, not professional development. But let’s step back a bit.
Does that actually argue for requiring that an undergraduate student interested
in paleontology take calculus? No, not quite. The ultimate goal cannot support
the claim that each and every typical course requirement is a necessary element
of the degree. What I think is important is that every liberal arts degree
reflect a diverse set of experiences, rather than a hyper-focused, specialized
formula that provides no broader perspective. What needs to be attained is the
diversity. Some programs of study recognize this, and are lenient and allow
exceptions or course substitutions, although not for every student. Going beyond
that, many students have ‘creative study’ majors, where student build their own
‘major’, their own program of study from available courses. Instead of being
hyper-focused and allowing students to escape prerequisites they find aren’t
useful, though, generally students in such programs tend to take diverse
courses, as it was their desire for an interdisciplinary focus that drove them
to creative studies to begin with. I think there’s a lot to be celebrated in
that. Now, that said, many undergraduates interested in paleontology do end up
taking calculus, and I think in the long-term, that course has benefits that
are difficult to measure or enumerate, even if they later lose the ability to
derive and integrate.</span></div>
<div class="MsoNormal" style="line-height: normal; margin-bottom: .0001pt; margin-bottom: 0in;">
<br /></div>
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<span style="font-family: "Times New Roman",serif; font-size: 12.0pt; mso-fareast-font-family: "Times New Roman";">So what do you think?</span></div>
dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-74036237283236073612015-04-03T01:12:00.000-07:002015-04-03T06:03:21.843-07:00What I Accomplished at the Paleobiology Database Hackathon, March 2015<p>Hello all! Recently I was able to attend the Paleobiology Database (PBDB) Hackathon that was held at UC Santa Cruz, and I wanted to talk to you about the experience and present some interesting products from my work at the hackathon.</p>
<div id="thoughts-on-the-hackathon-structure" class="section level1">
<h1>Thoughts on the Hackathon Structure</h1>
<p>Now, I’ve never attended another hackathon, but my preconceived notion is they tend to be entirely devoted to developing new software in a working-group setting. I think this particular meeting was ultimately more of a mixture between a hackathon and a PBDB API workshop. Much of the focus in shared discussions was on becoming familiar with the API, particularly the in-development version 1.2 of the API, and reporting issues as we encountered them while programming. The last was critical: if we hadn’t been trying to all program various items based on the API, we would have not encountered many of the issues we encountered; we were basically acting as Quality Assurance for the API software and the database, which is invaluable for future users to use the API effectively.</p>
<p>Now, I think that format worked quite well, but I think future PBDB hackathons (if there are plans for such) will probably hew closer to the typical hackathon model, as hopefully participants of future PBDB hacking events will encounter fewer issues and better documentation. There was also a learning curve issue: we probably needed more time so to first become proficient at the API as a group and then be able to work together on focused group projects. Overall, though, I think the main goal was to promote community excitement about the PBDB API, and I think that was accomplished in spades.</p>
<p>For me, the ‘workshop on the API’ aspect of the get-together was invaluable. I’ve been trying pretty hard for the last two months to understand the API’s output (as my previous blog posts will attest to) but one can only get so far by bothering Matt Clapham and others with emails.</p>
</div>
<div id="so-what-did-i-do" class="section level1">
<h1>So What Did I Do?</h1>
<p>Well, readers, I wrote a bunch of functions in R! I have even added the majority of them to <code>paleotree</code> on github, along with documentation, which means <strong>you</strong> can go play with them <strong>now</strong>! Just go install the in-development version of <code>paleotree</code> directly from github with package <code>devtools</code>…</p>
<pre class="r"><code>#get in-development paleotree version 2.4
library(devtools)
install_github("dwbapst/paleotree")</code></pre>
<p>Which we can check and see is named version 2.4:</p>
<pre class="r"><code>packageVersion("paleotree")</code></pre>
<pre><code>## [1] '2.4'</code></pre>
<p>So, now let’s load <code>paleotree</code>!</p>
<pre class="r"><code>#load paleotree
library(paleotree)</code></pre>
<pre><code>## Loading required package: ape</code></pre>
<p>You can use <code>?paleotree</code> to go peruse the help files for the more than seventy functions available in <code>paleotree</code>.</p>
</div>
<div id="functions-to-download-pbdb-data" class="section level1">
<h1>Functions to download PBDB data</h1>
<p>Now, what you won’t find in <code>paleotree</code> is the functions I wrote at the hackathon for downloading PBDB data. Why? Well: first, there’s already an <a href="http://cran.r-project.org/web/packages/paleobioDB/">R package for that</a>, <code>paleobioDB</code> (<a href="http://onlinelibrary.wiley.com/doi/10.1111/ecog.01154/abstract">Varela et al., 2015</a>). I have little interest in doing what others have already made their focus. Second, maintaining such functions to ensure functionality <strong>forever</strong> as I essentialy would like to ensure for all <code>paleotree</code> functions is difficult, as issues and corrections will be needed every time a new PBDB API version appeared. Note how the functions below call version 1.1 of the API.</p>
<p>The functions below were written to automate some aspects of the API for the ‘occurrences’ and ‘taxa’ download functionalities, respectively. In particular, the longer of the two, <code>easygetPBDBocc</code>, was written to strip out warning messages returned by the API, which can cause problems with simple uses of <code>read.csv</code> to read in PBDA data downloads using the API. This could be particularly useful if a user wants to repeatedly query the PBDB, say for a series of taxon names from a list, particularly if it is unknown whether all the taxon on that list have been formally entered into the PBDB and have occurrence data listed for them.</p>
<pre class="r"><code>easyGetPBDBocc<-function(taxa,show=c("ident","phylo")){
#cleans PBDB occurrence downloads of warnings
taxa<-paste(taxa,collapse=",")
taxa<-paste(unlist(strsplit(taxa,"_")),collapse="%20")
show<-paste(show,collapse=",")
command<-paste0("http://paleobiodb.org/data1.1/occs/list.txt?base_name=",
taxa,"&show=",show,"&limit=all",
collapse="")
command<-paste(unlist(strsplit(command,split=" ")),collapse="%20")
downData<-readLines(command)
if(length(grep("Warning",downData))!=0){
start<-grep("Records",downData)
warn<-downData[1:(start-1)]
warn<-sapply(warn, function(x)
paste0(unlist(strsplit(unlist(strsplit(x,'"')),",")),collapse=""))
warn<-paste0(warn,collapse="\n")
names(warn)<-NULL
mat<-downData[-(1:start)]
mat<-read.csv(textConnection(mat))
message(warn)
}else{
mat<-downData
mat<-read.csv(textConnection(mat))
}
return(mat)
}
easyGetPBDBtaxa<-function(taxon){
#let's get some taxonomic data
taxaData<-read.csv(paste0("http://paleobiodb.org/",
"data1.1/taxa/list.txt?base_name=",taxon,
"&rel=all_children&show=phylo,img&status=senior"))
return(taxaData)
}</code></pre>
<p>Note well, that <code>easyGetPBDBtaxa</code> will only return the senior names of taxa, so that we don’t have to remove junior synonyms from the resulting dataset.</p>
<p>Now, we can use these functions to download some example data for graptoloids from the Paleobiology Database API, version 1.1:</p>
<pre class="r"><code>graptOccPBDB<-easyGetPBDBocc("Graptoloidea")
graptTaxaPBDB<-easyGetPBDBtaxa("Graptoloidea")</code></pre>
<p>And let’s look at these datasets very briefly…</p>
<pre class="r"><code>head(graptOccPBDB)[,1:10]</code></pre>
<pre><code>## occurrence_no record_type reid_no superceded collection_no
## 1 2319 occurrence NA NA 270
## 2 2432 occurrence NA NA 279
## 3 2461 occurrence NA NA 281
## 4 2604 occurrence NA NA 288
## 5 2761 occurrence NA NA 297
## 6 2762 occurrence NA NA 297
## taxon_name taxon_rank taxon_no matched_name matched_rank
## 1 Hallograptus sp. genus 33673 Hallograptus genus
## 2 Schizograptus sp. genus 33761 Schizograptus genus
## 3 Didymograptus ? sp. genus 33655 Didymograptus genus
## 4 Didymograptus sp. genus 33655 Didymograptus genus
## 5 Diplograptus sp. genus 33660 Diplograptus genus
## 6 Didymograptus sp. genus 33655 Didymograptus genus</code></pre>
<pre class="r"><code>head(graptTaxaPBDB)[,1:10]</code></pre>
<pre><code>## taxon_no orig_no record_type associated_records rank
## 1 33606 33606 taxon NA order
## 2 166989 166989 taxon NA family
## 3 166991 166991 taxon NA subfamily
## 4 150197 150197 taxon NA family
## 5 166988 166988 taxon NA subfamily
## 6 33650 33650 taxon NA genus
## taxon_name common_name status parent_no senior_no
## 1 Graptoloidea NA belongs to 33534 33606
## 2 Retiolitidae NA belongs to 33606 166989
## 3 Plectograptinae NA belongs to 166989 166991
## 4 Diplograptidae NA belongs to 33606 150197
## 5 Retiolitinae NA belongs to 166989 166988
## 6 Dicellograptus NA belongs to 33606 33650</code></pre>
<p>Now what to we do with these big tables of data? Let’s look at what we can do with the occurrence data first.</p>
</div>
<div id="sorting-unique-taxa-from-occurrence-datasets-with-taxonsortpbdbocc" class="section level1">
<h1>Sorting Unique Taxa From Occurrence Datasets with taxonSortPBDBocc</h1>
<p>Having the occurrence data in a big table isn’t going to do us much good without some sorting of these occurrence into those assigned to separate, unique taxa. We can break these tables down into lists, where each element is a table of occurrences assigned to taxa at various taxonomic levels using the new <code>paleotree</code> function <code>taxonsortPBDBocc</code>, which debuted in the <a href="http://nemagraptus.blogspot.com/2015/03/converting-pbdb-occurence-data-to.html">last blog post</a>. This function received a lot of attention from me while at the hackathon and can now handle data from almost any vocabularly or API version.</p>
<p>As discussed in that previous post, there are several ways to pull taxa: from different taxonomic levels, but also deciding whether to pull the ‘informal’ taxa that have never been officially entered and yet are listed in the original information from the identification of the occurrence. We can also decide whether we want to keep occurrence that had some sort of indicator of taxonomic uncertainty in their identified taxon name.</p>
<p>One neat thing we can do is use <code>taxonSortPBDBocc</code> to count the number of taxa available for different taxonomic levels and levels of data ‘cleanliness’.</p>
<p>First, we can count just the formal genera:</p>
<pre class="r"><code>occGenus<-taxonSortPBDBocc(graptOccPBDB, rank="genus")
length(occGenus)</code></pre>
<pre><code>## [1] 133</code></pre>
<p>And then just formal species:</p>
<pre class="r"><code>occSpeciesFormal<-taxonSortPBDBocc(graptOccPBDB, rank="species")
length(occSpeciesFormal)</code></pre>
<pre><code>## [1] 20</code></pre>
<p>And, yep, there are fewer ‘formal’ graptoloid species in the PBDB then there are ‘formal’ genera. This must mean a majority of genera have no species formally assigned to them.</p>
<p>Now let’s also count the informal species, along with the formal species:</p>
<pre class="r"><code>occSpeciesInformal<-taxonSortPBDBocc(graptOccPBDB, rank="species",
onlyFormal=FALSE)
length(occSpeciesInformal)</code></pre>
<pre><code>## [1] 642</code></pre>
<p>And our numbers increase to something that might be realistic (to my eye), now that we have those ‘informal’ species.</p>
<p>Now let’s have the informal and formal species altogether, but let’s <strong>not</strong> throwout any occurrences with suspicious/uncertain taxon identifiers. This is really everything and the kitchen sink, as they say.</p>
<pre class="r"><code>occSpeciesEverything<-taxonSortPBDBocc(graptOccPBDB, rank="species",
onlyFormal=FALSE, cleanUncertain=FALSE)
length(occSpeciesEverything)</code></pre>
<pre><code>## [1] 734</code></pre>
<p>And we get even more species recovered.</p>
<p>Now, we can visualize the age uncertainty of the occurrences assigned to our species using a plotting function I wrote <a href="http://nemagraptus.blogspot.com/2015/02/how-do-we-treat-fossil-age-data-dates.html">a few weeks ago and posted to this blog</a>. This function is now in <code>paleotree</code> as <code>plotOccData</code>, and it takes taxon-sorted lists of occurrence data as its input, just as given by <code>taxonSortPBDBocc</code>. Let’s plot the formal species data for now:</p>
<pre class="r"><code>plotOccData(occSpeciesFormal)</code></pre>
<p><img 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" title alt width="672" /></p>
<p>Each of the horizontal lines is the age uncertainty of a single occurrence, and occurrences are visually sorted and color-coded by the taxa (in this case, species) that they below to. We can get something a little more complex if we try genera:</p>
<pre class="r"><code>plotOccData(occGenus)</code></pre>
<p><img src="data:image/png;base64,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" title alt width="672" /></p>
<p>But as there are many more taxa, there is a lot more going on in this figure.</p>
</div>
<div id="get-taxon-occurrence-data-into-a-timelist-object-with-occdata2timelist" class="section level1">
<h1>Get Taxon Occurrence Data into a ‘timeList’ object with occData2timeList</h1>
<p>Once we have a taxon-sorted list of occurrence tables, we can obtain a <code>timeList</code> object useable by many other <code>paleotree</code> functions via the function <code>occData2timeList</code>. This function also initially debuted in the <a href="http://nemagraptus.blogspot.com/2015/03/converting-pbdb-occurence-data-to.html">last blog post</a>. It is much-much improved now, in particular it returns (by default) the smallest bounds possible for the first and last appearance of each taxon, which are values that maximize the use of information content from all the occurrence data.</p>
<p>Let’s apply it to the dataset that contains cleaned occurrences, for both formal and informal species.</p>
<pre class="r"><code># use occData2timeList
graptTimeSpecies<-occData2timeList(occList=occSpeciesInformal)</code></pre>
<p>Let’s look at what we have. Every <code>timeList</code> object is composed of two matrices each with two columns: (1) the age bounds on the intervals, and (2) the respective first and last intervals of each taxon, given as the interval’s rownumber in the first matrix.</p>
<pre class="r"><code>head(graptTimeSpecies[[1]])</code></pre>
<pre><code>## startTime endTime
## [1,] 488.3 471.8
## [2,] 485.4 477.7
## [3,] 485.4 473.9
## [4,] 478.6 470.0
## [5,] 478.6 468.9
## [6,] 478.6 468.1</code></pre>
<pre class="r"><code>head(graptTimeSpecies[[2]])</code></pre>
<pre><code>## firstInt lastInt
## Abiesgraptus tenuiramosus 90 90
## Akidograptus acuminatus 59 62
## Akidograptus ascensus 59 59
## Amplexograptus arctus 19 19
## Amplexograptus bohemicus 58 58
## Amplexograptus confertus 18 34</code></pre>
<p>Our main purpose for getting a <code>timeList</code> object in <code>paleotree</code> is probably to time-scale a tree, but with one not being handy at the moment, let’s just do something a little more boring and compare the diversity curves for (formal and informal) species and for genera:</p>
<pre class="r"><code>graptTimeGenus<-occData2timeList(occList=occGenus)
taxicDivDisc(graptTimeSpecies)</code></pre>
<p><img 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" title alt width="672" /></p>
<pre class="r"><code>taxicDivDisc(graptTimeGenus)</code></pre>
<p><img 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" title alt width="672" /></p>
<p>We can see that the two are very different, <a href="http://nemagraptus.blogspot.com/2015/02/identifying-unique-species-in.html">as I noted last time</a>. The early Silurian spike in species diversity looks artificial to my eye, but there may also be something going on whether too many taxa have been lumped into the wastebin taxon <em>Monograptus</em>.</p>
<p>If only there was a way of visualizing the taxonomic structures in the PBDB…</p>
</div>
<div id="creating-a-taxon-tree-from-the-paleobiology-databases-taxonomic-data" class="section level1">
<h1>Creating a ‘Taxon-Tree’ from the Paleobiology Database’s Taxonomic Data</h1>
<p>Most of us are probably familiar with the superficial similarities between taxonomy, as a nested hierarchy, and phylogenies, which also are hierarchies with a nesting structure (except not everything needs to be equally nested from the topology’s point of view…). A commonly invoked metaphore is of taxonomic groups as to imagine that nested taxonomic groups are alike nested monophyletic clades, with no additional resolution. In other words, we could describe the taxonomy of a group as a phylogeny-like arrangedment of mostly-unresolved clusters nested within each other. It’ll look like a phylogeny but it is really just the taxonomic information portrayed in a new way.</p>
<p>However, in some ways, such ‘taxon-trees’ are already widely used in some fields as the analytical basis for various phylogenetic comparative methods. For example, much of <a href="http://phylodiversity.net/phylomatic/">Phylomatic</a>’s lower-taxonomic structure is derived from a taxon-tree like approach. Recently, <a href="http://sysbio.oxfordjournals.org/content/early/2015/03/23/sysbio.syv015.abstract">Soul and Friedman</a> examined the use of taxon-trees versus real cladograms in the fossil record and found (excitingly) that the use of outdated non-cladistic-based taxon-trees performed just as well in many ways as actual cladograms for a number of groups in the fossil record.</p>
<p>I don’t know if the PBDB’s taxonomy will ever be good enough that we could use a ‘taxon-tree’ for some group as the basis for comparative analyses, but for now we can use a taxon-tree approach to visualize what taxonomy is in the PBDB and visually search for weird errors.</p>
<p>The function I’ve written for this is <code>makePBDBtaxontree</code> which takes a taxonomic download for some group from the PBDB. This function is not 100% optimal, however, as the taxon-tree produced only captures the original Linnaen ranks. When version 1.2 of the API is released, I’ll be able to query the name of a taxon’s direct, most senior parent’s name and construct taxon-trees that way, which will likely add additional branching levels to the produced taxon-trees.</p>
<p>Let’s look at some example taxon-trees from various taxonomic groups:</p>
<pre class="r"><code>#graptoloids
graptTree<-makePBDBtaxontree(graptTaxaPBDB,"genus")
plot(graptTree,show.tip.label=FALSE,no.margin=TRUE,edge.width=0.35)
nodelabels(graptTree$node.label,adj=c(0,1/2))</code></pre>
<p><img src="data:image/png;base64,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" title alt width="672" /></p>
<pre class="r"><code>#conodonts
conoData<-easyGetPBDBtaxa("Conodonta")
conoTree<-makePBDBtaxontree(conoData,"genus")
plot(conoTree,show.tip.label=FALSE,no.margin=TRUE,edge.width=0.35)
nodelabels(conoTree$node.label,adj=c(0,1/2))</code></pre>
<p><img 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" title alt width="672" /></p>
<pre class="r"><code>#asaphid trilobites
asaData<-easyGetPBDBtaxa("Asaphida")
asaTree<-makePBDBtaxontree(asaData,"genus")
plot(asaTree,show.tip.label=FALSE,no.margin=TRUE,edge.width=0.35)
nodelabels(asaTree$node.label,adj=c(0,1/2))</code></pre>
<p><img 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" title alt width="672" /></p>
<pre class="r"><code>#Ornithischia
ornithData<-easyGetPBDBtaxa("Ornithischia")
#need to drop repeated taxon first: Hylaeosaurus
ornithData<-ornithData[-(which(ornithData[,"taxon_name"]=="Hylaeosaurus")[1]),]
ornithTree<-makePBDBtaxontree(ornithData,"genus")
plot(ornithTree,show.tip.label=FALSE,no.margin=TRUE,edge.width=0.35)
nodelabels(ornithTree$node.label,adj=c(0,1/2))</code></pre>
<p><img 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" title alt width="672" /></p>
<p>One thing you’ll notice in these trees is not all the edges seem to stretch to the same height from the root: that’s deliberate. If we are looking at genera in an order and there are genera on edges of <code>length=1</code>, those genera have been loosely assigned to that order with no class or family information. A longer edge would indicate the genus is simply part of a monotypic higher-taxon which has been lost as <code>ape</code> hates ‘singles’ (i.e. nodes with only a single descendant).</p>
<p>Now, those are pretty neat. However, we can go a step further and use our occurrence data to help us generate a time-scaled taxon-tree, which should be even more useful for seeing weird outliers in the taxonomy or occurrence data.</p>
<p>We can time-scale using function <code>bin_timePaleoPhy</code> and we’ll select arguments like <code>nonstoch.bin=TRUE</code> and <code>type="mbl"</code> to make a pretty time-scaled tree when we plot it.</p>
<pre class="r"><code>#can use the time data from occurrences, generated above
#let's time-scale this tree with paleotree
#in such a way to maximize prettiness
timeTree<-bin_timePaleoPhy(graptTree,timeList=graptTimeGenus,
nonstoch.bin=TRUE,type="mbl",vartime=3)</code></pre>
<pre><code>## Warning: Following taxa dropped from tree: Trichograptus, Anthograptus, Yutagraptus, Joamgsjamotes...
## Warning: Following taxa dropped from timeList: Nicholsonograptus</code></pre>
<p>This drops <strong>alot</strong> of taxa; why? Presumably some are due to mispelled taxon names, but it must be more than that. There must be a large number of senior graptoloid genera which exist in the taxonomic database but have no corresponding occurrences.</p>
<p>Now let’s load Mark Bell and Graeme Lloyd’s library <code>strap</code> and take <code>geoscalePhylo</code> for a spin.</p>
<pre class="r"><code>library(strap)</code></pre>
<pre><code>## Loading required package: geoscale</code></pre>
<pre class="r"><code>geoscalePhylo(timeTree, ages=timeTree$ranges.used)
nodelabels(timeTree$node.label,cex=0.7,adj=c(0.3,0))</code></pre>
<p><img 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" title alt width="672" /></p>
<p>Cool! For those who haven’t used <code>geoscalePhylo</code> before, the thicker black bars on the edges are each taxon’s stratigraphic range, in this case the <a href="http://nemagraptus.blogspot.com/2015/02/how-do-we-treat-fossil-age-data-dates.html">maximal range</a> of each taxon.</p>
<p>We can see some genera with suspiciously long ranges relative to closely related taxa with much shorter ranges, and some groups that seems to be out of place (e.g. many of these genera should be in the diplograptid group, there is no monograptids…).</p>
</div>
<div id="a-worked-example-rhynchonellida" class="section level1">
<h1>A Worked Example: Rhynchonellida</h1>
<p>Alright, now let’s take the above, where we mostly follow me playing around with a graptolite dataset, and let’s go back over these function with an entirely different group, the rynchonellid brachiopods. I have spent considerable time poking and proding the character data of this group as part of my current post-doctoral position with <a href="http://geology.ucdavis.edu/people/faculty/carlson.php">Sandy Carlson</a> at UC Davis. So, what does the <em>Rhynchonellida</em> look like in the PBDB?</p>
<pre class="r"><code>rynchData<-easyGetPBDBtaxa("Rhynchonellida")
#need to drop repeated taxon first: Rhynchonelloidea
rynchData<-rynchData[-(which(rynchData[,"taxon_name"]=="Rhynchonelloidea")[1]),]
rynchTree<-makePBDBtaxontree(rynchData,"genus")
plot(rynchTree,show.tip.label=FALSE,no.margin=TRUE,edge.width=0.35)
nodelabels(rynchTree$node.label,adj=c(0,1/2))</code></pre>
<p><img 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" title alt width="672" /></p>
<p>Well, we can see a number of families with genera nested within them, and a number of genera that appear to be in monotypic families. Let’s get the occurrence data in the right format and time-scale the taxon-tree:</p>
<pre class="r"><code>rynchOcc<-easyGetPBDBocc("Rhynchonellida")
rynchSortOcc<-taxonSortPBDBocc(rynchOcc,"genus")
rynchTimeList<-occData2timeList(rynchSortOcc)
rynchTimeTree<-bin_timePaleoPhy(rynchTree,timeList=rynchTimeList,
nonstoch.bin=TRUE,type="mbl",vartime=3)</code></pre>
<pre><code>## Warning: Following taxa dropped from tree: Nayunnella, Hispanirhynchia, Sphenarina, Xiaobangdaia, Colophragma...
## Warning: Following taxa dropped from timeList: Aethirhynchia, Agarhyncha, Akopovorhynchia, Allorhynchoides, Almerarhynchia...</code></pre>
<p>Even more taxa dropped than with the graptoloids… again, this is probably the effect of having taxa listed in the taxonomic part of the PBDB and not in the occurrence database. I’d say ‘or vice versa’, but we should only be placing formal senior genera on the taxon-tree, so they can’t be in the occurrence data but not the taxonomy data.</p>
<p>Let’s plot the time-scaled tree…</p>
<pre class="r"><code>geoscalePhylo(rynchTimeTree, ages=rynchTimeTree$ranges.used)
nodelabels(rynchTimeTree$node.label,cex=0.5,adj=c(0.3,0))</code></pre>
<p><img 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" title alt width="672" /></p>
<p>Well, the number of overlapping node labels make this a little messy, but hey, looks cool!</p>
<p>Anyway, you can find everything above (except the two functions for pulling PBDB data) in the version of <code>paleotree</code> now up on Github, soon coming to CRAN!</p>
<p>Well, until next time…</p>
</div>
dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-685639586935252722015-03-17T20:39:00.000-07:002015-03-17T20:39:22.006-07:00Converting PBDB Occurence data to paleotree's timeList format<p>Hello all! Let’s finally get into what I wanted to talk about a month ago: converting Paleobiology Database (PBDB) data from <code>paleobioDB</code> (or the API) into a format for <code>paleotree</code> functions!</p>
<p>But first…</p>
<div id="some-recent-updates-to-r-package-paleobiodb-since-my-last-post" class="section level1">
<h1>Some Recent Updates to R Package paleobioDB Since My Last Post</h1>
<pre class="r"><code># install newest development version of paleobioDB
library(devtools)
install_github("ropensci/paleobioDB")</code></pre>
<p>Hot on the heels of my last post and the related tickets I opened on the <code>paleobioDB</code> github repo, Sara Varela has swiftly updated <code>pbdb_temp_range</code> in <code>paleobioDB</code> so that only taxa formally recognized (and thus, synoymized) by the PBDB, <a href="https://github.com/ropensci/paleobioDB/issues/19">as described here</a>. This function now only identifies and return the taxa formally recognized by the PBDB.</p>
<pre class="r"><code>library(paleobioDB)</code></pre>
<pre><code>## Loading required package: raster
## Loading required package: sp
## Loading required package: maps</code></pre>
<pre class="r"><code>dicelloData<-pbdb_occurrences(limit="all", base_name="Dicellograptus", show=c("phylo","ident"))
pbdb_temp_range(dicelloData, rank="species", do.plot=FALSE)</code></pre>
<pre><code>## max min
## Dicellograptus anceps 455.8 443.7
## Dicellograptus ornatus 453.0 443.4
## Dicellograptus complanatus 453.0 443.7</code></pre>
<p>This now returns the three <em>Dicellograptus</em> species ‘formally’ identified (i.e. given species-level taxon IDs and have occurrences listed for them) within the PBDB. This misses informal taxa (see <a href="http://nemagraptus.blogspot.com/2015/02/identifying-unique-species-in.html">last post</a>), but that’s really the problem of anyone who works on a group with poorly-kept taxonomy in the PBDB.</p>
<p><em>(cough graptolites cough)</em></p>
<p>Let’s look at a non-graptolite example then.</p>
<pre class="r"><code>acoData<-pbdb_occurrences(limit="all", base_name="Acosarina minuta", show=c("phylo","ident"), vocab="pbdb")
pbdb_temp_range(acoData, rank="species", do.plot=FALSE)</code></pre>
<pre><code>## max min
## Acosarina minuta 303.7 251.3</code></pre>
<pre class="r"><code>unique(acoData$matched_no)</code></pre>
<pre><code>## [1] 125301</code></pre>
<p>Only a single species, just as it should be because all of the listed occurrences are synonymized to a single species within the PBDB’s taxonomic database.</p>
<pre class="r"><code>pbdb_temp_range(acoData, rank="genus", do.plot=FALSE)</code></pre>
<pre><code>## max min
## Acosarina 303.7 251.3</code></pre>
<p>And only a single genus, so the results at different levels of the taxonomic hierarchy are consistent!</p>
</div>
<div id="getting-pbdb-data-into-paleotree-differences-between-api-and-paleobiodb-downloads" class="section level1">
<h1>Getting PBDB Data into paleotree: Differences between API and paleobioDB downloads</h1>
<p>Preferably, we’d like to implement utilities that work on both data collected using <code>paleobioDB</code> or data collected using the PBDB API. So, let’s also download some data using the API:</p>
<pre class="r"><code>dicelloData2<-read.csv("http://paleobiodb.org/data1.1/occs/list.txt?base_name=Dicellograptus&show=ident,phylo&limit=all")</code></pre>
<p>Unfortunately, here’s where we run into a snag. We can see these datasets differ in their column names and, in some cases, contents.</p>
<pre class="r"><code>head(dicelloData)</code></pre>
<pre><code>## oid typ cid tna rnk tid mna mra mid
## 1:1 3393 occ 328 Dicellograptus sp. 5 33650 Dicellograptus 5 33650
## 1:2 4589 occ 378 Dicellograptus sp. 5 33650 Dicellograptus 5 33650
## 1:3 4634 occ 379 Dicellograptus sp. 5 33650 Dicellograptus 5 33650
## 1:4 4688 occ 380 Dicellograptus sp. 5 33650 Dicellograptus 5 33650
## 1:5 8957 occ 403 Dicellograptus sp. 5 33650 Dicellograptus 5 33650
## 1:6 9127 occ 424 Dicellograptus sp. 5 33650 Dicellograptus 5 33650
## oei eag lag rid gnl gnn odl
## 1:1 Black River 460.9 457.5 13 Dicellograptus 33650 Graptoloidea
## 1:2 Harju 452.0 443.7 13 Dicellograptus 33650 Graptoloidea
## 1:3 Harju 452.0 443.7 13 Dicellograptus 33650 Graptoloidea
## 1:4 Caradoc 460.9 449.5 13 Dicellograptus 33650 Graptoloidea
## 1:5 Ashgill 449.5 443.7 13 Dicellograptus 33650 Graptoloidea
## 1:6 Middle Ordovician 470.0 458.4 13 Dicellograptus 33650 Graptoloidea
## odn cll cln phl phn idt ids oli
## 1:1 33606 Graptolithina 33534 Hemichordata 33518 Dicellograptus sp. <NA>
## 1:2 33606 Graptolithina 33534 Hemichordata 33518 Dicellograptus sp. <NA>
## 1:3 33606 Graptolithina 33534 Hemichordata 33518 Dicellograptus sp. <NA>
## 1:4 33606 Graptolithina 33534 Hemichordata 33518 Dicellograptus sp. <NA>
## 1:5 33606 Graptolithina 33534 Hemichordata 33518 Dicellograptus sp. <NA>
## 1:6 33606 Graptolithina 33534 Hemichordata 33518 Dicellograptus sp. <NA>
## rst rss
## 1:1 <NA> <NA>
## 1:2 <NA> <NA>
## 1:3 <NA> <NA>
## 1:4 <NA> <NA>
## 1:5 <NA> <NA>
## 1:6 <NA> <NA></code></pre>
<pre class="r"><code>head(dicelloData2)</code></pre>
<pre><code>## occurrence_no record_type reid_no superceded collection_no
## 1 3393 occurrence NA NA 328
## 2 4589 occurrence NA NA 378
## 3 4634 occurrence NA NA 379
## 4 4688 occurrence NA NA 380
## 5 8957 occurrence NA NA 403
## 6 9127 occurrence NA NA 424
## taxon_name taxon_rank taxon_no matched_name matched_rank
## 1 Dicellograptus sp. genus 33650 Dicellograptus genus
## 2 Dicellograptus sp. genus 33650 Dicellograptus genus
## 3 Dicellograptus sp. genus 33650 Dicellograptus genus
## 4 Dicellograptus sp. genus 33650 Dicellograptus genus
## 5 Dicellograptus sp. genus 33650 Dicellograptus genus
## 6 Dicellograptus sp. genus 33650 Dicellograptus genus
## matched_no early_interval late_interval early_age late_age
## 1 33650 Black River Black River 460.9 457.5
## 2 33650 Harju Harju 452.0 443.7
## 3 33650 Harju Harju 452.0 443.7
## 4 33650 Caradoc Caradoc 460.9 449.5
## 5 33650 Ashgill Ashgill 449.5 443.7
## 6 33650 Middle Ordovician Middle Ordovician 470.0 458.4
## reference_no genus_name genus_reso subgenus_name subgenus_reso
## 1 13 Dicellograptus NA NA
## 2 13 Dicellograptus NA NA
## 3 13 Dicellograptus NA NA
## 4 13 Dicellograptus NA NA
## 5 13 Dicellograptus NA NA
## 6 13 Dicellograptus NA NA
## species_name species_reso genus genus_no family family_no
## 1 sp. Dicellograptus 33650 NA NA
## 2 sp. Dicellograptus 33650 NA NA
## 3 sp. Dicellograptus 33650 NA NA
## 4 sp. Dicellograptus 33650 NA NA
## 5 sp. Dicellograptus 33650 NA NA
## 6 sp. Dicellograptus 33650 NA NA
## order order_no class class_no phylum phylum_no
## 1 Graptoloidea 33606 Graptolithina 33534 Hemichordata 33518
## 2 Graptoloidea 33606 Graptolithina 33534 Hemichordata 33518
## 3 Graptoloidea 33606 Graptolithina 33534 Hemichordata 33518
## 4 Graptoloidea 33606 Graptolithina 33534 Hemichordata 33518
## 5 Graptoloidea 33606 Graptolithina 33534 Hemichordata 33518
## 6 Graptoloidea 33606 Graptolithina 33534 Hemichordata 33518</code></pre>
<p>Why is this? Because of the two different ‘vocabularies’, explained in <a href="http://nemagraptus.blogspot.com/2015/02/identifying-unique-species-in.html">the last blog post</a>, we can end up with PBDB data downloads with different column heads and contents. By default, <code>paleobioDB</code> returns data with the ‘com’ (compact) vocab, while PBDB API by default uses the more extended ‘pbdb’ vocab. While ‘com’ saves on data transfer for large tables, and the <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">API documentation</a> is clear about the changes in variable names, the changes in contents is less clear. For example, <code>taxon_rank</code> numbers the ranks in the ‘com’ vocab, but there doesn’t appear to be any documentation on what these numbers mean. All one can do is download the same data under both vocabs to figure it out; e.g. a rank of ‘3’ appears to be ‘species’.</p>
<p>As I noted <a href="http://nemagraptus.blogspot.com/2015/02/identifying-unique-species-in.html">before</a>, this makes a mess of things because it means those of us writing code to manipulate PBDB data either (a) write two seperate versions of their code to anticipate either ‘pbdb’ or ‘com’ datasets, which is what the authors of <code>paleobioDB</code> have done or (b) pick one and require users to only use one vocab. Personally, I find the first option to be dangerous from a programming view, because writing the same code twice can lead to toxic inconsistencies, such as when you have to go back to fix bugs at a later date and forget to fix them in the second version of the code. The second option is the easiest from a programming point of view, but likely to cause issues since the default for <code>paleobioDB</code> is ‘com’ vocab and the default for the PBDB API is ‘pbdb’ vocab, and any utilities written are likely to get use from both. Speaking from maintaining <code>paleotree</code> for the last three years, something like a warning statement about ‘you need to use the right vocab!!’ is exactly the sort of statement that just confuses users and fills your inbox. A third option might be to include lines that transform the needed variables of one vocab into the other, but without more documentation, that’s not entirely possible.</p>
<p>For the moment, I’m going to go with the second option and stick to ‘pbdb’ vocab since it is the clearest and easiest to read and manipulate. This means I’ll need to adjust the arguments for my <code>paleobioDB</code> <em>Dicellograptus</em> download to use ‘pbdb’ vocab.</p>
<pre class="r"><code>dicelloData<-pbdb_occurrences(limit="all", base_name="Dicellograptus", show=c("phylo","ident"), vocab="pbdb")
head(dicelloData)</code></pre>
<pre><code>## occurrence_no record_type collection_no taxon_name taxon_rank
## 1:1 3393 occurrence 328 Dicellograptus sp. genus
## 1:2 4589 occurrence 378 Dicellograptus sp. genus
## 1:3 4634 occurrence 379 Dicellograptus sp. genus
## 1:4 4688 occurrence 380 Dicellograptus sp. genus
## 1:5 8957 occurrence 403 Dicellograptus sp. genus
## 1:6 9127 occurrence 424 Dicellograptus sp. genus
## taxon_no matched_name matched_rank matched_no early_interval
## 1:1 33650 Dicellograptus genus 33650 Black River
## 1:2 33650 Dicellograptus genus 33650 Harju
## 1:3 33650 Dicellograptus genus 33650 Harju
## 1:4 33650 Dicellograptus genus 33650 Caradoc
## 1:5 33650 Dicellograptus genus 33650 Ashgill
## 1:6 33650 Dicellograptus genus 33650 Middle Ordovician
## early_age late_age reference_no genus genus_no order
## 1:1 460.9 457.5 13 Dicellograptus 33650 Graptoloidea
## 1:2 452.0 443.7 13 Dicellograptus 33650 Graptoloidea
## 1:3 452.0 443.7 13 Dicellograptus 33650 Graptoloidea
## 1:4 460.9 449.5 13 Dicellograptus 33650 Graptoloidea
## 1:5 449.5 443.7 13 Dicellograptus 33650 Graptoloidea
## 1:6 470.0 458.4 13 Dicellograptus 33650 Graptoloidea
## order_no class class_no phylum phylum_no genus_name
## 1:1 33606 Graptolithina 33534 Hemichordata 33518 Dicellograptus
## 1:2 33606 Graptolithina 33534 Hemichordata 33518 Dicellograptus
## 1:3 33606 Graptolithina 33534 Hemichordata 33518 Dicellograptus
## 1:4 33606 Graptolithina 33534 Hemichordata 33518 Dicellograptus
## 1:5 33606 Graptolithina 33534 Hemichordata 33518 Dicellograptus
## 1:6 33606 Graptolithina 33534 Hemichordata 33518 Dicellograptus
## species_name late_interval genus_reso species_reso
## 1:1 sp. <NA> <NA> <NA>
## 1:2 sp. <NA> <NA> <NA>
## 1:3 sp. <NA> <NA> <NA>
## 1:4 sp. <NA> <NA> <NA>
## 1:5 sp. <NA> <NA> <NA>
## 1:6 sp. <NA> <NA> <NA></code></pre>
<p>There’s also <em>another</em> issue of trying to write functions useful for both PBDB API and <code>paleobioDB</code> downloads. In addtion to <code>paleobioDB</code> (dropping empty variables)[<a href="https://github.com/ropensci/paleobioDB/issues/18" class="uri">https://github.com/ropensci/paleobioDB/issues/18</a>], empty variable entries are assigned <code>NA</code> while the <code>read.csv</code> procedure used above for downloading PBDB API data replaces empty variables with <code>""</code> (i.e. an empty string). Empty variables, however, get assigned <code>NA</code> by <code>read.csv</code>, which is very inconsistent.</p>
<p>Here’s some examples from the API downloaded data:</p>
<pre class="r"><code>#an empty variable
as.character(dicelloData2$subgenus_reso[1:20])</code></pre>
<pre><code>## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA</code></pre>
<pre class="r"><code>#a variable with both empty and filled entries
as.character(dicelloData2$genus_reso[1:20])</code></pre>
<pre><code>## [1] "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" "" ""
## [18] "" "?" ""</code></pre>
<p>And, for comparison, the <code>paleobioDB</code> data (note the empty variable has been dropped):</p>
<pre class="r"><code>#an empty variable
as.character(dicelloData$subgenus_reso[1:20])</code></pre>
<pre><code>## character(0)</code></pre>
<pre class="r"><code>#a variable with both empty and filled entries
as.character(dicelloData$genus_reso[1:20])</code></pre>
<pre><code>## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [18] NA "?" NA</code></pre>
<p>One way to fix this is via the argument <code>na.strings</code> in <code>read.csv</code>.</p>
<pre class="r"><code>dicelloData3<-read.csv("http://paleobiodb.org/data1.1/occs/list.txt?base_name=Dicellograptus&show=ident,phylo&limit=all",
na.strings=c("NA",""))
#a variable with both empty and filled entries
as.character(dicelloData3$genus_reso[1:20])</code></pre>
<pre><code>## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [18] NA "?" NA</code></pre>
<p>A better solution, however, may be to simply put a check in any function cleaning the data to convert empty string values (<code>""</code>) to <code>NA</code>s, like so.</p>
<pre class="r"><code>#making a copy of the original API download
dicelloData3<-dicelloData2
#replace empty values with NA
dicelloData3[dicelloData3==""]<-NA
#a variable with both empty and filled entries
as.character(dicelloData3$genus_reso[1:20])</code></pre>
<pre><code>## [1] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## [18] NA "?" NA</code></pre>
<p>Looks good.</p>
</div>
<div id="sorting-out-unique-taxa" class="section level1">
<h1>Sorting Out Unique Taxa</h1>
<p>Alright, now that we picked a consistent vocabulary, we need to write a function that sorts occurrence data into the unique taxa of a user-selected taxonomic rank. I think the preferable way to do this is to make a list where each element is a ‘unique’ taxon and all the occurrences attributed to it are under that element as a table. Additionally, we’ll have to allow the function to pull either just the ‘formal’ identified taxa or ‘all’ the identified taxa. This is necessary if we want species that are listed under their genus and never got assigned a species-level taxonomic ID in the PBDB.</p>
<p>Its important we consider the order of data manipulations we need to perform in this process. Formal identified taxa need to be pulled first, and then informal taxa pulled from whatever occurrences are left over. Furthermore, its possible (but hopefully rare) that the same taxon might be listed both formally and informally in a set of PBDB occurrences, and it would be preferable that the ‘informal’ occurrences for a taxon be concatenated to the ‘formal’ occurrences for that same taxon, rather than be treated as separate taxon.</p>
<p>Now, the functions I used as references when writing this function were <code>paleobioDB</code>’s <code>pbdb_temp_range</code> function (<a href="https://github.com/ropensci/paleobioDB/blob/master/R/pbdb_temporal_functions.R#L64-178">code here</a>), and <a href="http://people.ucsc.edu/~mclapham">Matthew Clapham</a>’s <code>taxonClean</code> function (<a href="https://github.com/mclapham/PBDB-R-scripts/blob/master/taxonClean.R">code here</a>). I took a somewhat different approach though. Both of these write seperate code for the different taxonomic ranks, which is vaguely unsatisfying from a programming perspective, as it could lead to inconsistencies where you fail to update all parts. Thankfully, except for species, one can get the formal taxonomic name for a given taxonomic rank by looking for the variable with the same name as the taxonomic rank. Additionally, we can look for informal occurrences of a taxon by then checking for occurrences with <code>taxon_rank</code> identical to the required rank (although it seems this is rarely important).</p>
<p>And, as noted above, we need a check to convert empty <code>""</code> values to <code>NA</code>s.</p>
<pre class="r"><code>taxonSortPBDBocc<-function(data,rank, onlyFormal=TRUE, cleanUncertain=TRUE,
cleanResoValues=c(NA, '"', "", "n. sp.", "n. gen.")){
#this function inspired by Matt Clapham's taxonClean and paleobioDB's pbdb_temp_range
#onlyFormal=FALSE;rank="species"
#onlyFormal=FALSE;rank="genus"
#pull occurrences out of a data table and sort by unique taxa into a list structure
#First, some warning checks to see if occurrences downloaded correctly:
# pbdb vocab and showing phylo and ident data
#the following is taken with minor modification from code in paleobioDB package
if (any("tna"==colnames(data))){
stop("need to add 'vocab='pbdbd'' to pbdb_occurrences query\n *or*\n 'vocab=pbdb' to your PBDB API query")
}
if (!any("genus_name"==colnames(data)) | !any("genus"==colnames(data))){
stop("need to add 'show=c('phylo', 'ident')' to pbdb_occurrences query\n *or*\n 'show=ident,phylo' to PBDB API query")
}
#additional checks for rank
if(length(rank)!=1){stop("length of rank must be 1")}
if(!any(sapply(c("species","genus","family","order","class","phylum"),function(x) x==rank))){
stop("rank must be one of 'species', 'genus', 'family', 'order', 'class' or 'phylum'")}
#for inconsistencies between rank and onlyFormal - NOT TRUE, IGNORE THIS CHECK
#if(!onlyFormal & (rank!="species" | rank!="genus")){
# stop("Informal taxon does not exist for above genus level, please use 'onlyFormal=TRUE'")}
#need to replace any empty string values with NAs (due perhaps to use of read.csv with the API)
data[data==""]<-NA
#now, pull taxa using the relevant variable from 'phylo' for formal ID
#this matches what paleobioDB now does
#sort occurrences by unique taxa in each level and then append valid names
if(rank=="species"){
if(cleanUncertain){ #remove uncertain species taxonomy
data<-data[data[,"species_reso"] %in% cleanResoValues,,drop=FALSE]
}
#first formal taxa
#get species names for taxa formally recognized as species level
whichFormal<-(data[,"matched_rank"]==rank)
taxonVar<-as.character(data[,"matched_name"])
taxaNames<-as.character(unique(taxonVar[whichFormal])) #get unique taxa
#drop empty entries (occurrences listed at a higher taxonomic level, formally)
taxaNames<-taxaNames[!is.na(taxaNames)]
#take rows these taxa are in and drop them into elements of a list
sortedOcc<-lapply(taxaNames,function(x) data[which(x==taxonVar),,drop=FALSE])
names(sortedOcc)<-taxaNames
if(!onlyFormal){
#now use taxon_rank to identify useful informal occurrence
taxonVar2<-data[,c("genus_name","species_name")]
taxonVar2<-apply(taxonVar2,1,paste,collapse=" ")
#which of these occs are useful as informal occs
stillUseful<-which(!whichFormal & (data[,"taxon_rank"]==rank))
taxaNames2<-as.character(unique(taxonVar2[stillUseful]))
#drop any weird empties
taxaNames2<-taxaNames2[taxaNames2!=" " & !is.na(taxaNames2)]
sortedOcc2<-lapply(taxaNames2,function(x) data[which(x==taxonVar2),,drop=FALSE])
names(sortedOcc2)<-taxaNames2
# merge sortedocc2 with sortedOcc
share1<-sapply(taxaNames,function(x) any(x==taxaNames2))
share2<-sapply(taxaNames2,function(x) any(x==taxaNames))
if(sum(!share1)>0){sortedOccU<-sortedOcc[!share1]}else{sortedOccU<-list()}
if(sum(!share2)>0){sortedOcc2U<-sortedOcc2[!share2]}else{sortedOcc2U<-list()}
#and for shared names
if(sum(share1)>0){
shared<-lapply(taxaNames[share1],function(x)
cbind(sharedOcc[[taxaNames==x]],sharedOcc2[[taxaNames2==x]]))
names(shared)<-names(taxaNames[share1])
}else{shared<-list()}
sortedOcc<-c(sortedOccU,sortedOcc2U,shared)
}
}else{ #if not at the species rank
if(cleanUncertain & rank=="genus"){ #if rank=genus and removing uncertain taxonomy
data<-data[data[,"genus_reso"] %in% cleanResoValues,,drop=FALSE]
}
taxonVar<-data[,rank] #then our taxonomic variable of interest is just so!
taxaNames<-as.character(unique(taxonVar)) #get unique taxa
taxaNames<-taxaNames[!is.na(taxaNames)]
#take rows these taxa are in and drop them into elements of a list
sortedOcc<-lapply(taxaNames,function(x) data[which(x==taxonVar),,drop=FALSE])
names(sortedOcc)<-taxaNames
if(!onlyFormal){
#now use taxon_rank to identify useful informal occurrence
taxonVar2<-data[,"taxon_name"]
#which of these occs are useful as informal occs
stillUseful<-which(is.na(taxonVar) & (data[,"taxon_rank"]==rank))
taxaNames2<-as.character(unique(taxonVar2[stillUseful]))
taxaNames2<-taxaNames2[!is.na(taxaNames2)]
sortedOcc2<-lapply(taxaNames2,function(x) data[which(x==taxonVar2),,drop=FALSE])
names(sortedOcc2)<-taxaNames2
# merge sortedocc2 with sortedOcc
share1<-sapply(taxaNames,function(x) any(x==taxaNames2))
share2<-sapply(taxaNames2,function(x) any(x==taxaNames))
if(sum(!share1)>0){sortedOccU<-sortedOcc[!share1]}else{sortedOccU<-list()}
if(sum(!share2)>0){sortedOcc2U<-sortedOcc2[!share2]}else{sortedOcc2U<-list()}
#and for shared names
if(sum(share1)>0){
shared<-lapply(taxaNames[share1],function(x)
cbind(sharedOcc[[taxaNames==x]],sharedOcc2[[taxaNames2==x]]))
names(shared)<-names(taxaNames[share1])
}else{shared<-list()}
sortedOcc<-c(sortedOccU,sortedOcc2U,shared)
}
}
# sort occurrences by taxon name
sortedOcc<-sortedOcc[order(names(sortedOcc))]
return(sortedOcc)
}</code></pre>
<p>(I apologize for the terribly uneven indenting above. A strange copy/paste error involving tabs in Rstudio, Notepad++ and R GUI, and the more I tab, the worse it gets. I promise a clean-looking version in the <code>paleotree</code> github repo soon.)</p>
<p>Now, in the following, I have dropped or revised some of the checks from <code>taxonClean</code> and <code>pbdb_temp_ranges</code>.</p>
<ul>
<li><code>pbdb_temp_ranges</code> originally (before my issue tickets) checked to see if the same taxon was listed under multiple <code>taxon_no</code> ID numbers, and returned an error if this was true. This resulted in some interesting cases where the error arose due to subtle differences in taxonomic name, like <em>Pseudoclimacograptus (Metaclimacograptus) hughesi</em> and <em>Pseudoclimacograptus hughesi</em> being listed (for whatever reason) under different <code>taxon_no</code> ID numbers. I’m blanket presuming that if a taxon’s listed taxon name (e.g. formal names <code>matched_name</code>, <code>genus</code>, etc. as well as informal like <code>genus_name + species_name</code> and <code>taxon_name</code>.) are the same, its the same taxon. This assumption is tempered by the order of operations I discussed above: formal taxa are identified first, and informal taxa are identified from the occurrences that are ‘leftover’, and informal occurrences assigned to a taxon with a formal ID are concatenated to the ‘formal’ occurrences. Otherwise, we could mistakingly link the same occurrences to multiple taxa.
<ul>
<li>For what its worth, with respect to my above example, it looks like all taxa with <code>taxon_name</code> of <em>Pseudoclimacograptus (Metaclimacograptus) hughesi</em> or <em>Pseudoclimacograptus hughesi</em> have identical <code>genus_name</code> and <code>species_name</code> variables… but not identical <code>matched_name</code> variables, as only some of these occurrences have been assigned to formal taxon <em>Diplograptus_hughesi</em>, while others are assigned to genus-level formal taxa. But that’s a whole other can of worms: the inconsistent taxonomy of graptolites in the PBDB.</li>
</ul></li>
<li><code>taxonClean</code> removes taxa with question marks, <em>cf.</em> or other taxonomic-uncertainty flim-flam from the names it uses to identify unique taxa. Now there are two seperate concerns here:
<ol style="list-style-type: decimal">
<li>Occurrences with taxonomic uncertainy in our data. This really only pertains when <code>rank</code> is <code>"species"</code> or <code>"genus"</code>, and can be easily taken care of by removing occurrences where <code>species_reso</code> or <code>genus_reso</code> does not equal a ‘safe’ value, a functionality controlled by the argument <code>cleanUncertain</code>, which is <code>TRUE</code> by default. The ‘safe’ values for <code>species_reso</code> or <code>genus_reso</code> are listed in the <code>cleanResoValues</code> argument, and by default includes common notifiers like <em>‘n. sp.’</em> which indicate something other than taxonomic uncertainty.</li>
<li>The ‘cleaning’ of names so they don’t have unwanted taxonomic cruff on them, like that one sock in your laundry that attracts all the distasteful lint. This is particularly problematic as I use names, and not taxon ID numbers, to match occurrences as having the same tazon. However, my use-case is somewhat different from Clapham’s, as <code>cleanTaxon</code> uses <code>taxon_name</code>, while I use <code>matched_name</code> for formal species and the appropriate taxonomic variable for higher-taxa (i.e. <code>genus</code> for genera) and, for informal taxa, combining <code>genus_name</code> and <code>species_name</code> (for species), with <code>taxon_name</code> only being called for referencing informal supraspecific taxa. My working assumption is that taxonomic name rubbish has been kept out of <code>matched_name</code> and the various ‘formal’ supraspecific taxon variables, as well as <code>genus_name</code> and <code>species_name</code> for the taxonomic names given in the reference for that occurrence. That could be a bad assumption, but so far I haven’t found any. I know this rubbish exists in <code>taxon_name</code>, but (a) its hard to identify every use-case of such name-trash, and plus the case where an occurrence isn’t given a formal higher-taxon is very rare (in the data I’ve looked at).</li>
</ol></li>
</ul>
<p>Now, let’s test this function with data from <code>paleobioDB</code>. If we look at the <em>Dicellograptus</em> data, we should find only a single genus.</p>
<pre class="r"><code>x<-taxonSortPBDBocc(dicelloData, rank="genus", onlyFormal=TRUE)
names(x)</code></pre>
<pre><code>## [1] "Dicellograptus"</code></pre>
<p>Yep! And with the dataset downloaded using the API, we should get the same:</p>
<pre class="r"><code>x<-taxonSortPBDBocc(dicelloData2, rank="genus", onlyFormal=TRUE)
names(x)</code></pre>
<pre><code>## [1] "Dicellograptus"</code></pre>
<p>Yes. Now, let’s try to just retrieve ‘formal’ species from the <code>paleobioDB</code> dataset, and look at what’s in the first element:</p>
<pre class="r"><code>x<-taxonSortPBDBocc(dicelloData, rank="species", onlyFormal=TRUE)
names(x)</code></pre>
<pre><code>## [1] "Dicellograptus anceps" "Dicellograptus complanatus"
## [3] "Dicellograptus ornatus"</code></pre>
<pre class="r"><code>head(x[[1]])</code></pre>
<pre><code>## occurrence_no record_type collection_no taxon_name
## 1:17 52248 occurrence 3875 Dicellograptus anceps
## 1:214 1143907 occurrence 145603 Dicellograptus anceps
## 1:227 1143994 occurrence 145612 Dicellograptus anceps
## taxon_rank taxon_no matched_name matched_rank matched_no
## 1:17 species 306364 Dicellograptus anceps species 306364
## 1:214 species 306364 Dicellograptus anceps species 306364
## 1:227 species 306364 Dicellograptus anceps species 306364
## early_interval early_age late_age reference_no genus
## 1:17 Rawtheyan 455.8 445.6 227 Dicellograptus
## 1:214 Ashgillian 449.5 443.7 47085 Dicellograptus
## 1:227 Ashgillian 449.5 443.7 47087 Dicellograptus
## genus_no order order_no class class_no phylum
## 1:17 33650 Graptoloidea 33606 Graptolithina 33534 Hemichordata
## 1:214 33650 Graptoloidea 33606 Graptolithina 33534 Hemichordata
## 1:227 33650 Graptoloidea 33606 Graptolithina 33534 Hemichordata
## phylum_no genus_name species_name late_interval genus_reso
## 1:17 33518 Dicellograptus anceps Kralodvorian <NA>
## 1:214 33518 Dicellograptus anceps <NA> <NA>
## 1:227 33518 Dicellograptus anceps <NA> <NA>
## species_reso
## 1:17 <NA>
## 1:214 <NA>
## 1:227 <NA></code></pre>
<p>Okay, 3 formal species and occurrence data for a single species (<em>Dicellograptus anceps</em>), just like we’d expect. And now both formal and informal species:</p>
<pre class="r"><code>x<-taxonSortPBDBocc(dicelloData, rank="species", onlyFormal=FALSE)
names(x)</code></pre>
<pre><code>## [1] "Dicellograptus alector" "Dicellograptus anceps"
## [3] "Dicellograptus complanatus" "Dicellograptus divaricatus"
## [5] "Dicellograptus flexuosus" "Dicellograptus gurleyi"
## [7] "Dicellograptus intermedius" "Dicellograptus intortus"
## [9] "Dicellograptus johnstrupi" "Dicellograptus mensurans"
## [11] "Dicellograptus minor" "Dicellograptus mirabilis"
## [13] "Dicellograptus morrisi" "Dicellograptus ornatus"
## [15] "Dicellograptus russonioides" "Dicellograptus sextans"
## [17] "Dicellograptus tumidus" "Dicellograptus turgidus"</code></pre>
<p>A big increase in the number of species recovered! Now, we might be curious if this number further increases if we retain occurrences with uncertain taxonomy:</p>
<pre class="r"><code>x<-taxonSortPBDBocc(dicelloData, rank="species", onlyFormal=FALSE, cleanUncertain=FALSE)
names(x)</code></pre>
<pre><code>## [1] "Dicellograptus alector" "Dicellograptus anceps"
## [3] "Dicellograptus angulatus" "Dicellograptus caduceus"
## [5] "Dicellograptus complanatus" "Dicellograptus divaricatus"
## [7] "Dicellograptus elegans" "Dicellograptus flexuosus"
## [9] "Dicellograptus forchammeri" "Dicellograptus gurleyi"
## [11] "Dicellograptus intermedius" "Dicellograptus intortus"
## [13] "Dicellograptus johnstrupi" "Dicellograptus mensurans"
## [15] "Dicellograptus minor" "Dicellograptus mirabilis"
## [17] "Dicellograptus moffatensis" "Dicellograptus morrisi"
## [19] "Dicellograptus ornatus" "Dicellograptus pumilis"
## [21] "Dicellograptus russonioides" "Dicellograptus sextans"
## [23] "Dicellograptus smithi" "Dicellograptus tumidus"
## [25] "Dicellograptus turgidus" "Dicellograptus vagus"</code></pre>
<p>Oooh, even more species of questionable taxonomic validity!</p>
<p>That’s enough <em>Dicellograptus</em> for the moment. Now, let’s try our example brachiopod data from before. We should only recover one species, even if we allow it to pull informal taxa too:</p>
<pre class="r"><code>x<-taxonSortPBDBocc(acoData, rank="species", onlyFormal=FALSE)
names(x)</code></pre>
<pre><code>## [1] "Acosarina minuta"</code></pre>
<p>Yep, looks good!</p>
</div>
<div id="converting-occurrences-data-to-a-timelist-data-object" class="section level1">
<h1>Converting Occurrences Data to a timeList Data Object</h1>
<p>Alright, now we can finally do what I meant to show you in <strong>January</strong>: turn these PBDB occurrences into a <code>paleotree</code> <code>timelist</code> data format. As you might recall from <a href="http://nemagraptus.blogspot.com/2013/06/a-tutorial-to-cal3-time-scaling-using.html">some</a> <a href="http://nemagraptus.blogspot.com/2015/02/how-do-we-treat-fossil-age-data-dates.html">previous posts</a>, <code>timeList</code> data objects are lists composed of two matrices, the first matrix giving the start and end times of intervals, and the second matrix giving the intervals in which taxa are first and last observed.</p>
<p>Now, I imagine there might be more reasons to need to convert occurrence data to a <code>timeList</code> object than just those involving the PBDB (such as output from simulation function <code>sampleRanges</code>), so I’ll allow this function to look either for (a) variables named <code>early_age</code> and <code>late_age</code> as under ‘pbdb’ vocab, or, (b) if each element of the list is a two-column matrix, use each pair of values as the earliest and latest time bounds for the listed occurrences.</p>
<pre class="r"><code>occData2timeList<-function(occList){
# the following is all original, though inspired by paleobioDB code
#need checks
#pull first list entry
exOcc<-occList[[1]]
#test if all occList entries have same number of columns
if(!all(sapply(occList,function(x) ncol(x)==ncol(exOcc)))){
stop("Not all occList entries have same number of columns?")}
#will assume all data given in this manner either has columns named "" and ""
#or has only two columns
if(!any(colnames(exOcc)=="early_age") | !any(colnames(exOcc)=="late_age")){
if(!ncol(exOcc)!=2){stop("")}else{
ageSelector<-1:2}
}else{ageSelector<-c("early_age","late_age")}
#get intervals in which taxa appear
taxaInt<-lapply(occList,function(x) x[,ageSelector])
#get earliest and latest intervals the taxa appear in
taxaMin<-lapply(taxaInt,function(x) x[x[,1]==max(x[,1]),,drop=FALSE])
taxaMax<-lapply(taxaInt,function(x) x[x[,2]==min(x[,2]),,drop=FALSE])
#get smallest intervals
taxaMin<-sapply(taxaMin,function(x) if(nrow(x)>1){
x[which((-apply(x,1,diff))==min(-apply(x,1,diff)))[1],]
}else{x})
taxaMax<-sapply(taxaMax,function(x) if(nrow(x)>1){
x[which((-apply(x,1,diff))==min(-apply(x,1,diff)))[1],]
}else{x})
#transpose and remove weird list attribute
taxaMin<-t(taxaMin)
taxaMax<-t(taxaMax)
taxaMin<-cbind(unlist(taxaMin[,1]),unlist(taxaMin[,2]))
taxaMax<-cbind(unlist(taxaMax[,1]),unlist(taxaMax[,2]))
#make interval list
intTimes<-unique(rbind(taxaMin,taxaMax))
intTimes<-intTimes[order(-intTimes[,1],-intTimes[,2]),]
#now assign taxa first and last intervals
firstInt<-apply(taxaMin,1,function(x)
which(apply(intTimes,1,function(y) identical(y,x))))
lastInt<-apply(taxaMax,1,function(x)
which(apply(intTimes,1,function(y) identical(y,x))))
taxonTimes<-cbind(firstInt,lastInt)
rownames(taxonTimes)<-names(occList)
#package it together
dimnames(intTimes)<-list(NULL,c("startTime","endTime"))
res<-list(intTimes=intTimes,taxonTimes=taxonTimes)
return(res)
}</code></pre>
<p>Let’s try it out!</p>
<pre class="r"><code>sortDicelloOcc<-taxonSortPBDBocc(dicelloData,rank="species",onlyFormal=FALSE)
dicelloTimeList<-occData2timeList(occList=sortDicelloOcc)
dicelloTimeList</code></pre>
<pre><code>## $intTimes
## startTime endTime
## [1,] 471.8 457.5
## [2,] 467.3 458.4
## [3,] 460.9 456.1
## [4,] 460.9 449.5
## [5,] 458.4 443.4
## [6,] 455.8 445.6
## [7,] 453.0 445.2
## [8,] 449.5 445.6
## [9,] 449.5 443.7
## [10,] 445.2 443.4
##
## $taxonTimes
## firstInt lastInt
## Dicellograptus alector 4 9
## Dicellograptus anceps 6 9
## Dicellograptus complanatus 7 9
## Dicellograptus divaricatus 3 3
## Dicellograptus flexuosus 4 4
## Dicellograptus gurleyi 4 4
## Dicellograptus intermedius 2 3
## Dicellograptus intortus 3 3
## Dicellograptus johnstrupi 4 4
## Dicellograptus mensurans 4 4
## Dicellograptus minor 7 9
## Dicellograptus mirabilis 9 9
## Dicellograptus morrisi 4 8
## Dicellograptus ornatus 7 10
## Dicellograptus russonioides 3 3
## Dicellograptus sextans 1 5
## Dicellograptus tumidus 9 9
## Dicellograptus turgidus 9 9</code></pre>
<p>Looks like a timeList to me!</p>
<p>Okay, now we’re ready to try using the data with <code>paleotree</code>! Now, the main use will probably be for time-scaling a tree using functions like <code>bin_timePaleoPhy</code>, but since we lack a tree at this very moment to time-scale, let’s just use one of <code>paleotree</code>’s diversity curve for now. I realize that’s a bit of a letdown, maybe, since <code>paleobioDB</code> already has diversity curve functions (although they work different from <code>paleotree</code>’s).</p>
<pre class="r"><code>library(paleotree)
taxicDivDisc(dicelloTimeList)</code></pre>
<p><img src="data:image/png;base64,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" title alt width="672" /></p>
<p>Other things we could do is to get at sampling rates from these estimates, which I’ll be covering in a future blog post (perhaps the next post?? Who knows!).</p>
</div>
<div id="a-full-worked-example" class="section level1">
<h1>A Full Worked Example</h1>
<p>My intention is to add <code>taxonSortPBDBocc</code> and <code>occData2timeList</code> to <code>paleotree</code> very soon, but for now users will have to source them into R manually. Assuming you do source these two functions, or that you are reading this <strong>in the future</strong> and have a new version of <code>paleotree</code> with these two functions, here’s a complete worked example with all graptoloids, using <code>paleobioDB</code> to download the data:</p>
<pre class="r"><code>library(paleotree)
library(paleobioDB)
taxon<-"Graptoloidea"
graptOcc<-pbdb_occurrences(limit="all", base_name=taxon,
show=c("phylo","ident"), vocab="pbdb")
#for species (formal ID only)
sortGraptOcc<-taxonSortPBDBocc(graptOcc,rank="species",onlyFormal=TRUE)
graptTimeList<-occData2timeList(occList=sortGraptOcc)
taxicDivDisc(graptTimeList)</code></pre>
<p><img 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" title alt width="672" /></p>
<pre class="r"><code>#for species (all)
sortGraptOcc<-taxonSortPBDBocc(graptOcc,rank="species",onlyFormal=FALSE)
graptTimeList<-occData2timeList(occList=sortGraptOcc)
taxicDivDisc(graptTimeList)</code></pre>
<p><img 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" title alt width="672" /></p>
<pre class="r"><code>#for genera (formal ID only)
sortGraptOcc<-taxonSortPBDBocc(graptOcc,rank="genus",onlyFormal=TRUE)
graptTimeList<-occData2timeList(occList=sortGraptOcc)
taxicDivDisc(graptTimeList)</code></pre>
<p><img 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" title alt width="672" /></p>
<pre class="r"><code>#for family (formal ID only)
sortGraptOcc<-taxonSortPBDBocc(graptOcc,rank="family",onlyFormal=TRUE)
graptTimeList<-occData2timeList(occList=sortGraptOcc)
taxicDivDisc(graptTimeList)</code></pre>
<p><img 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" title alt width="672" /></p>
<pre class="r"><code># number of occurrences listed for each family
sapply(sortGraptOcc,nrow)</code></pre>
<pre><code>## Abrograptidae Diplograptidae Phyllograptidae Retiolitidae
## 4 625 95 100
## Sinograptidae
## 5</code></pre>
<p>Unsurprisingly, we can see that there are more genera given formal IDs in the PBDB than there are formal species. Of course, the number of (unsynonymized) informal species swamp out both, with an enormous number of informal species in the early Silurian, creating a noticeable and somewhat odd peak. Some of the dicordence between the species and genus counts is almost certainly due to the general trend of simplification in graptolite colony structure, which leads to there being many species of Silurian monograptids placed in the polyphyletic wastebin taxon <em>Monograptus</em>, which means Silurian generic richness of graptolites is almost certainly not a very good metaphor for changes in species-level diversity. Simultaneously, though, it would take some investigation to figure out how the PBDB has over 200 contemporaneous early Silurian graptoloids. Very curious.</p>
<p>You might note from the table of number of occurrences per family that searching for graptoloids has skipped the paraphyletic family <em>Anisograptidae</em>. The ‘anisograptids’ were the paraphyletic ancestors for the traditional graptoloids (i.e. Section <em>Graptoloidea</em> Lapworth) and included in the Subdivision <em>Graptoloida</em> by Maletz et al. (<a href="http://www.geology.cz/bulletin/contents/art1108">2009</a>), as this group was also planktonic and had a nematophorous sicula like all other graptoloids (well, except for the ones who lose their sicula…). It isn’t traditionally included in the graptoloids as the ‘anisograptids’ had not lost the bithecae of their dendroid graptolite ancestors. Anyway, all this means is that the above figures do not depict all the planktonic graptolites (i.e. the graptoloids as defined by Maletz et al., 2009).</p>
<p>Anyway, there we go: how to get PBDB API and <code>paleobioDB</code> data downloads to talk to <code>paleotree</code>.</p>
<p>PS: Hopefully, most of this will not become irrelevant with (version 1.2 of the PBDB API)[<a href="https://twitter.com/meclapham/status/577896469207748608" class="uri">https://twitter.com/meclapham/status/577896469207748608</a>]!</p>
</div>
dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-65060001697607933762015-02-23T23:39:00.000-08:002015-02-24T08:00:13.445-08:00Identifying Unique Species in Paleobiology Database Occurrence Downloads<div id="introduction" class="section level1">
<h1>Introduction</h1>
<p>Hello all!</p>
<p>I’ve had an interesting week or so having conversations with several individuals about best practices for identifying ‘species’ level taxa in <a href="http://nemagraptus.blogspot.com/2015/02/what-is-right-way-to-identify-unique.html">fossil occurrence data</a> from the <a href="http://paleobiodb.org/">Paleobiology Database</a> following <a href="http://nemagraptus.blogspot.com/2015/02/what-is-right-way-to-identify-unique.html">my post two weeks ago</a> where I found that several utilities associated with the PBDB identified ‘species’ in different ways. Let’s try to hit this issue really <strong>hard</strong> today.</p>
</div>
<div id="paleobiology-database-vocabulary" class="section level1">
<h1>Paleobiology Database Vocabulary</h1>
<p>First, in my last post, I was a little confusing since I referred to the same variable by its full name and a shortening that seemed to be used only by the <code>paleobioDB</code> package (<a href="http://onlinelibrary.wiley.com/doi/10.1111/ecog.01154/abstract">Varela et al., in press</a>); it turns out this is simply the difference between variable headings returned by the Paleobiology Database’s API depending on <a href="http://paleobiodb.org/">special parameter</a> <code>vocab</code>, which can either be <code>pbdb</code> (full variable names) or <code>com</code> (for the ‘compact’, shortened terminology). You can easily change this in <code>paleobioDB</code> data-pulling functions using argument <code>vocab</code>.</p>
<pre class="r"><code>library(paleobioDB)</code></pre>
<pre><code>## Loading required package: raster
## Loading required package: sp
## Loading required package: maps</code></pre>
<pre class="r"><code>head(pbdb_occurrences(limit="all", base_name="Dicellograptus", show=c("ident"), vocab="com"))</code></pre>
<pre><code>## oid typ cid tna rnk tid mna mra mid
## 1:1 3393 occ 328 Dicellograptus sp. 5 33650 Dicellograptus 5 33650
## 1:2 4589 occ 378 Dicellograptus sp. 5 33650 Dicellograptus 5 33650
## 1:3 4634 occ 379 Dicellograptus sp. 5 33650 Dicellograptus 5 33650
## 1:4 4688 occ 380 Dicellograptus sp. 5 33650 Dicellograptus 5 33650
## 1:5 8957 occ 403 Dicellograptus sp. 5 33650 Dicellograptus 5 33650
## 1:6 9127 occ 424 Dicellograptus sp. 5 33650 Dicellograptus 5 33650
## oei eag lag rid idt ids oli rst rss
## 1:1 Black River 460.9 457.5 13 Dicellograptus sp. <NA> <NA> <NA>
## 1:2 Harju 452.0 443.7 13 Dicellograptus sp. <NA> <NA> <NA>
## 1:3 Harju 452.0 443.7 13 Dicellograptus sp. <NA> <NA> <NA>
## 1:4 Caradoc 460.9 449.5 13 Dicellograptus sp. <NA> <NA> <NA>
## 1:5 Ashgill 449.5 443.7 13 Dicellograptus sp. <NA> <NA> <NA>
## 1:6 Middle Ordovician 470.0 458.4 13 Dicellograptus sp. <NA> <NA> <NA></code></pre>
<pre class="r"><code>head(pbdb_occurrences(limit="all", base_name="Dicellograptus", show=c("ident"), vocab="pbdb"))</code></pre>
<pre><code>## occurrence_no record_type collection_no taxon_name taxon_rank
## 1:1 3393 occurrence 328 Dicellograptus sp. genus
## 1:2 4589 occurrence 378 Dicellograptus sp. genus
## 1:3 4634 occurrence 379 Dicellograptus sp. genus
## 1:4 4688 occurrence 380 Dicellograptus sp. genus
## 1:5 8957 occurrence 403 Dicellograptus sp. genus
## 1:6 9127 occurrence 424 Dicellograptus sp. genus
## taxon_no matched_name matched_rank matched_no early_interval
## 1:1 33650 Dicellograptus genus 33650 Black River
## 1:2 33650 Dicellograptus genus 33650 Harju
## 1:3 33650 Dicellograptus genus 33650 Harju
## 1:4 33650 Dicellograptus genus 33650 Caradoc
## 1:5 33650 Dicellograptus genus 33650 Ashgill
## 1:6 33650 Dicellograptus genus 33650 Middle Ordovician
## early_age late_age reference_no genus_name species_name
## 1:1 460.9 457.5 13 Dicellograptus sp.
## 1:2 452.0 443.7 13 Dicellograptus sp.
## 1:3 452.0 443.7 13 Dicellograptus sp.
## 1:4 460.9 449.5 13 Dicellograptus sp.
## 1:5 449.5 443.7 13 Dicellograptus sp.
## 1:6 470.0 458.4 13 Dicellograptus sp.
## late_interval genus_reso species_reso
## 1:1 <NA> <NA> <NA>
## 1:2 <NA> <NA> <NA>
## 1:3 <NA> <NA> <NA>
## 1:4 <NA> <NA> <NA>
## 1:5 <NA> <NA> <NA>
## 1:6 <NA> <NA> <NA></code></pre>
<p>Note that some variables change their formatting depending on whether <code>pbdb</code> or <code>com</code> vocabulary are used; for example, <code>taxon_rank</code> is full nouns (‘genus’, ‘species’, etc) with <code>pbdb</code>, but is a numerical coding with <code>com</code>.</p>
<p>For this post (and future posts), I’ll try to use the full <code>pbdb</code> terminology; you can always check the <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">API page for PBDB occurrence data</a> if you want the corresponding <code>com</code> label though.</p>
<hr />
</div>
<div id="a-brief-aside-paleobiodb-package-versus-the-raw-api-in-r" class="section level1">
<h1>A brief aside… paleobioDB package versus the raw API in R</h1>
<p><a href="http://geoscience.wisc.edu/geoscience/people/faculty/shanan-peters/">Shanan Peters</a> has usefully <a href="https://twitter.com/shananpeters/status/565716274878361603">pointed out</a> that we shouldn’t mistake <code>paleobioDB</code> as the only way to access the PBDB API. After all, the API will presumably continue to evolve and thus the <code>paleobioDB</code> package <em>may</em> not be up-to-date with the API’s various arguments. Instead of using <code>paleobioDB</code> we could instead access the API directly, for example:</p>
<pre class="r"><code>rawAPI<-read.csv("http://paleobiodb.org/data1.1/occs/list.txt?base_name=Dicellograptus&show=ident&limit=all")
head(rawAPI)</code></pre>
<pre><code>## occurrence_no record_type reid_no superceded collection_no
## 1 3393 occurrence NA NA 328
## 2 4589 occurrence NA NA 378
## 3 4634 occurrence NA NA 379
## 4 4688 occurrence NA NA 380
## 5 8957 occurrence NA NA 403
## 6 9127 occurrence NA NA 424
## taxon_name taxon_rank taxon_no matched_name matched_rank
## 1 Dicellograptus sp. genus 33650 Dicellograptus genus
## 2 Dicellograptus sp. genus 33650 Dicellograptus genus
## 3 Dicellograptus sp. genus 33650 Dicellograptus genus
## 4 Dicellograptus sp. genus 33650 Dicellograptus genus
## 5 Dicellograptus sp. genus 33650 Dicellograptus genus
## 6 Dicellograptus sp. genus 33650 Dicellograptus genus
## matched_no early_interval late_interval early_age late_age
## 1 33650 Black River Black River 460.9 457.5
## 2 33650 Harju Harju 452.0 443.7
## 3 33650 Harju Harju 452.0 443.7
## 4 33650 Caradoc Caradoc 460.9 449.5
## 5 33650 Ashgill Ashgill 449.5 443.7
## 6 33650 Middle Ordovician Middle Ordovician 470.0 458.4
## reference_no genus_name genus_reso subgenus_name subgenus_reso
## 1 13 Dicellograptus NA NA
## 2 13 Dicellograptus NA NA
## 3 13 Dicellograptus NA NA
## 4 13 Dicellograptus NA NA
## 5 13 Dicellograptus NA NA
## 6 13 Dicellograptus NA NA
## species_name species_reso
## 1 sp.
## 2 sp.
## 3 sp.
## 4 sp.
## 5 sp.
## 6 sp.</code></pre>
<p><strong>Note</strong>, however, some small differences between these otherwise very similar requests. In particular, <code>paleobioDB</code> seems to have removed several variables related to data cleaning, like whether an occurrence has been superceded by another occurrence or new taxonomic assignment. I’ve traced the code used in the function through <code>paleobioDB</code>’s <a href="https://github.com/ropensci/paleobioDB/blob/master/R/pbdb_querys.R">github repo</a> and I’m not sure why these don’t show up.</p>
<pre class="r"><code>PBDBpack<-pbdb_occurrences(limit="all", base_name="Dicellograptus", show=c("ident"), vocab="pbdb")
# same number of columns in both?
ncol(rawAPI)==ncol(PBDBpack)</code></pre>
<pre><code>## [1] FALSE</code></pre>
<pre class="r"><code># Nope! Which columns aren't in the paleobioDB output
(missingCol<-colnames(rawAPI)[sapply(colnames(rawAPI),function(x) !any(x==colnames(PBDBpack)))])</code></pre>
<pre><code>## [1] "reid_no" "superceded" "subgenus_name" "subgenus_reso"</code></pre>
<p>Perhaps there is a cleaning step where <code>paleobioDB</code> (or Paleobiology Database, when it responds to <a href="http://testpaleodb.geology.wisc.edu/data1.1/formats/json_doc.html">JSON requests</a>) is removing empty columns (i.e. contains only <code>NA</code>s).</p>
<pre class="r"><code># Are the missing columns just full of NAs?
sapply(missingCol,function(x) all(is.na(rawAPI[,x])))</code></pre>
<pre><code>## reid_no superceded subgenus_name subgenus_reso
## TRUE TRUE TRUE TRUE</code></pre>
<p>There’s no mention to this phenomenon that I can find in paleobioDB’s manual (or any evidence of column dropping in the code of the package’s repo) or the documentation for the Paleobiology Database’s API, however. It’s a little odd because it means one could query, for example, for abundance data (<code>show=c("abund")</code>) and not get back the columns relating to abundance because they are empty. This could create issues for code that expects variables to always be present in a given data download.</p>
<p>For example, here is an extremely contrived example for the bizarre reticulate graptoloid genus Reteograptus <a href="http://www.jstor.org/stable/1304243">(see Finney, 1980, for some morphological details)</a>, which is probably a <a href="http://phenomena.nationalgeographic.com/2015/02/02/sciencespeak-lazarus-taxon/">lazarus taxon</a> of the abrograptids (according to Finney).</p>
<pre class="r"><code>rawAPI<-read.csv("http://paleobiodb.org/data1.1/occs/list.txt?base_name=Reteograptus&show=abund&limit=all")
PBDBpack<-pbdb_occurrences(limit="all", base_name="Reteograptus", show=c("abund"), vocab="pbdb")
# Which columns aren't in the paleobioDB output
(missingCol<-colnames(rawAPI)[sapply(colnames(rawAPI),function(x) !any(x==colnames(PBDBpack)))])</code></pre>
<pre><code>## [1] "reid_no" "superceded" "abund_value" "abund_unit"</code></pre>
<pre class="r"><code>#let's look at the abundance values from the rawAPI; are they all NAs?
apply(rawAPI[,c("abund_value","abund_unit")],2,function(x) all(is.na(x)))</code></pre>
<pre><code>## abund_value abund_unit
## TRUE TRUE</code></pre>
<p>There does not appear to be an argument to alter this variable-dropping behavior. I’ve opened an issue ticket <a href="https://github.com/ropensci/paleobioDB/issues/18">here</a> at the <code>paleobioDB</code> repo.</p>
<p>The ultimate result of the is that we can’t always depend on the same variables to (a) be present, (b) have the same column headings or (c) have the same variable codings for tables of PBDB occurrence data, because it depends greatly on how the data was downloaded and which options were used, going beyond the choices involving <code>show =</code>. The authors of <code>paleobioDB</code> package seem to have dealt with this by writing two separate versions of functions for analyzing occurrence data within each function (<a href="https://github.com/ropensci/paleobioDB/blob/master/R/pbdb_temporal_functions.R#L64-176">example</a>), which is inefficient but perhaps necessary.</p>
<hr />
</div>
<div id="distinguishing-species-in-paleobiology-database-occurrence-data" class="section level1">
<h1>Distinguishing Species in Paleobiology Database Occurrence Data</h1>
<p>It turns out there is no single ‘right’ answer to how to identify unique species in a set of PBDB occurrence data and assign occurrences to these distinct species. To understand the different ways we might perform this task, we should cover the relevant variables and what the information contained within is. The following variables can be obtained by setting the <code>show</code> to both <code>ident</code> and <code>phylo</code>, whether within the API or <code>paleobioDB</code>.</p>
<p>Some of the following information comes from a very informative discussion I have had with <a href="http://people.ucsc.edu/~mclapham/">Matthew Clapham</a>. However, personallylly, I’m still working through understanding all these variables, and intentionally trying to do this with as little fact-checking by email as possible. This is partly to demonstrate the limitations of the current documentation and also to avoid drowning certain individuals in emails. Better I save up all my questions for the end, right? I will be honest and say I still don’t entirely understand <code>reid_no</code> and <code>superceded</code>. So don’t blame my sources, blame me for when I get things wrong, because that’s part of this little mental experiment.</p>
<p>An important distinction is that one set of variables refers to the taxonomic identification for an occurrence reported within the <strong>original reference</strong>:</p>
<ul>
<li><p><code>taxon_name</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “The taxonomic name by which this occurrence is identified.”</p></li>
<li><p><code>taxon_rank</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “The taxonomic rank of the name, if this can be determined.”</p></li>
<li><p><code>genus_name</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “The taxonomic name (less species) by which this occurrence was identified. This is often a genus, but may be a higher taxon.”</p></li>
<li><p><code>genus_reso</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “The resolution of this taxonomic name, i.e. sensu lato or aff.”</p></li>
<li><p><code>species_name</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “The species name (if any) by which this occurrence was identified”</p></li>
<li><p><code>species_reso</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “The resolution of the species name, i.e. sensu lato or n. sp”</p></li>
</ul>
<p>Related to this group, we have the variable <code>taxon_no</code>:</p>
<ul>
<li><code>taxon_no</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “The unique identifier of the identified taxonomic name. If this is empty, then the name was never entered into the taxonomic hierarchy stored in this database and we have no further information about the classification of this occurrence.”</li>
</ul>
<p>…which identifies which taxon ID is assigned to the original taxon identification. However, many taxa apparently get entered without being given unique taxon IDs, and are instead assigned tentatively to a higher taxon. For example, there are many occurrences listed with distinct <code>species_names</code> that are listed under a <code>taxon_no</code> for a genus-level taxon.</p>
<p>A different set of variables refers to the <strong>formal taxon assignment</strong> currently given within the PBDB’s present state of taxonomic standardization, which attempts to control for synonymies:</p>
<ul>
<li><p><code>matched_name</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “The senior synonym and/or currently accepted spelling of the closest matching name in the database to the identified taxonomic name, if any is known, and if this name is different from the value of taxon_name.”</p></li>
<li><p><code>matched_rank</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “The taxonomic rank of the matched name, if different from the value of taxon_rank”</p></li>
<li><p><code>matched_no</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “The unique identifier of the closest matching name in the database to the identified taxonomic name, if any is known.”</p></li>
<li><p><code>genus</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “The name of the genus in which this occurrence is classified”</p></li>
<li><p><code>genus_no</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “The identifier of the genus in which this occurrence is classified”</p></li>
</ul>
<p>Again, because many lower-taxa get informally assigned to the same higher-taxon ID on a tentative tentatively, <code>taxon_no</code> (and thus <code>matched_no</code> and <code>matched_name</code> in addition) may be the same for occurences listed as multiple distinct species going by <code>taxon_rank</code> and <code>species_name</code>.</p>
<p>Finally, the occurrence itself might be reidentified or superceded with a new taxonomic assignment, which can be found in:</p>
<ul>
<li><p><code>reid_no</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “If this occurrence was reidentified, a positive integer that uniquely identifies the reidentification”</p></li>
<li><p><code>superceded</code> <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">described as</a> “The value of this field will be true if this occurrence was later identified under a different taxon”</p></li>
</ul>
<p>These aren’t exhaustive lists, and if we wanted to discuss pulling out unique supraspecific taxa, we would need to consider additional variables such as <code>family_name</code> reported by the <code>show=phylo</code> parameter.</p>
<p>It turns out the <em>Dicellograptus</em> example I keep using provides an example of <em>one type</em> of disagreement we might see in groups where there has been little standardization and where many species have been entered ‘informally’ as tentative assignments to a higher taxon. So let’s focus on the above variables in that dataset:</p>
<pre class="r"><code>#taxonomic variables of interest
taxonVar<-c("reid_no","superceded", #PBDB occurrence reidentification info
#original taxon info for occurrence
"taxon_name","taxon_rank","genus_name","genus_reso","species_name","species_reso",
#standardized formal taxon info
"taxon_no","matched_name","matched_rank","matched_no","genus","genus_no")
# due to the column dropping issue, we can't use paleobioDB package:
#dicelloData<-pbdb_occurrences(limit="all", base_name="Dicellograptus", show=c("ident","phylo"), vocab="pbdb")
# so we'll use the API instead
dicelloData<-read.csv("http://paleobiodb.org/data1.1/occs/list.txt?base_name=Dicellograptus&show=ident,phylo&limit=all")
head(dicelloData[,taxonVar])</code></pre>
<pre><code>## reid_no superceded taxon_name taxon_rank genus_name
## 1 NA NA Dicellograptus sp. genus Dicellograptus
## 2 NA NA Dicellograptus sp. genus Dicellograptus
## 3 NA NA Dicellograptus sp. genus Dicellograptus
## 4 NA NA Dicellograptus sp. genus Dicellograptus
## 5 NA NA Dicellograptus sp. genus Dicellograptus
## 6 NA NA Dicellograptus sp. genus Dicellograptus
## genus_reso species_name species_reso taxon_no matched_name
## 1 sp. 33650 Dicellograptus
## 2 sp. 33650 Dicellograptus
## 3 sp. 33650 Dicellograptus
## 4 sp. 33650 Dicellograptus
## 5 sp. 33650 Dicellograptus
## 6 sp. 33650 Dicellograptus
## matched_rank matched_no genus genus_no
## 1 genus 33650 Dicellograptus 33650
## 2 genus 33650 Dicellograptus 33650
## 3 genus 33650 Dicellograptus 33650
## 4 genus 33650 Dicellograptus 33650
## 5 genus 33650 Dicellograptus 33650
## 6 genus 33650 Dicellograptus 33650</code></pre>
<p>Just from the first few lines, we can also see a number of <em>Dicellograptus</em> occurrences that were never resolved beyond the genus level and thus are just assigned to the <em>Dicellograptus</em> genus taxon ID (<code>taxon_no = 33650</code>), and the <code>matched_name</code> and other ‘matched’ information is the same, because <em>Dicellograptus</em> has never been synonymized (which is good, because its a valid genus, so it shouldn’t be synonymized).</p>
<p>We can see these listed as “Species lacking formal opinion data” <a href="http://paleobiodb.org/cgi-bin/bridge.pl?a=checkTaxonInfo&taxon_no=33650&is_real_user=1">here</a>.</p>
<p>But how many unique <code>taxon_name</code> and <code>species_name</code> values are assigned to <code>taxon_no = 33650</code>?</p>
<pre class="r"><code>unique(dicelloData[dicelloData$taxon_no==33650,"taxon_name"])</code></pre>
<pre><code>## [1] Dicellograptus sp. Dicellograptus ? sp.
## [3] Dicellograptus alector n. sp. Dicellograptus gurleyi
## [5] Dicellograptus mensurans Dicellograptus sextans cf.
## [7] Dicellograptus spp. Dicellograptus sextans
## [9] Dicellograptus vagus cf. Dicellograptus intortus
## [11] Dicellograptus divaricatus cf. Dicellograptus caduceus cf.
## [13] Dicellograptus forchammeri cf. Dicellograptus intortus cf.
## [15] Dicellograptus moffatensis cf. Dicellograptus intermedius n. sp.
## [17] Dicellograptus divaricatus Dicellograptus russonioides n. sp.
## [19] Dicellograptus johnstrupi Dicellograptus morrisi
## [21] Dicellograptus gurleyi cf. Dicellograptus elegans cf.
## [23] Dicellograptus morrisi cf. Dicellograptus smithi cf.
## [25] Dicellograptus sp. ? Dicellograptus alector
## [27] Dicellograptus alector cf. Dicellograptus ? angulatus cf.
## [29] Dicellograptus angulatus cf. Dicellograptus flexuosus
## [31] Dicellograptus minor Dicellograptus tumidus
## [33] Dicellograptus turgidus Dicellograptus mirabilis
## 40 Levels: Dicellograptus ? angulatus cf. ... Dicellograptus vagus cf.</code></pre>
<pre class="r"><code>unique(dicelloData[dicelloData$taxon_no==33650,"species_name"])</code></pre>
<pre><code>## [1] sp. alector gurleyi mensurans sextans
## [6] spp. vagus intortus divaricatus caduceus
## [11] forchammeri moffatensis intermedius russonioides johnstrupi
## [16] morrisi elegans smithi angulatus flexuosus
## [21] minor tumidus turgidus mirabilis
## 27 Levels: alector anceps angulatus caduceus complanatus ... vagus</code></pre>
<p>Thankfully, all of these are given a <code>taxon_rank</code> of species… right? We can check this by removing any occurrence not given a <code>taxon_rank</code> of species and looking at the unique <code>taxon_name</code> values.</p>
<pre class="r"><code>unique(dicelloData[dicelloData$taxon_no==33650 & dicelloData$taxon_rank!="species","taxon_name"])</code></pre>
<pre><code>## [1] Dicellograptus sp. Dicellograptus ? sp. Dicellograptus spp.
## [4] Dicellograptus sp. ?
## 40 Levels: Dicellograptus ? angulatus cf. ... Dicellograptus vagus cf.</code></pre>
<p>Okay, in <em>Dicellograptus</em>, at least, all the informal species assigned formally to a genus-level taxon are listed correctly as species.</p>
<p>We can examine <em>another sort</em> of the disagreement among these fields by looking at datasets where there has been a lot of taxonomic standarization in the PBDB (i.e. <em>not graptolites</em>). Matt Clapham showed me a particularly interesting read-out for a query involving <em>Acosarina minuta</em>, a Permian brachiopod (…perhaps a minute brachiopod?), filtering for the same variables as above:</p>
<pre class="r"><code># so we'll use the API, like above...
# Note that the following won't work except from web browser address bar or other utility
# which automatically (and silently) replaces " " with "%20"
acoData1<-read.csv("http://paleobiodb.org/data1.1/occs/list.txt?base_name=Acosarina minuta&show=ident,phylo")
colnames(acoData1)</code></pre>
<pre><code>## [1] "occurrence_no" "record_type" "reid_no" "superceded"
## [5] "collection_no" "taxon_name" "taxon_rank" "taxon_no"
## [9] "matched_name" "matched_rank" "matched_no" "early_interval"
## [13] "late_interval" "early_age" "late_age" "reference_no"</code></pre>
<pre class="r"><code># the query terminated at the " " in the taxon name and never got to other parameters, like 'show'
# (This tripped me up for an hour. Don't judge me!)
# Okay, now the right way
acoData<-read.csv("http://paleobiodb.org/data1.1/occs/list.txt?base_name=Acosarina%20minuta&show=ident,phylo")
head(acoData[,match(taxonVar,colnames(acoData))])</code></pre>
<pre><code>## reid_no superceded taxon_name taxon_rank genus_name
## 1 NA NA Orthotichia indica species Orthotichia
## 2 20839 NA Acosarina minuta species Acosarina
## 3 NA NA Acosarina minuta species Acosarina
## 4 NA NA Acosarina dorsisulcata species Acosarina
## 5 NA NA Acosarina dorsisulcata species Acosarina
## 6 NA NA Acosarina dorsisulcata species Acosarina
## genus_reso species_name species_reso taxon_no matched_name
## 1 indica 177488 Acosarina minuta
## 2 minuta 125301 Acosarina minuta
## 3 minuta 125301 Acosarina minuta
## 4 dorsisulcata 126711 Acosarina minuta
## 5 dorsisulcata 126711 Acosarina minuta
## 6 dorsisulcata 126711 Acosarina minuta
## matched_rank matched_no genus genus_no
## 1 species 125301 Acosarina 26652
## 2 species 125301 Acosarina 26652
## 3 species 125301 Acosarina 26652
## 4 species 125301 Acosarina 26652
## 5 species 125301 Acosarina 26652
## 6 species 125301 Acosarina 26652</code></pre>
<p>All of the occurrences listed here are formally identified in PBDB’s standardized taxonomy as <em>Acosarina minuta</em>. That’s pretty easy to verify by looking at unique values of <code>matched_name</code> and <code>matched_no</code>:</p>
<pre class="r"><code>unique(acoData$matched_name)</code></pre>
<pre><code>## [1] Acosarina minuta
## Levels: Acosarina minuta</code></pre>
<pre class="r"><code>unique(acoData$matched_no)</code></pre>
<pre><code>## [1] 125301</code></pre>
<p>However, we can see even just from the <code>head()</code> that there are multiple values of <code>taxon_name</code>, <code>taxon_no</code>, <code>genus_name</code> and <code>species_name</code> formally reassigned to the ‘matched’ taxon <em>Acosarina minuta</em>. Here’s a full list:</p>
<pre class="r"><code>unique(acoData[,c("taxon_name","taxon_no","genus_name","species_name")])</code></pre>
<pre><code>## taxon_name taxon_no genus_name species_name
## 1 Orthotichia indica 177488 Orthotichia indica
## 2 Acosarina minuta 125301 Acosarina minuta
## 4 Acosarina dorsisulcata 126711 Acosarina dorsisulcata
## 15 Acosarina dorsisulcata n. sp. 126711 Acosarina dorsisulcata
## 49 Orthis indica 125296 Orthis indica
## 58 Acosarina indica 175512 Acosarina indica
## 99 Kotlaia capillosa 123689 Kotlaia capillosa
## 100 Kotlaia n. gen. capillosa n. sp. 123689 Kotlaia capillosa
## 103 Orthotichia minuta 199434 Orthotichia minuta
## 110 Acosarina minuta cf. 125301 Acosarina minuta
## 111 Sunacosarina campana 190895 Sunacosarina campana
## 134 Acosarina kanmerai n. sp. 206333 Acosarina kanmerai
## 142 Orthis (Schizophoria) indica ? 125296 Orthis indica
## 143 Orthis (Schizophoria) indica 125296 Orthis indica
## 217 Dalmanella indica 255744 Dalmanella indica
## 234 Acosarina ? minuta 125301 Acosarina minuta</code></pre>
<p>So, how do we navigate these variables to identify unique species in our datasets?</p>
</div>
<div id="potentially-misleading-ways-to-identify-species-in-pbdb-occurrence-data" class="section level1">
<h1>Potentially Misleading Ways To Identify Species in PBDB Occurrence Data</h1>
<p><a href="http://nemagraptus.blogspot.com/2015/02/what-is-right-way-to-identify-unique.html">In the last post</a>, I mentioned that a number of my friends, as it turned out, used very different criteria for distinguishing taxa from PBDB occurrence data, criteria which also varied among the formal ‘apps’ of the Paleobiology Database.</p>
<p>Personally, I’d intuitively chosen (<a href="http://nemagraptus.blogspot.com/2015/02/how-do-we-treat-fossil-age-data-dates.html">two blog posts ago</a>) to use <code>species_name</code> alone, filtered on <code>taxon_rank = "species"</code>. It turns out several of my friends also use <code>species_name</code>. Another friend uses a combination of <code>taxon_name</code> and <code>taxon_no</code>. Strategies based on <code>taxon_name</code> or <code>species_name</code> are (at best) not making use of the potential for information of taxonomic synonymies in the PBDB and (at worst) in groups that have undergone a considerable amount of qualified taxonomic standardization, using these variables may be very misleading relative to the current state of knowledge.</p>
<p>It should also be clear that <code>taxon_no</code> alone could be a misleading way to distinguish unique species in occurrence data, even if filtered to species-ranked occurrences. <a href="http://nemagraptus.blogspot.com/2015/02/what-is-right-way-to-identify-unique.html">Referring to the last post</a>, I showed that <code>paleobioDB</code> package uses <code>taxon_no</code> to identify unique species in occurrences, filtered to <code>taxon_rank</code> of species. The function <code>match()</code> is then used to find <code>genus_name</code> and <code>species_name</code> which are then combined into a single species label. We can see <a href="https://github.com/ropensci/paleobioDB/blob/master/R/pbdb_temporal_functions.R#L72-80">the code here</a>. As we can see above, even filtering <code>taxon_no</code> to species-level occurrences via <code>taxon_rank</code> would result in mistakingly grouping a number of informally entered species that are formally assigned to their genus as a single species-level taxon with an exaggerated number of occurrences assigned to it. Furthermore, the use of <code>match()</code> returns the first and only the first match in a list, so the assigned species-label is an arbitrary function of whatever species comes first in a table of occurrences. This results in the mistaken reporting of <em>Dicellograptus alector</em> from <em>Dicellograptus</em> occurrence data below:</p>
<pre class="r"><code>require(paleobioDB)
dicelloData<-pbdb_occurrences(limit="all", base_name="Dicellograptus", show=c("phylo","ident"))
pbdb_temp_range(dicelloData, rank="species", do.plot=FALSE)</code></pre>
<pre><code>## max min
## Dicellograptus alector 471.8 443.4
## Dicellograptus anceps 455.8 443.7
## Dicellograptus ornatus 453.0 443.4
## Dicellograptus complanatus 453.0 443.7</code></pre>
<p>I’ve opened an issue ticket for this issue on the <code>paleobioDB</code> repo <a href="https://github.com/ropensci/paleobioDB/issues/19">here</a>.</p>
<p>It turns out this <strong>isn’t</strong> what PBDB Navigator does, as I had incorrectly guessed <a href="http://nemagraptus.blogspot.com/2015/02/what-is-right-way-to-identify-unique.html">last post</a>. PBDB Navigator is doing something much more complex, counting formal taxa that are listed as daughter taxa. The reason why <em>Dicellograptus gravis</em> appears despite having no occurrences in the PBDB is because that taxon was formally entered without any occurrences. This is discussed more <a href="https://github.com/paleobiodb/navigator/issues/9">here</a>, where I had thought it was a bug and reported it as such, but it was more of a… misinterpretation. Similarly, <a href="http://paleobiodb.org/cgi-bin/bridge.pl">PBDB Classic</a> / <a href="http://fossilworks.org/">Fossilworks</a> appears to use the taxonomic standarization in the database to return ranges for all formally-defined species appropriately (under the Download function), but returns any informally defined species in addition.</p>
</div>
<div id="a-framework-for-identifying-unique-species" class="section level1">
<h1>A Framework for Identifying Unique Species</h1>
<p>So, how should we go about pulling species-level taxa from our occurrence data? If we want all ‘species’ information possible, then we must do two seperate processes:</p>
<ol style="list-style-type: decimal">
<li><p>Pull out all formally-defined species, with their <code>matched_name</code> species name. This should be as simple as using <code>matched_rank</code> to filter on species-level formal taxa and pulling out the unique <code>matched_no</code> values.</p></li>
<li><p>Pull out all informal species that are assigned formally to supraspecific taxa. This should be possible on filtering only for taxa that do <strong>not</strong> have a <code>matched_rank = species</code> assignment but have a <code>taxon_rank = species</code> value, and then pulling on unique values of <code>species_name</code>.</p></li>
</ol>
<p>Obviously, the second is optional if we want to only keep formal species; depends on our data.</p>
<p>There are some complexities to the above for actually generating the correct labels for these species-level taxa. First, there is no corresponding <code>species_name</code>-like variable for <code>matched_taxon</code> as there is with <code>genus_name</code> and <code>genus</code>. The most reasonable method seems to be splitting the species name from the genus name in <code>matched_name</code> and combining it with <code>genus</code>, but presumably this should have the same effect as just using <code>matched_name</code> whole.</p>
<p>Let’s look at a function that tries to follow this ‘informal versus formal’ dichotomy. Here’s <code>taxonClean</code> from Matthew Clapham’s github repo <a href="https://github.com/mclapham/PBDB-R-scripts">PBDB-R-scripts</a>, function script located <a href="https://github.com/mclapham/PBDB-R-scripts/blob/master/taxonClean.R">here</a>:</p>
<pre class="r"><code>taxonClean <- function(occurrences, tax_level="genus", formal_id="no") {
#FILE PREPARATION AND DATA CLEANING
#file preparation and data cleaning for species level analysis
if(tax_level=="species"){
#removes rows where species qualified by cf. or aff., question mark, ex gr. or quotation mark
resolved_occs <- subset(occurrences, occurrences$species_reso=="" | occurrences$species_reso=="n. sp.")
if (formal_id=="yes") {
#deletes occurrences not resolved to species level using formally-classified species
cleaned_occs <- subset(resolved_occs, resolved_occs$matched_rank=="species")
cleaned_occs$final_taxon <- cleaned_occs$matched_name
} else {
#deletes occurrences not resolved to species level using classified and unclassified species
resolved_occs <- subset(resolved_occs, resolved_occs$taxon_rank=="species")
#strips all extraneous qualifiers from taxon name
resolved_occs$taxon_name <- gsub("\" ", "", resolved_occs$taxon_name)
resolved_occs$taxon_name <- gsub("cf. ", "", resolved_occs$taxon_name)
resolved_occs$taxon_name <- gsub("aff. ", "", resolved_occs$taxon_name)
resolved_occs$taxon_name <- gsub("\\? ", "", resolved_occs$taxon_name)
resolved_occs$taxon_name <- gsub("n. gen. ", "", resolved_occs$taxon_name)
resolved_occs$taxon_name <- gsub(" n. sp.", "", resolved_occs$taxon_name)
resolved_occs$taxon_name <- gsub(" sensu lato", "", resolved_occs$taxon_name)
resolved_occs$taxon_name <- gsub(" informal", "", resolved_occs$taxon_name)
#separates classified and unclassified occurrences
classified_occs <- subset(resolved_occs, resolved_occs$matched_rank=="species")
unclassified_occs <- subset(resolved_occs, resolved_occs$matched_rank!="species")
classified_occs$final_taxon <- classified_occs$matched_name
unclassified_occs$final_taxon <- unclassified_occs$taxon_name
#combines classified and unclassified occurrences
cleaned_occs <- rbind(classified_occs, unclassified_occs)
}
#or file preparation and data cleaning for family level analysis
} else if (tax_level=="family") {
#deletes occurrences not resolved to at least family level
resolved_occs <- subset(occurrences, occurrences$matched_rank <= 9)
#deletes occurrences where genus or family are qualified with question mark, quotations, cf. or aff.
if(length(grep("\\? ", resolved_occs$taxon_name)) > 0){
resolved_occs <- resolved_occs[-grep("\\? ", resolved_occs$taxon_name),]
}
if(length(grep("\" ", resolved_occs$taxon_name)) > 0){
resolved_occs <- resolved_occs[-grep("\" ", resolved_occs$taxon_name),]
}
if(length(grep("cf. ", resolved_occs$taxon_name)) > 0){
resolved_occs <- resolved_occs[-grep("cf. ", resolved_occs$taxon_name),]
}
if(length(grep("aff. ", resolved_occs$taxon_name)) > 0){
resolved_occs <- resolved_occs[-grep("aff. ", resolved_occs$taxon_name),]
}
cleaned_occs <- resolved_occs
cleaned_occs$final_taxon <- cleaned_occs$family
#or file preparation and data cleaning for genus level analysis
} else {
#deletes occurrences not resolved to at least genus level using classified and unclassified species
resolved_occs <- subset(occurrences, occurrences$matched_rank <= 5)
#deletes occurrences where genus is qualified with question mark, quotations, cf. or aff.
cleaned_occs <- subset(resolved_occs, resolved_occs$genus_reso=="" | resolved_occs$genus_reso=="n. gen.")
#extracts genus name from matched_name string
cleaned_occs$final_taxon <- gsub(" .*", "", cleaned_occs$matched_name)
}
cleaned_occs
}</code></pre>
<p>(I made some small code alterations because I am just personally terrified by the idea of not embracing the lines of control structure (i.e. <code>if</code>, <code>for</code>, etc.) in curly braces! There was several actual errors: an erroneous <code>resolved_occs</code> before <code>resolved_occs</code> was called, also an attempt to call <code>matched_name = 3</code> rather than <code>matched_name = "species"</code>…)</p>
<p>Note that <code>taxonClean</code> depends on <code>vocab = "pbdb"</code>, which differs from the default for data functions in package <code>paleobioDB</code>. This function will return a table of occurrences, pared down only to the occurrences of taxa at the appropriate level and with their valid taxon labels in a new variable labeled <code>final_taxon</code>.</p>
<p>Let’s see what it does with our <em>Dicellograptus</em> data, using the API.</p>
<pre class="r"><code>dicelloData<-read.csv("http://paleobiodb.org/data1.1/occs/list.txt?base_name=Dicellograptus&show=ident,phylo&limit=all")
# with informal species
cleanOccs<-taxonClean(dicelloData, tax_level="species", formal_id="no")
unique(cleanOccs$final_taxon)</code></pre>
<pre><code>## [1] Dicellograptus anceps Dicellograptus complanatus
## [3] Dicellograptus ornatus Dicellograptus alector
## [5] Dicellograptus gurleyi Dicellograptus mensurans
## [7] Dicellograptus sextans Dicellograptus intortus
## [9] Dicellograptus intermedius Dicellograptus divaricatus
## [11] Dicellograptus russonioides Dicellograptus johnstrupi
## [13] Dicellograptus morrisi Dicellograptus flexuosus
## [15] Dicellograptus minor Dicellograptus tumidus
## [17] Dicellograptus turgidus Dicellograptus mirabilis
## 19 Levels: Dicellograptus ... Dicellograptus mirabilis</code></pre>
<pre class="r"><code># only formal species
cleanOccs<-taxonClean(dicelloData, tax_level="species", formal_id="yes")
unique(cleanOccs$final_taxon)</code></pre>
<pre><code>## [1] Dicellograptus anceps Dicellograptus complanatus
## [3] Dicellograptus ornatus
## 4 Levels: Dicellograptus ... Dicellograptus ornatus</code></pre>
<p>For me though, I’d probably approach this issue in a slight different way than <code>taxonClean</code>… I’ll show what I cook up next time, when I (<em>hopefully</em>) finally show some stuff I did weeks ago integrating <code>paleobioDB</code> data downloads with <code>paleotree</code> functions.</p>
</div>
dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-14258720469174219172015-02-11T15:50:00.000-08:002015-02-11T15:50:55.541-08:00What is the right way to identify unique species in Paleobiology Database downloads?<p><em>(Continuing with the RMarkdown-to-Blogger experiment! I apologize for any mysterious indentations that I can’t seem to make go away! Sorry!)</em></p>
<p>So, let’s recap:</p>
<p><a href="http://nemagraptus.blogspot.com/2015/02/how-do-we-treat-fossil-age-data-dates.html">Last post</a>, we had some fun pulling data out of the <a href="http://paleobiodb.org/">Paleobiology Database</a> using R package <code>paleobioDB</code> (<a href="http://onlinelibrary.wiley.com/doi/10.1111/ecog.01154/abstract">Varela et al., in press</a>). We even made a nice plot of age uncertainty in occurrences sorted by species in the delightful graptolite genus <em>Dicellograptus</em>. Let’s see that pretty plot again, although we’ll hide some of the R code this time!</p>
<pre class="r"><code>library(paleobioDB)</code></pre>
<pre class="r"><code>dicelloData<-pbdb_occurrences(limit="all", base_name="Dicellograptus", show=c("phylo","ident"))
dicelloData<-dicelloData[dicelloData$rnk==3,] #keep only occurrences of taxa resolved to species level
plotOccPBDB(dicelloData,"Dicellograptus Species")</code></pre>
<p><img src="data:image/png;base64,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" title alt width="672" /></p>
<p>Now, we had a little mystery when we decided to look at species ranges from this same data using function <code>pbdb_temp_range</code> in package <code>paleobioDB</code>.</p>
<pre class="r"><code>pbdb_temp_range(dicelloData, rank="species", do.plot=FALSE)</code></pre>
<pre><code>## max min
## Dicellograptus alector 471.8 443.4
## Dicellograptus anceps 455.8 443.7
## Dicellograptus ornatus 453.0 443.4
## Dicellograptus complanatus 453.0 443.7</code></pre>
<p>The occurrences I plotted above show something like <strong>more than twenty</strong> species. <code>pbdb_temp_range</code> shows only <strong>four species</strong>. What is going on?</p>
<p>Well, for the occurrence plot above, I used the <em>species_name</em> column (the column <code>$ids</code>, see <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">here</a> for its description in the paleobioDB API documentation) to distinguish different species, which we can see contains more than 20 unique identifiers:</p>
<pre class="r"><code>as.character(unique(dicelloData$ids))</code></pre>
<pre><code>## [1] "anceps" "alector" "gurleyi" "mensurans"
## [5] "sextans" "complanatus" "vagus" "intortus"
## [9] "divaricatus" "caduceus" "forchammeri" "moffatensis"
## [13] "intermedius" "russonioides" "johnstrupi" "morrisi"
## [17] "elegans" "smithi" "angulatus" "ornatus"
## [21] "flexuosus" "minor" "tumidus" "turgidus"
## [25] "mirabilis"</code></pre>
<p>However, while <code>pbdb_temp_range</code> uses the various ‘name’ entries for identifying independent taxa at other taxonomic levels, <code>pbdb_temp_range</code> uses <em>taxon_no</em> (column <code>$tid</code>) to identify unique species-level taxa. We can see this in the code in <code>paleobioDB</code>’s <a href="https://github.com/ropensci/paleobioDB/blob/master/R/pbdb_temporal_functions.R#L127-133">GitHub repo</a>.</p>
<p>This is suggestive that <em>taxon_no</em> is the Paleobiology Database’s way of handling taxonomic synonymy among species. A quick survey of a few friends who actually use PBDB data on a regular basis (unlike me, I’m a PBDB-newb) reveals some… uh, lack of clarity on this point. Well, a lack of clarity often suggests lack of clear documentation. Indeed, the <a href="http://paleobiodb.org/data1.1/occs/list_doc.html">paleobioDB API documentation</a> describes <em>taxon_no</em> simply as the “unique identifier of the identified taxonomic name” and is remarkably silent on suggesting whether it might indicate unique species-level identities. Looking through the rest of the API documentation, its never said explicitly, but one might infer that this is true, as long as taxonomic rank (column <code>$rnk</code>) has been been limited to species-level taxa only. I.e., all species-level taxa with unique taxon-IDs are valid species-level taxa.</p>
<p>I wonder though: aren’t there cases of synonymized supraspecific taxa? These must be missed if uniqueness at those levels in <code>paleobioDB</code> package is determined based on the various ‘names’ qualifiers. This issue seems like quite the tough nut. It seems properly sorting data out of an occurrances download from the PBDB may require directly referencing the taxonomic database of the PBDB as well.</p>
<p>Anyway, we can see that <code>$tid</code> matches the species returned by <code>pbdb_temp_range</code>, which finds its names for the unique <code>tid</code> values using <code>match()</code>, which just means the species name reported by <code>pbdb_temp_range</code> is whatever species name is listed first with that <code>tid</code> value in a given data table.</p>
<pre class="r"><code>unique(dicelloData$tid)</code></pre>
<pre><code>## [1] 306364 33650 306226 306367</code></pre>
<pre class="r"><code>as.character(sapply(unique(dicelloData$tid),function(x) dicelloData$ids[match(x,dicelloData$tid)]))</code></pre>
<pre><code>## [1] "anceps" "alector" "complanatus" "ornatus"</code></pre>
<p>Well, that solves that mystery, but is <code>tid</code> actually the right way to identify unique valid species in a dataset? I wonder, as this isn’t written down anywhere…</p>
<p>I decided to pursue this further and went to <a href="http://fossilworks.org/">Fossilworks</a>, a mirror database of the Paleobiology Database which I mentioned in the previous post. I used the <a href="http://fossilworks.org/?a=displayBasicDownloadForm">download form</a> at Fossilworks to pull the species ranges for <em>Dicellograptus</em>. It returns ranges for <strong>27 separate species</strong>; here’s a summary showing just the first four columns and small selection of ranges:</p>
<table>
<thead>
<tr class="header">
<th align="left">genus</th>
<th align="left">species</th>
<th align="left">base of range (Ma)</th>
<th align="left">top of range (Ma)</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">Dicellograptus</td>
<td align="left">alector</td>
<td align="left">456.1</td>
<td align="left">443.7</td>
</tr>
<tr class="even">
<td align="left">Dicellograptus</td>
<td align="left">anceps</td>
<td align="left">449.5</td>
<td align="left">445.6</td>
</tr>
<tr class="odd">
<td align="left">Dicellograptus</td>
<td align="left">angulatus</td>
<td align="left">456.1</td>
<td align="left">449.5</td>
</tr>
<tr class="even">
<td align="left">Dicellograptus</td>
<td align="left">caduceus</td>
<td align="left">460.9</td>
<td align="left">456.1</td>
</tr>
<tr class="odd">
<td align="left">Dicellograptus</td>
<td align="left">complanatus</td>
<td align="left">449.5</td>
<td align="left">445.6</td>
</tr>
<tr class="even">
<td align="left">Dicellograptus</td>
<td align="left">divaricatus</td>
<td align="left">468.1</td>
<td align="left">456.1</td>
</tr>
<tr class="odd">
<td align="left">…</td>
<td align="left">…</td>
<td align="left">…</td>
<td align="left">…</td>
</tr>
<tr class="even">
<td align="left">Dicellograptus</td>
<td align="left">vagus</td>
<td align="left">460.9</td>
<td align="left">456.1</td>
</tr>
</tbody>
</table>
<p>I also get 27 species with the <a href="http://paleobiodb.org/cgi-bin/bridge.pl?a=displayBasicDownloadForm">download form using PBDB classic</a> at paleobioDB (also discussed in the last post). So, these applications are clearly using <em>species_name</em> and not <em>taxon_no</em> to distinguish what species are in the occurrences data.</p>
<p>Okay, I thought, this is, uh… inconsistent.</p>
<p>Recently, the Paleobiology Database revealed a new interface, called <a href="http://paleobiodb.org/navigator/">PBDB Navigator</a>, which uses a map and various chronological and taxonomic filters to sort through the PBDB collection data. How many species does Navigator report for <em>Dicellograptus</em>?</p>
<pre><code>Taxon name (number of occurrences in the database)
Dicellograptus complanatus (1)
Dicellograptus ornatus (1)
Dicellograptus anceps (1)
Dicellograptus gravis (1) </code></pre>
<p>Huh. Okay, so Navigator uses <em>taxon_no</em> to identify individual unique species, unlike the Classic PBDB which is on the same website but uses <em>species_name</em>.</p>
<p>This is making my head hurt. What’s the answer? Maybe someone could pipe up in the comments.</p>
<p>Anyway, until next time!</p>
dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-85522637047145156372015-02-11T14:19:00.000-08:002015-02-11T14:19:32.685-08:00How Do We Treat Fossil Age Data? Dates, Ranges and Occurrences<p>(Hello all! I’m trying Rmarkdown for writing blog posts! Let’s see how this goes.)</p>
<p>I spend a lot of time thinking about how the datasets we (paleontologists and biologists) work with differ in their structure and information content. The obvious difference is how much paleontological data differs from the sort of data we have for living organisms in evolutionary biology (neontology… whatever…) but the one we so often overlook is how datasets from the fossil record can differ greatly from each other. I talk a lot in my book chapter last year on how much paleontological datasets for different groups of organisms or time-intervals can differ and how this impacts our intention to try phylogeny-based analyses of macroevolution (<a href="https://drive.google.com/file/d/0B_xvEcEvKno_dTM1YWp1czM3MGs/edit">you can check out my chapter here, thanks to the publisher</a>, including its horrid easter eggs referring to a certain popular series of Game Boy games).</p>
<p>That chapter barely scratched the surface in terms of <em>‘things to know about paleo data’</em>. Recently, while playing with <a href="http://www.paleobioDB.org">Paleobiology Database</a> data using the recently release R package <a href="http://cran.r-project.org/web/packages/paleobioDB">paleobioDB</a>, I had some additional thoughts on how we think of and use chronostratigraphic data for fossil taxa.</p>
<p>Warning: Many of you who are paleontology are probably already familiar with a lot of what I’m about to say. In some regards, I’m very mindful that some of my biologist friends read this, so I’m trying to cover what they might not be so familiar with. Also, much of it is a retread of previous blog posts. A major goal of this blog post is an attempt to describe a set of terms for relating how different types of fossil age datasets relate to one another in terms of their information content (in a sense, their <a href="https://en.wikipedia.org/wiki/Ontology_%28information_science%29">ontology</a>), and that requires discussing some of the tiniest of minutia. I also don’t think I’m right about everything I say below–I think there is a lot more thinking we need to have about the ‘ontology’ of paleontological age data.</p>
<div id="four-date-age-data-timelist" class="section level1">
<h1>Four-Date Age Data (“timeList”)</h1>
<p>I’ve thought a few ways about to talk about these issues, and the best I can do is to start with how I encountered taxonomic age data initially. So, graptoloids (the planktonic graptolites, my favorite group… why aren’t they yours too?) have a very well known fossil record. Skipping through some of the vagueries of Paleozoic time-scale making, you can generally tell based upon the species composition of a graptolite fossil assemblage which graptolite biostratigraphic zone you are in, meaning that the rocks you’re looking at come before ‘this graptolite’ showing up for the first time but after some other graptolite species shows up. The same taxa are often found across multiple continents, so there is a global correlation of such zones. The use of some complicated annealing software allows us to put absolute dates on the first appearance of graptolite taxa (Sadler et al., 2009), so if you know the graptolite zone of a rock, you know what little segment of (about) a few million years that rock is from. What nice little time-keepers… Its really too bad graptoloids are extinct, isn’t it?</p>
<p>Anyway, most of the data recorded in papers referring to when particular graptoloid taxa appear in the geologic record is often just which graptolite zones these species or genera first and last appear within. This is understandable, as some taxa can be found pretty continuously throughout their stratigraphic range, and all the information important for stratigraphic correlation are in these times of first and last appearance. In many cases, the data is also very well-behaved in that these graptolite zones don’t overlap and are of a rougly equal length (although maybe with more variation than we’d like; Sadler et al., 2009). This is exactly the sort of data I was first exposed to and dealt with in some undergrad research (published in Bapst et al., 2012). You can find the data <a href="http://datadryad.org/resource/doi:10.5061/dryad.d24sb3h8">here</a>), or you can play with it in <code>paleotree</code> because I recently added it as example data. Let’s take a look at that sort of data.</p>
<pre class="r"><code>library(paleotree)</code></pre>
<pre><code>## Loading required package: ape</code></pre>
<pre class="r"><code>data(graptDisparity)</code></pre>
<p>Our item of interest is <code>graptRanges</code>, which is a <em>timeList</em> object I’ve talked about on this blog before and composed of two matrices: a matrix of the earliest and latest ages for some intervals, in this case graptolite biostratigraphic zones, which are intervals denoted by the first appearance of the taxa the zones are named after.</p>
<pre class="r"><code>head(graptRanges[[1]])</code></pre>
<pre><code>## start_time end_time
## Nemagraptus gracilis (Gi1) 460.86 456.35
## Orthograptus calcaratus (Gi2) 456.35 455.29
## Diplograptus lanceolatus (Ea1) 455.29 452.21
## Diplacanthograptus spiniferus (Ea2) 452.21 449.73
## Dicellograptus kirki (Ea3) 449.73 448.96
## Dicranograptus gravis (Ea4) 448.96 448.57</code></pre>
<p>…and first and last intervals of appearance for some taxa.</p>
<pre class="r"><code>head(graptRanges[[2]])</code></pre>
<pre><code>## first_int last_int
## 'Bulmanograptus' macilentus 16 16
## 'Monograptus' arciformis 16 17
## 'Monograptus' austerus 15 17
## 'Paramplexograptus' kiliani 12 13
## 'Paramplexograptus' paucispinus 12 13
## 'Prisitiograptus' fragilis 16 19</code></pre>
<p>Information-wise, this means every taxon has <strong>four dated values</strong> associated with it: the earliest and latest dates for the intervals in which a taxon first and last appears. By separating the two into separate matrices (effectively making the data structure no longer <em>‘flat’</em>) we remove some redundancy of having to list the same dates for different taxa which first or last appear in the same interval. Essentially, we’re compressing the information content of our taxon range data. Losing this redundancy is good because it minimizes needing to update the dates more than once.</p>
<p>Note that when we have consecutive, non-overlapping intervals (like with the graptoloid data above), its possible we could compress the information content even further; for example, we could just have the start dates of intervals listed in the interval times matrix, and their end-date is, by implication, the start date of the next interval listed. <code>paleotree</code> retains the second column however so that it can handle those cases where intervals do overlap or are non-consecutive. Going in the other direction, we might want more than two numbers to define dates for a given collection, if we had more information about dates than simply a minimum and maximum bound with a flat probability density inbetween (for example, if the fossils themselves can be dated using geochemistry, such as Strontium isotopes), but that sort of age information is not common.</p>
</div>
<div id="two-date-age-data-timedata" class="section level1">
<h1>Two-Date Age Data (“timeData”)</h1>
<p>Particularly in discussions I had on this blog last year, I stressed that there was two other sorts of datasets that might be confused with this. If each taxon was known from only a single collection in the fossil record, such that their first appearance was their last appearance, than we would only have two dates known: the earliest and latest dates for that particular rock it was found in. Alternatively, in an extremely well resolved fossil record, we might have exceptional certainty about the dating of fossil remains, such the first and last appearance dates (FADs and LADs) of taxa were known from strata of extremely precise dating with negligble uncertainty.</p>
<p>Both situations are simply special cases of the four-date datasets; you could still describe them with four dates, but the values would largely be redundant. For comparability across datasets, though, it may be preferable to define taxa as</p>
<p>In both of these cases, each listed taxon has <strong>two dated values</strong> associated with it, but they imply very different things. I’ve encountered issues with people mistaking one for the other when it comes to providing input to <code>paleotree</code> functions, like <code>timePaleoPhy</code>, hence some recent (in the past year) changes to how <code>timePaleoPhy</code> handles age data (which the help file refers to as “timeData”) to make it more explicit. It doesn’t help that data reflecting age uncertainty of point observations is probably the most common type of data that people seem to have in-hand when attempting to time-scale phylogenies of fossil taxa, while I myself had in hand data of precise first and last appearances when I initially developed <code>paleotree</code>.</p>
<p>One could imagine cases where both of the above special cases are true: i.e., (1) taxa are known from singular remains and appear to not have any persistent morphotaxon duration and (2) the stratigraphic time-scale (this is sometimes called an ‘age model’) is so well-resolved that dates are known with exceptional precision; this means every fossil taxon’s age could be defined by a <strong>single dated value</strong>. Such datasets are very rare, and you won’t find anything that assumes such data as input in <code>paleotree</code>.</p>
<p>Ultimately, the commonly used functions for time-scaling phylogenies in the fossil record, my <code>timePaleoPhy</code> and strap’s <code>DatePhylo</code> use two-date age data like this. <code>paleotree</code>’s <code>bin_timePaleoPhy</code> and <code>bin_cal3TimePaleoPhy</code> are just wrappers which take the four-date timeList datasets, randomly sample ages within the intervals and hand their respective time-scaling functions two-date age data.</p>
</div>
<div id="occurrence-data" class="section level1">
<h1>Occurrence Data</h1>
<p>Although its possible to describe the age ranges of fossil taxa from many groups as the four-date-value type above, this format is a reduction in information content; a summary of only what is needed to know about the timing of their first and last appearances. Other than those cases where some taxon is found continuously from its first to last appearance, a given taxon may be observed to occur in a larger number of fossil collections. Thus for each of a given taxon, we might end up with two dates, reflecting the earliest and latest dates bounding the age of each collection. I’ll call this occurrence data (which I think is what everyone else calls it, but I’m not certain 100% because, well, paleontologists can be inconsistent with their terminology).</p>
<p>The Paleobiology Database is probably the data source most accessible to anyone who wants age data from the fossil record, and its raw data is in the form of such occurrences within collections. With the recently introduction of the new <code>paleobioDB</code> package (<a href="http://onlinelibrary.wiley.com/doi/10.1111/ecog.01154/abstract">Varela et al., in press</a>), the use of paleobioDB (aka PBDB) occurrence data is probably going to increase.</p>
<p>Let’s look at an example of occurrence data, using the <code>paleobioDB</code> package to download some PBDB records. To make this fun, we’ll look up this blog’s mascot…</p>
<pre class="r"><code>library(paleobioDB)</code></pre>
<pre><code>## Loading required package: raster
## Loading required package: sp
##
## Attaching package: 'raster'
##
## The following objects are masked from 'package:ape':
##
## rotate, zoom
##
## Loading required package: maps</code></pre>
<pre class="r"><code>nemaData<-pbdb_occurrences(limit="all", base_name="Nemagraptus", show=c("phylo","ident"))
nemaData<-nemaData[nemaData$rnk==3,] #keep only occurrences of taxa resolved to species level
head(nemaData)</code></pre>
<pre><code>## oid typ cid tna rnk tid oei
## 1:4 93753 occ 6986 Nemagraptus gracilis 3 33700 Middle Ordovician
## 1:16 118184 occ 8776 Nemagraptus gracilis 3 33700 Caradoc
## 1:20 118736 occ 8887 Nemagraptus gracilis 3 33700 Actonian
## 1:22 118977 occ 9035 Nemagraptus gracilis 3 33700 Gisbornian
## 1:36 598661 occ 63445 Nemagraptus gracilis 3 33700 Gisbornian
## 1:38 601803 occ 63967 Nemagraptus ? gracilis 3 33700 Gisbornian
## eag lag rid odl odn cll cln phl
## 1:4 470.0 458.4 437 Graptoloidea 33606 Graptolithina 33534 Hemichordata
## 1:16 460.9 449.5 613 Graptoloidea 33606 Graptolithina 33534 Hemichordata
## 1:20 455.8 449.5 607 Graptoloidea 33606 Graptolithina 33534 Hemichordata
## 1:22 460.9 456.1 623 Graptoloidea 33606 Graptolithina 33534 Hemichordata
## 1:36 460.9 456.1 18179 Graptoloidea 33606 Graptolithina 33534 Hemichordata
## 1:38 460.9 456.1 18428 Graptoloidea 33606 Graptolithina 33534 Hemichordata
## phn idt ids mna mra oli rst rss
## 1:4 33518 Nemagraptus gracilis Nemagraptus 5 <NA> <NA> <NA>
## 1:16 33518 Nemagraptus gracilis Nemagraptus 5 <NA> <NA> <NA>
## 1:20 33518 Nemagraptus gracilis Nemagraptus 5 Onnian <NA> <NA>
## 1:22 33518 Nemagraptus gracilis Nemagraptus 5 <NA> <NA> <NA>
## 1:36 33518 Nemagraptus gracilis Nemagraptus 5 <NA> <NA> <NA>
## 1:38 33518 Nemagraptus gracilis Nemagraptus 5 <NA> ? <NA></code></pre>
<p>We can see each row of the obtained data-frame corresponds to a different individual occurrence of some named taxon. Myltiple occurrences are listed for the same taxon. We can simplify this by looking at just the taxon name listed for the occurrence and the earliest and latest age bounds for the associated collections.</p>
<pre class="r"><code>head(nemaData)[,c("tna","eag","lag")]</code></pre>
<pre><code>## tna eag lag
## 1:4 Nemagraptus gracilis 470.0 458.4
## 1:16 Nemagraptus gracilis 460.9 449.5
## 1:20 Nemagraptus gracilis 455.8 449.5
## 1:22 Nemagraptus gracilis 460.9 456.1
## 1:36 Nemagraptus gracilis 460.9 456.1
## 1:38 Nemagraptus ? gracilis 460.9 456.1</code></pre>
<p>We can already see that many of these occurrences overlap with each other and are not from intervals of the same size. Now, PBDB data is noisy and some of that is due to noise, but some of it is just due to differences in how well collections can be chronostratigraphically constrained. So, here we have a data set where taxa may be represented by hundreds of date-values, effectively two date bounds for every collection a taxon occurs in.</p>
<p>We could try visualizing this; first let’s group the occurrences by species…</p>
<pre class="r"><code>occList<-lapply(unique(nemaData$ids),function(x) nemaData[x==nemaData$ids,c("eag","lag"), drop=FALSE])
occList<-lapply(occList,function(x) x[order(x[,1]),, drop=FALSE])
names(occList)<-unique(nemaData$ids)
print(occList)</code></pre>
<pre><code>## $gracilis
## eag lag
## 1:20 455.8 449.5
## 1:48 458.4 453.0
## 1:16 460.9 449.5
## 1:22 460.9 456.1
## 1:36 460.9 456.1
## 1:38 460.9 456.1
## 1:41 460.9 456.1
## 1:43 460.9 456.1
## 1:45 460.9 449.5
## 1:46 460.9 449.5
## 1:49 460.9 456.1
## 1:4 470.0 458.4
##
## $exilis
## eag lag
## 1:39 460.9 456.1
## 1:40 460.9 456.1
## 1:42 460.9 456.1
## 2 460.9 449.5</code></pre>
<p>And now let’s plot the bounds of the age uncertainty for each of the occurrences, color-coded for their species.</p>
<pre class="r"><code>par(yaxt="n")
plot(0, 0, type="n", xlim=c(max(sapply(occList,max))*1.02, min(sapply(occList,min)))*.98,
ylim=c(0,sum(sapply(occList,nrow))+(2*length(occList))),
ylab = "", xlab = "Time (Ma)", main="Age Uncertainty of PBDB Occurrences for Nemagraptus Species")
occColors<-rainbow(length(occList))
count<-1
for(i in 1:length(occList)){
for(j in 1:nrow(occList[[i]])){
lines(occList[[i]][j,],c(count,count),lwd=3,col=occColors[i])
count<-count+1
}
count<-count+2
}</code></pre>
<p><img 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" title alt width="672" /></p>
<p>That wasn’t so impressive, as <em>Nemagraptus</em> is a rather species-poor graptolite genus. Let’s try its close dicranograptid cousin genus, <em>Dicellograptus</em>. This time, we’ll make the plotting code a function.</p>
<pre class="r"><code>plotOccPBDB<-function(pbdbOccData,groupLabel=NULL,occColors=NULL,lineWidth=NULL,xlims=NULL){
#where pbdbOccData is a matrix returned by function pbdb_occurrences in package paleobioDB
#
#order taxa by species (add more taxonomic levels later?)
occList<-lapply(unique(pbdbOccData$ids),function(x) pbdbOccData[x==pbdbOccData$ids,c("eag","lag"), drop=FALSE])
#
#
#order taxa by earliest occurrence
occList<-occList[order(sapply(occList,max))]
#order the occurrences within taxa
occList<-lapply(occList,function(x) x[order(x[,1]),, drop=FALSE])
names(occList)<-unique(pbdbOccData$ids)
#
#set xlims
if(is.null(xlims)){
xlims<-c(max(sapply(occList,max)), min(sapply(occList,min)))
xlimMod<-(xlims[1]-xlims[2])*0.01
xlims<-c(xlims[1]+xlimMod,xlims[2]-xlimMod)
}
#initiate the plot with a modifiable main title
par(yaxt="n")
plot(0, 0, type="n", xlim=xlims,
ylim=c(0,sum(sapply(occList,nrow))+(2*length(occList))),
ylab = "", xlab = "Time (Ma)",
main=ifelse(is.null(groupLabel),"Age Uncertainty of PBDB Occurrences",
paste("Age Uncertainty of PBDB Occurrences for",groupLabel)))
#set colors
if(is.null(occColors)){
occColors<-sample(rainbow(length(occList))) #scramble colors
}
#set line width
if(is.null(lineWidth)){
lineWidth<-(-0.02*nrow(pbdbOccData))+3.2
lineWidth<-ifelse(lineWidth>0,lineWidth,0.01)
}
#now plot the occurrences as lines
count<-1
for(i in 1:length(occList)){
for(j in 1:nrow(occList[[i]])){
lines(occList[[i]][j,],c(count,count),lwd=lineWidth,col=occColors[i])
count<-count+1
}
count<-count+2
}
#return the occList as an invisible data object
return(invisible(occList))
}
dicelloData<-pbdb_occurrences(limit="all", base_name="Dicellograptus", show=c("phylo","ident"))
dicelloData<-dicelloData[dicelloData$rnk==3,] #keep only occurrences of taxa resolved to species level
plotOccPBDB(dicelloData,"Dicellograptus Species")</code></pre>
<p><img src="data:image/png;base64,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" title alt width="672" /></p>
<p>Each color shown here is a different taxon, each line is the age uncertainty of a different occurrence. We can see for dicellograptids, the age uncertainty may potentially be a lot greater than the true sampled age ranges themselves. Many of these collections have age uncertainties that overlap and are not contiguous.</p>
<p>Now, the issue here is that for many statistical analyses that we apply in the fossil record, they use only the first and last appearance times, and they also often presume that these appearances are reported in contiguous, non-overlapping intervals of roughly equal length (let’s call that <strong>‘well-behaved’</strong> four-date data). Why? Well, most of these methods depend on the simple modeling trick that if we have contiguous intervals of roughly equal size, it becomes very easy to calculate the probability of observing a taxon X intervals into the future (or the past), given a sampling parameter. Obviously, there are considerable challenges in translating a dataset of loose occurrences to such well-behaved interval data, and that’s where my mind is right now.</p>
<p>Now, all that said, there are some analytical programs that take occurrence data natively. In particular, <a href="http://sourceforge.net/projects/pyrate/">PyRate</a> is a Python package that applies a Bayesian MCMC algorithm to estimate taxonomic origination, extinction and sampling rates from occurrence data. The method is described <a href="http://sysbio.oxfordjournals.org/content/63/3/349">here</a> and the program itself is described in MEE <a href="http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12263/abstract">here</a>. One could easily set up a functions to use [paleobioDB] package to download PBDB and use PyRate’s <a href="www.sourceforge.net/projects/pyrate/files/">data preparations R functions</a> to produce datasets for analysis in Python. Ultimately, PyRate solves the issue using a similar solution to the one I advocate via <code>bin_timePaleoPhy</code> (I discuss this more below): treating the age uncertainty of occurrences as uniform uncertainty bounds on the actual age of taxa and repeatedly randomly sampling ages from within those bounds and running the PyRate analysis. The sampling of ages is technically done in R; the Python component of PyRate only accepts <strong>single-date</strong> ‘point’ age data for occurrences as input. The results of multiple analyses can then be considered to see how much influence the uncertainty in taxon ages has on the resulting.</p>
<p>But PyRate doesn’t replace every pre-existing analysis of paleobiological data, and the ‘repeatedly sample and consider the resulting distributions’ trick isn’t an applicable solution to those analyses. So, how do we translate occurrence data into ranges within contiguous intervals? How does the manner in which we do this matter (or not) for the analyses we do? I’ve given this question a lot of thought previously with respect to time-scaling phylogenies of fossil taxa, but what about analyses where phylogenies aren’t involved? In particular, there are a number of functions in <code>paleotree</code> for estimating sampling rates and sampling probabilities that depend on having data from contiguous intervals (e.g., <code>getSampProbDisc</code> or <code>make_durationFreqDisc</code> or <code>make_inverseSurv</code>). These are questions I’ll try to address in future posts, along with a long-promised post about porting data from <code>paleobioDB</code> into <code>paleotree</code> format.</p>
<p>However, on a related node…</p>
</div>
<div id="what-does-the-paleobiology-database-report-as-two-date-age-ranges" class="section level1">
<h1>What does the Paleobiology Database report as two-date age ranges?</h1>
<p>Some analyses only accept the two-date range data, and so people have invented algorithms for obtaining such datasets from occurrence data or four-date range data. However, such datasets must represent either precise age ranges or age uncertainty on a single occurrence, and thus information is being lost when we translate to two-dates (except in the rare cases where each taxon is known from only a single collection, and thus the two-dates are simply age uncertainty on singular finds). One can imagine some simple algorithms for obtaining two-date ranges, such as <strong>‘maximum range’</strong>, i.e. take the earliest possible age of the oldest occurrence for a taxon and the latest possible age of the youngest occurrence. Although algorithms such as this retain some information about the geologic age of taxa, my opinion is that the information loss about age uncertainty may impact many analyses, perhaps adding an analytical bias. An alternative philosophy, which I espouse in the <code>bin_timePaleoPhy</code> function in <code>paleotree</code>, is to random draw precise FADs and LADs from four-date ranges (or occurrence data), run an analysis (such as time-scaling a phylogeny of fossil taxa), save the resulting test statistic or parameters of interest, and repeat this procedure many times to generate sampled distributions of the test statistics / parameters. Such a procedure retains (rather than loses) the uncertainty component of the ages into our results, but as I said above when discussing PyRate, this procedure isn’t applicable to every type of analysis.</p>
<p>Two of the various utilities or “apps” (<em>…ugh</em>) associated with the Paleobiology Database will return <em>two-date</em> age ranges for a search query. I became very curious how these ranges were calculated and so I wanted to report on my investigations.</p>
<p><strong>FirstApp</strong></p>
<p>Before I get into these various ways of returning ranges from occurrence data, I should mention a relavant <strong>not</strong>-range-returning application that deals with fossil taxon ages, <a href="https://mclapham.shinyapps.io/firstApp/">FirstApp</a>, a module by Matt Clapham. FirstApp takes paleobioDB data for a given taxon and displays occurrences through time as a density histogram, with occurrences placed as points at the earliest age boundary of the collections they occur within.</p>
<div id="ranges-in-paleobiology-database-classic-fossilworks" class="section level2">
<h2>Ranges in Paleobiology Database Classic / Fossilworks</h2>
<p>First, if you go to the present Paleobiology Database <a href="http://paleobiodb.org">website</a>, you have the option of using <a href="http://paleobiodb.org/cgi-bin/bridge.pl?">PBDB Classic</a> which is a search query engine, with a download option (located <a href="http://paleobiodb.org/cgi-bin/bridge.pl?a=displayBasicDownloadForm%22">here</a>). You can access an almost identical interface at <a href="http://fossilworks.org/">FossilWorks</a>, which mirrors the paleobioDB data along with a number of analytical tools previously housed at the PBDB. FossilWork’s basic download option is located <a href="http://fossilworks.org/?a=displayBasicDownloadForm">here</a>. The algorithm for calculating two-date ranges used is somewhat complex but doesn’t appear to be described in any documentation, so I went straight to the “horse’s mouth” (his words), and asked John Alroy himself. I’ll paraphrase our conversation.</p>
<p>John has developed this <strong>“zone-of-overlap”</strong> algorithm to deal with obtaining ranges from the realities of messy occurrence data described above: age uncertainties (i.e. stratigraphic intervals) that overlap, of different lengths and don’t sort into an orderly hierarchy. Rather than returning the earliest possible date and the latest possible date like with the maximum-range algorithm described above, the zone-of-overlap algorithm “finds the oldest base that is older than at least part of all the intervals and the youngest that is younger than at least part of all the intervals” (direct quote, John Alroy).</p>
<p>Here’s three specific examples of how these ranges are calculated, taken from direct quotes from our email conversation. Two are from John and the third is from myself; as such, they are almost comedic examples of our taxonomic preferences:</p>
<ul>
<li><p>“…for example, if the two oldest occurrences are respectively Paleocene (not better resolved than that) and Thanetian then the base has to be the base of the Thanetian.”</p></li>
<li><p>“Another example would be Hemphillian (Mio-Pliocene) and Pliocene. These work out to a range going from base of the Pliocene to the top of the Hemphillian even though the Hemphillian is ‘older’ (based on its base) and the Pliocene ‘younger.’”</p></li>
<li><p>“If I follow correctly, some taxon listed in the PBDB with two occurrences, in a Katian collection (base ~456 Ma) and a <em>Dicellograptus anceps</em> zone (base is ~449 Ma) collection respectively, the lower age bound returned returned will be ~449 Ma.”</p></li>
</ul>
<p>In other words, for calculating the first appearance datum (FAD), the algorithm looks for all occurrences that overlap with the age range of the earliest-most occurrence, obtains their earliest boundary ages and returns the <strong>latest-most</strong> earliest age boundary among these overlapping occurrences. Similarly, for calculating the latest appearance datum (LAD), the algorithm looks for all occurrences that overlap with the age range of the latest-most occurrence, obtains their latest boundary ages and returns the <strong>earliest-most</strong> latest age boundary among these overlapping occurrences. On theoretical grounds, one could probably describe the zone-of-overlap algorithm as minimizing taxonomic age ranges by assuming that overlapping occurrences probably describe a very similar FAD or LAD, and thus picks the one that extends the taxonomic range the least.</p>
<p>However, this does come with a downside that if these occurrences are not repeated attempts to capture the same FAD or LAD, then the zone-of-overlap algorithm isn’t an accurate depiction of the uncertainty in the ages; the age of the rocks containing the observed FADs and LADs may actually be outside of the reported range, unlike the maximum-range algorithm, which must contain the entire observed age range for a taxon as long as ourour interval dates for collections is accurate. Consider my graptolite zone example above; there is a possibility that the collection from the Katian is actually from an earlier graptolite zone than the <em>D. anceps</em> zone, maybe the <em>Dicranograptus kirki</em> zone (~453 Ma). Better stratigraphic resolution on a collection might very well provide such information in the future, but zone-of-overlap algorithm doesn’t give any weight to that possibility. On the other hand, John argues that a large number of taxa in the PBDB are listed from collections with very broadly defined intervals of time, and so some way is needed to downweight the effect of that lack of stratigraphic interval resolution. Given the nature of the data, the loss of uncertainty may be a necessary evil under the zone-of-overlap algorithm if you want to be as conservative as possible (say with providing molecular clock calibration dates for phylogenetic divergence dates for crown clades). I think the likely answer here is dependent strongly on the group we’re interested in and the reason we want range dates.</p>
</div>
<div id="ranges-in-r-package-paleobiodb" class="section level2">
<h2>Ranges in R package <em>paleobioDB</em></h2>
<p>The <code>paleobioDB</code> package also has a function for obtaining and plotting <strong>two-date</strong> age ranges, the function <code>pbdb_temp_range</code>. Here’s a species-level example using the <em>Dicellograptus</em> data above, with the plotting functionality turned off.</p>
<pre class="r"><code>pbdb_temp_range(dicelloData, rank="species", do.plot=FALSE)</code></pre>
<pre><code>## max min
## Dicellograptus alector 471.8 443.4
## Dicellograptus anceps 455.8 443.7
## Dicellograptus ornatus 453.0 443.4
## Dicellograptus complanatus 453.0 443.7</code></pre>
<p>So what are these ranges? The manual file for <code>pbdb_temp_range</code> just refers to them as temporal ranges or time spans of taxa.</p>
<p>Well, we can go to the Github repo for <code>paleobioDB</code> and read the code for <code>pbdb_temp_range</code> directly, <a href="https://github.com/ropensci/paleobioDB/blob/master/R/pbdb_temporal_functions.R#L127-1128">here’s the relevant bit</a> for species. It appears that the ranges reported by <code>pbdb_temp_range</code> are the maximum-ranges, the earliest and latest possible bounds on a taxon’s age range.</p>
</div>
</div>
<div id="wait-wait-hold-on-why-are-there-only-four-species-listed" class="section level1">
<h1>Wait, wait, hold on, why are there only four species listed…</h1>
<p>So, tangentially, you’re probably wondering why so few species are reported, as we can see from the occurrences figure above that a much larger number appears to be contained within the downloaded occurrences table. Uh… I’ll address that in my next blog post.</p>
<hr />
<p>PS: I just discovered how paleobioDB’s API allows you to call <a href="http://phylopic.org/">PhyloPic</a> images for taxa, woah! Here, check out Nemagraptus:</p>
<p><a href="http://paleobiodb.org/data1.1/taxa/thumb.png?id=281">http://paleobiodb.org/data1.1/taxa/thumb.png?id=281</a></p>
<p>Sweet!</p>
</div>
dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-73441075995167051012014-07-30T13:36:00.002-07:002014-07-30T13:40:51.361-07:00Creating pretty plots of trees timescaled with paleotree via strap's geoscalePhyloFor anyone who cares, paleotree was not written for pretty plotting; other things were just more important to me. I don't have a particularly well-honed sense of aesthetics anyway; have you seen my recent Paleobiology paper, A.K.A. the grande parade of boxplots?<br />
<br />
Over the years, I've gotten people asking me how they plot their fossil-taxon trees against, for example, a geological time-scale and I haven't been really able to tell these people what to do. But that changed recently, thanks to Mark Bell's and Graeme Lloyd's new package <i>strap</i>.<br />
<br />
http://cran.r-project.org/web/packages/strap/<br />
<br />
In addition to its own timescaling function and functions for assessing stratigraphic consistency, their package includes the extremely useful function <i>geoscalePhylo</i> which makes use of Mark Bell's geoscale package. This function takes a time-scaled tree and plots it against a geologic scale and, optionally, will display taxon ranges as thickened bars on the terminal branches.<br />
<br />
Recently, I got one or two questions about using this function with trees time-scaled using paleotree, and so I decided to post an example using a dataset with a discrete interval range data; i.e. one of those datasets written for <i>bin_timePaleoPhy</i> and associated functions. Its mostly straightforward to do this, particularly as the <i>'bin_'</i> time-scaling functions additionally outputs the taxon ranges used for a particular tree. we do need to do a minor step where we rename some columns in the range matrix for <i>strap</i> to accept these ranges.<br />
<br />
So, here's a short R script that uses the example graptolite dataset from
paleotree, as used in the <a href="http://nemagraptus.blogspot.com/2013/06/a-tutorial-to-cal3-time-scaling-using.html">cal3 tutorial</a> from last year (i.e. Bates et al., 2005). See the old blog post for details about constructing the data files. <br />
<br />
Now, you can just install strap and paleotree and give this script a whirl...<br />
___________________________________________________________________<br />
<span style="font-family: "Courier New",Courier,monospace;"><br />library(paleotree)<br />library(strap)<br /><br />data(retiolitinae)<br /><br />#need time-scaled tree<br /> </span><span style="font-family: "Courier New",Courier,monospace;"><span style="font-family: "Courier New",Courier,monospace;"> #</span>randomly draw FADs/LADs from within grapt biozones<br />timetree<-bin_timePaleoPhy(tree=retioTree,timeList=retioRanges,<br /> type="basic",add.term=FALSE)<br /><br />#get the ranges used by the bin_ function<br />rangesUsed<-timetree$ranges.used<br />colnames(rangesUsed)<-c("FAD","LAD")<br /><br />#now plot it<br />geoscalePhylo(tree=timetree,ages=rangesUsed,ranges=TRUE)</span><br />
<br />
<br />
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<br />
___________________________________________________________________________<br />
<br />
Now, this is *much* prettier than anything paleotree makes, but we can do *much* better still.<br />
<br />
First, this tree is a single random draw, an insignificant what-if produced by drawing the first and last appearance dates of taxa from the discrete intervals they are known to first and last occur in (here, Silurian graptolite biozones). For plotting purposes this is undesirable because you could remake this plot ten times and get ten slightly different looking plots. A better alternative, one matching plots in the literature, is to display taxa as ranging from the very start of the first interval they are found in to the very end of the last interval they occur in. This can be done easily with <i>bin_timePaleoPhy</i>'s argument <i>nonstoch.bin</i>.<br />
<br />
Secondly, the simple minimum-node-ages "basic" timescaling method almost always introduces artificial zero-length branches that, when plotted, make dichotomous nodes look confusingly like polytomies. Furthermore, this can obscure any polytomies your phylogeny contained to begin with. So, for purposes of plotting, we can use one of the simple, arbitrary, post-hoc timescaling methods available in <i>paleotree</i> or <i>strap</i>. These methods rescale branches so there aren't any zero-length branches, but how realistic these various alternatives to "basic" are is another matter (you might want to read my most recent Paleobiology paper). Nevertheless, they are useful for showing relationships, as long as any one looking at it knows the apparent timing of divergences on the figure isn't even a best guess, just a random stab in the dark that's most probably dead wrong.<br />
<br />
(Anyone who regularly reads paleontological papers will probably have that understanding.)<br />
<br />
Finally, we can make the plot prettier just by playing with the graphical parameters of geoscalePhylo: make the text bigger, widen the branches, adjust the time-scale so we can see the Silurian diversification of the Retiolitinae with respect to the Ordovician and Devonian, etc. <br />
<br />
And so... <br />
____________________________________________________________________________<br />
<br />
<span style="font-family: "Courier New",Courier,monospace;">#now let's make it prettier</span><span style="font-family: "Courier New",Courier,monospace;"><span style="font-family: "Courier New",Courier,monospace;"><span style="font-family: "Courier New",Courier,monospace;"> </span></span></span><br />
<span style="font-family: "Courier New",Courier,monospace;"><span style="font-family: "Courier New",Courier,monospace;"><span style="font-family: "Courier New",Courier,monospace;"> </span></span>#non-random FADs and LADs</span><br />
<span style="font-family: "Courier New",Courier,monospace;"><span style="font-family: "Courier New",Courier,monospace;"><span style="font-family: "Courier New",Courier,monospace;"> </span></span>#also use a 'prettier' time-scaling method<br /><br />timetree<-bin_timePaleoPhy(tree=retioTree,timeList=retioRanges,<br /> type="mbl",vartime=1,nonstoch.bin=TRUE,add.term=FALSE)</span><br />
<span style="font-family: "Courier New",Courier,monospace;"><br />rangesUsed<-timetree$ranges.used<br />colnames(rangesUsed)<-c("FAD","LAD")</span><br />
<span style="font-family: "Courier New",Courier,monospace;"><br />geoscalePhylo(tree=timetree,ages=rangesUsed,ranges=TRUE,<br /> cex.tip=0.8,cex.ts=0.55,cex.age=0.5,x.lim=c(413,445),width=2)</span><br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhOlHrJUKqBo5ggQDsiAczBrR_2hdatGWrL_ESfed-iKz3hAfNSIRvD1qOM1xwVMNk6ew77AC_waXz0qXyUXg5FXXl2UwRieRv1wpPVBp07ioHu9wkzhvg12GisHf60WLWbmLzcKdOvXRK/s1600/retio_geoscalePhylo_07-29-14.jpeg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjhOlHrJUKqBo5ggQDsiAczBrR_2hdatGWrL_ESfed-iKz3hAfNSIRvD1qOM1xwVMNk6ew77AC_waXz0qXyUXg5FXXl2UwRieRv1wpPVBp07ioHu9wkzhvg12GisHf60WLWbmLzcKdOvXRK/s1600/retio_geoscalePhylo_07-29-14.jpeg" height="319" width="320" /></a></div>
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_________________________________________________________________<br />
<br />
Ta-da! <i>Very</i> pretty!<br />
<br />
Enjoy!<br />
-Davedwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-75529571078703900952014-02-05T06:51:00.001-08:002014-02-05T06:51:57.851-08:00Raising A White Flag, paleotree v2.0 and the Reciprocal Monophyly of Various Hemichordate GroupsHello all!<br />
<br />
We have a lot to talk about today.<br />
<br />
Last time, I bemoaned users who were misunderstanding my functions. Well, I have surrendered. timePaleoPhy is now happy to take taxon dates as min-max bounds. In the newest version of paleotree, version 2.0, you can find this functionality in the new argument dateTreatment for timePaleoPhy and other time-scaling functions.<br />
<br />
In fact, a lot has changed in paleotree lately. Last night, I uploaded paleotree 2.0 to CRAN, and it should be making its way to your favorite repository as a binary of your choice soon. This, and the previous update, paleotree 1.9, have changed a lot about paleotree! <br />
<br />
All these changes are kinda fitting, given that paleotree is now version 2.0, and the turning over of a new version number usually a rapid change in the structure of a package. However don't really try to read too much into that, its really just a happy accident. There's a lot of very extreme opinions some people have about version numbering and I was a little naive when I started programming. So, way back in January of 2012, I just decided to go with a simple system where every public release except the most minor would be given a new increment of 0.1, and the first public release would be 1.0. I'm sure some people think that's a hideous way to do version numbering, but whatever. It ain't your package, dudes.<br />
<br />
As always, you can look at paleotree's CHANGELOG to see what's new, but here's the last two entries for your reading pleasure.<br />
<br />
__________________________________________________<br />
<br />
Version - 1.9 - 01-02-14<br />
-Changed all functions to individual R script files rather than single master script. Why didn't I do this years ago??!<br />
<br />
-All help files converted to an roxygen2 format within function scripts<br />
<br />
-At suggestion of Fabricio Villalobos, added option to expandTaxonTree that allows branch lengths to be retained and the added lower-taxa are connected with zero-length branches. Only useful for very specific purposes, please use with caution.<br />
<br />
-Changed all lines which checked for class "phylo" of input objects to use is(obj,"phylo") instead of class(obj)=="phylo" per recommendation of Carl Boettiger<br />
-Added new warning line to taxicDivDisc so people who try to pass it matrices with character strings in the matrix will get more helpful warning messages<br />
<br />
-Upgraded probAnc and qSProb2comp based on equations in Foote (1996) for all three modes of origination, also fixed some errors in previous versions<br />
<br />
-Added new function pqr2Ps which uses Emily King's exact derivation for the joint probability of a clade being (a) going extinct but sampled on an infinite time scale and (b) never going extinct on an infinite time scale<br />
<br />
-Removed internal Ps function from cal3, now pqr2Ps which is exported directly to namespace<br />
<br />
-Converted to new way to handle likelihood functions in paleotree, moving to a function-as-an-object system like diversitree.<br />
<br />
-Following on last point, added new functions for fitting models of duration frequency data, replacing getSampProbDisc and getSampRateCont<br />
<br />
-More models to follow in future versions of paleotree: added new function footeValues which will (eventually) support a release of a function that implements Foote's (2001, 2003, 2005) inverse survivorship models<br />
<br />
-Modified diversitree's 'constrain' function to make a paleotree version named constrainParPaleo which is both entirely separate and fulfills needs such as constraining many similarly named parameters to a single value<br />
<br />
-See new functions listed in ?modelMethods for manipulating functions in the new model format<br />
<br />
-Added some not-exported hidden functions for use by the various model-handling functions<br />
<br />
-Added a 'terrible idea' function optimPaleo which simplifies using optim with new parameter bounds functions. This function is entirely for pedagogical reasons and may be removed later.<br />
<br />
-Added new function horizonSampRate which uses ML estimator from Solow and Smith (1997) for estimating sampling rate from precise durations in continuous time and number of sampled horizons<br />
<br />
-Added new function perCapitaRates which estimates the per-capita origination and extinction rates from discrete interval data, following the methods from Foote (2000)<br />
<br />
<br />
Version - 2.0 - 02-03-14<br />
-Changed parInit to use uniform distribution
to randomly draw initial parameter values between bounds, rather than
take mid value between bounds<br />
<br />
-Changed how time-scaling functions
dealt with node.mins argument; can no longer use node.mins with a
dataset that has unshared taxa that are to be dropped<br />
<br />
-Altered
example for use of node.mins in help files for time-scaling functions;
thanks John Clarke of Oxford for the heads up! Also other modifications
were made to the help descriptions, clarifying that node.mins can be
used to constrain the minimum age for the root node.<br />
<br />
-Also on a
different issue brought to my attention by John Clarke, added an error
message to paleotree when 'equal' is attempted by the edges leading to the
root are zero-length (because 'equal' cannot run under this situation!)<br />
<br />
-Added new function collapseNodes that collapses specific user-defined nodes, either forward or backward<br />
<br />
-Made all lines checking for dichotomous trees check both with is.binary.tree() *and* is.rooted()<br />
<br />
-Added
new function dateNodes which returns the dates of the internal and tip
nodes of a phylogeny on an absolute time-scale, with respect to the
$root.time element if one exists<br />
<br />
-On a trial basis, I have added
new function inverseSurv which attempts to replicate the forward and
inverse survivorship modeling applied by Foote (2001, 2003, 2005) and is
useable with the newly implemented constrainParPaleo framework
implemented in the previous version of paleotree. I am not yet convinced
this function is a 100% faithful replicate of the original method.<br />
-fixed
error in plotTraitgram where if trait data was entered in same order as
tree$tip.label, trait data was not resorted prior to running ace<br />
<br />
-'equal'
method in timePaleoPhy wasn't returning same result as 'equal' method
in DatePhylo from Graeme Lloyd's original code. This turned out to be a
result of differences in how we ordered nodes: Graeme ordered them by
time or distance-from-the-root (using dist.nodes) and I was using
node.depth, which counts number of branching events. This choice
shouldn't make a considerable difference on the performance of the
algorithm, but does produce some differences in the resulting
time-scaled trees. For consistency, I have change timePaleoPhy to match
Graeme's algorithm.<br />
<br />
-altered timePaleoPhy and cal3timePaleoPhy to
allow the point date occurrences with the first and second column of
timeData interpreted as bounds instead, using the argument
dateTreatment="minMax"<br />
<br />
-related to above change, the argument
rand.obs was removed from timePaleoPhy and cal3TimePaleoPhy as no longer
necessary, this functionality is now available via
dateTreatment="randObs"<br />
<br />
-Although it may be strange to not report
a lack of a change, but still have not added the finite time window
approach for durationFreq<br />
<br />
________________________________________________________<br />
<br />
Hahah, and as you might guess from that last one, I've still got some more new things and changes for paleotree in store in the next few months. I included it here because I was actually partway through adding this option and had to undo those additions through commenting, since I wanted to push this version to CRAM (the undoing worked, I think, but I can't be certain, so best to add it to the CHANGELOG). <br />
<br />
As always, let me know what you think of the new paleotree functionality!<br />
<br />
So what else?<br />
<br />
Well, hemichordate phylogeny has been shaken back and forth a little lately. You might have missed them, so here's a short list:<br />
<br />
http://onlinelibrary.wiley.com/doi/10.1002/jez.b.22510/full<br />
http://link.springer.com/article/10.1007/s00114-013-1117-3<br />
http://www.biolbull.org/content/225/3/194.abstract<br />
<br />
The Stach article in particular is covered by Cambrian Mammal's blog:<br />
http://cambrianmammal.wordpress.com/2014/01/27/the-use-of-a-larva<br />
<br />
The Cannon et al. is really neat: with greater gene and taxon coverage than a few years ago, it looks like the different hemichordate groups really are reciprocally monophyletic and not nested, and maybe even the Rhabdopleura and Cepholdiscus groups are even reciprocally monophyletic. Big implications for what the stem deuterostome looked like... and even bigger implications for the stem graptolite.... if you're into that sort of thing. ;)<br />
<br />
Oh, and finally, I'm also a brachiopod worker now! I've begun a remote post-doc with Sandy Carlson at UC Davis. I'm looking forward to doing some neat things with her and the rest of her lab!<br />
<br />
-Davedwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-87404211265366894882013-12-13T22:48:00.001-08:002013-12-13T22:48:12.950-08:00Stratigraphic Uncertainty, timePaleoPhy versus bin_timePaleoPhy, and My Own Paleontological MyopiaHi all!<br />
<br />
So, its been a rough week! I've gotten more emails about issues or questions about paleotree this week than I have in the past four months! Which is awesome, cause I love talking to people about using paleotree, but there's something that's been worrying me in some of these emails. These emails have given me a grave concern that a number of users don't understand the distinction between two key time-scaling functions in paleotree: timePaleoPhy and bin_timePaleoPhy. The problem is, not understanding this difference is extremely dangerous, as they could generate time-scaled trees that don't make sense without realizing it.<br />
<br />
But I can't really blame them, because I think its my fault as the package author to explain this distinction clearly enough... the problem is I didn't understand something critical and fundamental three years ago: Our Fossil Records are Different.<br />
<br />
See, the fossil record for your group is probably really different from the fossil record I'm working on, and that means not only how we do things is different, but the sort of assumptions we come to the table with and what we think X means is different. And that sounds kind of obvious, until you meet someone whose fossil record is radically different from yours and they don't even have a concept for this one thing you thought everybody knew about.<br />
<br />
The problem is that people are using timePaleoPhy who probably shouldn't be using timePaleoPhy, when they should be using bin_timePaleoPhy instead. It isn't helped by the argument 'rand.obs' in timePaleoPhy, which some interpreted as representing how to bring stratigraphic uncertainty into an analysis. After explaining to several users over email why I was concerned about their use of paleotree, I felt like a specific blog post on the topic was needed.<br />
<br />
But to understand where this misconception of some users comes from, and why I think it stems from my own misconceptions in the past that maybe fossil records were more similar than I know now, I've also got to explain my reasons for writing both functions the way I did.<br />
<br />
So, I wrote timePaleoPhy first, when I had some stratigraphic data for graptolite ranges, with stratigraphic meters scaled to an absolute time-scale. Since graptolite species often persist over time, I had a matrix of first and last appearance dates (FADs and LADs, respectively), like so...<br />
<br />
Normalograptus_normalis 457.9 438.3<br />
Climacograptus_typicalis 463.2 442.8<br />
<br />
(This is totally fake data; I don't even know if typicalis is still a Climacograptus, but maybe its the type?...I can't remember at this very moment.)<br />
<br />
The dates here represent actual ranges; i.e. N. normalis is first found in rocks dated 457.9 Ma and is found fairly continuously until you get to rocks dated 438.3 Ma. Of course, the rocks aren't actually 'dated' so precisely, more like we're assuming continuous sedimentation rates and so we're inferring those dates using the absolute dates we do have.<br />
<br />
This means when we time-scale the tree, unless we're allowing for ancestors (which we don't normally do unless we know something about apoomorphic and plesiomorphic taxa) the clade containing N. normalis has to be at least as old as 457.9 Ma.<br />
<br />
And that's what timePaleoPhy does, if you hand it data like above, you get back a tree where the nodes are as old as the dates in the first column (the taxon names should be row labels), because it thinks those are first appearance dates, known very precisely.<br />
<br />
A different question is where the tips go (or 'when', really). As I've talked about before here on this blog and in published papers, this is a difficult question. For most things, such as looking at a tree or estimating lineage diversity through time, you'd want the whole taxon range 'added' to the tree, so you should use the LAD as the tip-date. However, comparative methods for studying trait evolution assume that the date of tips represents the date of the population from which trait values were taken. Of course, maybe your trait doesn't vary much (or at all) over the duration of the taxon, but that's a story for another time. In the papers I've written, I often call these tip-dates 'times of observation'... cause I think some reviewer didn't like 'tip-date', or maybe it was a committee member. Well, reviewers seem to dislike 'times of observation' too. <br />
<br />
(I can't freakin' win when it comes to vocabulary.)<br />
<br />
At the time, I was most interested in comparative methods, so I assumed this would be true of everyone else. In general, it seemed like people were using the FAD primarily in the literature (or at least, I thought so at the time) as the tip-date for comparative methods, so I made the FAD the default tip-dates that timePaleoPhy spits out. To get timePaleoPhy to use the LAD as the tip-date, you have to set add.term=TRUE, which adds the range to the terminal branch the taxon sits on.<br />
<br />
But using the FAD or the LAD wasn't really a satisfying solution: the tip-date in a comparative analysis supposed to be the time of the sampled population (hence why I call them 'times of observation'). For my long-ago purpose, I had I had size measurements from a bunch of figured specimens for graptolites but with very little data on 'when' those specimens came from. They were from 'the Late Ordovician' or 'Early Silurian' and I didn't have enough information to figure out a more specific tip-date. So that's pretty much useless
information for figuring out the 'when' for these specimens, and if I
want to do some PCM, well the tips are supposed to represent the date of the population where I measured the trait. I just know the Normalograptus normalis specimen I measured has a 4.2 mm long doodad. Using the FAD or the LAD wasn't really an honest approach given the uncertainty involved.<br />
<br />
So... rand.obs is how I dealt with this uncertainty, still using the FAD for N. normalis to adjust the clade age, but allowing the tips to fall at some date randomly selected between the FAD and the LAD. Maybe I had measured doodad length from a normalis specimen from 441.3 Ma,
so, the tip would be at that date, but the clade constraining that taxon would still be constrained by the 457.9 Ma first appearance date. Note that rand.obs only works if we also set add.term=TRUE, cause the way I decided to do it (and no, I'm not changing it) was that these random observation dates were a temporary replacement of the LADs. Thankfully, timePaleoPhy returns an error if you ever try using rand.obs=TRUE while add.term=FALSE, or else people might get very confused.<br />
<br />
Later, I needed to make use of a much less precise dataset, where taxon first and last occurrences were graptolite zones. So like, the different zones had minimum and maximum dates associated with their starting and ending, like this...<br />
<br />
zone_A 466.2 452.4<br />
zone_B 452.4 448.5<br />
zone_C 448.5 441.1<br />
zone_D 441.1 436.2<br />
zone_E 436.2 431.0<br />
<br />
(This is totally fake cause graptolite zones are usually shorter than that.)<br />
<br />
To keep track of everything, I needed some way of recording the dates for the intervals and which intervals taxa first and last showed up in. Normally you see this thing displaying in papers as a range chart, where species A will have Xs in intervals 1, 2, 3, and then not 4, meaning it last appeared in interval 4. This seemed like a fairly non-straightforward way of handling the information, particularly as I was only really interested in the first and last interval each taxon appeared in.<br />
<br />
So, what I did was I listed the first and last interval that each taxon was known from as the row of the interval matrix. Like so:<br />
<br />
N._normalis 1 4<br />
C._typicalis 1 3<br />
<br />
So, if we look at this in reference to the prior matrix, this tells us that N. normalis first appears in zone A and last appears in zone D, which means it might have first shown up anywhere between 466.2 and 452.4 Ma and last appeared somewhere between 441.1 and 436.2 Ma. And so now, if we have both this 'taxon times' matrix and the 'interval times' matrix above, we have all the information on the dates associated with when these taxa were first and last occurring.<br />
<br />
In paleotree, I decided to call a list that contained both matrices a 'timeList' object because as you have no doubt deduced, I have terrible taste when it comes to coming up with new vocabulary. A lot of functions in paleotree use these timeList objects. (It's not a real object class, cause I didn't understand S3 classes yet when I wrote paleotree.) Anyway, I went with a list of two matrices rather than two seperate matrices because the taxonTimes matrix doesn't make sense without reference to the intervalTimes matrix and so I felt I needed to keep them sorted together. To get a timeList, you can just make the two matrices by hand in your favorite spreadsheet program, read them into R and then combine as a list. The last step is as simple as typing time<-list(intervalTimes,taxonTimes) into the terminal. Note that the interval names and taxon names are rownames, so the matrices should both have only two
columns each.<br />
<br />
Now, to time-scale a tree with this sort of data, we need to somehow get precise first and last appearance dates and then make a tree from there. So I made bin_timePaleoPhy to draw dates from these intervals, under a uniform probability distribution, and then use the randomly drawn dates to make a time-scaled tree. So, maybe normalis was first seen
at 454.2 or 461.8 Ma, and last seen at 440.7 Ma, or 438.8 Ma. Or lots of other combinations! We can resample those dates and get all sorts of new FADs. and LADs and make lots of time-scaled trees. Unlike timePaleoPhy, bin_timePaleoPhy actually does something about stratigraphic uncertainty, at least as far as having data in discrete intervals goes.<br />
<br />
But this doesn't fix the unknown times of observation issue for trait data... so bin_timePaleoPhy also has a rand.obs argument just like , so that we can let our observed doodad length for N. normalis come from 452.6 Ma or 446.3 Ma or whatever, depending on the set of FADs and LADs we plucked from the intervals in a given run. Again, just like with timePaleoPhy, the date of the nodes are still going to depend on the FADs, rand.obs is only going to impact the tip-dates, those enigmatic 'times of observation'.<br />
<br />
So, let's be clear:<br />
<br />
1) timePaleoPhy does *not* deal in stratigraphic uncertainty of appearance dates: it believes the numbers you are handing it are very precise first and last appearance dates, which exist for some fossil records but probably not most. It does deal with uncertainty in the times of observation of specimen measurement, though, which is what rand.obs is for.<br />
<br />
2) bin_timePaleoPhy *does* deal with stratigraphic uncertainty, but it requires slightly more complicated input: two matrices, as a list.<br />
<br />
And I think, somehow, I said something that is confusing people about these two facts. I'm repeatedly getting emails from people that think the matrix for timePaleoPhy is a matrix of the minimum and maximum ages for a point occurrence of a fossil. In other words, their fossil taxa only appear at a single point in time which is imprecisely known, and so their dates are the bounds on that single date, rather than two very precisely known dates that represent the first and last appearances.<br />
<br />
This practice is very worrying, because if they put those dates in and then set rand.obs=TRUE and add.term=TRUE, they'll think their pulling their taxon occurrence dates randomly from their min and max dates... which is true for the tip-dates. But the nodes are being pushed back in time to those minimum dates, which really means that clades are 'as old as they could possibly be' or something wacky like that. And I bet people have done this without realizing that's what is happening! <i>Its absolute nonsense and this nonsense has to stop.</i><br />
<br />
I think the real issues is that when I wrote these functions, I was excessively myopic. I looked at things like a
graptolite worker and I thought everyone had data like graptolite data, with taxa that typically first and last appeared at different times. I actually didn't realize that other people had data where it was more common to see things like 'species A is known from one skeleton, found somewhere in the early Cretaceous'. Some vert paleo friends of mine have kindly helped me understand that this isn't true, at least, for lots of vert datasets: they actually have taxa known from single collections that may be as poorly unconstrained as 'anywhere within a 40 million year interval', which, well, that blows my mind every single time I read this sentence.<br />
<br />
So, given the apparent and continual misuse of timePaleoPhy, I have actually considered pulling the function entirely from the CRAN version of paleotree, or hiding the function, making it unavailable to regular users. This isn't ideal: some people, such as those who work with detailed biostratigraphic records such as for index fossils and microfossils, actually have precisely dated first and last appearances. I don't know if anyone like that uses paleotree yet, but I want to keep the door open for them! Plus, bin_timePaleoPhy is really just a dumb wrapper for timePaleoPhy. If someone really wants to write their own wrapper for timePaleoPhy, that's fine! I'm happy for them.<br />
<br />
But honestly, the majority of people won't have data that's infinitely precise. They should be using bin_timePaleoPhy. (Or you know, bin_cal3TimePaleoPhy, but maybe I shouldn't get ahead of myself...) <br />
<br />
So, the real question is how to convince people to want to use bin_timePaleoPhy. Well, although I just explained the function from a grapt-point-of-view, I didn't code it for that only that type of data. The timeList objects takes by bin_timePaleoPhy and other paleotree functions can actually be much less formal than my example (well, a few specific functions have very specific needs, but that isn't true of bin_timePaleoPhy). <br />
<br />
For one thing, the intervals in the interval matrix can be completely messy. For example, you could have an interval matrix that looks like:<br />
Lower Cumberbatchian 134.5 129.4<br />
Cumberbatchian-Martinian 134.5 109.6<br />
Cumberbatchian-Bloomean 134.5 99.8<br />
Middle Upper Martinian 107.5 105.8<br />
Martinian-Bloomean 115.4 99.8<br />
<br />
What I've discovered in a recent collaborative project is if you get data from the PBDB, you'll probably have to have an interval matrix like this. It's okay. But if you have multiple collections for a taxa, try to get the most precise first and last intervals.<br />
<br />
For example, say your taxa only ever appear in single interval, well than the first and last intervals are identical, so you'd just structure the second part of the input list so the
columns matched, like so:<br />
<br />
Rareosaurus 1 1<br />
Uniqueodon 2 2<br />
<br />
This is a little more tricky if your taxa also only ever appear in a single collection. By default, bin_timePaleoPhy assumes taxa first and last appear at different dates within an interval. For example, Rareosaurus will not be assigned matching FADs and LADs. However, if you set the argument point.occur=TRUE, then bin_timePaleoPhy will treat all your taxa as if their first and last occurrences are identical: i.e. all your taxa will be treated as point occurrences.<br />
<br />
Of course, I have made it so that bin_timePaleoPhy has a little more functionality, to deal with those special cases that often crop up with uncertainty in appearance times; these can be usually handled with what I called the 'sites' matrix. For example, perhaps the first specimens of two taxa appear in the same fossil assemblage. That single fossil assemblage may be very poor resolved to a wide interval, but we know the first appearance date for both taxa should be the same date. If you make a site matrix as I describe in the bin_timePaleoPhy help file, and set the site number the same for the first appearance of both taxa, then bin_timePaleoPhy will always use the same randomly-drawn date for those taxa. You could do this for lots of taxa, or do this to account for a site where some taxa have their last appearance at and other taxa first appear at, or all sorts of neat modifications. I have to be honest and point out that this sites matrix idea was first suggested by Jon Mitchell over lunch about three years ago.<br />
<br />
In reality, the point.occur argument I described is just a simplified way of modifying the site matrix so all first and last intervals are coded as having the same 'site' for each taxon. If you want to have only some taxa in a dataset constrained to be point occurrances, you can just specify a custom-made site matrix where only those taxa have their first and last interval constrained to be the same.<br />
<br />
To be truly honest though, I don't think its a lack of advertising about the options offered by bin_timePaleoPhy that stops people from using it... really, its the timeList input format. A large number of people have suggested that this is a very obscure way of recording this data and
more than a few have complained that its just not amendable to their
use. Peter Smits told me that the timeList was specifically very 'ugly' to have to
use two matrices. Overall, I have a feeling that I just did a shoddy job of describing timeList objects in the help files to begin with.<br />
<br />
Well, I don't know what to do about it guys! Coming up with the two-matrix fix was the best idea I had after thinking about it for a while, and while I admit its inelegant and while I admit that the timeList structure is pretty dissimilar from the typical data structure you might see in a paleontological dataset (as opposed to a taxon by interval range chart), I just don't see a better solution, even though I can that its actively driving people to misuse the functions in paleotree in ways that I think are unpredictable and a little dangerous.<br />
<br />
But I know that I don't know everything... so if anyone has a better idea of how to make the bin_timePaleoPhy input work better, I'd love to hear it and implement your idea in paleotree! Do you see a better, simpler way of conveying the same information? Or, is there a data format that you use, that you'd like to have a function for converting into a timeList? Let me know in the comments below.<br />
<br />
For those of you interested, I give a real example of a very nicely behaved timeList object in my time-scaling method tutorial from this summer, which you can find here:<br />
<a href="http://nemagraptus.blogspot.com/2013/06/a-tutorial-to-cal3-time-scaling-using.html" target="_blank">http://nemagraptus.blogspot.<wbr></wbr>com/2013/06/a-tutorial-to-<wbr></wbr>cal3-time-scaling-using.html</a><br />
<br />
On a more philosophical note, I think those of us who write methods for paleontological data have to keep in mind that Fossil Records are Different. Had I been less nearsighted and graptolite-oriented in my design and help file writing in the beginning, maybe this user preference for the wrong function could have been avoided, and so for that, I really only can blame myself.<br />
<br />
Finally, if you're reading this, and you think you might be one of those people who sent me an email this week and contributed to my concerns, I just want to thank you for emailing me and forcing me to confront the fact that there was something about my software that users were finding consistently confusing. I still love reading and responding to every paleotree email I get.<br />
<br />
-Davedwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-73884361893028597022013-06-17T13:32:00.002-07:002013-06-17T13:32:16.738-07:00Sampling Rates and Dealing with Fossil Records Less Than Conducive to Their Estimation<br />
<br />
So, in my last post, I went at length into a lot of the nuances we need to consider when we estimate sampling rates and sampling probabilities from the frequency distribution of ranges in the fossil record. This gave me the impetus to do something I'd been meaning to do for a while: actually track down the various estimates of sampling probability or sampling rate, turn them all into per Lmy sampling rates and compare them. Here they are, ordered from greatest to least:<br />
<br />
Sampling Rate (per Lmy) Taxon Reference<br />
17.4577776 Oklahoma Trilobites Species (Single Locality) Foote and Raup, 1996<br />
1.660731207 Cenozoic Macroperforate Planktic Forams Ezard et al., 2011<br />
1.046292901 Neogene Iberian Mammal Genera Alba et al., 2001<br />
0.868900035 Phanerozoic Brachiopoda Genera Foote and Sepkoski,1999<br />
0.797580429 Neogene Iberian Mammal Species Alba et al., 2001<br />
0.483501825 Phanerozoic Brachiopoda Genera Foote and Sepkoski,1999<br />
0.437808292 Phanerozoic Cephalopoda Genera Foote and Sepkoski,1999<br />
0.434450018 Phanerozoic Conodonta Genera Foote and Sepkoski,1999<br />
0.410974389 N.A. Cenozoic Mammal Species Foote and Raup, 1996<br />
0.408044166 European Jurassic Bivalve Species Foote and Raup, 1996<br />
0.385502461 Phanerozoic Trilobita Genera Foote and Sepkoski,1999<br />
0.357475065 Phanerozoic Graptolithina Genera Foote and Sepkoski,1999<br />
0.334331480 Phanerozoic Cephalopoda Genera Foote and Sepkoski,1999<br />
0.244922481 Phanerozoic Bryozoa Genera Foote and Sepkoski,1999<br />
0.214987601 Phanerozoic Bryozoa Genera Foote and Sepkoski,1999<br />
0.201575023 Phanerozoic Conodonta Genera Foote and Sepkoski,1999<br />
0.196147211 Phanerozoic Echinodea Genera Foote and Sepkoski,1999<br />
0.192764386 Phanerozoic Trilobita Genera Foote and Sepkoski,1999<br />
0.182563024 Phanerozoic Graptolithina Genera Foote and Sepkoski,1999<br />
0.159239636 Phanerozoic Echinodea Genera Foote and Sepkoski,1999<br />
0.153449104 Phanerozoic Gastropoda Genera Foote and Sepkoski,1999<br />
0.145183217 Phanerozoic Ostracoda Genera Foote and Sepkoski,1999<br />
0.133448941 Phanerozoic Bivalvia Genera Foote and Sepkoski,1999<br />
0.127046142 Phanerozoic Ostracoda Genera Foote and Sepkoski,1999<br />
0.122426282 Phanerozoic Anthozoa Genera Foote and Sepkoski,1999<br />
0.116261536 Phanerozoic Porifera Genera Foote and Sepkoski,1999<br />
0.115432413 Phanerozoic Blastozoa Genera Foote and Sepkoski,1999<br />
0.112799434 Phanerozoic Bivalvia Genera Foote and Sepkoski,1999<br />
0.099553348 Phanerozoic Blastozoa Genera Foote and Sepkoski,1999<br />
0.099553348 Phanerozoic Gastropoda Genera Foote and Sepkoski,1999<br />
0.099021026 Early Paleozoic Crinoid Genera Foote and Raup, 1996<br />
0.093263457 Phanerozoic Anthozoa Genera Foote and Sepkoski,1999<br />
0.086915600 Phanerozoic Porifera Genera Foote and Sepkoski,1999<br />
0.084205114 Phanerozoic Crinoidea Genera Foote and Sepkoski,1999<br />
0.078324167 Phanerozoic Crinoidea Genera Foote and Sepkoski,1999<br />
0.075548263 Phanerozoic Malacostraca Genera Foote and Sepkoski,1999<br />
0.067297159 Phanerozoic Osteichtyes Genera Foote and Sepkoski,1999<br />
0.054279636 Phanerozoic Asterozoa Genera Foote and Sepkoski,1999<br />
0.052305831 Phanerozoic Asterozoa Genera Foote and Sepkoski,1999<br />
0.039758685 Phanerozoic Malacostraca Genera Foote and Sepkoski,1999<br />
0.029548896 Phanerozoic Osteichtyes Genera Foote and Sepkoski,1999<br />
0.023242431 Phanerozoic Chondrichtyes Genera Foote and Sepkoski,1999<br />
0.011674604 Phanerozoic Chondrichtyes Genera Foote and Sepkoski,1999<br />
0.009677980 Phanerozoic Polychaeta Genera Foote and Sepkoski,1999<br />
0.009326054 Phanerozoic Polychaeta Genera Foote and Sepkoski,1999<br />
0.008051675 Cenozoic Chiroptera Genera Eiting and Gunnell, 2009<br />
<br />
Most of these were published as sampling probabilities, so I just found the mean interval length and got the sampling rater per Lmy using my paleotree function 'sProb2sRate'. The Foote and Sepkoski values weren't published as numbers, just shown in a plot, so I used a ruler rather than send an email to my old advisor. It's just a blog post, not a research paper.<br />
<br />
Note that every group in Foote and Sepkoski had two sampling probabilities plotted, for two different systems of how to break the Phanerozoic into smaller intervals. Interestingly, this reveals considerable variation in the estimated sampling rate, suggesting that the estimates from freqRat or from the maximum likelihood variant can be sensitive to how we break up intervals (as I suggested in the last blog post).<br />
<br />
The rate estimated for the Oklahoma trilobite data is wildly high, but note this is a densely sampled single locality in the Ordovician. Beware the disconnect between global estimates and local estimates... and beware the fact that they don't cleanly scale between each other. If all of your taxa are from Australia, if you do a global analysis, it will be just an analysis of Australia that you're only pretending is global. Maybe that is the 'global' rate, but it depends on your assumptions about whether there are taxa that you didn't sample in non-Australia regions of the world. There's also taxonomic issues here too: many of these analyses were done at the supraspecific level (note that doesn't mean persistent taxa are only at the genus level and above; paleontologists just have a tendency to analyze genera, not species).<br />
<br />
Now let's look at the other end of the scale. We've got some fish groups, then polychaetes, then bats. Makes sense right? If you had to imagine a really bad fossil record, squishy worms and fragile little bats sounds about right. But hold on. Let's ask ourselves, what's missing from this list. Well, there aren't any terrestrial groups on this other than mammals. I used to think this was just because no one had done it yet.<br />
<br />
Then I started talking to various vertebrate paleontologists and researchers working with datasets from non-Cenozoic vertebrate paleontology datasets and they told me that in other vertebrates groups, like dinosaurs, every species is found in what essentially amounts to a single stratigraphic interval, at least at the global scale. They might be found throughout a unit or formation that represents a limited window of time, like the Morrison or whatever, but you don't find the same morphotaxon persisting across intervals like you do in invertebrates. The people I talk to often call it 'point occurrence' data so I'll use that term for the rest of this post, even though that isn't exactly ideal: 'point-occurrence' really isn't completely accurate if some of those taxa are found at multiple horizons in a unit or formation, its less descriptive than we might desire and 'point occurrence' is sometimes used to mean other things. (There's other terms too like 'singleton' which could apply here, but their usage is even more muddled.)<br />
<br />
Disclaimer: I know nothing about vertebrates or their fossil record. As
evidence, I present that I squeaked by with a C- in Primate
Anthropology, my one attempt to learn anything about vertebrates. I felt
it was important to start off this post with that, because, great
golly, I don't want anyone reading this and thinking I know anything about the vertebrate fossil record. Heck, I haven't even seen any
datasets firsthand. All my information is hearsay from other scientists, most of whom contacted me in trying to time-scale their trees. If someone can show me a range chart that has a bunch of sauropod species persisting through multiple geologic intervals, then great, I'd love to see it.<br />
<br />
So, anyway, I found this description of vertebrate paleo datasets to be really strange. Why are they so different from the ones we can get sampling rates from, like those above? One explanation I have been given is that this occurs because groups like dinosaurs are just more poorly sampled than shelly invertebrate groups. If I try to simulate a really poorly sampled clade but still have a number of sampled taxa, under a simple model where extinction and sampling are homogenous Poisson processes, I can't make all the taxa point occurrences. There's a real good reason for this: as the sampling rate decreases, the number of original true taxa must increase, such that the sampled taxa is a tiny portion of a vast unsampled diversity. As the number of total taxa increases, you get more and more extremely long-lived taxa, a tiny minority, but they become so increasingly long-live that the probability of NOT sampling any of them one of them twice (and thus getting a persistent taxon) is incredibly low. I cannot avoid simulated datasets with persistent taxa, not under this simple model of extinction and sampling. This is true even if I break up the time-scale of these simulations into discrete intervals. I still get persistent taxa with first and last appearances sampled in multiple intervals.<br />
<br />
So, to me, that means it isn't just 'poorer sampling'. There needs to be something else going on. We're missing some explanatory factor that could cause morphotaxa to either be very 'short-lived' in the fossil record and/or would make it very difficult to sample any morphotaxon that might survive into another stratigraphic interval. Of the former, it is possible to imagine a fossil record of point occurrences resulting from extremely high anagenesis rates, such that species really don't have any opportunity to be persistent. However, that means there's something about dinosaurs and other such groups that is fundamentally different from groups like North American mammals and fossil invertebrates. It doesn't need to represent a real evolutionary difference: it might be that differences in the available material gives more characters and thus allows for much much finer species delimination (or, possibly, that species delimination is being overly aggressive). The later, where some factor makes it difficult to sample any persist morphotaxon more than once, could occur if there is some sort of complex regional alternation in the deposition and preservation of rock packages that could preserve vertebrates; i.e. North America get the Morrison, then nothing for a long time, something like that (I have no idea if the preceding statement is true). But then why the shift going from Mesozoic dinosaurs to Cenozoic mammals?<br />
<br />
Remember, to use the freqRat or the variants of it, you need to have some persistent taxa so that means there are some persistent bat taxa, found in multiple stages (as that's what Eitling and Gunnell used) but not dinosaurs. Now, an important distinction here is maybe it's that Eitling and Gunnell used genera. So, okay, forget bats. But why do Alroy's Cenozoic mammal species have persistent taxa? (as analyzed in Foote and Raup) Whatever the reason for this point occurrence pattern in some groups, it will probably be obscure for a while. I'd bet it is probably the result of multiple factors.<br />
<br />
But, to get back to the practical point, what's a researcher with a dataset unanimously dominated by point occurrence taxa supposed to do if they want to use cal3, which absolutely requires an estimate of the sampling rate? (We'll ignore also needing branching and extinction rate estimates for the moment.)<br />
<br />
It's really quite a conundrum. These are my thoughts.<br />
<br />
First is that the freqRat and the ML variant aren't the only ways to get sampling rate. Foote (2004) also introduced a survivorship curve method, but that also won't work if you don't have persistent taxa. Solow and Smith (1997) presented a method that obtained sampling rate estimates from the waiting time distribution between sampling events in single taxa; that information definitely isn't available here, at least not at the global, phylogenetic scale we're trying to get sampling rates for.. There's also capture-mark-recapture methods, which also estimate sampling probabilities, but I don't know much about them or whether they could be applied to data that effectively has no recaptures. An idea that's been banging around in my skull is that Friedman and Brazeau (2012) introduced an idea that might bear water: get sampling estimates from the ghost branch distribution. But I think that issue is a lot more complicated than Friedman and Brazeau suggested, and there's some theoretical roadblocks relating to differentiation patterns (budding versus bifurcation, anagenesis, etc) that I haven't thought around yet.<br />
<br />
So, that exhausts the methods (that I know of) for actually estimating the sampling rate.<br />
<br />
One utter kludge would be to just pull them out of thin air. Ask yourself what fossil records on the chart above are probably of similar quality and use those values. Even better, choose a range of likely values and use those. The problem with this is that maybe your fossil record is even worse than the ones described here. Also, these values are for a limited selection of data: most of them are global and their relationship to regional datasets is thus obscure. Many of them are at the generic level, particularly the more poorly sampled records, and it is difficult to envision any way of converting a generic estimate of sampling to a species-level estimate (although maybe paraclade models might offer such a route; see Patzkowsky, 1995; Foote, 2012). Overall, I cannot recommend that pulling values out of thin air is the right way to use cal3. It's a quick and dirty way of getting something done, just like using a 'taxon tree' as a replacement for a cladogram, but its ultimately difficult to robustly defend the assumptions you'd be making in such an analysis. Class projects and pilot work can have quick and dirty techniques, but for real research we want to be able to trust the conclusions of, we would want something that involves less guesswork.<br />
<br />
And there's an addition problem to such cross-eyed guesstimations: remember my simulation woes above? We can't get point occurrence data under our typical models of sampling and extinction events, where they occur as under a Poisson process. We know the assumptions of our models are not valid in those cases. And that means even ignoring the 'thin air' part of pulling sampling rates out of thin air, the models that cal3 assumes probably don't apply to this type of data. And it's not just cal3 that assumes those models; that's the typical assumptions of many paleobiological methods that involve putting any sort of expectation on a sampling process. Of course, just because model assumptions are violated doesn't mean a method will return the wrong answer. Methods can be more robust than that. However, without knowing what leads to point occurrence type data it is hard to make predictions, since we don't know exactly what about our model assumptions are incorrect. My presumption would be that if this data type occurs because sampling is much more complicated than a global Poisson process, then cal3 will give fairly wrong answers. Again, though, that's probably also true of many other methods too. If, instead, it's because these are groups where morphotaxa are extremely finely delimited, such that you can't get persistent taxa, then maybe the cal3 method is okay. This violation of model assumptions just means morphotaxa are 'terminating' via anagenetic pseudoextinction much more often than lineages terminating due to extinction. In other words, there's a disconnect between the morphological differentiation of lineages and their birth-death dynamics. cal3's assumption have more to do with the probability of not sampling a clade at all or not, something which should be independent of the morphological differentiation pattern within that clade.<br />
<br />
Regardless, and some people have pushed me on this point, I do not claim that the cal3 method is applicable to all datasets. I fully admit that it is not! I did this to time-scale graptolite phylogenies and while I want the method to be as useful and general for as many people as possible, the only reasonable starting point for making an explicitly model-based time-scaling method was to start with the simplest models of sampling and diversification possible. Simple models are the only logical foundation for moving forward. And while I might be able to realistically defend the assumptions of those simple models in graptolites or other 'well-sampled' groups, as a scientific community, we have a lot more work ahead of us to tackle every dataset. Even though sampling processes are as important as origination and extinction in shaping the fossil record, if not more so, I think we still spend much more effort in understanding and modeling the dynamics of diversification in paleobiology than sampling processes.<br />
<br />
But this difficulty in applying cal3 to every dataset poses a new question: are the other time-scaling methods that we have available sufficient for the sort of phylogeny-based analyses of evolution ('phylogenetic comparative methods') that we want to do in the fossil record? <br />
<br />
Well, we can talk more about that this Sunday in Snowbird, if you'll be there. ;)<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=149<br />
<br />
Cheers,<br />
-Davedwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-10061806233351338242013-06-14T00:09:00.003-07:002013-12-12T16:19:09.373-08:00A Tutorial to cal3 Time-Scaling Using a Real DatasetHi guys!<br />
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So, my paper on time-scaling methods for trees of fossil taxa has been accepted to Methods in Ecology and Evolution! You won't see it right away though; it's part of a special issue of MEE and all the papers included in it will be held back for a simultaneous online release, probably in August (I guess?). Anyway, until then, you can go read chapter 3 of my dissertation, it's mostly the same thing, and its up on UMI Proquest now. Or you can contact me for a copy.<br />
<br />
Anyway, to commemorate the acceptance, I have actually pulled a real dataset out of the literature and written up a tutorial about applying the various time-scaling methods to this data, in particular the cal3 method. The data in question (of course) a graptolite group, the Silurian retiolitids. It's a generic dataset, but, hey, it's just a tutorial right! Chill, dudes.<br />
<br />
Such a tutorial was persuasively argued for by one of my reviewers at MEE, but I had to respond with "I don't have the room!" However, I've got infinite room here on this blog and also it means anyone can come see it here, for free, forever (well as long as Blogger exists...). So here it is! Thanks to Reviewer #1 for making such a strong case for it. Sorry, Reviewer #1, but I did not take you up on that other suggestion that I use a vertebrate dataset example. I just know graptolites better, and vertebrates are a headache to get sampling rates from (I'll touch on that in a later blog post, thanks).<br />
<br />
Now, this is a very LONG tutorial. Why? I want to cover in detail all the hiccups that can happen on the way to wanting to time-scale a tree, and I really wanted to nail down what the data structure looks like. So, apologies for the length, but I think anyone actually attempting this stuff will appreciate the detail I go into, particularly, how the degree we should exercise caution and skepticism in estimating sampling rates.<br />
<br />
I've written this tutorial much like a cave man from 1984 might have, as an overly chatty, heavily commented R script. If someone who knows LaTEX and Sweave better than me wants to helpfully convert this to a document suitable for release as a vignette file for paleotree well... Well, I will buy that person a few beers at Evolution! Otherwise, you guys are getting just this: an R script. <br />
<br />
I've pasted the script text below, with figures inserted and a few key console outputs. The images overhang the margins. I don't know why and I can't seem to fix it. Stupid Blogger! But rather than read my poorly formatted blog post, I instead recommend downloading the script and the dataset and going through it yourself in R! It's more... interactive, that way!<br />
<br />
You can get the retiolitid data file and the R script as a zipped archive here:<br />
http://webpages.sdsmt.edu/~dbapst/cal3tutorial/cal3tutorial.zip<br />
<br />
This gave me the idea for another post about published estimates of sampling rates, which I'll put up in a few days. I hope you all enjoy this tutorial as much as I enjoyed writing it,<br />
-Dave<br />
<br />
<br />
#############################################################<br />
#A Tutorial to Time-scaling Methods with the Retiolitinae<br />
#David W. Bapst<br />
#06-12-13<br />
<br />
#Introduction<br />
<br />
#The package paleotree has functions for time-scaling phylogenies of fossil<br />
#taxa but none of the help examples give a concrete case of the functions being<br />
#applied to real data. Instead, I opted to only give examples using simulated data.<br />
#The reasons for this are simply that real data tends to be very complex, and<br />
#although the functions can certainly be applied to real data, it will often require<br />
#more effort than what can really be put in an R help file.<br />
#<br />
#However, without a clear example of applying the functions to real data, I have<br />
#opened the door for lots of confusion about how a user should do this. Here, I<br />
#use a simple dataset of graptoloid generic ranges and relationships from a recent<br />
#publication (Bates et al., 2005) to exemplify how the time-scaling functions in<br />
#in paleotree work, what the data input looks like and just how complicated even<br />
#a simple real dataset can be.<br />
#<br />
#By the way, our example data today is of the Silurian retiolitids, which are really<br />
#cool looking. Check 'em out! <br />
#http://www.insugeo.org.ar/libros/cg_18/FIG_1.jpg<br />
<br />
######################################<br />
#Know Your Data<br />
<br />
#First, we need to load paleotree...<br />
library(paleotree)<br />
<br />
#Next, we need to load the retiolitid data file, which needs to be in our working dir.<br />
load("retiolitinae.rda")<br />
<br />
#You can also load the data directly from the current version of paleotree:<br />
data(retiolitinae)<br />
#if this doesn't work, you have an old version of paleotree! Go download a new one. <br />
<br />
<br />
#And that should have added two new objects to our workspace, retioTree and retioRanges.<br />
<br />
#Let's look at retioRanges first. This is our temporal/interval data.<br />
<br />
#retioRanges<br />
<br />
str(retioRanges)<br />
<br />
> str(retioRanges)<br />
List of 2<br />
$ int.times :'data.frame': 20 obs. of 2 variables:<br />
..$ start_time: num [1:20] 440 439 437 437 436 ...<br />
..$ end_time : num [1:20] 439 437 437 436 435 ...<br />
$ taxon.times:'data.frame': 22 obs. of 2 variables:<br />
..$ first_int: int [1:22] 8 6 2 8 8 8 8 13 8 13 ...<br />
..$ last_int : int [1:22] 8 10 10 8 14 14 14 14 14 14 ...<br />
<br />
#It's a list with two components, both matrices. Let's see what they are!<br />
<br />
#First component:<br />
retioRanges[[1]]<br />
<br />
> retioRanges[[1]]<br />
start_time end_time<br />
Coronograptus cyphus 440.18 439.37<br />
Demirastrites triangulatus - Demirastrites pectinatus 439.37 437.46<br />
Pernerograptus argenteus 437.46 436.97<br />
Lituigraptus convolutus 436.97 435.85<br />
Stimulograptus sedgwicki 435.85 434.90<br />
Spirograptus guerichi 434.90 432.50<br />
Spirograptus turriculatus - Stretograptus crispus 432.50 430.44<br />
Monoclimacis griestoniensis - Monoclimacis crenulata 430.44 429.43<br />
Spirograptus spiralis 429.43 429.01<br />
Nanograptus lapworthi - Cyrtograptus insectus 429.01 427.07<br />
Crytograptus centhfugus - Cyrtograptus murchisoni 427.07 426.53<br />
Monograptus riccartonensis - Monograptus belophorus 426.53 426.06<br />
Cyrtograptus rigidus - Cyrtograptus perneri 426.06 424.92<br />
Cyrtograptus lundgreni 424.92 424.02<br />
Gothograptus nassa - Pristiograptus dubius 424.02 423.09<br />
Colonograptus preadeubeli - Colonograptus deubeli 423.09 422.81<br />
Colonograptus ludensis 422.81 422.65<br />
Neolobograptus nilssoni - Lobograptus progenitor 422.65 422.35<br />
Lobograptus scanicus 422.35 421.27<br />
Saetograptus leintwardnensis 421.27 420.44<br />
<br />
#A matrix. Each row is a different zone (a biostratigraphically defined interval, <br />
#marked by the appearance of new 'index' fossil taxa), the row names are the index <br />
#fossils for those zones and the start and end times are listed in the two columns <br />
#of the matrix. I sometimes refer to this as the 'interval times' or 'int.times' <br />
#matrix in paleotree documentation.<br />
<br />
#Now let's look at the second component of retioRanges:<br />
<br />
retioRanges[[2]]<br />
<br />
> retioRanges[[2]]<br />
first_int last_int<br />
Rotaretiolites 8 8<br />
Pseudoplegmatograptus 6 10<br />
Pseudoretiolites 2 10<br />
Dabashanograptus 8 8<br />
Retiolites 8 14<br />
Stomatograptus 8 14<br />
Paraplectograptus 8 14<br />
Pseudoplectograptus 13 14<br />
Sokolovograptus 8 14<br />
Eisenackograptus 13 14<br />
Sagenograptus 14 14<br />
Cometograptus 14 14<br />
Plectograptus 17 19<br />
Plectodinemagraptus 20 20<br />
Semiplectograptus 19 20<br />
Baculograptus 14 16<br />
Doliograptus 15 15<br />
Gothograptus 14 16<br />
Papiliograptus 16 16<br />
Spinograptus 16 17<br />
Holoretiolites 18 18<br />
Neogothograptus 18 18<br />
<br />
#Another matrix. Each row in this matrix is a taxon, the rownames tell you what<br />
#taxon (note that this was a generic-level analysis) and the columns are which<br />
#interval (graptolite zones) each taxon first and last appeared in the fossil record.<br />
#The numbers in these columns are references to the rows of the interval times matrix,<br />
#for example, Rotaretiolites first and last appears in interval on the 8th row (which <br />
#is the Monoclimacis griestoniensis - Monoclimacis crenulata zone). I usually refer <br />
#to this matrix as the 'taxon times' or 'taxon.times' matrix.<br />
<br />
############################################################<br />
#A Digression on Discrete Interval Data in paleotree<br />
<br />
#This list format composed of two matrices, an int.times matrix and a taxon.times <br />
#matrix with two columns each, is the keystone data input of all analyses in paleotree<br />
#using data where taxa are only in discrete time intervals. I usually call such objects<br />
#"timeList" objects in paleotree, particularly in the input arguments of functions.<br />
#<br />
#Most paleontological data is only know from discrete intervals, because it is<br />
#fundamentally difficult to place the beds that fossils are found at a specific date.<br />
#Instead, most fossils can only be placed as coming from discrete intervals, and our<br />
#ability to resolve the appearance dates of a fossil can differ widely based on the<br />
#stratigraphy of a locality, such that one fossil taxon may be first known from a two<br />
#million year long interval in the early Triassic, while its sister taxon may be <br />
#very hard to pin down, maybe as generalized as listed as first appearing in <br />
#the 'Triassic'.<br />
#<br />
#The graptolite data here are exceptional well resolved; note that in the interval-time<br />
#matrix (the first component of retioRanges), many of the intervals are less than a<br />
#million years long. All of the occurrences are also resolved to a single zone, and<br />
#none of the zones overlap or contain one another. This doesn't have to be the case.<br />
#In our example above with the two taxa known from the Triassic, we would list the <br />
#short eartly Triassic interval as one row in the interval times matrix and the entire<br />
#Triassic as another row. The respective taxa would refer to those respective intervals.<br />
#Most paleotree functions won't care about overlapping intervals, but there is an<br />
#important exception. getSampProbDisc and the freqRat, both of which are applied below,<br />
#CANNOT use such data. Those functions assume the intervals they are containing are <br />
#successive and of roughly similar length. Here, the graptolite data are all successive<br />
#but as we can see, there is some variation in interval length.<br />
#<br />
#Now, there are exceptional datasets composed of continuous time data, which require<br />
#a much simpler data structure than discrete interval data. All that is needed in <br />
#paleotree isjust a matrix of first and last appearance dates). Such datasets tend to <br />
#be for very recent fossil records, microfossil data from cores or computationally<br />
#correlated stratigraphic columns, such as the output from CONOP (Sadler, 2001).<br />
#<br />
#However, if you know your fossil appearances aren't well resolved, you really<br />
#should take that uncertainty into account using this 'timeList' structure.<br />
#Getting data into a 'timeList' structure isn't too difficult: just compose<br />
#the interval times and taxon times matrices seperately in your favorite spreadsheet<br />
#program, read them into R with read.table() and then compose them into a list (using, <br />
#of course, list()...). You can then save that list with save() or dput() and reuse it.<br />
<br />
###################################################################<br />
#Okay, enough of that! Let's try drawing the diversity curve for this <br />
#discrete interval data.<br />
<br />
taxicDivDisc(retioRanges)<br />
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" style="cursor: move;" /></a> <br />
<br />
<br />
#taxicDivDisc counts up all the taxa that COULD occur in each interval (remember<br />
#my point about overlapping intervals, above). We're okay here, because our data is<br />
#in successive intervals. Notice how there's a big generic diversity drop around 424 MYA.<br />
#Also notice how the 'blocky'-ness of the diversity curve also displays a little<br />
#information about interval length: notice the extremely short graptolite zone near<br />
#423 MYA.<br />
<br />
#Alright, let's move on to our other data object, retioTree. We can plot it using ape...<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
</div>
plot(retioTree)<br />
<a href="http://www.blogger.com/blogger.g?blogID=497393262310058111" style="margin-left: 1em; margin-right: 1em;"><img alt="" border="0" src="data:image/png;base64,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" style="cursor: move;" /></a> <br />
<br />
<br />
#And we get an unscaled tree, partially unresolved (i.e. some polytomies). Cool.<br />
<a href="http://www.blogger.com/blogger.g?blogID=497393262310058111" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"></a><br />
#Now that I've explained discrete interval data in paleotree ad nauseum to you, let's <br />
#actually apply time-scaling methods. Since this is discrete interval data,<br />
#we'll time-scale using the bin_timePaleoPhy function, which applies the simplest<br />
#time-scaling algorithms to discrete interval data, by randomly assigning first and<br />
#last appearance dates within each interval to each taxon, every time it creates a<br />
#new time-scaled tree. The idea is to do this many times, create many time-scaled trees<br />
#but for now, let's just create one.<br />
#<br />
#The simplest time-scaling algorithm for cladograms of fossil taxa is the 'basic' <br />
#method (Norell, 1992; Smith, 1994) which just means every clade is as old as the <br />
#oldest appearance of any taxon in that clade. Let's try that one for now.<br />
<br />
#Basic Time-Scaling<br />
bin_timePaleoPhy(tree=retioTree,timeList=retioRanges,type="basic",<br />
<a href="http://www.blogger.com/blogger.g?blogID=497393262310058111" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"></a><a href="http://www.blogger.com/blogger.g?blogID=497393262310058111" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"></a><a href="http://www.blogger.com/blogger.g?blogID=497393262310058111" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"></a> ntrees=1,plot=TRUE)<br />
<br />
<br />
> bin_timePaleoPhy(tree=retioTree,timeList=retioRanges,type="basic",<br />
+ ntrees=1,plot=TRUE)<br />
Warning: Do not interpret a single tree; dates are stochastically pulled from uniform distributions<br />
<br />
Phylogenetic tree with 22 tips and 13 internal nodes.<br />
<br />
Tip labels:<br />
Neogothograptus, Holoretiolites, Spinograptus, Papiliograptus, Gothograptus, Doliograptus, ...<br />
<br />
Rooted; includes branch lengths. <br />
<img alt="" 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" /><br />
<br />
#Hey look, it warned us not to interpret a single tree. Heed that warning!<br />
#<br />
#Okay, we can see the cladogram and the time-scaled tree produced. Note three things.<br />
#<br />
#First, the polytomies were not resolved. We'll deal with that in a moment.<br />
#<br />
#Second, the little time-axis at the bottom is relative to the latest tip of the tree <br />
#being at 0, which is unfortunate, as the latest tip of the tree is at ~420 MYA. (I have <br />
#tried but failed to figure out how to alter this. It would require me adding an altered <br />
#plot.phylo to paleotree, which I am incredibly loathe to do.) So, that time axis just <br />
#tells us about relative timing, which still makes it pretty informative.<br />
#<br />
#Third, the 'location' of the tips of our time-scaled tree have been placed at the<br />
#FIRST appearance dates of our taxa, not the LAST appearance dates ('FADs' versus <br />
#'LADs'). I call this choice of 'location' for our tips the 'time of observation' issue.<br />
#This issue really only rears its head when you have persistent morphotaxa, like these<br />
#graptolite genera, which occur/are morphologically recognizable across multiple <br />
#time intervals. Obviously, if we want to know something about lineage diversification,<br />
#we want to include those 'terminal' ranges of the taxa and add them to the terminal<br />
#branches of our time-scaled tree. We might want to do something different if we're<br />
#dealing with analyses of trait evolution. We should think more carefully about what our<br />
#'time of observation' for our taxa really is!<br />
#<br />
#Note that if our time of observation is FAD, then all the branches are essentially<br />
#posited unsampled 'ghost' branches or 'ghost' lineages.<br />
#<br />
#Now let's try including the ranges, using argument 'add.term':<br />
<br />
#Basic Time-Scaling with Terminal Taxon Ranges<br />
<br />
bin_timePaleoPhy(tree=retioTree,timeList=retioRanges,type="basic",<br />
add.term=TRUE,ntrees=1,plot=TRUE)<br />
<br />
> bin_timePaleoPhy(tree=retioTree,timeList=retioRanges,type="basic",<br />
+ add.term=TRUE,ntrees=1,plot=TRUE)<br />
Warning: Do not interpret a single tree; dates are stochastically pulled from uniform distributions<br />
<br />
Phylogenetic tree with 22 tips and 13 internal nodes.<br />
<br />
Tip labels:<br />
Neogothograptus, Holoretiolites, Spinograptus, Papiliograptus, Gothograptus, Doliograptus, ...<br />
<br />
Rooted; includes branch lengths.<br />
<img alt="" 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" /><br />
<br />
#Alright, we can compare and see what changed. For example, we can see that the tip with<br />
#Pseudoretiolites shifted, because it's a "long-lived" morphotaxa, so whether <br />
#its time of observation is its first or last appearance makes a large difference.<br />
<br />
#Polytomies are still unresolved. Let's randomly resolve those nodes, forcing the trees<br />
#to be dichotomous. This is another action which is stochastic and that shouldn't be done<br />
#to create a single tree, but rather should be done to create many time-scaled trees.<br />
#The argument of importance here to change is 'randres'.<br />
<br />
#Basic Time-Scaling with Terminal Ranges and Polytomies Randomly Resolved<br />
<br />
bin_timePaleoPhy(tree=retioTree,timeList=retioRanges,type="basic",<br />
randres=TRUE,add.term=TRUE,ntrees=1,plot=TRUE)<br />
<br />
> bin_timePaleoPhy(tree=retioTree,timeList=retioRanges,type="basic",<br />
+ randres=TRUE,add.term=TRUE,ntrees=1,plot=TRUE)<br />
Warning: Do not interpret a single tree; dates are stochastically pulled from uniform distributions<br />
Warning: Do not interpret a single randomly-resolved tree<br />
<br />
Phylogenetic tree with 22 tips and 21 internal nodes.<br />
<br />
Tip labels:<br />
Neogothograptus, Holoretiolites, Spinograptus, Papiliograptus, Gothograptus, Doliograptus, ...<br />
<br />
Rooted; includes branch lengths.<br />
<img alt="" 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" /><br />
<br />
#Alright, we've resolved those nodes, as we can see in the cladogram plotted at the top.<br />
#But look at the time-scaled tree! Why are there those 'apparent' polytomies?<br />
#The reason is Zero-Length Branches, which occur in time-scaling phylogenies of fossil<br />
#taxa when a more derived taxon nested within a clade occurs earlier in the fossil record<br />
#than other taxa in that clade. It pushes nodes up against each other under the<br />
#basic time-scaling method, forcing those divergences to occur simultaneously.<br />
<br />
#To avoid this, we need to choose a new time-scaling algorithm. One option is the<br />
#'Minimum Branch Length' method (MBL; Laurin, 2004), where we will force all the branch<br />
#lengths in the tree to be some minimum length and thus push nodes back in time.<br />
#To do this, we'll change the the 'type' argument to "mbl" and the minimum length is set<br />
#using the 'variable time' or 'vartime' argument, which we'll set to 1 MY as an example.<br />
<br />
#min branch length time-scaling<br />
bin_timePaleoPhy(tree=retioTree,timeList=retioRanges,type="mbl",<br />
vartime=1,randres=TRUE,add.term=TRUE,ntrees=1,plot=TRUE)<br />
<br />
> bin_timePaleoPhy(tree=retioTree,timeList=retioRanges,type="mbl",<br />
+ vartime=1,randres=TRUE,add.term=TRUE,ntrees=1,plot=TRUE)<br />
Warning: Do not interpret a single tree; dates are stochastically pulled from uniform distributions<br />
Warning: Do not interpret a single randomly-resolved tree<br />
<br />
Phylogenetic tree with 22 tips and 21 internal nodes.<br />
<br />
Tip labels:<br />
Neogothograptus, Holoretiolites, Spinograptus, Papiliograptus, Gothograptus, Doliograptus, ...<br />
<br />
Rooted; includes branch lengths. <br />
<img alt="" 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" /><br />
<br />
#Wow, that changed things, didn't it? You can see that the MBL method has added ALOT of<br />
#extra branch-length to our tree, essentially positing a lot more unobserved <br />
#evolutionary history across this clade. Notice also the long 'spine' of nodes seperated <br />
#by 1 MY going all the way down most of the tree, due to the ladder-like structure of <br />
#the tree. <br />
#<br />
#Obviously, the choices we make while time-scaling a tree makes assumptions, whether <br />
#they are implicit or explicit, about how much evolutionary history is unsampled. <br />
#Perhaps 1 MY is too long of a minimum branch length for this dataset; we wouldn't<br />
#expect this much 'ghost' evolutionary history. But how can we test that or even take<br />
#into account how much 'ghost' evolutionary history we would expect? And even if we did<br />
#know how much missing time there should be on the phylogeny, we would be extremely <br />
#uncertain in where to place it.<br />
#<br />
#The solution I suggest in a recent paper is a stochastic time-scaling method, where<br />
#node ages are shifted around in time at random while keeping the tree consisten with<br />
#the appearance times of the taxa, loosely alike to jiggling a zipper. This method <br />
#biases the node ages to be more often sampled at further or closer in time, relative <br />
#to likely we think the corresponding amount of unobserved evolutionary is. That amount<br />
#is not just a function of how frequently sampled the fossil record is: it is also<br />
#dependent on how often new lineages originate via branching and go extinct. Thus, this<br />
#time-scaling method requires three rates to calibrate its use: sampling, branching<br />
#and extinction, and thus I named it 'cal3'. <br />
<br />
#These three rates must be calculated before we can apply cal3. <br />
#How? paleotree has the tools!<br />
<br />
#########################################<br />
#Getting Sampling Rate, Other Rates and Learning to Be Skeptical<br />
<br />
#For discrete interval data, there are two paleotree functions of use for ultimately<br />
#calculating sampling rates from our range data.<br />
<br />
#First, we can try the freqRat (Foote and Raup, 1996), which is just a simple<br />
#ratio of the frequencies of taxa found in just one, two and three intervals.<br />
#Let's apply that to retioRanges and tell it to plot the histogram of taxon durations.<br />
<br />
freqRat(retioRanges,plot=TRUE)<br />
<br />
> freqRat(retioRanges,plot=TRUE)<br />
freqRat <br />
0.5925926 <br />
<img alt="" src="data:image/png;base64,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" /><br />
<br />
#Okay, so the number the freqRat spit out (0.59) is a sampling PROBABILITY, the probability of <br />
#a taxon being sampled at least once per interval. It's not a sampling rate... yet.<br />
#<br />
#But we need to consider the assumptions and drawbacks of the freqRat first.<br />
#<br />
#The freqRat, like getSampProbDisc below, assumes that extinction and sampling are<br />
#Poisson processes, such that the waiting times between events will be exponentially<br />
#distributed. Under such a process, we'd expect the histogram of taxon durations to be<br />
#roughly similar to an exponential distribution still, with the slope of a log scale<br />
#indicative of the extinction rate, but with the number of taxa that occur in only a<br />
#single interval increased greatly. In addition, it is assumed that extinction and <br />
#sampling event frequency has been relatively constant across the entire sample.<br />
#<br />
#The retiolitid data kind of match our expectation: there is an over abundance of taxa<br />
#known from a single interval. However the number of taxa with longer durations is <br />
#patchy. In particular, there are no taxa known from three graptolite zones. This <br />
#probably just reflects the low sample size of the dataset (22) but also reveals a<br />
#a weakness of the method: just the first three frequency values are used in the<br />
#freqRat. If our sample size is low, or if intervals are so finely broke up that there<br />
#are few taxa found in only one, two or three intervals, the freqRat may give poor if<br />
#not extremely misleading results.<br />
#<br />
#getSampProbDisc is an alternative which uses a maximum likelihood optimization to find<br />
#the best fitting sampling probability and extinction rate, using the entire distribution<br />
#of taxon durations.<br />
<br />
SPres <- getSampProbDisc(retioRanges)<br />
SPres<br />
<br />
> SPres<br />
$Title<br />
[1] "Analysis with 1 time bins and 0 groupings ( 0 and 0 States),with 2 parameters and 22 taxa"<br />
<br />
$parameters<br />
extRate sampProb Completeness <br />
0.2028359 0.5454152 0.8672921 <br />
<br />
$log.likelihood<br />
[1] -41.40539<br />
<br />
$AICc<br />
[1] 87.44237<br />
<br />
$convergence<br />
[1] 0<br />
<br />
$message<br />
[1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"<br />
<br />
#We can see that getSampProbDisc gives a lot of output! Of importance is whether the<br />
#optimizer converged; if $convergence is 0, then we're probably okay. The best-fit<br />
#parameters are at the top. The sampling probability per graptolite zone is 0.54.<br />
#<br />
#Now is the time to be suspicious. Is this a realistic value? To do that, we need to<br />
#compare this value to other published values, but those don't exist, not for graptolite<br />
#zones. Instead, we need to find out how long our graptolite zones are and relate it to<br />
#time. As I pointed out above, our zones are uneven in length, an unfortunate reality of<br />
#the geologic record. But how uneven?<br />
<br />
intLength<--apply(retioRanges[[1]],1,diff)<br />
hist(intLength)<br />
<img alt="" src="data:image/png;base64,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" /><br />
meanInt<-mean(intLength)<br />
meanInt<br />
<br />
> meanInt<br />
[1] 0.987<br />
<br />
#Okay, so we can see there's a lot of short zones and some zones longer than one, but<br />
#altogether the mean is close to 1 million years. Is this an okay amount of variation?<br />
#It's hard to tell, or even to test. My recommendation if you're dataset was like this<br />
#would be to create several versions of the range data, glomming smaller zones onto one<br />
#another to try to create as even length intervals as possible, and then run your<br />
#analyses across those. If there's no difference, you're fine. I'm not going to<br />
#give an example of that here, but if you're playing with real data, I'd recommend you<br />
#try that. Instead, let's go with the idea that our graptolite zones are roughly a <br />
#million years long, so we have a 0.54 probability of sampling a retiolitid genus at<br />
#least once per million year interval.<br />
#<br />
#Another way we could use to consider our estimated sampling probability is compare<br />
#the completeness values, which are not dependent on interval length. These represent<br />
#the proportion of total taxa expected to be sampled in the fossil record. In this case,<br />
#the retiolitid fossil record is estimated as being 87% complete.<br />
#<br />
#Unfortunately, both completeness and sampling probability can have a dependency<br />
#on extinction rate, because these variables depend on how long taxa survive before<br />
#being sampled or the end of a given interval. The instantaneous rate of sampling<br />
#rates, the rate of a sampling event occurring per lineage million years, is the only<br />
#sampling variable that can be estimated independently of extinction rate.<br />
#<br />
#We can transform sampling probability to sampling rate using the function sProb2sRate<br />
#and the mean interval length:<br />
<br />
sRate <- sProb2sRate(SPres[[2]][2],int.length=meanInt)<br />
sRate<br />
<br />
> sRate<br />
[1] 0.7987547<br />
<br />
#So, a sampling rate of 0.79 per Lmy.<br />
#<br />
#So, to return to the issue I raised earlier: are our estimates of sampling reasonable?<br />
#Well, there's aren't a whole lot of published estimates of sampling variables. I got<br />
#rather sidetracked by that and went through the literature and collected rate estimates<br />
#from as many publications as possible. (I'll post those later on my blog.)<br />
#<br />
#Foote and Sepkoski gave the only previous graptoloid sampling estimates; specifically<br />
#generic sampling probabilities. I've converted those to sampling rates, which<br />
#are about 0.18-0.35 per Lmy (Foote and Sepkoski gave different estimates based on<br />
#different ways of breaking up intervals). So, our estimate for the retiolitids is<br />
#nearly twice that. This makes our estimates suspicious but not unrealistic: its <br />
#possible that retiolitids are twice better sampled than other graptolite genera. It <br />
#may also be because of factors we haven't accounted for. Remember how the diversity<br />
#curve showed what looked like an extinction event? That was the well-known 'lundgreni <br />
#event'. That, or some other variation in sampling or extinction rate, may be enough<br />
#to have biased our estimates. Alternatively, maybe our sample size it too low to<br />
#get as good an estimate as we'd like.<br />
#<br />
#getSampProbDisc allows for us to fit different parameters to different groups of taxa<br />
#in our dataset, such as taxa in different time-intervals or different clades. This<br />
#can allow us to consider such potential sources of variation, and use AIC to compare<br />
#the fit of these models. Unfortunately, in this case with only 22 taxa, splitting the<br />
#dataset doesn't seem very feasible, so I won't try that here.<br />
#<br />
#For the sake of this tutorial, I'll continue with the sampling rate we estimated,<br />
#but in a real analysis we would probably want to spend a lot more time assessing the<br />
#accuracy of our estimate.<br />
#<br />
#Now, for cal3, we also need extinction rate and branching rate. One way to get those<br />
#would be to estimate them using per capita rates (Foote, 2000), but getSampProbDisc <br />
#also calculates extinction rate, and its better to take this joint estimate then get<br />
#extinction rate from a seperate analysis. We might also assume that branching rate is<br />
#equal to extinction rate. This is an extremely simplifying assumption, but one that<br />
#oddly seems to hold true in the fossil record (Stanely, 1979; Sepkoski, 1998). In<br />
#general, I'd suggest getting rates from both getSampProbDisc and from a per capita<br />
#rates analysis (not yet implemented in paleotree), and comparing the rates they<br />
#estimate, and also to test if branching rate is similar to extinction rate.<br />
<br />
#To get the extinction rate (also the branching rate for this tutorial) from<br />
#getSampProbDisc, we simply divide the extinction rate it calculated by the interval<br />
#length.<br />
<br />
divRate<-SPres[[2]][1]/meanInt<br />
<br />
#Alright and now we have everything we need to use cal3 with the retiolitid dataset.<br />
#Note however that all of the above won't apply for every sort of dataset we might<br />
#want to analyze: if your data doesn't have persistent taxa, then all taxa occur in<br />
#a single interval and freqRat and getSampProbDisc will be of no use to you, whatsoever.<br />
#There's are really any direct method that let you estimate sampling rates in those<br />
#cases, but there might be some workarounds. I'll talk more about that in the blog post<br />
#where I give all those published sampling rate estimates I've been collecting.<br />
<br />
########################################################<br />
#And Now, We Can Finally Time-Scale with cal3!<br />
<br />
#By default, cal3 randomly resolves polytomies and adds on the terminal ranges of<br />
#taxa, unlike the timePaleoPhy function we applied in an earlier part of this tutorial.<br />
#Just like with timePaleoPhy, there is a 'bin' version of cal3TimePaleoPhy for<br />
#discrete time interval data.<br />
<br />
ttree <- bin_cal3TimePaleoPhy(retioTree,retioRanges,brRate=divRate,extRate=divRate,<br />
sampRate=sRate,ntrees=1,plot=TRUE)<br />
<br />
<a href="http://www.blogger.com/blogger.g?blogID=497393262310058111" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"></a><a href="http://www.blogger.com/blogger.g?blogID=497393262310058111" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"></a><a href="http://www.blogger.com/blogger.g?blogID=497393262310058111" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"></a>> ttree <- bin_cal3TimePaleoPhy(retioTree,retioRanges,brRate=divRate,extRate=divRate,<br />
+ sampRate=sRate,ntrees=1,plot=TRUE)<br />
Warning: Do not interpret a single cal3 time-scaled tree<br />
Warning: Do not interpret a single tree; dates are stochastically pulled from uniform distributions<br />
<img alt="" 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" /><br />
<br />
#Note well the copious warnings about not interpreting a single tree! cal3 is a <br />
#stochastic time-scaling method.<br />
#<br />
#With such high sampling rates, nodes seperated by ZLBs under basic only move a little<br />
#back further in time: it's unlikely that there is much more unobserved evolutionary<br />
#history.<br />
#<br />
#We can see the diversity curve assumed by this tree...<br />
<br />
phyloDiv(ttree)<br />
<br />
<img alt="" src="data:image/png;base64,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" /><br />
<br />
#But if we really wanted to see the uncertainty in the phylogenetic diversity estimate<br />
#we would need to create a sample of time-scaled phylogenies and calculate<br />
#the median diversity curve across that sample. (Function multiDiv can do this!)<br />
#<br />
#Let's try making a hundred trees and putting them in multiDiv.<br />
<br />
ttrees <- bin_cal3TimePaleoPhy(retioTree,retioRanges,brRate=divRate,extRate=divRate,<br />
sampRate=sRate,ntrees=100,plot=FALSE)<br />
multiDiv(ttrees)<br />
<br />
<img alt="" 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" /><br />
<br />
#The thick black line is the median diversity, the gray blocks are 95% quantiles<br />
#across the sample of time-scaled trees.<br />
<br />
#Lastly, we can also change the times of observation in cal3 using the argument <br />
#'FAD.only'...<br />
<br />
bin_cal3TimePaleoPhy(retioTree,retioRanges,brRate=divRate,extRate=divRate,<br />
sampRate=sRate,ntrees=1,FAD.only=TRUE,plot=TRUE)<br />
<br />
> bin_cal3TimePaleoPhy(retioTree,retioRanges,brRate=divRate,extRate=divRate,<br />
+ sampRate=sRate,ntrees=1,FAD.only=TRUE,plot=TRUE)<br />
Warning: Do not interpret a single cal3 time-scaled tree<br />
Warning: Do not interpret a single tree; dates are stochastically pulled from uniform distributions<br />
<br />
Phylogenetic tree with 22 tips and 21 internal nodes.<br />
<br />
Tip labels:<br />
Rotaretiolites, Pseudoretiolites, Pseudoplegmatograptus, Dabashanograptus, Stomatograptus, Retiolites, ...<br />
<br />
Rooted; includes branch lengths.<br />
<img alt="" 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" /><br />
<br />
#Cool? Now you should know everything you need to play around with cal3!<br />
<br />
###################################################<br />
#References<br />
#Source for cladogram and zonal ranges for genera:<br />
#Bates, D. E. B., A. Kozlowska, and A. C. Lenz. 2005. Silurian retiolitid graptolites: Morphology and evolution. Acta Palaeontologica Polonica 50(4):705-720.<br />
<br />
#Source for interval dates for graptolite zones:<br />
#Sadler, P. M., R. A. Cooper, and M. Melchin. 2009. High-resolution, early Paleozoic (Ordovician-Silurian) time scales. Geological Society of America Bulletin 121(5-6):887-906.<br />
<br />
#Other references:<br />
<a href="http://www.blogger.com/blogger.g?blogID=497393262310058111" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="http://www.blogger.com/blogger.g?blogID=497393262310058111" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a><a href="http://www.blogger.com/blogger.g?blogID=497393262310058111" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a>#Foote, M. 1997. Estimating Taxonomic Durations and Preservation Probability. Paleobiology 23(3):278-300.<br />
#Foote, M. 2000. Origination and extinction components of taxonomic diversity: general problems. Pp. 74-102. In D. H. Erwin, and S. L. Wing, eds. Deep Time: Paleobiology's Perspective. The Paleontological Society, Lawrence, Kansas.<br />
#Foote, M., and D. M. Raup. 1996. Fossil preservation and the stratigraphic ranges of taxa. Paleobiology 22(2):121-140.<br />
#Foote, M., and J. J. Sepkoski. 1999. Absolute measures of the completeness of the fossil record. Nature 398(6726):415-417.<br />
#Laurin, M. 2004. The Evolution of Body Size, Cope's Rule and the Origin of Amniotes. Systematic Biology 53(4):594-622.<br />
#Norell, M. A. 1992. Taxic origin and temporal diversity: the effect of phylogeny. Pp. 89-118. In M. J. Novacek, and Q. D. Wheeler, eds. Extinction and phylogeny. Columbia University Press, New York.<br />
#Sadler, P. 2001. Constrained optimization approaches to the paleobiologic correlation and seriation problems: a user's guide and reference manual to the CONOP program family, v.6.1. University of California, Riverside.<br />
#Sepkoski, J. J. 1998. Rates of speciation in the fossil record. Philosophical Transactions: Biological Sciences 353(1366):315-326.<br />
#Smith, A. B. 1994. Systematics and the fossil record: documenting evolutionary patterns. Blackwell Scientific, Oxford.<br />
#Stanley, S. M. 1979. Macroevolution: Patterns and Process. W. H. Freeman, Co., San Francisco.<br />
<br />dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-63379670833218108172013-04-25T13:15:00.000-07:002013-04-29T08:07:00.842-07:00Why YOU should go to Evolution 2013!Hello all,<br />
<br />
It's April 25th, 2013, and that means Evolution 2013 is a little less than two month away. Now, a lesser known thing about Evolution meetings is (a) you only have to submit a title unless you're trying for a special student presentation award and (b) you can view everyone's submission as they submit on a public website:<br />
<br />
<a href="http://evo2013.evolutionmeeting.org/engine/search/index.php?func=summary">Evolution 2013 - Presentation Search</a>
<br />
<br />
ALSO, the organizers have extended presentation deadlines, you still have until TOMORROW (April 26th) to submit your presentation! Still on the fence about whether to go? Well, here's one (cough) short list of reasons why you should go.<br />
<br />
Once upon a time, I remember some people who told me I probably wouldn't enjoy my time at Evolution: that the majority of the talks involved genetic evolution. (And I work on an extinct group so...) Well look down and tell me if that's true in 2013. <br />
<br />
If you have any interests in paleontology, phylogenetics or macroevolution, you'll find something below of interest, I guarantee it. This is an amazing time to be a young scientist working in this area. Also, we really need to see more paleontology at Evolution! Note well though that many of the Evolution talks given by paleontologists aren't even listed under paleontology: many of us are giving macroevolution, phylogenetics, biogeography or community ecology and evolution.<br />
<br />
<br />
<br />
Here's the list:<br />
<br />
A generalized kappa statistic for estimating phylogenetic signal from multivariate data<br />
Dean Adams<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=454<br />
<br />
A genomic approach to understanding allopatric speciation in tropical montane birds<br />
Ben Winger and John Bates<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=62<br />
<br />
A linear-time algorithm for Gaussian and non-Gaussian trait evolution models.<br />
Ho, Lam; Ane, Cecile<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=51<br />
<br />
An artifact caused by undersampling optimal trees in supermatrix analyses of locally sampled characters<br />
Pitkin Simmons, Mark; Goloboff, Pablo.<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=98<br />
<br />
A new birth-death model recovers the K-Pg mass-extinction event in mammals.<br />
Hoehna, Sebastian<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1206<br />
<br />
Arbor: Comparative Analysis Workflows for the Tree of Life<br />
Harmon, Luke, Baumes, Jeff, Hughes, Charlie, Soberon, Jorge, Specht, Chelsea, Thacker, Robert, Turner, Wes, Lisle, Curtis<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=668<br />
<br />
A simple index of the strength of convergent evolution.<br />
Arbuckle, Kevin<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=823<br />
<br />
Assembly & early diversification of modern reef fishes.<br />
Price, Samantha, Wainwright, Peter<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=249<br />
<br />
Association between colony life-history and polymorphism in Cheilostome br.<br />
Carl Simpson<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=659<br />
<br />
A variable rates approach to quantifying the processes underlying trait co-variation.<br />
Smaers, Jeroen<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=654<br />
<br />
Bayesian inference of biogeographical histories for hundreds of discrete areas<br />
Landis, Michael; Matzke, Nicholas; Moore, Brian; Huelsenbeck, John <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=194<br />
<br />
Bayesian inference of phylogeny from partitioned data<br />
Moore, Brian, Fredrik, Ronquist, McGuire, Jim, Huelsenbeck, John <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1176<br />
<br />
Better interpretation of patterns of trait evolution using a novel reversible-jump method of detecting adaptive regimes from phylogenetic comparative data<br />
Josef Uyeda and Luke Harmon<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=601<br />
<br />
Beyond the lower-bound: the fossil record can provide empirically-informed prior distributions on node ages in poorly-sampled groups<br />
Graeme Lloyd, Matt Friedman and Mark Bell<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1134<br />
<br />
Coevolution, diversification, and biogeography of a specialized insect-plant pollination mutualism on oceanic islands<br />
David Hembry<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=553<br />
<br />
Constraints on mammalian forelimb development: Insights from developmental disparity<br />
Ross, Darcy; Marcot, Jonathan; Betteridge, Keith; Nascone-Yoder, Nanette; Bailey, Scott; Sears, Karen<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=794<br />
<br />
Deflating trees: improving Bayesian branch-length estimates using informed priors<br />
Nelson, Brad, Andersen, John, Brown, Jeremy<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=674<br />
<br />
Detecting Genomic Introgression at the Phylogenetic Scale<br />
Eaton, Deren; Ree, Richard<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=538<br />
<br />
Distance-based phylogenetic algorithms around a polytomy<br />
Davidson, Ruth; Sullivant, Seth<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=472<br />
<br />
Diversification of a diverse lineage of Neotropical rodents (Caviomorpha: Octodontoidea): integrating DNA sequences, fossils, and species traits<br />
Nate Upham and Bruce Patterson<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=406<br />
<br />Duplicate gene evolution on the Y-chromosome: insights from ampliconic genes of Indonesian macaques<br />Hermina Ghenu, Ben Bolker and Ben Evans<br />http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=875<br />
<br />
Dynamic variation in rates of body mass evolution in birds<br />
McEntee, Jay P; Ruhfel, Brad; Burleigh, J. Gordon<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=754<br />
<br />
Estimating Phylogeny from microRNA Data: A Critical Appraisal<br />
Thomson, Robert; Plachetzki, David; Mahler, D. Luke; Moore, Brian<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1203<br />
<br />
Evo-devo to Everest: travels with the trilobite time lords<br />
Nigel Hughes<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=571<br />
<br />
Evolution and the Levels of Lineage<br />
Matt Haber<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=862<br />
<br />
Evolution of desert biotas<br />
John Wiens<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=908<br />
<br />
Expeditionary Science in Deep Time: Motor for Paradigm Change<br />
Paul Sereno<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1104<br />
<br />
Experience with maximum likelihood morphometric methods that use phylogenies<br />
Joe Felsenstein<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=229<br />
<br />
Exploring Divergence and Convergence in the Hyperdiverse Myrmicine Ants<br />
Ward, Philip; Schultz, Ted; Fisher, Brian; Brady, Sean<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1287<br />
<br />
Exploring the pelagic abyss<br />
Karen Osborn<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=458<br />
<br />
Founder-event speciation dramatically improves likelihoods and alters parameter inference in Dispersal-Extinction-Cladogenesis (DEC) analyses<br />
Nicholas Matzke<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1242 <br />
<br />
Fossils as terminals: a total-evidence analysis to estimate angiosperm divergence date<br />
Magallon, Susana, Doyle, James<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=852<br />
<br />
Freeloader to free-living: lineage diversification and morphological evolution of the megadiverse marine bivalve clade Galeommatoidea<br />
Li, Jingchun; Ó Foighil, Diarmaid; Strong, Ellen<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=182<br />
<br />
From 'Bigmessidae' to Merulinidae: integrating molecules and morphology to resolve the systematics of scleractinian corals.<br />
Huang, Danwei; Smith, Nathan; Budd, Nancy<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=74<br />
<br />
Genetic correlates of morphological diversity in Costa Rican army ants<br />
Winston, Max; Moreau, Corrie<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=35<br />
<br />
Geometric morphometrics for analysis of any type of symmetry: Methods and biological applications<br />
Savriama, Yoland; Klingenberg, Christian Peter; Neustupa, Jiří; Gómez, José María; Francisco, Perfectti<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=952<br />
<br />
How clades expand in size/shape space: integrating fossil and Recent data to evaluate the role of clade age, species richness and position in morphospace<br />
Shan Huang and David Jablonski<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=803<br />
<br />
How cryptic is cryptic diversity? Machine learning approaches to fine scale variation in the morphology of Emys marmorata.<br />
Smits, Peter D; Angielczyk, Kenneth D; Parham, James F <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=425<br />
<br />
How do measures of "signal" relate to patterns of traits distributed on a phylogenetic tree?<br />
Forrestel, Elisabeth J.; Donoghue, Michael <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1241<br />
<br />
How good is good enough? - Taxonomy and phylogeny in evolutionary analyses.<br />
Laura Soul, Graeme Lloyd and Matt Friedman<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=825<br />
<br />
How to quantify the magnitude and significance of convergent evolution<br />
C. Tristan Stayton<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=957<br />
<br />
Identifying hidden rate changes in the evolution of a binary morphological character: the evolution of plant habit in campanulid angiosperms<br />
Beaulieu, Jeremy; O'Meara, Brian C.; Donoghue, Michael <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1127<br />
<br />
Indirect defensive traits and lineage diversification rates in plants<br />
Weber, Marjorie; Agrawal, Anurag<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=36<br />
<br />
Integrating Phylogeny, Climatic Niche, and Biogeographic History to Explain Growth Form Evolution in the Spine-shield Euphorbias (E. sect. Euphorbia, Euphorbiaceae)<br />
Dorsey, Brian; Berry, Paul <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=115<br />
<br />
Late Quaternary climate velocity, contemporary environmental gradients, and marine biodiversity patterns on coral reefs<br />
Sbrocco, Elizabeth J.<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=716<br />
<br />
Losing to the Red Queen I: a tale of two rates.<br />
Quental, Tiago Bosisio; Marshall, Charles <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=504<br />
<br />
Losing to the Red Queen II: when non-equlibrial processes trump diversity dependence<br />
Marshall, Charles; Quental, Tiago Bosisio <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1108<br />
<br />
Lung evolution in Amniotes<br />
Colleen Farmer<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1210<br />
<br />
Lying through your teeth: saturation and non independence in morphological data, and what to do about it<br />
Davalos, Liliana M; Velazco, Paul M; Warsi, Omar M; Smits, Peter D; Simmons, Nancy B<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=646<br />
<br />
Macroevolutionary dynamics of diversification across extant bats<br />
Shi, Jeff; Badgley, Catherine; Rabosky, Daniel<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=217<br />
<br />
Macroevolutionary patterns of trait diversification in social insects: a phylogenetic analysis of caste evolution using turtle ants<br />
Scott Powell<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=834<br />
<br />
MCDUSA: A Monte Carlo method for more reliable detection of lineage-specific rates of diversification<br />
Michael May and Brian Moore<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1140<br />
<br />
Missing data lead to holes in the tree of life<br />
Cabezas, Patricia; Drew, Bryan; Gazis, Romina; Swithers, Kristen; Deng, Jiabin; Rodriguez, Roseanna; Katz, Laura; Crandall, Keith; Hibbett, David; Soltis, Douglas<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=512<br />
<br />
Models of species diversification and the age-richness relationship paradigm<br />
Sánchez-Reyes, Luna Luisa; Magallón, Susana<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=417<br />
<br />
Morphology and exploitation in ray-finned fishes using crowdsourced data<br />
Jonathan Chang, Dan Rabosky, Mike Alfaro<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1293 <br />
<br />
Multi-Dimensional Optimization - A Mathematic Model For Evolution<br />
Leng, Xuguang<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=87<br />
<br />
New graphical methods for visualizing comparative data on phylogenies<br />
Liam Revell<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=954<br />
<br />
Node-based analysis of clade distribution<br />
Catherine Graham<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1179<br />
<br />
Open Tree of Life: large-scale phylogenetic data synthesis<br />
Karen Cranston<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=586<br />
<br />
Patterns of phenotypic correlations and integration in animals and plants<br />
Conner, Jeffrey K; Cooper, Idelle; La Rosa, Raffica; Perez, Samuel; Royer, Anne <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=323<br />
<br />
Phylogenetic ANCOVA: The study of adaptation and phenotypic radiation when combining continuous and categorical traits<br />
Fuentes, Jesualdo; Martins, Emilia<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=740<br />
<br />
Phylogenomics offers insights into hemichordate evolution<br />
Cannon, Johanna; Kocot, Kevin; Santos, Scott; Swalla, Billie; Halanych, Ken<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1014<br />
<br />
Sexual dichromatism and speciation rate in birds<br />
HUANG, HUATENG; Rabosky, Daniel <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=488<br />
<br />
Tempo of diversification across a 900-species muroid-rodent phylogeny; what do different methods tell us?<br />
Schenk, John, Steppan, Scott<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=234<br />
<br />
Testing irreversibility/Dollo's law using ancestral state reconstruction: How hard is it?<br />
David Swofford<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1130<br />
<br />
Testing the strength of evolutionary correlations between dental morphology and diet in extant Carnivora<br />
Sam Hopkins and Sam Price<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1285<br />
<br />
The discovery and documentation of convergent evolution in the morphology and mechanics of turtle shells<br />
C. Tristan Stayton<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=645<br />
<br />
The evolution of cichlid craniofacial diversity<br />
Matthew McGee, Sam Borstein, Peter Wainwright<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1233<br />
<br />
The evolution of ecological specialization in birds<br />
Oswald, Jessica A.; Burleigh, J. Gordon <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=298<br />
<br />
The evolution of neopilionid harvestmen and a new, putative Gondwanan relic from New Caledonia<br />
Krentzel, Dallas; Cokendolpher, James<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=660<br />
<br />
The Fossilized Birth-Death Process: A Coherent Model of Fossil Calibration for Bayesian Divergence Time Estimation<br />
Tracy Heath, John Huelsenbeck and Tanja Stadler<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=193<br />
<br />
The Genomic Nature of Linnaean Genera<br />
Masalia, Rishi R.; Barker, Michael <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=339<br />
<br />
The genome of the big-eyed arboreal ant Pseudomyrmex gracilis<br />
Ben Rubin and Corrie Moreau<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=524<br />
<br />
The Impact of Time-Scaling Methods on Phylogenetic Comparative Methods in the Fossil Record<br />
David Bapst<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=149<br />
<br />
The inference of basal snake phylogenetic relationships: The importance of combining molecular, morphological, and fossil data<br />
Harrington, Sean<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1150<br />
<br />
The major features of plant trait evolution, or: how I learned to stop worrying and love the model<br />
Pennell, Matthew; Cornwell, William; Harmon, Luke<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=935<br />
<br />
The Signal and the Noise: Why we're still searching for resolution of deep phylogenies<br />
Showers Corneli, Patrice<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=17<br />
<br />
Tracking the evolution of fur color patterns in the short-tailed opossum (Monodelphis: Didelphidae): common ancestry or independent evolution?<br />
Pavan, Silvia; Jansa, Sharon; Voss, Rob <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=559<br />
<br />
When are missing data problematic for phylogenetic estimation from phenotypic data sets?<br />
April Wright and David Hillis<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=231<br />
<br />
Why do species not adapt but go extinct?<br />
Bokma, Folmer<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1187<br />
<br />
Wing shape evolution in social wasps and phylogenetic signal.<br />
Perrard, Adrien, Baylac, Michel, Carpenter, James M., Villemant, Claire <br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=326<br />
<br />
<br />
............................<br />
<br />
... And last but not least, the three presidential speeches:<br />
<br />
The dechronization of E. coli: A 25-year love story<br />
Richard Lenski<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=847<br />
<br />
Speciation and latitude<br />
Dolph Schluter<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1142<br />
<br />
Systematic biology two decades after Snowbird 1993 and w(h)ither the species tree?<br />
Jack Sullivan<br />
http://evo2013.evolutionmeeting.org/engine/search/index.php?func=detail&aid=1212<br />
<br />
<br />
.......................<br />
<br />
Okay guys, now tell me about all the amazing talk submissions I missed, cause I know I must have missed a bunch!<br />
<br />
Also, go submit your own talk title! Go make the list above out-dated in the next 24 hours! Get to it!<br />
<br />
-Davedwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-84063428590759202862013-02-21T22:40:00.003-08:002013-02-24T11:26:49.929-08:00How to Find a Paleontological Phylogeny (For Those With a Rapidly Approaching Deadline)<div class="separator" style="clear: both; text-align: left;">
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<span style="font-size: small;"><span style="font-family: inherit;">Hello all! Recently I got some email requests about where to find paleontological phylogenies, particularly for invertebrate groups. I wrote some responses and then asked if I could post my answer to them here, as I expect they aren't the only ones to have such issues. I mainly got the sense these individuals wanted trees for use with comparative analyses (macroevolutionary analyses) and found me through my authorship of paleotree, the predominant function of which is to prepare a phylogenetic dataset for such evolutionary analyses.</span></span></div>
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<span style="font-size: small;"><span style="font-family: inherit;">So, I'll assume for this that you, dear reader, are a person in need of a phylogenetic tree of some fossil taxa, particularly invertebrate fossil taxa. Furthermore, I'll assume that the best answer doesn't apply here: that if one really wants a good, believable tree, one should go make a tree the hard way, using cladistics or other phylogeny-building methods and a morphological matrix. The main reason this may not apply is maybe you, dear reader, is if you want a tree for a class project or some other short-term goal, such as to just prove you can apply some method of interest even partially to a group. So you are looking for a tree without needing to mak<span style="font-size: small;">e a completely new tree<span style="font-size: small;"> yourself.</span></span></span></span></div>
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<span style="font-size: small;"><span style="font-family: inherit;">First off, not all groups have experienced equal effort in understanding their relationships using algorithmic phylogenetics (i.e. maximum parsimony, likelihood, Bayesian, etc). The difference in relative effort to make trees using quantitative methods from morphological matrices is particularly large between vertebrate and invertebrate groups, but there is even variation about invertebrate groups. Check out this figure (below) from Neige et al. (2007). The main point of that paper
was to
show how little worked ammonites are using cladistics, but they also reviewed phylogenetic effort in a number of
other major fossiliferous group. Some groups, such as bivalves,
also have not been the focus of much analysis. In contrast, echinoderms and trilobites
have been the focus of considerable cladistic work. Partly its cultural
(sometimes there are just too few workers who are acquainted with
cladistic methodology) and also its just
because some groups, like bivalves, often don't preserve many of the important
systematic characters needed for cladistic analysis. In some groups, systematic characters are dominated by continuous measurements, and there is still a lively debate about how to use those traits to determine relationships reliably.</span></span></div>
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<span style="font-size: small;"><span style="font-family: inherit;"><img alt="" height="640" 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" 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<span style="font-size: small;"><span style="font-family: inherit;">This means if you want to work on a specific group, you may not be able to find a tree built using an explicit cladistic analysis. But let's hope this isn't the case for the moment. Where would you find such a phylogenetic hypothesis, if it existed? There's no general database for specifically paleontological phylogenies. While paleontologists were ahead of the curve by putting collection and occurrence data online in the PBDB
years ago, collecting phylogenetics data has lagged behind. There are
some morphological datasets and tree files on MorphBank, treeBASE and Dryad, but if you have a specific group you're interested in, particularly invertebrates, they probably don't contain it. </span></span><br />
<br />
<span style="font-size: small;"><span style="font-family: inherit;">EDIT (02-22-13)<span style="font-size: small;">:</span></span></span><br />
<span style="font-size: small;"><span style="font-family: inherit;"><span style="font-size: small;">Graeme Lloyd, who <span style="font-size: small;">I knew had been keeping a number of fossil vertebrate <span style="font-size: small;">character matrices and trees on his website, pointed out that he expanded at some point to also include <span style="font-size: small;">invertebrate <span style="font-size: small;">groups too! So, <span style="font-size: small;">thanks for proving me wrong, Graeme!</span> </span></span></span></span></span></span></span><span style="font-size: small;"><span style="font-family: inherit;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;"></span>(I clearly don't <span style="font-size: small;">read</span> Graeme's website <span style="font-size: small;">often</span> enough...) </span></span></span></span></span></span><br />
<br />
<span style="font-size: small;"><span style="font-family: inherit;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;">http://www.graemetlloyd.com/matr.html </span></span></span></span></span></span><br />
<br />
<span style="font-size: small;"><span style="font-family: inherit;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;">EDIT (02-24-13)<span style="font-size: small;">: Graeme <span style="font-size: small;">has commented he doesn't currently have any invert matrices up<span style="font-size: small;"> just yet, so his lists at the moment just reflect literature where you could find matrices<span style="font-size: small;">. Still, that's quite a service!</span></span></span></span></span></span></span></span></span></span><br />
<span style="font-size: small;"><span style="font-family: inherit;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;"> </span></span></span></span></span></span><br />
<span style="font-size: small;"><span style="font-family: inherit;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-family: inherit;">Also, <span style="font-size: small;">I noticed<span style="font-size: small;"> a</span> Bristol datab<span style="font-size: small;">ase Graeme mentione<span style="font-size: small;">d</span> which I was unaware of contains a number of matrice<span style="font-size: small;">s</span>.</span></span></span></span></span></span></span></span></span> Again though, coverage <span style="font-size: small;">is<span style="font-size: small;"> still pretty spotty for some groups.</span></span></span> <br />
<span style="font-size: small;"><span style="font-family: inherit;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-size: small;"><span style="font-family: inherit;"><span style="font-size: small;"><span style="font-size: small;">http://palaeo.gly.bris.ac.uk/cladestore/</span></span></span></span></span></span></span></span></span></span> <br />
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<span style="font-size: small;"><span style="font-family: inherit;">So that means you'll have to turn to the primary literature to find trees to use, probably using some well chosen keywords in Google Scholar. Now, let's assume you find the tree you want, which in 99% of the cases will be some cladogram (I recommend the consensus trees...). Now, the tree itself may be offered in supplemental files or you might be able to get it by contact the authors, but let's be perfectly realistic and admit that sometimes those avenues won't work, or they may only work on very long time-scales (hard drives get fried, babies are had and emails go unanswered, etc). Instead, you'll probably need to use what's actually at hand: the tree as printed on the page. I've done this myself quite a bit, and there are some handy programs out there that automate this, but I've always just copied the tree out by hand. </span></span></div>
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<br />
<span style="font-size: small;"><span style="font-family: inherit;">For small trees, the simplest way to do this is to copy trees out as Newick string. Newick is just a quick way to write
relationships among taxa as a series of nested parentheses, with sister
lineages seperated by commas (sometimes also called 'phylip' format). A quick
example of Newick format for ctenophore, man, graptolite, fly and brachiopod
genera: (Pleurobranchia,((Homo,Nemagraptus)(Drosophila,Terebratula))) You can
read that into the programming language R as a text file using read.tree() in library ape. For large trees this can get tedious: you could also write out part of the tree and then modify it, by adding taxa, removing taxa and collapsing clades in a GUI, such as Mesquite. R has some of these tools but it doesn't make wholesale tree editing easy. If you're eventual goal is to put the tree in R, I've found it is sometimes necessary to save Mesquite files as PHYLIP format and then open them and resave them in TreeFig before using read.tree. If you're curious how long this will take, I've often found I can copy over a large tree in half a day or so.</span></span></div>
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<span style="font-size: small;"><span style="font-family: inherit;">Now, let's continue on the path of assuming you found a cladogram. (Yes, reader who cannot find a cladogram, I'll get to you in a moment.) If your question just needs the nesting relationships shown by the cladogram, you're done. You got what you wanted. But maybe you want to go further. Maybe you want to make time-scaled phylogenies, which is what paleotree is for, and probably why you're reading this blog to begin with. These time-scaled phylogenies are what we want for making most evolutionary inferences.</span></span></div>
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<span style="font-size: small;"><span style="font-family: inherit;">If you want a time-scaled phylogeny, you'll need to find stratigraphic data to use, in addition to the cladogram. These stratigraphic data should record at least which intervals or dates the taxa you want to analyze first and last appear at. These might be in the same publication as the cladogram you found, but they may not. You will probably need to go look at published range charts for this group and spend some time figuring out where obscure regional stages correlated to the global time-scale (this is where Gradstein and Ogg volumes become very useful). You should also check the PBDB, although for some groups the data can be very coarse and will requite some cleaning. Again, there is a right way to do this, but again I'm imagining you have a class project you want to complete with some rapidly approaching deadline. To do anything with paleotree, you
just need to get the tree to the point you can read it into R as a 'phylo'
object and read the ranges in as a matrix. The functions in paleotree can be
applied once you have that. Note, by the way, about these stratigraphic ranges, I'm assuming you have almost all the known species in your dataset. If you are doing something like sampling one species per family, the first appearance times of taxa in your dataset will be poor indicators of when clades branched from each other, at least using the sort of methods I offer in paleotree.
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<span style="font-size: small;"><span style="font-family: inherit;">Okay, so what if you couldn't find a cladogram for the group you wanted to work on? Or what if you found a cladogram, but its at the genus-level and your data is species? Or what if the cladogram has half the species you want to analyze, but not the other half, and its non-random with respect to your question (like, let's say body size affects taphonomy in your group, so the cladogram has all the big individuals, but you're interested in body size evolution...). Well, you have several possibilities. You may just be out of luck and maybe you will need to start over or consider making a tree yourself.</span></span><br />
<br />
<span style="font-size: small;"><span style="font-family: inherit;">If it's just the problem that your taxa are on a bunch of trees and you don't know how to make any sense of all the conflicting relationships, <span style="font-size: small;">don't worry, supertree methods were invented for a reason and you might want to look into those. However, more likely the issue is the taxa you want on the tree have never been on the tree. So, what you might want to do is</span> look for data on relationships that isn't the product of an explicit phylogenetic analysis. For example, some invert groups have a number of stratophenetic diagrams, which are built based on expert opinion relating to
morphological and stratigraphic data. There is also an increasing number of analyses being conducted with informal trees built from a combination of cladistic hypotheses and traditional taxonomic data (like the widely-used Phylomatic for plants;
Webb and Donoghue, 2006), or just from taxonomic data alone (e.g. Green et al.,
2011 or 'Common Tree', see <a href="http://schamberlain.github.com/2013/02/common-tree/" target="_blank"><span style="color: blue;">http://schamberlain.github.com/2013/02/common-tree/</span></a>). These non-cladistic options, or options which are only partly based on cladistic evidence, *might* be okay to use. </span></span></div>
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<span style="font-size: small;"><span style="font-family: inherit;"> Whether such shortcuts are acceptable approaches or not are you and
your question. You should carefully consider how sensitive your analyses are to uncertain relationships.
It's not really an easy question to ask, but maybe for a first-pass rough cut
(such as for a class project), a summary hypothesis based on taxonomic information in
replace of a tree may be okay. Some people will feel very strongly about this one way or another, and you'll ultimately be the one who will have to defend what you did and how you did it. The function expandTaxonTree in paleotree can be
useful if you do decide to go this route, as it can do helpful things like turning a rough genus-level tree
into a species-level tree by treating each genus as a soft polytomy. (Also, as I stated above, if you
want a time-scaled tree of fossil taxa, you still need stratigraphic ranges,
unless you are using a stratophenetic tree, in which case it should already be
time-scaled.) </span></span><br />
<br />
<span style="font-size: small;"><span style="font-family: inherit;">Now, for those of you who read this and maybe think <span style="font-size: small;">this post could <span style="font-size: small;">inadvertently</span> serve to inspire risky b<span style="font-size: small;">ehavior, <span style="font-size: small;">I agree you might have a<span style="font-size: small;"> point. Phylogenetics <span style="font-size: small;">can be slow, because good phylogenetics require<span style="font-size: small;">s patience<span style="font-size: small;">, care and due consideration</span></span>. A</span></span></span></span></span></span></span>ll I can say is that students will always <span style="font-size: small;"><span style="font-size: small;">end up in situations where they need datasets as part of coursework (or whatever) and this questions will thus always come up, because making a new morphology-<span style="font-size: small;">based</span> tree from scratch isn't a feasible solution for most classes (or most students). Overall, I hope people go and do simple analyses with back-of-the-envelope trees, see how awesome phylogeny-based analyses are and <span style="font-size: small;">get inspired to construct trees<span style="font-size: small;"> for doing those same analyses as part of a larger project where they have more time.</span></span></span></span></div>
<span style="font-size: small;"><span style="font-family: inherit;"><br /></span></span>
<br />
<div style="margin-left: 1em; margin-right: 1em; text-align: left;">
<span style="font-size: small;"><span style="font-family: inherit;">Anyway, I hope all this helps!</span></span><br />
<span style="font-size: small;"><span style="font-family: inherit;">-Dave</span></span></div>
dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com2tag:blogger.com,1999:blog-497393262310058111.post-8892469553066688892012-11-23T00:23:00.000-08:002012-11-23T00:23:56.452-08:00Playing Mountain Witch 11-21-12<!--[if gte mso 9]><xml>
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I recently played a game of Mountain Witch which I would
like to tell everyone about ! It was so amazing I must share!</div>
<div class="MsoNormal">
<br /></div>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><img alt="" border="0" class="cmuImage" height="500" id="cmuMainImage" src="https://images-na.ssl-images-amazon.com/images/G/01/ciu/a8/bd/f0e3c060ada00203a969d110.L.jpg" style="height: 500px; margin-left: auto; margin-right: auto; width: 324px;" width="324" /></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Stole this Amazon. Yeah, that's how I roll.</td></tr>
</tbody></table>
<div class="MsoNormal">
Obviously, I can only relate my account of events. It would
be interesting to know what the other players were experiencing; there were
certainly some bits I missed by leaving the room. I’m going to try to switch
back and forth between what segments of the fiction which emerged and an
account of the actual play.</div>
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<br /></div>
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We had Josh as GM and (if you don’t know Mountain Witch) the
rest of us played ronin hired to take on the dangerous task of killing the
powerful and god-like Mountain Witch. A fantastic wealth was promised to us if
we could do this task. </div>
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<br /></div>
<div class="MsoNormal">
Justin playing Kagome (Dog, Yellow), who was a female samurai
whose lord had been defeated in battle and who planned to kill herself after
getting the money to completing some final task. Important to the game later,
Kagome could smell fear itself and she had the preternatural skill to shoot
anything she could smell. Alice played gray-haired Miyoko (Tiger, Silver) who
was an older samurai who had become ronin after he made a reckless tactical
mistake that led to his lord’s son dying in a rout, and hoped to reprove his
loyalty with the reward money. Miyoko had a thousand shuriken hidden on his
body and the ability to browbeat those who disagreed with him. Colin played
Shigeru (Dragon/Green), a strong warrior who had willingly left his lord’s
service, intent on raising funds for his own army and banner men, thus becoming
his own lord. He had his armor and warhorse with him, which he rarely
dismounted, meaning he often spoke down to the rest of us. Peter played Honzo
(Red, Monkey), a man with plain features and red armor, who had been thrown out
of his lord’s service due to an overheard insult. Honzo was a sneak, able to go
where he liked without notice. I played Goro (Blue, Rat), a cynical man with
sharp features who rumors say killed his wife and his lord after discovering
them in a delicate situation. Blue tattoos covered his skin and he the ability
to speak to birds and had a loyal crow who perched on his shoulder, Tsuba.</div>
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<br /></div>
<div class="MsoNormal">
Note I only discussed a few of the powers, those I remember
that became important to the fiction. Character creation was very good, we did
powers like ala Settlers of Cataan town placement, where started at one end and
then curled back around in opposite order (and then back again), so that there
was some free equality in how powers were decided. It was probably unnecessary
though, cause I think we each had very different ideas of what ronin powers
should be.</div>
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<br /></div>
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An interesting thing which has happened is that there’s
already been considerable drift in what I can remember from the game. Was Honzo
shorter than the rest of us? Peter is somewhat short and as the game went on,
he deliberately stooped in his chair, becoming even shorter. But I’m pretty
certain Honzo started off at average height. I don’t remember if Colin
described Shigeru as a giant, but that’s what he was by the end in my mind: a
towering man of incredible strength. None of this was stated *I think*, and so
I worry that means many of the things I say here may very much be not even have
been part of the spoken narrative, but speaking with Peter suggests I was not
the only one who saw these same shifts in our characters. Peter speaks of how
Kagome became sterner and more like steel, how Miyoko became desperate and
tired, more ferocious in his mission, how Goro (me) became more cryptic, solemn
and quiet. I saw all of these things too in play, but I don’t think they were
actually stated. Some of these shifts in appearance and personality (like my
character’s) contrasted pretty strongly with the original characterization in
act 1.</div>
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<br /></div>
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The first scene in Act 1 started with Honzo looking over at
us and saying “Shall we go?” and starting our way into the forest at the base
of the mountain. Soon we were in an encounter with wolves, which turned violent
thanks to Kagome. Honzo and Goro stayed out of the fight, which ended quickly.
Shortly after, a servant girl named Kono appeared from the forest, pleading
with Shigeru to not kill the Mountain Witch. She claimed that both of them had
once been in the Witch’s employ, but he refused to deal with her. Further on,
the ronin had to cross a bridge in the forest and had to deal with the ghost of
a warrior who had once followed Miyoko, but was browbeat by both Miyoko and
Honzo to let them cross. Goro crossed on his own, leaping across the river.
Kagome did not trust Goro and tried to follow, only to almost drown. Tsuba told
Goro that Goro had to save Kagome, although Goro was angry that Kagome did not
trust him. Still, he reluctantly had Tsuba drop a rope in the river to pull her
in. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
All of the above had been framed by Josh, who then passed it
on to one of us to frame, based on our dark fate. I volunteered and narrated us
coming across a man holding his daughter, who was dead of mysterious cause.
Kagome identified correctly that she was poisoned, having committed suicide in
the same way as her mother. The father was distraught, and tried to kill
himself with the poison to find answers to his daughter’s death. Miyoko
considered this dishonorable and took the poison with her. Honzo, as he did
repeatedly later in the game, claimed all of this was an illusion, trickery
from the witch.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Next, we entered a place which Honzo later called the Vale
of Dead Flesh, where we were attached by the restless dead, only to be saved by
a magic word from Honzo. He claimed he knew this word because he had, in fact,
been up the mountain to the Witch before but refused to explain how or why. The
rest of the ronin presumed the word could only come from the Witch. We then
came to a withered holy tree, tended by a spirit disguised a priest. Only
Kagome considered herself worthy to go near the holy place, and it turned out
the priest held a message for Miyoko. Kagome broke the sealed message open, to
discover that it was from Ai, the former charge of Miyoko.<span style="mso-spacerun: yes;"> </span>She was headstrong, and she was going up the
mountain to face the Mountain Witch before us (maybe to save Miyoko from facing
the Witch?). Following this, (Peter framing) we found a burial mound at a
crossroads, which caused Honzo to freak out. Kagome shot it with her rifle and
a severed child’s hand tumbled out of the mound. Goro recognized the hand and
freaked out, but the hand vanished mysteriously in the ensuing chaos. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
At this point no one trusted anything: both Shigeru and Honzo
appeared to be (at least former) servants of the Witch and Goro regularly
whispered things to Tsuba in bird-tongue who would then fly away and return
some time later. Miyoko and Kagome were fast relying on each other as the only
two trustworthy members of the ronin. Shigeru constantly tried to deflect
suspicion on himself by questioning the motives of Honzo and Goro. Goro was
adamant that his goal was to kill the witch and Honzo was equally adamant that
his goal was to complete ‘the Mission’. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Kagome did not trust either Goro or Honzo. As the group
crested a hill, she whispered to herself that she had smelled no fear yet on
one of the men she travelled with, and that her mission would be to kill the
Witch, but also to make this man feel fear and kill him.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
We camped (end of Act 1), taking turns at watch. There was a
scene I wasn’t in the room for, where I think the Witch invaded the dreams of
Miyoko and/or Kagome, and there was something involving the bottle of poison
(??).<span style="mso-spacerun: yes;"> </span>Later in the night, during Goro’s
watch, which Kagome forced him to share with her, Kagome saw him conversing
again with his crow, who flew off, up the mountain. </div>
<div class="MsoNormal">
Trust point change time! It was clear Kagome didn’t trust
Goro, and I reciprocated by dropping trust points with Kagome down to one. I
increased Goro’s trust for Honzo, but I was the only one, and Miyoko. Overall,
much trust was lost for both Honzo and Goro.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
In the morning (now Act 2), the warriors were greeted by a
friendly 40 foot tall rock giant, who claimed he was the chancellor of the
Witch. No one trusted him. He gave the ronin a bundle of food and passed on a
message from the Witch, who politely suggested they should give up their
mission. He also deposited a bundle of gold, a gift for the true servant of the
Witch among the group. Only Shigeru touched either of these bundles, taking
some food while the others were leaving. Goro told the chancellor to tell the
Witch that nothing would turn Goro from his task. As the ronin walked off,
Tsuba flew back, snatching a note hidden among the Chancellor’s gold. The crow
gave it to Goro who read it and tried to throw it away in a sudden fit. Shigeru
grabbed the note with his spear and read it: it was “Kikuya”, the name of
Goro’s dead wife… and a name which Miyoko had heard Kagome utter in her sleep
last night. Tensions between Goro and Kagome almost peaked, but Kagome won the
staring match. Goro came away only knowing she knew… something about his wife.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
We came to a crossroads, where we could travel either by
tunnels or by the precipice road, and Kono appeared to plead with Shigeru again
and declaring her love for him, asking him to think of the things the Witch had
done for both her and Shigeru. Shigeru (or someone) threatened to kill her if
she did not leave. Taking the precipice road, the ronin were faced with ice
demons and their fierce winds, who tried to shove them off the mountain. Goro
sliced the wave with his sword, but Tsuba was grabbed by the wind and flung off
far down the mountain (I narrated this). Both Honzo and Shigeru lost their
footing and nearly died, but Honzo used another magic word and Shigeru called
on the Witch to honor their deal. Having survived, the ronin quickly made it to
an inviting cave where Honzo and Shigeru might have been interrogated if the
cave wasn’t already occupied (I framed). An old man sat by a campfire and
offered to tell samurai a tale as they tended to their wounds and dried out by
the fire. He told of an emperor in a far off land, who realized he was
disliked, so he made himself so hated that his people stormed the palace and
killed him, but he had last laugh because- at which point in the story, Goro
sliced off the old man’s head and covered Shigeru and Kagome with blood.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
The ronin exploded into distrust, with Shigeru trying to
deflect suspicion on himself by instead trying to claim that Goro and Honzo
must be spies of the witch. Honzo’s sanity was clearly slipping and Peter was
fantastic, stooping to look smaller and smaller, grinning and mumbling
constantly about how they had to complete the Mission. The nearly violent
dynamic between Colin as Shigeru (who grew somehow to be a giant samurai full
of arrogance and bluster) versus the impish and unwell Honzo was probably the
most memorable part of the game. It was clear Honzo had been up the mountains
many times and Goro seemed to withdrawl emotionally following killing the old
man, becoming ever more solemn and cryptic, only being adamant that they must
kill the Witch. Honzo seemed to have almost become a risk but his clear
experience with the mountain path was too valuable to force him from the group.
Goro asked Honzo if he was the ‘next one’ but Honzo had no idea what Goro was
talking about.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Miyoko seemed the most sane of the ronin, but not for long.
Travelling on, they found a scabbard for a woman’s sword, which Miyoko
remembered giving to Ai. It was clear that a struggle had occurred here but
there was not trace of Miyoko’s former student. He became obsessed with moving
on to the palace and making sure Ai did not die like the men who Miyoko had
once led to their doom. As they entered the volcano, they snuck past several
trolls, preparing a mortal for dinner. Honzo recognized the man as his former
lord but was completely indifferent to this emotionally, stating that it was
clearly another illusion. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
The ronin made their way down to the Witch’s stronghold, to
the bridge which marked the edge of his demesne. There, Kikuya’s ghost appeared
and warned her sister, Kagome, to not trust Him, and she seemed to not notice
Goro’s presence at all. Goro went into emotional trauma again, begging Kikuya
for answers: was she having an affair with his lord? Had their deaths been
righteous or not? Did she forgive him for what had happened to their daughter?
No response and the ghost left having given its message to Kagome. Suddenly, as
if a spell was broken, Goro could see that Kagome was nearly identical to his
dead wife. He begged Kagome to forgive him, that he had thought Kikuya was
keeping something from him, that he was sorry that he had killed Kikuya.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="mso-spacerun: yes;"> </span>Kagome informed him
that he hadn’t killed Kikuya… Kagome WAS Kikuya! The two sisters had switched
their names when Goro had become arranged as ‘Kagome’s husband-to-be, so that
Kikuya was spared the harsh life of a woman samurai. Goro was in tears, as
‘Kagome’ told him that they would kill the witch, but then she would kill him
for the death of her sister. Goro, suddenly angry, told Kagome that the only
thing that mattered was that the witch must die.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Trust points continued to evaporate and cluster although I
can’t remember all the details. Goro trusted Honzo and Shigeru highly (4 and 3)
to kill the witch, but both of them could not trust him nor each other. Miyoko
continued to trust Goro somewhat (2) and Kagome (who had been saved repeatedly
now by Goro, but also intended to kill him) gave Goro a single trust point.
Goro trust Miyoko also to kill the witch, but could not trust Kagome, knowing
that Kagome meant to kill him. Miyoko and Kagome continued to be bound tightly
in trust. I think trust evaporated for Honzo completely except for Goro’s
trust, and the same for Shigeru.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
At the start of act three, Tsuba reappeared, landing on
Goro’s shoulder, where he told Goro that the shadow puppet was ready. Goro
nodded. The ronin entered a great plain before the Witch’s mighty fortress,
only to face an army of several thousand vicious Oni. In the face of great and
violent death, the ronin ran, with Shigeru taking lead and pulling a note from
his armor with a map of the fortress on it. The oni pursued with no mercy.
Miyoko tripped and Kagome tried to help him, only for Goro to grab both of them
from behind and pull them up. He told Kagome he had no intention of letting the
real Kikuya die also. At a shear cliff wall, Shigeru uttered a magic word which
opened a secret entrance to the Witch’s library and then sealed it after the
other ronin stepped through, saving everyone from death. Goro wondered where
the note had come from, and the crow whispered that he’d given the note to
Shigeru. (In play we knew it came from the Witch, and there was some confusion
when I said that the crow had told me this. I think some thought I was making a
joke. I was not.) Shigeru explained he had once worked for the Witch but he was
now completely devoted to killing the witch.</div>
<div class="MsoNormal">
In the library, Honzo began leading the ronin through
hallway after maze-like hallway, searching for the book of souls. Shigeru and
Goro separated from the others, with Kagome following Goro refusing to let him
be alone and contact the Witch. She was now convinced that Goro was the servant
of the witch. Miyoko stayed with Honzo, who Kagome and Miyoko agreed was much
more dangerous than Shigeru.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span style="mso-spacerun: yes;"> </span>(Colin narrating)
Shigeru, by himself, wandered to an isolated section, where he pulled out a
hand mirror. The hideous visage of the Witch formed there, and told Shigeru
that he had done well, having brought the ronin to the stronghold so that the
Witch might absorb their souls. Shigeru thanked his lord and saw a vision of
the vast wealth and mighty kingdom he would rule as his reward. Shigeru added
that he wanted his love (Kono?) by his side. In play, there was an odd bit here
where several of us rechecked our Dark Fate cards. I had some pretty complex
narrative plans in particular, and I felt somewhat impatient to have my own
dark conference with the Witch to clarify what I’d been hinting at all
game-long. It wasn’t going to happen though, not with Kagome around. I just had
to take solace in the fact that I knew my Dark Fate gave me the end-all-be-all
narrative control over the elements of my own Dark Fate and that I’d be able to
tie my outlook in eventually.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Honzo found the tome of souls (he knew exactly where it was
already, having been there before). It was a giant book, bound by chains and
lightning. It could kill any mortal who touched it. Honzo was unharmed as he
leafed through it, because he had no soul and proceeded to look for the entry
that would tell him where his soul was. It was in the Witch’s throneroom.
Miyoko requested desperately if they could go to the dungeon, where perhaps Ai
was being kept, but Honzo informed him that he had looked up Ai in the tome and
she was also in the throne room. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Meanwhile, Goro revealed all to Kagome: He had killed her
sister and his lord in a moment of passion, believing that they were cheating
behind his back, and that then he had run in cowardice. In absentia, Goro was
punished by his lord’s family by his daughter with Kikuya being executed. His
sin knew no end, but he could make it right. He asked Kagome to trust him: if
the Witch died, ‘Kikuya’ would be returned to this world. Kagome said she would
help in killing the Witch, but Goro would die soon after if there was any
treachery. Goro conceded to this. Shigeru, Miyoko and Honzo reappeared, at
which Goro tried to sneak away. It was almost too easy for Kagome to shoot Goro
through a bookcase, leaving him wounded and unable to get away. He was not,
would not, be out of Kagome’s sight again.</div>
<div class="MsoNormal">
It was at this point that Josh said he knew what all our
dark fates were. He was mostly wrong. I don’t think any of us knew who the
others fates were.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
We made our way through the palace until we got to the hall
of crystals, the anteroom to the throne room. (Alice narrating) Miyoko glanced
in one crystal and suddenly was back as the master samurai, teaching tender Ai
the art of the blade. His soul began to leak out into the crystal and Kagome
grabbed him, trying to tear his gaze from the crystal, only for her own eyes to
fall on one. (Justin narrating) Kagome saw an image of her and her sisters as
happy innocent children, but she pushed this to the back of her mind and saved
Miyoko. (Colin narrating) Shigeru looked and could only see his anger and
jealously, how he had seen the riches of the lords and how wanted them,
particularly the love they received. He would lead the rest of us to our deaths
and take all of the wives of his former lords as his own. (Peter narrating) Honzo
walked through unimpeded, for he had no soul.</div>
<div class="MsoNormal">
(I narrating) Goro looked at the crystal and saw first
himself, as a samurai. He had led his lord’s men to victory, and there was a
grand celebration. Then he looked over and saw his wife and his lord talking
quietly. Goro’s heart darkened at that moment. He always knew his wife held
something from him, had some secret she would not tell him. Was it that she did
not love him, maybe loved another? Then the vision changed, now it was later,
after Goro had killed his wife and lord in anger and killed his daughter in
cowardice. It was now the throne room of the god-like Mountain Witch, with the
mighty monster of ice and fire that was the Witch sitting on his throne. A ruined
man was dragged in by Oni, “We found this one trying to commit suicide in the
forest below!” they cried. Let me die, Goro pleaded. The Mountain Witch laughed
and offered to undo Goro’s sins. The Witch told Goro of the Emperor’s tale,
this time including the ending: that the hated emperor had arranged for a
double and so it was not the Emperor that was killed. Instead, the hated
Emperor had abandoned his life as a disliked ruler and escaped to die at an old
age, as a happy and fat peasant. The Witch wanted to do the same: he no longer
wished to be hunted and hated but instead to leave his power and trapping
behind. So, here was the plan: the Witch had selected several powerful ronin
and Goro’s task was to lead them up the mountain. They all had their reasons to
hate and despise the Witch and he would give them even more, except one, who he
had groomed to be the Witch’s replacement, to become the next Witch. All Goro
had to do was help the Witch fake his own death and destruction.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Finally, the visions faded and now it was a reflection of
Goro, but the thing on his should was not his crow, was not Tsuba, it had never
been. It was the Witch. The Witch had been with the ronin as Goro’s crow the
entire journey. The thing that sat on the throne in the next room was the
shadow-doll, ready to be slaughtered. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
We entered the throne room (no one shifted Trust), and saw
the giant Witch-Shadow-doll of ice and fire sitting on the throne. Ai was
trapped in a block of ice, forced to dance for his enjoyment. Miyoko stepped
forward and battle commenced, with Honzo immediately running off to look for
his soul among the witch’s treasures. Shigeru went after him (I think?),
because he needed all of our souls to be absorbed into the Witch (or was it the
shadow puppet? was the witch lying to someone? there was still doubt here for
us at the table about who knew the truth of the situation). I cannot remember
what Kagome was doing during this portion, but I know that Miyoko and Goro
fought the ‘Witch’, with Goro eventually distracting it while getting burned by
fire. The distraction worked long enough for Miyoko to kill the god monster,
filling throne room with a rolling cloud of steam and melted ice. The crow
cawed and Goro dropped his blade. The bird took off down a distant hallway,
past piles of treasure and wealth. Goro followed, followed by Kagome, readying
her rifle.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Miyoko strode forward and dug Ai out of the melting ice,
making sure she was okay. Shigeru, full of rage and fury that the Witch was
dead, attacked Miyoko and the two dueled as Ai watched helplessly. It ended
with Miyoko’s shuriken in Shigeru’s eye, his blood seeping out into the melted
water that had once formed the ice-body of the Witch/shadowdoll.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Far off in a distance section of the throne room, Honzo
found the pot with his name on it, opened it and… it was empty. Suddenly he
remembered, very briefly: he had no soul. He never had! He was just a creation
of the Witch’s, given memories as a toy, a plaything, an eternal traveler who
would forever gather ronin and then shepherd them up the mountain to the
stronghold, while he looked for his soul. And then… poof, Honzo simply stopped
existing.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Meanwhile, the crow landed on a giant clay pot, next to two
other giant pots. The bird cawed several instructions to Goro and then flew off
to freedom, out a window, supposedly to a new life where it was no longer the
Witch. Goro smashed the pots open: within one was his former lord, within the second
was ‘Kikuya’/the real Kagome, and the third held their daughter. Each pot was
also full of rice vinegar, and as each spilled out, they coughed and began to
breathe. The witch had kept his promise: this was Kikuya and the others truly
returned to life. The samurai known as Kagome ran forward to cradle her sister.
Goro begged for forgiveness… he no longer needed her to return to give him
answers, he knew now, thanks to Kagome, what secret she had been keeping. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
But even in this moment of happiness, some sins cannot be
fully undone, some things cannot return to how they were. ‘Kagome’ would not
take her revenge, she would not kill Goro. But she did force Goro to leave, to
turn his back and never come near her sister or niece ever again. His reward
and other riches for killing the Witch would be split between his wife and his
lord, now returned to life, to make up for his crime. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Our epilogues are great, and I’m gonna do a bit of curating
here in the order I report them. This is not the order we narrated them.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Miyoko returned to his former lord and begged forgiveness, giving
his lord his money, and finishing Ai’s training. <span style="mso-spacerun: yes;"> </span>Kagome, once she had made sure that her sister
and niece were well taken care of and that Goro would never come back, killed
herself, following her fallen lord into the afterlife. Goro left and never
picked up a weapon again, living a lonely life as a monk, but knowing some
peace in that his sin was undone and his family lived once more.<span style="mso-spacerun: yes;"> </span>At night though, he would hear a crow and
wonder if perhaps he had replaced one crime with another. </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
In the throne room below, the water the shadow puppet was
made of began to freeze again, reforming, only now Shigeru’s soul was pulled
out his dying body, his blood intermingling, his flesh changing. He became the
new Mountain Witch, with all the power and riches he had ever wanted, all the
maidens and wives he could ever desire, but trapped in a body of ice and fire
with no escape and no way to love another.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Tsuba, i.e. the crow, i.e. the Witch, flew around the stronghold
several times after the ronin left, then flew into a tiny room at the top of a
tower. Kono sat there and the crow landed on her shoulder. “Again, again!” she
cried. “Let’s do it again!” </div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Then, at the base of the mountain, we see an average looking
man with plain features, wearing red armor. He glances over a group of ronin he is with. “Shall
we go?” he says to them.</div>
<div class="MsoNormal">
<br />.....<br /> </div>
<div class="MsoNormal">
With the game over, we revealed all of our dark fates. As
may be apparent, none except Honzo’s and Kagome’s had been absolutely clear up
until then: Honzo had the other mission, Kagome was revenge, Shigeru was Love,
Miyoko was Loyalty and Goro was Unholy Pact. From what I understand, Josh’s
guesses were mostly off. Somehow, and this wasn’t intentional, most people had
become convinced my dark fate was love.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
So, some thoughts.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
So, my biggest takeaway, the one that hit me after playing
it was how cohesive the story was at the end. Normally, improv and sharing
narration is great but… in my experience, someone always bends a little to
silliness somewhere or we all forget some plot thread and stuff is left
unanswered at the end. I don’t play a terrible lot of narrativist games, but
this is just something I’d accepted a side-product of allowing shared
narration. I didn’t really get that this time. I don’t know exactly why, maybe
it’s cause narration with respect to our dark fates was inviolate. Maybe other
players felt differently, but I look back on what I just wrote here and its
just freakin’ incredible how cohesive and amazing all of it was.</div>
<div class="MsoNormal">
So, I think I’d laid enough clues that my little spiel in
the crystal room wasn’t a crazy sudden plot punch from the right, but at the
same time Kagome hadn’t given me many options to reveal this narrative twist.
(Just how should an Unholy Pact-man expose his status to the players and not
the characters when one character was watching him like a hawk?) To be honest,
I had been laying the foundation for this twist since Act 1. I’d be interested
to know how much of a shocker this was from the other players. All I know is
that I knew that I considered this my narrative claim as I had the Unholy Pact
as my dark fate: this was the deal the Witch had made with Goro. I think that
having the Dark Fate as your own thing that no one could mess with was really
awesome because it let me. <br />
<br />
Now, here’s a thing… I could only explain Colin’s narration as Shigeru being
kind of set-up by the Witch (and, let’s be honest, the things the Witch was
promising Shigeru fit rather well with an interpretation that Shigeru was to
become the replacement Witch). I was really trying hard to make it so it didn’t
de-protagonize him and that just seemed the best option. Becoming the Mountain
Witch is pretty awesome, well, to me at least.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
The way it worked out, I was very glad that it had been
unable to get exposed until right before we entered the throne room and it
worked out wonderfully, but that sort of 20 ton thermonuclear plot bomb so
close to the climax could have gone so very badly in many other games. I also
wouldn’t do a twist where the Witch’s unholy pact was to get killed again, but
I think this one time it really worked out well.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
Most of us had not played Mountain Witch before; I don’t
know really if anyone had played it before other than Josh. I hadn’t, I had
just wanted to (and to read it… speaking of which, Kleinart, I can’t wait to
buy the next edition now.). Josh, Peter and I were fairly familiar with
narrativist story games. Colin and Alice were new to these types of games. I do
not know Justin’s background. We made pretty good use of the rules. There was A
LOT of fishing (AKA the Mountain Witch trick, i.e. the GM asking leading
questions). So much fishing! Fishing was probably more common than Josh
actually just making statements of narration. And it all worked out pretty
great. In terms of the mechanics on the sheet, we didn’t make a lot of use of
some of them. Betrayal came up, but only once or twice. We never(?) stole
narration from each other, I think. There was a lot of aiding throughout the
game.</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
So, anyway. That session was freaking incredible.</div>
dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-14643955125541078272012-07-26T14:18:00.000-07:002012-07-26T14:18:44.730-07:00Resolving Polytomies According to Temporal OrderHello everyone!<br />
<br />
A user of paleotree recently asked about resolving non-bifurcating nodes according to the order of stratigraphic appearance. If you're familiar with the library ape, you know there's a function called multi2di, which resolves polytomies randomly into bifurcating nodes. But what if we want to include information we have about WHEN taxa show up in the fossil record, assuming that taxa which show up later are more likely to be closely related?<br />
<br />
My library paleotree has the Sampling Rate Calibrated time-scaling methods (SRC timescaling), which will resolve polytomies according to a probabilistic approach about gaps in the fossil record. This function can allow for ancestor-descendant relationships, although that aspect can be shut off or minimized to the user's discretion. However, how SRC methods work and whether it works well isn't something that's known to anyone but me, (maybe) my committee and whoever has asked me about it. Also, it needs an estimate of sampling rate, which isn't always obtainable.<br />
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So, as another option for resolving polytomies, I've made a new function called timeLadderTree, which takes polytomies and turns them into little pectinate (ladder-like) sub-trees. Each lineage in the pectinate sub-tree is ordered according to the time of first-appearance datum (FAD) for each lineage, for both clades and single taxa. This method of resolving polytomies assumes that the order of
stratigraphic appearance perfectly depicts the order of branching. This
may not be a good assumption for poorly sampled fossil records, but hey, maybe it's still better than assuming a completely random solution for a polytomy. <br />
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I'd paste the code here, but it's really long. If you really want it, email me or you can get this function either from the public paleotree library source file on github, which I'm pushing to as we speak, or just wait until the next paleotree release (sometime in the next month). Also in the newest release, I'll be including this function as an additional option for timePaleoPhy and bin_timePaleoPhy to resolve the tree with. The new argument will be called 'timeres' and setting it to TRUE will cause trees output with those functions to have polytomies resolved according to time of first appearance. (Obviously, this is incompatible with also setting the argument 'randres' for random resolution via multi2di to TRUE and doing such will return a warning).<br />
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Okay, so how does it work? Well, let's say we have data and some stratigraphic ranges that look like so...<br />
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...And we get a nice pectinate tree.<br />
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What that poorly-scrawled M$Paint image means is we need a tree in R (in 'phylo' format) with polytomies and a set of temporal data, in timeData format as is standard for taxon ranges in continuous time in paleotree. timeData matrices look like the following, where row-names are the taxon names.<br />
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FAD LAD<br />
t1 170.391157 147.8839782<br />
t2 158.519694 152.6571314<br />
t3 149.415686 128.5232837<br />
t4 ... ...<br />
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Note that this function is for resolving tree when a continuous time-scale is known. For discrete time-scales, where appearance dates are known from intervals rather than from specific points in time, users should use the function bin_timePaleoPhy, which stochastically produces arrangements of dates in discrete intervals in the course of time-scaling. This would be the preferred way of doing things with datasets where taxa are known on discrete time-scales (most data in paleontology, probably...).
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You might wonder how this function handles ties. Well, taxa with the same identical first appearance date will be ordered randomly. Thus, the output could be slightly stochastic (it'll differ each time you run it), but this only occurs when taxa descended from the first node have the same first appearance datum in continuous time exist. This is probably uncommon with real data on continuous time-scales. Thus, resolving polytomies based on time-order will probably produce a single same tree each time you use it, unlike multi2di which is guaranteed to not produce the same tree each time. It's a pretty strong assumption though, to make, that order of appearance perfectly predicts branching order, though!<br />
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Because simulating clades in paleotree often produces partially unresolved trees (for reasons I explained last time) we can test this function pretty easily.<br />
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library(paleotree)<br />
set.seed(444)<br />
taxa<-simFossilTaxa(0.1,0.1,mintaxa=100)<br />
tree<-taxa2cladogram(taxa)<br />
ranges<-sampleRanges(taxa,r=0.5)<br />
tree1<-timeLadderTree(tree,ranges)<br />
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And we can see the difference in our two trees, with the second have some very pectinate-looking regions...<br />
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layout(1:2)<br />
plot(ladderize(tree),show.tip.label=FALSE)<br />
plot(ladderize(tree1),show.tip.label=FALSE)<br />
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(Apologies for all the white-space, I didn't crop this one before pasting it...)</div>
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As always, let me know if you have any new ideas for paleotree that you would find useful in your own work!<br />
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-Dave </div>dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com4tag:blogger.com,1999:blog-497393262310058111.post-91232157478898040712012-05-01T15:18:00.000-07:002012-05-01T15:18:29.319-07:00Simulating the Fossil Record: Incomplete Sampling and HatsAlright, here we go for part 3 of my series on simulating in the fossil record. If having explicit models of how morphological differentiation evolves in lineages is critical to understanding observed patterns of diversification in the fossil record, than the second major difference is incomplete sampling. Sampling issues are important to every field, but paleontologists and geologists has always been especially concerned with accounting for the incomplete and gap-filled nature of the fossil record.<br />
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To appropriately model the sort of data we generally have in reality, simulations of diversification as observed in the fossil record must consider some model of incomplete sampling. In continuous time, the simplest model treats sampling events as a Poisson process, just as the simplest models of speciation and extinction treat those events as Poisson processes. Under this model, the waiting times between events are exponentially distributed with some instantaneous per-lineage-time-unit rate parameter, generally called <i>r</i> (Foote, 1997). <br />
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(Tangent on terminology: <i>r</i> matches alright with the general paleobiological usage of <i>p</i> and <i>q</i> for speciation/origination and extinction rates, but not so much for the biological usage which uses <i>lambda</i> and <i>mu</i> for those same rates. Stadler (2010), however, defined sampling rate as a variable (independently possibly, given no references to the paleontological literature) and used <i>psi</i>. For me, though, it'll always be lower-case <i>r</i>.)<br />
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Here's an old figure (I need to heavily revise it) from my in-the-works paper on time-scaling methods which illustrates sampling in the fossil record:<br />
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As in previous similar figures, (a) is the original ranges and relationships of some morph-taxa, (b) is one possible outcome under a Poisson-process sampling model and (c) are the temporal ranges we would recover for those taxa we sampled, in continuous time (more or less; F should be a one-timer). (d) is a tangential bonus, just showing what sort of relationships we would resolve among the sampled taxa using morphology-based cladistics (see the last post for why there is a polytomy.)<br />
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As we can see in this simple model, incomplete sampling slices the early and later parts of a taxon's history off, if that taxon is sampled at all. In continuous-time, a majority of taxa will probably have zero-length observed durations ('one-timers'; Foote, 1997). In general, longer-lived taxa will be more likely to get sampled at all and to have positive-length durations, so the probability of sampling any given taxon is not the same.<br />
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There are a number of additional complications in how taxa are sampled in the fossil record which make this Poisson model of sampling in continuous time unrealistic. Generally, the ability to temporally resolve the 'date' that any particular taxon is sampled is not so great, and can have considerable error bars. This is generally done with relative dating, using 'biozones', where time is defined based on the appearance of zonal taxa (graptolites happen to be great for this purpose). In some cases, these can be well resolved temporally by correlation with absolute dating (Sadler et al., 2009), for example, Sadler et al. presented global graptolite zones a few hundred thousand years long and for which the start and end dates can be resolved within a few thousand years. However, biozones tend to not extend to the global level and even then the appearance of taxa tends to not by synchronous globally (Sadler, 2011; Loydell, 2012). Some taxa are better than others for defining short biozones. If your Ordovician rocks only have corals, for example, it might be very difficult to correlate those globally with precision, unless other information is available.<br />
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Geologists have constructed hierarchical time-scales with eras, periods and stages, all rigorously defined (or proposed to be) based on particular 'type sections', just as Linnean taxa must be based on type specimens. Although we now have global-level systems for much the Phanerozoic (the last ~500 Ma), with the starts and ends of intervals attached to the boundaries between bio-zones, many finds from previous decades are still only reported in terms of more regional systems of intervals, which can be very difficult to correlate to the global system. Thus, in general, our finds are really known from more discretely known intervals, and the order of events within a given interval may be very difficult to resolve. (e.g. A find of <i>Normalograptus normalis</i> within the <i>N. extraordinarius</i> biozone could come from anywhere within the <i>N. extraordinarius </i>biozone.)<br />
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In general, a way to deal with these additional complexities of how the nature of correlation and time-scaling of the rock record itself works is to impose a system of discrete intervals on a set of continuous-time sampling events. I think this makes the most sense, as we generally simulate branching processes as the result of instantaneous rates, we should speak of sampling in terms of an instantaneous rate. Some previous studies placed lineages, generated in continuous time, into a discrete time framework and then sample them within those intervals under some per-interval sampling probability. However, the relationship between the instantaneous sampling rate (r) and the per-interval sampling probability (R; Foote and Raup, 1996) is not exactly simple, although it can be loosely approximated (see the function sRate2sProb in paleotree). The per-interval probability assumes taxa span the entire interval, which may not be true as average interval length increases relative to average taxon duration. Also, the discrete time intervals are imposed secondarily by us, the geologists, and so it just makes more sense to me that we should simulate sampling on lineages in continuous time first.<br />
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Simulating sampling originally in continuous time actually allows for very quick simulations of sampling. simFossilTaxa makes use of the Poisson process nature of the processes it simulates to only consider the waiting times between events. Its sister function, sampleRanges, does the same thing by default, pulling the waiting times for sampling events from an exponential distribution. It is then very simple to apply binTimeData to the output from sampleRanges, which produces (by default) ranges placed into intervals of equal length, but (as of version 1.3) allows for user-input ranges as an alternative. A future modification may allows for intervals to be defined based on the origin/extinction of taxa, which would be more realistic as the real discrete intervals of the geologic record are often based on such biostratigraphic events (for example, periods were often defined with mass extinctions placed at their boundary, as this created a considerable amount of faunal turnover that allowed for biostratigraphic dissection).<br />
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For example, we can simulate sampling on example dataset from the R help file for sampleRanges, with the sampling rate set to 0.5 per Ltu (lineage*time-units). The flat line at top is the (unvarying) sampling rate over time and below it is a diversity curve produced from one simulation of sampling.<br />
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However, we may want to go further. Liow et al. (2010) represented an important step forward in the field of 'simulating diversification in the fossil record' by allowing for very complex sampling models, which went beyond the 1-parameter Poisson model. The newest version of paleotree includes a greatly updated version of sampleRanges which includes these models.<br />
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For example, we might think that as we get closer to the present, more of the rock
record is available and preservation is better so we are more likely to
sample taxa. A very simple model of this would be a linear increase in sampling rate over time. <br />
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We can simulate this by changing the parameter rTimeRatio, which is basically the increase over time from the start of a clade's origin to its end. The input sampling rate becomes the mean sampling for the dataset in this case (the sampling rate observed at a clade's midpoint. For example, here is a plot of sampling rate for a clade varying over time when rTimeRation=5, along with the observed diversity curve produced by simulating sampling under that model. This plot can be easily reproduced with the examples code in the sampleRanges help page in paleotree 1.3, by the way! You can go run it yourself!<br />
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Another model is the 'hat'. Various studies in the last half-decade have suggested that taxa tend to be most abundant and geographically wide-spread in the middle of their geologic duration: the rise and fall of species and genera . Also remarkably, this rise and fall looks like a remarkably normal-looking symmetrical curve. (Lee Hsiang Liow tends to refer to this as the "hat" in her work.) This would lead one to expect that perhaps the rise and fall of taxa might also influence sampling, such that the probability of sampling is highest in the middle of a taxon's temporal range.<br />
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sampleRanges can handle this by changing the alpha and beta parameters: when they are set several times higher to 1 and are equal, you get a bell-curve-looking symmetrical distribution. Here, with alpha=beta=4, we get the following, with taxon range represented by a single symmetrical 'hat' of sampling rate increase and decreasing over its range. Again, the sampling rate input (0.5) becomes the 'mean sampling rate' for the dataset.<br />
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(A stray thought: presently, the shape of the hats are dependent on the taxon range, such that simulations with some extant taxa will have those taxa decrease in sampling rate as their range appears to 'end' at the modern. Hmm. But what would be more realistic? Choosing how 'far' they are with their hat at random using a uniform distribution or placing the peak arbitrarily at the modern? Hmm.... something to think about.)<br />
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But what if sampling increases absolutely with time AND there is a general tendency for better sampling in the middle of a taxon's range? If we force sampling to always be zero at the end of each taxon's range (as its range gets smaller and smaller...) we would expect this to look like hats which are growing bigger in size as time goes on. If we set rTimeRation=5 and alpha=beta=4, we get...<br />
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Finally, the new sampleRanges also allows for among-lineage variation in sampling rate. For example, we could imagine traits which determine sampling rate (such as shell thickness) increasing and decreasing as a trait evolving under Brownian Motion. An example of this is also included in the sampleRanges help examples and produces a pattern sampling rates which looks like:<br />
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sampleRanges can go one step further and even consider a model where lineages vary in their intrinsic 'mean' sampling rate (on a per-lineage basis), have 'hat' shaped sampling rate curves AND sampling rate is overall increasing over their duration.<br />
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In this case, the 'hats' end up looking like they are sideways, as if
stylishly placed on one's head (with a chilling echo of Clockwork
Orange). This is because the interpretation of rTimeRatio differs when
per-lineage sampling rates are input, such that the input sampling rates
represent the per-lineage mean, thus requiring sampling rate to
increase over the duration of each taxon. The peaks of the hats can't increase like in the pull-of-the-recent+hat model above, so instead the whole hat tips forward. I realize that this somewhat different model behavior may seem undesirably, but there's a good reason for this: I want the models in sampleRanges to be collapsible: setting alpha=beta=rTimeRatio=1 will produce the simple Poisson model, because the input sampling rate is treated as the 'mean' sampling rate. If per-lineage sampling rates are used, this means the interpretation of what those sampling rates imply will be very different than when a single rate parameter is input for the whole dataset. </div>
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Including the hat model, pull-of-the-recent and among-lineage variation are good steps forward, but there are further expansions which can be made on these simulations of incomplete sampling. Holland and Patzkowsky (1999) presented a model where sampling was a function of preservation bias and changes in the sedimentary environment ('facies') producing the rock record with a depositional basin. Implementing a facies model of sampling in paleotree would be a lot of work, as it would require a model for simulating how facies are preserved within a given basin, but it would also be an important step toward realism. The produced fossil records would thus represent the observed sampling events in different sedimentary basins and several such records would have to be concatenated to produce a 'global' account of events. (So the number of basins sampled itself would be a parameter...). This would open up some incredible opportunities for understanding how the fossil record and the rock record should relate in a simple model (well, as simple as possible). What does sampling under the hat model, pull of the recent and facies-shifts look like? Are the fossil records produced by all these complexities even distinguishable from data produced by the Poisson model of sampling?</div>
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Well, that ends this series of posts. So, who is excited to simulate the fossil record? I know I am! I love being a paleobiologist because I get to sit around and come up with fun toys for simulating the fossil record. :D<br />
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Finally, in other news, my paleotree paper just got accepted to MEE! I'll post here as soon as its up for download.</div>dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-36159912855848335212012-04-29T15:00:00.002-07:002012-04-29T15:00:30.916-07:00Simulating the Fossil Record: Cryptic Species, Phylogenies and Resolvable CladesOkay, so let's pick up where we left off with a small tangent.<br />
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First things first: I released a new version of paleotree, version 1.3! It has a number of new elements, particularly one item I will talk to you about today: cryptic cladogenesis.<br />
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In the last missive, I argued that any simulator of "diversification in the fossil record" had to be enmeshed with some assumptions (i.e. model) of how morphologically distinguishable taxonomic units arise, as these are the basic units that we (paleontologists) can identify, relate and measure. These morphologically-deliminated, sometimes temporally-extensive units can represent something very different than equivalent taxa in evolutionary biology (Forey et al., 2004; Ezard et al., 2012).<br />
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To clarify some of my explanations last time, it may be helpful to think of two general classes of events. First, one could have 'anagenesis', where a lineage experiences a morphological change that is geologically-sudden, producing a new 'descendant' morphotaxon distinguishable from the previous 'ancestor' which no longer appears. As we generally define and distinguish taxa based on discrete or meristic characters, like the presence or absence of spines, one can think about these events as the change of one or more such characters. If they ever change again, then that would be another 'anagenetic' event and another new morphotaxon would origination, and so on.<br />
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(Note that this is different from how most people define anagenesis! Most of the literature using this term is referring to changes in continuous traits, particularly traits which don't (generally) get used to distinguish taxa. I'm only interested in shifts between recognizable morphotaxa, so I'm limiting my usage of anagenesis to describe that and being totally agnostic to how non-systematically-informative traits vary within lineages.)<br />
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Now let's think about how branching events, cladogenesis, comes into this. Let's limit ourselves to only bifurcating events, which produce two daughter lineages. We can contextualize the 'bifurcating and budding cladogenesis' of the last post by considering these as all part of a system of whether morphological change must happen in one, both or neither of the daughter lineages. Like so:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhKOGY8EnicgedGoSx_p3R_ikUg0bHOjbZY9EmvQveifoQpaWeCAsMbUcaWfGEgL4xXnf8U1Jufkr4Tjk5yVRsX-9OpXk-uhhC-Jk1UFK_Q-ZaXZPmkItvCqeIdQCoriz069qJ7l0zzWH9q/s1600/Morphological+Patterns+of+Speciation.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="113" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhKOGY8EnicgedGoSx_p3R_ikUg0bHOjbZY9EmvQveifoQpaWeCAsMbUcaWfGEgL4xXnf8U1Jufkr4Tjk5yVRsX-9OpXk-uhhC-Jk1UFK_Q-ZaXZPmkItvCqeIdQCoriz069qJ7l0zzWH9q/s320/Morphological+Patterns+of+Speciation.jpg" width="320" /></a></div>
In cryptic cladogenesis, both daughter would continue to be diagnosed as the same morphotaxon as the ancestor, in budding one of the daughter lineages becomes a new morphotaxon while the other experiences no morphological shifts, while in bifurcating cladogenesis, two new morphotaxa arise and the ancestor no longer exists. We can describe any model of how distinguishable morphotaxa arise in the fossil record as some mixture of these four event classes (anagenesis, cryptic clado., budding clado. and bifurcating clado), which even more simply can be described as 'shifts within branches and shifts at branching events'. If we can describe these processes in a model, then we can include most previously described models of morphological differentiation, at least the ones described for processes on geologic timescales.<br />
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The function simFossilTaxa can simulate all of these and any mixture of these processes, within the (generally assumed) constraint that diversification and the morphological shifts occur as Poisson processes. The big major change I had to do to allow for cryptic cladogenesis in paletree 1.3 was a new column which describes which morphotaxon each lineage would be assigned to (due to being functionally identical).<br />
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With this new feature, we can do fun things like simulating only under cryptic cladogenesis and anagenesis. This gives us patterns like these, using a particularly relevant example.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgVsAz1Ljd8hG5FbuGd6M3YDE8F-4ZEEMFjMXFd6E3t5lcCfP2vED3sTgReGwVIKeZy_JpcnU8SLR6uWEsJtYgzCAmqMGaL4xdEOrxbd1-FYKGXDYVyjOwV5QvJogCKqwuWBGIrHD6K6khu/s1600/cryptic+cladogenesis+in+the+fossil+record+with+eevees+04-13-12.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="300" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgVsAz1Ljd8hG5FbuGd6M3YDE8F-4ZEEMFjMXFd6E3t5lcCfP2vED3sTgReGwVIKeZy_JpcnU8SLR6uWEsJtYgzCAmqMGaL4xdEOrxbd1-FYKGXDYVyjOwV5QvJogCKqwuWBGIrHD6K6khu/s400/cryptic+cladogenesis+in+the+fossil+record+with+eevees+04-13-12.jpg" width="400" /></a></div>
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Okay, now, this post is supposed to be about how we can turn simulated data from simFossilTaxa into cladograms and phylogenies, using the functions <span style="font-family: "Courier New",Courier,monospace;">taxa2cladogram</span> and <span style="font-family: "Courier New",Courier,monospace;">taxa2phylo</span>. Just how does paleotree do that, really?<br />
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Well, check out this figure that I totally wish I had room for putting in my MEE submission on paleotree.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhSbJ2MDlcD09i5Ay_rtjqQo66wQNANpX3YGRYySwcIvbcLEYe0gW2Xd6fQRiOivcGW8jGVFbCD8aqZfNRhYu8QbPpBNr2EQ7TgIB3by7usFxOkCtcWzg97rErE3TZzddIkwuNXL-IJB-6z/s1600/taxa2cladogram+and+taxa2phylo+figure.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="211" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhSbJ2MDlcD09i5Ay_rtjqQo66wQNANpX3YGRYySwcIvbcLEYe0gW2Xd6fQRiOivcGW8jGVFbCD8aqZfNRhYu8QbPpBNr2EQ7TgIB3by7usFxOkCtcWzg97rErE3TZzddIkwuNXL-IJB-6z/s320/taxa2cladogram+and+taxa2phylo+figure.jpg" width="320" /></a></div>
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<br />
So, let me walk you through this. In (a), we have three morphotaxa,
related to each other by budding cladogenesis and (b), (c) and (d) are
various phylogenetic interpretations of that data.<br />
<br />
In
particular, (b) is the result of transforming such a dataset into an
uncscaled cladogram with taxa2cladogram. This is an unscaled set of
nesting relationships (i.e. clades), containing all the clades that
could be resolved with morphological data, assuming that shifts in
systematic characters can only occur when new morpho-taxa originate.
(This is a pretty good assumption: if we see shifts in systematic
characters within a lineage, we generally start calling the critters a
new name in the fossil record!) The distinctions between morphotaxa are
captured in all that information output by simFossilTaxa.<br />
<br />
Note that in this case, you get a polytomy. For cases where there is a
single ancestor, static in systematic characters and multiple
descendants via budding cladogenesis, you get a polytomy, which was
originally shown by Smith (1994) and Wagner and Erwin (1995). This is
true if you sample two descendants and an ancestor or just three
descendants. You can also get it if you have bifurcating cladogenesis
and sample ancestors. You will end up with more than two taxa that
contain no actual synapomorphies, although in practice this would
actually look like either some poorly-supported relationships (on a set
of most parsimonious trees) or a polytomy (on a consensus tree).<br />
<br />
I've
been looking at this issue in great detail lately, with respect to
varying how shifts occur under the various models of morphological
differentiation we've discussed and with varying rates of sampling in
the fossil record. I've decided to write this up as a chapter for my
dissertation, so I can't say much at the moment, but the short answer is
that it could be a very serious issue: some simulations have have very
few resolvable clades at realistic sampling parameters.<br />
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<br />
Now, (b) and (c) are a little more complicated,
representing different ways of translating (a) into time-scaled
phylogenies using taxa2phylo. The first thing to understand is that
there is no such thing as a single time-scaled tree that will describe
the relationships for lineages that span intervals of time. None!<br />
<br />
All
we can do is talk about the relationships about populations at
particular points in time. We might want to do this, for example, if we
want to simulate continuous traits evolving on the tree using any of the
typically used trait simulators in ape or geiger. We would need to pick
a particular 'time' of observation of our simulated morphotaxa in order
to even have a time-scaled description of relationships at those dates.<br />
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<br />
That's what taxa2phylo does. It constructs the time-scaled tree which
perfectly describes the set of relationships among for particular points
in time within the simulated ranges of taxa. The taxonomic identity of
branches is lost, leaving only the historical patterns of branching that
get us to our points of interest. 'Ancestral' taxa which have multiple
descendants (like taxon A) get chopped up into segments which become separate branches on the resulting output tree. <br />
<br />
<br />
The figure above shows how different the result can be for
different choices of 'observation times'. For (b), the time of interest
is the first appearance times of the taxa (I call this the observation
times or 'obs times' in the arguments for the function taxa2phylo). For
(c), these are the mid-points of the taxon ranges. By default, the
observation times used in taxa2phylo are the last appearances times
which are not directly figured above but would essentially produce a
tree with the branch lengths and branching events equivalent to the
simulated dataset, except that taxa which went pseudo-extinct (such as
in a bifurcation or anagenesis event) would be attached to the tree as a
tip with a zero-length terminal branch.<br />
<br />
<br />
taxa2phylo should not be used for any purpose but simulation: it doesn't represent anything but a perfect
representation of the phylogenetic and temporal relationships. In
particular, this is good for simulating datasets in simulators that
require a tree (like rTraitCont) but not for testing whether a
tree-based analysis works. Using the unscaled partially-unresolved
cladograms (from taxa2cladogram) and sampled fossil occurrences, in
particular on discrete interval time-scales, will be a more accurate
description of the type of data recoverable in the fossil record.<br />
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<br />
Okay, so that's how I can turn simulated fossil records into trees with paleotree! Next post will be about the aforementioned sampling of the fossil record and how paleotree simulates it!dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com4tag:blogger.com,1999:blog-497393262310058111.post-7305466769380789272012-04-03T06:15:00.007-07:002012-04-04T03:14:18.417-07:00Simulating the Fossil Record: Diversification and Morpho-TaxaHello all! Fair warning: this is going to be a really long post.<br /><p class="MsoNormal">Recently I submitted a short description of my library, <span style="font-weight: bold;">paleotree</span>, as an Applications paper to Methods in Ecology and Evolution and got back some great reviews. However, based on the confusion of my reviewers on some of the points, I clearly flubbed some of the explaining about how the most basic simulation functions work. I've also had similar questions from some users based on some of the concepts I discuss in the documentation. Unfortunately, Applications papers in MEE have rather restrictive word-count limits, so while I think of how to write a clear SHORT description for MEE, here’s an extra-long version which probably won’t see the light of day in a real publication. I'm going to pitch this as if readers aren't familiar with things like 'birth-death models' so bear with me if you are all too familiar with birth-death models.<br /></p><p class="MsoNormal">So, here's the central question: how does paleotree simulate the fossil record and what makes it different from the sort of simulators already out there? Today I'm just going to talk about how diversification simulation in paleotree works, specifically with respect to the different modes of morphological-branching offered by the function simFossilTaxa.<br /></p><p class="MsoNormal">First, let's lay out what models and simulations are.<br /></p><p class="MsoNormal">First, let's talk about <span style="font-weight: bold;">models</span>. Models (in my view) are just sets of simplified assumptions we make to describe the world around us; you could make an argument that hypotheses and theories are just models which are testing or have tested repeatedly. All of science, pretty much, is just coming up with models and asking how relatively well they describe our observations. I'd argue that scientists really can't know much more than the relative fit of models to our observations; there is always the potential that we haven't yet considered the One Awesome Model to Explain Everything. (I should jump in here and point out that I'm a weak-instrumentalist when it comes to philosophy of science... I think there is really some true system of how things work but I definitely don't think we can actually describe that system, just approximate it with our feeble little models.) The important thing about working with models is to always recognize the vast full sum of the assumptions and pinpoint which assumptions might make a big difference.<br /></p><p class="MsoNormal">Models stack on top of each other in ways that can make things pretty hard to do this sort of thought-dissection, but its so important to do it! Think about this: when we take the standard arithmetic mean of some values when we want a single value to describe the central tendency of our observations (say, we take the mean of all the kids in a second grade class), we are assuming the underlying data structure is normally-distributed. If that assumption is really wrong, then the mean won't... heh, mean much at all! The mean time you spend waiting in lines probably isn't the value that you experience most often when waiting in lines at the bank, as the distribution of those waiting times is probably exponentially or gamma distributed. However, the mean in that cause could give you valuable information about the rate that bank tellers are processing customers. There's a whole field of statisticians devoted to understand the math of such queues, which are particularly important for designing effective call centers (who knew those were based on science... geesh...).<br /></p><p class="MsoNormal">So, if that what models are, what's a <span style="font-weight: bold;">simulation</span>? What do I mean when I say we are interested in 'simulating the fossil record'? <span style="font-weight: bold;">Simulations</span> are models of <span style="font-weight: bold;">stochastic</span> processes (processes where the result can differ in outcome, as opposed to <span style="font-weight: bold;">deterministic</span> processes) which attempt to recreate the steps that create those outcomes. A likelihood equation that describes how likely a given parameter ism given some observed data, is not a simulation. A simulation is generally more like a board game, and in fact does not really need to be computational: Monopoly is a simulation of what its like to be a real estate mogul in Atlantic City, it just makes a bunch of assumptions we probably don't think are realistic about that system. Of course, for really complex processes with many steps, we'd like to automate them and run them many many times very quickly, which is why turning simulations into computer code is so valuable. That said, I find that coding simulations is incredibly analogous to game design, which happens to be my one hobby. Just gotta keep track of where little particles are moving through and simplifying the rules and the steps involved without negatively affecting the outcome.</p> <p class="MsoNormal">Okay, so that's Dave's view on Models and Simulations. Obviously, simulating the fossil record is actually a meaningless phrase, so let's go a step further and now what we really want is to simulate the fossil record, specifically, the patterns of taxonomic diversification in the fossil record. For example, maybe we would like to create a little model that can recreate patterns like Sepkoski’s classic curve of taxonomic diversity across the Phanerozoic (the last 500 million years). For example, here's the family-level curve from Raup and Sepkoski, 1982.<br /></p><p class="MsoNormal"><a href="http://www.st-edmunds.cam.ac.uk/CIS/rolston/images/figure1.gif"><img style="display:block; margin:0px auto 10px; text-align:center;cursor:pointer; cursor:hand;width: 500px; height: 381px;" src="http://www.st-edmunds.cam.ac.uk/CIS/rolston/images/figure1.gif" alt="" border="0" /></a></p> <p class="MsoNormal">If you haven't seen this before, this plot basically juxtaposes the number of marine taxonomic families (y-axis) that Jack found described in the paleontological literature as occurring in a given time interval against time before present (x-axis). That's a bit of a simplification, actually; technically Jack only tallied the first time the family showed up and the last time it was found, as the vast number of lineages do not have continuously sampled temporal ranges in the fossil record.</p><p class="MsoNormal">Note that this is a plot of taxonomic families: Sepkoski's curves were done at the supraspecific (above species) level. There's a lot of reasons for this and some people think this is a pretty big problem with Sepkoski's work, but let's just sidestep that for now. For our intents, we want to make simulated datasets like Sepkoski's, where the units function more or less like single species lineages do.<br /></p><p class="MsoNormal">Obviously, this plot and its ilk have had a huge affect on paleontology as a science and the understanding of the history of life for even non-scientists, as it reveals a pattern of ups and downs (evolutionary radiations and extinction events) with a general trend of increase to the present. There's also a general appearance of flat plateaus, where diversity may follow an equilibrium, maybe diversity-dependent diversification. So, we might infer a lot of things at face value from this curve, but we might want a deeper understand than that. Maybe we'd like to know how the processes that produced this curve work, and for that we would turn to describing those processes as models and simulating them. In other words, simulating the fossil record.<br /><br />Okay, so, how would we actually make a simulation that creates data like that...?</p><p class="MsoNormal">A good starting point is to talk about how biologists simulate such processes, such as if they want to simulate the phylogenetic relationships among Darwin's finches. Here's an example of such a tree, using the example data in geiger (Harmon et al., 2008; I have heard this is probably not the real phylogeny of Darwin's finches (there's apparently a lot of uncertainty involved), but I don't know anything at all about bird phylogenetics and it makes a nice example).<br /></p><p style="text-align: center;" class="MsoNormal"><img style="width: 495px; height: 369px;" src="data:image/png;base64,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" alt="" /></p><p class="MsoNormal">This is a time-scaled phylogeny, so time proceeds from the left (where the most recent common ancestor is) to the right, where the living descendants are listed by their species names or genus name. Cool!<br /></p><p class="MsoNormal">Notice that time-scaled molecular phylogenies of living animals produces these phylogenies where all the tips are at the same point in time. We call these <span style="font-weight: bold;">ultrametric</span> phylogenies, as opposed to the <span style="font-weight: bold;">non-ultrametric</span> trees you might expect of extinct fossil taxa, where the tips are at different distances from the roots. Note also that the vertical axis doesn't really mean anything here at all; the tree is only presented as it is so we can easily see the relationships and the tip labels.<br /></p><p class="MsoNormal">Evolutionary biologists have it a bit easier, really, than a paleontologists trying to simulate the full fossil record. In evolutionary biology and paleontology, there are a set of special models which describe population growth that can also be applied to describe a branching process like a phylogeny. In general, you see people use a <span style="font-weight: bold;">pure-birth process</span>, where there is some speciation (birth) rate and no extinction (extinction rate = 0), or a <span style="font-weight: bold;">birth-death process</span>, where there is speciation and a non-zero extinction rate. Events (speciation or extinction) are modeled as Poisson processes, with exponentially-distributed waiting times between them. Biologists can just simulate a pure-birth or birth-death process (Nee, 2007) until they hit some maximum time or maximum number of co-extant lineages and save the relationships among those taxa. For example, we might get a tree with a hundred taxa like this:</p><p style="text-align: center;" class="MsoNormal"><img style="width: 326px; height: 284px;" src="data:image/png;base64,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" alt="" /></p><p class="MsoNormal">The result is the sort of phylogeny we would expect to observe for the living members of a clade, if we (a) sampled all the taxa alive today and (b) perfectly reconstructed the branching relationships and the timing of the branching events. Do these assumptions matter? Probably, but in general when evolutionary biologists want to test whether a method works, they assume these, maybe relaxing (a).<br /></p><p class="MsoNormal">So, let's take a tree without dropping the extinct taxa. That will be like simulating the fossil record, right? Right? (Note this isn't the same tree as above.)<br /><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgNIfbtGjsj9IHepr_O35lDbm2naxh0Pw2bCEM7mybPvtlno8BWiedjYrxMAyie3i25acf_zTsGWbIkPsthFh9l3lisvqJzPenbFmQr6YG7R7Bq3AkplEtFS8tsi2JmgqOZdwe-Ehdi1Whr/s1600/cool_tree_from_simPaleoTrees_02-10-12.jpeg"><img style="display:block; margin:0px auto 10px; text-align:center;cursor:pointer; cursor:hand;width: 400px; height: 310px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgNIfbtGjsj9IHepr_O35lDbm2naxh0Pw2bCEM7mybPvtlno8BWiedjYrxMAyie3i25acf_zTsGWbIkPsthFh9l3lisvqJzPenbFmQr6YG7R7Bq3AkplEtFS8tsi2JmgqOZdwe-Ehdi1Whr/s400/cool_tree_from_simPaleoTrees_02-10-12.jpeg" alt="" id="BLOGGER_PHOTO_ID_5727178712970172450" border="0" /></a>Note that since this is a simulation, the timescale is arbitrary, just as the number of turns in a board game is only informative relative to how much happens in a turn (the third turn of Diplomacy can be three hours into the game!). We could take a tree like that, count up when speciation and extinction events happen and construct a curve like Jack Sepkoski's above (the resulting curve this particular tree would superficially similar to Jack's curve, actually).<br /></p><p class="MsoNormal">But how good of an approximation is this simulation? Well, its kind-of-but-not-really like the fossil record. Obviously, there are some assumptions here which might make using such output as a simulation an unrealistic way of simulating diversification in the fossil record, just as some of the simplicities of Monopoly would cause us not to think its a realistic simulator of the ups and downs of property values. Let's sort our brains and think about how it differs:<br /></p><ol><li>Almost every real dataset from the fossil record is incompletely sampled. The above is perfectly, completely sampled. (Even ole Chuck Darwin would say this is unrealistic! ;) For those of you who don't get the joke, Darwin famously blamed the lack of intermediates in the fossil record on it being incomplete, like a half-burned book with half the pages missing. Probably not a bad allusion.)<br /></li><li>The relationships among the taxa is perfectly known, with no uncertainty. This is probably unrealistic, as we rarely can resolve relationships with zero uncertainty in the fossil record. This doesn't really affect things at the moment, though if we're just interested in the diversity curve as long as we still independently know the times of speciation and extinction and we're measuring diversity like Jack did.<br /></li><li>Taxa in the fossil record are identified, distinguished and recognized based on morphology; if we accept the above as a 'fossil' record, then every time a branch starts and ends, we say they are morphologically distinct.</li><li>Events in the fossil record rarely can be resolved down to a continuous time-scale; rather a discrete time-scale is generally necessary where first and last appearances are only known to occur within bins (discrete intervals) that may last as long as 10 million years. This could have a big impact on our data.<br /></li><li>To return to a point previously made, Sepkoski's data was at the familial or generic taxonomic level, not the species level. This obviously could make a big difference on the sort of patterns we pick up; for example, if it was a plot of marine phyla, the curve would flatten after the Cambrian! There are various ways of dealing with this in simulations by having a hierarchical branching model(Patzkowsky, 1993; Foote, 2011) but for now let's ignore it. I think the more phylogenetically-minded paleontologists who will be using paleotree will be interested more in simulating species-level patterns. Creating a quick function for converting data to higher level taxa is a neat idea for a future improvement for paleotree, though!</li></ol><p class="MsoNormal">So, 1 is a big problem, because we'd like to know how incomplete sampling affects things. We know that sampling can make a huge difference in the fossil record based on decades of literature, and as Matt Friedman once told me (and I paraphrase), paleontology is the science of studying a degraded biological record. Dealing with that degradation is central to the art of being paleobiologists, I think. 2 isn't a problem if we just want a diversity curve, so let's ignore that for now. We don't really know whether 3 is a good or a bad assumption and we will just ignore 4 and 5 for the moment. So, maybe if we can just account for sampling we'll have some usable simulations?<br /><br />Unfortunately, morphological identification and sampling are tangled. Why is this? It is because the basic unit of paleontological study is the morphologically-defined taxon, and it is these units which are sampled or not sampled in time. Consider one of the most basic assumption of most paleobiological data, which is range-through. For those who do not know what range-through is, let's return to how Jack was analyzing his data. He was looking for the first and last appearances of taxonomic families and genera in the systematic literature and considering any time in between those dates as the taxon being present. This is range-through. It's practical a Lyellian law of biochronology: 'If the paleontology community can morphologically recognize a specimen as being of a taxon at time 1 and at time 2, then it is present in all the samples in between.' This is why many paleo datasets are in the form of first and last appearances (this isn't true for the PBDB, though). Obviously, this sort of an assumption could be misled by convergence. At the same time, paleontologists can't identify truly cryptic species in the fossil record: two lineages which are morphologically identical would be defined as a single morphotaxon. </p><p class="MsoNormal">My point here is that no matter we choose here, we need to make some sort of assumption, either implicitly or explicitly, about the morphological distinctiveness of taxa. It is always better to be explicit when it comes to models. There is a variety of ways in which taxa could relate to each other morphologically and change morphology with respect to branching events. Some of this is obviously tied up with puncuated equilibrium, which posited that morphological change only occurred as sudden 'punctuated' shifts at cladogenesis (another term for branching events which produce new species). This idea is still in dispute.<br /></p><p class="MsoNormal">A simple model that is often used in paleontology is <span style="font-weight: bold;">budding cladogenesis</span>, where at each branching event there is an ancestral taxon which persists and a single daughter lineage which 'buds' off and is immediately morphologically distinct from its ancestor. Morphological change never happens outside of these budding events. (Foote, 1996, describes these modes, as does Wagner and Erwin (1995) who simply refer to this as 'cladogenesis'. Rohlf et al., 1990, also use these models but refer to it as the 'punctuational' model.) The budding cladogenesis model does not really necessitate punctuated equilibrium: all that would be required is for new species to become morphologically distinct rapidly after speciation, relative to the geological timescale. The lack of change outside of the budding events is generally treated more as a 'nuisance parameter' (a term for an assumption considered to be of small effect in a model) instead of biological reality.<br /></p><p class="MsoNormal">For your reference, here's a little schematic of what this mode of morpho-taxon-branching is supposed to look like, from Foote (1996), along with some other modes I'll introduce in a bit.</p> <p style="text-align: center;" class="MsoNormal"><img style="width: 535px; height: 266px;" 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" alt="" /></p> <p class="MsoNormal">Again, like the phylogenies above, the horizontal axis isn't meaningful; it just designates shifts between morphotaxa we would consistently recognize.<br /></p><p class="MsoNormal">For the purpose of approximating diversity count data like what Sepkoski analyzed, we could probably just assume this single model of budding morphological change. Along with a simple model of sampling in the fossil record such as sampleRanges in paleotree (which I'll cover in a future post), we could probably have an acceptably realistic model of those sort of dynamics.<br /></p><p class="MsoNormal">There's a very similar model of morphological change, generally referred to as <span style="font-weight: bold;">bifurcating cladogenesis</span>. In that model, each branching event produces two new species which are morphologically distinct from their ancestor, which to an observer looking at data from the fossil record would appear to superficially go extinct at that branching event. We generally call such events 'pseudo-extinction'. Just like in the pure-budding model, there's not morphological shifts at all except at the branching events.</p>This model, just like the budding model, is probably an adequate model for morphotaxa and simulating continuous-time diversity curves. The datasets will differ from the budding model because the morphological shifts along both daughter lineages mean that taxa cannot persist through speciation events. Remember range-through? No more range-through. If A is our ancestor and B and C are its descendants, the nature of the fossil record would lead us to expect some gap in time between when we last sample A and its pseudo extinction, and a gap in time between the origination of B and C and their first observed appearances in the sampled fossil record. Maybe not a huge bias, but we begin to see how our assumptions about morphology are important to how we simulate even the diversification of lineages.<br /><p class="MsoNormal">What if we want to go further? What if we bin our temporal data like Sepkoski did? For either the budding model or the bifurcating model, we would expect some bias because long intervals will appear to have many taxa present which might individually have been short-lived, but we can intuitively expect this bias. If we bin datasets produced under bifurcation, we find a new bias has appeared which wasn't present with the budding cladogenesis datasets. The presence of a pseudo-extinct ancestor in the same interval as both of its descendants will inflate the taxonomic diversity counts and also make the data itself look more volatile, with more apparent speciations and extinction per interval.<br /><br />Enough to make a difference? Well, it really depends on the rates and what sort of a difference we're interested in. It's different, but whether that matters really comes down to what matters for the question you're asking. My point here is that considering how morphological change is occurring is an important aspect of simulating diversification dynamics in the fossil record. If we want to talk about how we recognize changes in diversity, we should be thinking about is how morphotaxa are changing and being differentiated. Its as important as the assumptions we make about whether there is extinction or not and thus inseparable from any real simulator of diversification in the fossil record.<br /></p><p class="MsoNormal">For this reason, simFossilTaxa, which is a function in paleotree designed to simulate the birth and death of species in the fossil record, explicitly uses a budding cladogenesis model, as described in the documentation for paleotree. The function also allows for varying levels of bifurcating cladogenesis and for <span style="font-weight: bold;">anagenesis</span>, when a lineage in the fossil record shifts morphologically, such that it is no longer identified as the same morphotaxon and thus creating apparent pseudo-extinction event and pseudo-speciation event. Thus, one can even simulate datasets where morphological change was occurring under budding, bifurcation and anagenesis.<br /><br />In terms of what this actually means, it means differences in how the units of the output get broken down. All else held constant, a clade simulated with a positive rate of anagenesis will just have more arbitrary devisions into morphotaxa wit shorter durations. Because it makes many other things easier, the output of the simFossilTaxa is meant to already be in the units of morphotaxa that would be recognize by some imaginary systematicist. Also, as the number of morphotaxa identified across the evolutionary history of a group is often the criteria of sampling used in paleobiological studies, I feel it is important to have the morphological shifts which distinguish morphotaxa interwoven into a diversification model rather than place these shifts secondarily. Otherwise, it would actually be much more complicated to simulate a dataset of '1000 taxa'.<br /></p><p class="MsoNormal">So, yes, simFossilTaxa isn't simulating morphology, but models of morphological change are still necessary to touch upon to describe diversification in the fossil record. That said, the models offered in simFossilTaxa allow for a lot of variability previous to other simulators, but not the full range. What if you don't think that speciation/branching events and morphological change need to be tied at all? (A world without any punc-eq like phenomena or character displacement, basically.) simFossilTaxa is not designed to simulate data under this model. It's on the to-do list, though. In that model, taxa would be 'seen' as their ancestor until some anagenetic shift happened to distinguish them from their descendants.<br /></p><p class="MsoNormal">We would imagine the mean duration of these nascent, cryptic lineages to be something like the reciprocal of the summed instantaneous rates of anagenesis and extinction combined, in other words, they would last as long until either extinction or anagenesis occurred (remember that the reciprocal of the rate in an exponential process is the mean waiting time, and the rate of an exponential process where either event A or event B can happen is the sum of the respective rates; see the really detailed and handy Wikipedia article on the Exponential Distribution). It would be a little more complicated than this, because there's also the chance of these nascent lineages branching off and producing new also-cryptic species. Humbug!<br /><br />Now, you can approximate the 'pure-anagenesis' model in simFossilTaxa under a particular scenario where you thought anagenesis was happening very frequently (so much so that stasis was very rare). You could just assume new taxa will never be cryptic then and simulate this scenario as bifurcating cladogenesis and increase the rate of anagenesis arbitrary high in simFossilTaxa. Of course, this may violate some assumptions of the basic way simFossilTaxa does things. As in the birth-death models I described above, I treat anagenesis as a Poisson process, a statistical model which assumes events are fairly rare.</p><p class="MsoNormal">You might wonder which of any of these models of morphological change is supported by the fossil record. Well, Folmer Bokma and his lab has done some work with molecular phylogenies and models of trait change which suggests that large portion of morphological change, but not the majority, occurs at branching events. Gene Hunt has found similar things that he has presented at conferences, using fossil data, and he's previously published that stasis seems to be pretty common in the fossil record. The only studies of whether change is generally more by budding or bifurcation are by Wagner and Erwin (1995), who found generally much more probable events of budding cladogenesis compared to bifurcation.<br /><br />That last finding is a bit shocking, though! Under a model where speciation and morphological shifts are unrelated, if the rate of the later was high enough not to commonly have those un-seen cryptic lineages, you would expect apparent bifurcation to happen sometime to (i.e. have anagenesis occur shortly after in both daughter lineages). So, either (a) the past history of life is rife with unidentified cryptic species or (b) there really is a relationship between branching events and morphological change and it tends to be asymmetrical.<br /></p><p class="MsoNormal">Anyway, to sum up all of the above, modeling and simulating the fossil record is complex and it is important to consider all the caveats and assumptions one is making, as these may have a large effect on the result. In simFossilTaxa, the diversification of lineages, which is generally modeled as non-morphological births and deaths, is tightly interwoven with models of how recognizable morphotaxa start and end in the fossil record. How one morphologically interprets taxa has big implications for how we reconstruct things like history of diversity. It matters even more for our next topic: how do we simulate extracting morphological relationships from the fossil record?<br /></p>Later, I'll be posting more about how the simulators in paleotree work. The next post will be about how I can take patterns of diversification from simFossilTaxa and pull different approximations of relationships from the taxonomic units within, and I'll discuss a little about a recent result I had. The third and last post will be on incomplete sampling in the fossil record and I'll discuss how I'm implementing some new features in sampleRanges to include a variety of models of sampling in the fossil record.<br /><br />Edit 04-04-12:<br />I mention at the start about this post being initiated by working on some revisions, but I just want to be clear: the reviews for my paleotree paper were pretty nice! Thanks, reviewers! I just don't have a venue without a word count, other than my blog, where I can fully get into these issues about how we simulate diversification in the fossil record. And yet, this information needs to be out there.<br /><p class="MsoNormal"> </p>dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com1tag:blogger.com,1999:blog-497393262310058111.post-24516078104194609032012-03-08T08:55:00.012-08:002012-03-08T14:34:30.444-08:00Simulating Budding CladogeneticTrait ChangeSo, an important realization I just had. I think some of you who read this will find it interesting, so here you go:<br /><br />Imagine you want to simulate cladogenetic trait evolution, where traits change only when a branching event occurs on a tree. (This is what we would expect if Puncuated Equilibrium is valid). For simplicity, let's say the changes act as under a Brownian motion diffusion model. Let's also assume we're looking at a species-level tree, so the branching events are also speciation events, making this also a 'speciational' model of trait evolution.<br /><br />Most of the time, this is modeled in the comparative methods literature by taking a phylogeny, setting the branches all equal to one and simulating trait evolution across the branches. However, this implicitly assumes that the trait change is symmetric. What if its asymmetric? And, you might be wondering, what the heck do I mean by that?<br /><br />Well, check out this figure.<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj5euVXTQYQn_wRtOX5caWFuV-XRxRfrJNwWBSuCpQX6_DxWYurzQ170l1NycqhLrIhwb93Cif0VJctJEoxIf0wb-x63nAHgPFuoaouIoXlLUFdqCQL4okqy81q8is56PVCPgWCAqAXe6Ye/s1600/speciational+trait+change.jpg"><img style="display:block; margin:0px auto 10px; text-align:center;cursor:pointer; cursor:hand;width: 400px; height: 141px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj5euVXTQYQn_wRtOX5caWFuV-XRxRfrJNwWBSuCpQX6_DxWYurzQ170l1NycqhLrIhwb93Cif0VJctJEoxIf0wb-x63nAHgPFuoaouIoXlLUFdqCQL4okqy81q8is56PVCPgWCAqAXe6Ye/s400/speciational+trait+change.jpg" alt="" id="BLOGGER_PHOTO_ID_5717657849369531922" border="0" /></a>So what is going on here? Let's start with the first diagram. The vertical axis is time and the horizontal axis is trait space. If you're a paleontologist, you probably see diagrams like this all the time as stratophenetic hypotheses of the relationships among taxa in a group. This describes a general pattern observed in some groups in the fossil record, where we have morphotaxa that are (more or less) morphologically static, with some separation between them. The model of speciation inferred is referred to as budding cladogenesis (e.g. Foote, 1996). When speciation/branching occurs, the 'ancestral' taxon doesn't change at all, while there is considerable change leading to the daughter taxon.<br /><br />Of course, we don't really know that the (generally unobserved) changes were actually that sudden and happened exactly at the same time that lineage branching (cladogenesis/speciation), occurred, because the fossil record is under-sampled. The important thing is that budding cladogenesis is a possible interpretation of how morphotaxa are related in the fossil record.<br /><br />In this example, which is meant to represent a simulated dataset of evolution under budding cladogenesis, we have a dataset of four taxa, where A is ancestral to taxa B, C and D. A remained static through the cladogenesis events that led to those four taxa. The second diagram is a time-scaled phylogeny describing the relationships among the populations present at the first occurrence of these four taxa. (If the taxa are static in a model of budding-cladogenesis, then simulating things to their first appearance datum should be okay.)<br /><br />The problem with the budding cladogenesis model and simulating trait change in this scenario is that its asymmetric: only one of the lineages sees any change at speciation events. I realized this earlier this week when I was using the function simFossilTaxa and taxa2phylo from my paleotree library. taxa2phylo converts taxon ranges into a phylogeny, as depicted by the second diagram above. I was simulating clades under budding cladogenesis (as above), converting the datasets to phylogenies, then simulating trait evolution using standard Brownian Motion (BM) simulations on phylogenies (like rtraitcont or fastBM in the ape and phytools packages, respectively). To simulate cladogenetic trait change, I set all the branches in the second diagram to 1 and simulated BM.<br /><br />But this was wrong! If I did this for the example above, the trait value of taxa B would be just a function of the trait value of A above and a single speciation event. That's what's we want (look at the first diagram). But for C and D, their trait values also become a function of trait evolution which occurred at previous nodes in the tree, when their trait values should actually be independent of those events: i.e. the values of C and D should simulated just like B, only being a function of their ancestor (A) and single trait value change.<br /><br />I have another function in paleotree which translates a 'taxa' matrix from simFossilTaxa into a cladogram of nested relationships (assuming that systematic characters don't vary over morphotaxon ranges). However, this is just as useless in this situation, as there are no nested relationships in the four taxa above, so they just form one big polytomy. If you try simulating BM on that using standard methods, the trait values of all the taxa will equally depend on some ancestor (which is not inferred to be A). Yeesh!<br /><br />Note that if the changes were symmetric in the model (as in a bifurcating cladogenesis model) than all would be fine and dandy: species traits always shift after speciation; there is none of this static ancestor stuff. But for the asymmetric case, you need to have information about which branch experienced the change. One could always just randomly pick one, if all a user had was a topology, but that isn't the case here. I'd like the changes to match to the budding pattern in the simulation I've already done! (Who knows how sampling of morphotaxa in time could affect these things...)<br /><br />Instea,d we'll need a specialized function that simulates trait evolution using the taxa matrix itself, since that's the only place where we have the data on the ancestor-descendant relationships and the stasis of particular lineages. Using Liam Revell's excellent <a href="http://phytools.blogspot.com/2011/01/brownian-motion-simulation.html">description</a> (also <a href="http://phytools.blogspot.com/2011/01/simulating-bm-part-ii.html">here</a> and <a href="http://phytools.blogspot.com/2011/01/addendum-to-fast-brownian-simulation.html">here</a>) of how he constructed fastBM for his phytools package, I was able to cobble together a little function for simulating cladogenetic trait evolution on the output from simFossilTaxa. Here it is (this will be included in the next release of paleotree):<br /><br />cladogeneticTraitCont<-function(taxa,rate=1,meanChange=0,rootTrait=0){<br />#simulate speciational trait evolution for datasets from simFossilTaxa<br />#idiot proofing<br />taxa<-taxa[order(taxa[,1]),]<br />if(any(taxa[-1,2]>=taxa[-1,1])){stop("Ancestors have higher IDs than Descendants?")}<br />anctaxa<-sapply(taxa[-1,2],function(x) which(x==taxa[,1]))<br />traits<-rootTrait<br />for(i in 2:nrow(taxa)){<br /> traits[i]<-traits[anctaxa[i-1]]+rnorm(1,mean=meanChange,sd=sqrt(rate))<br /> }<br />names(traits)<-paste("t",taxa[,1],sep="")<br />return(traits)<br />}<br /><br />We can use plotTraitgram to see what the evolutionary pattern would like, under a model where we infer ancestral traits via time-dependent BM (so not cladogenetic/speciational). Obviously, that ain't true for our simulated data, but we wouldn't know that a priori for most datasets so let's do it anyway.<br /><br />library(paleotree)<br />set.seed(444)<br />taxa<-simFossilTaxa(0.1,0.1,mintaxa=100,plot=T)<br />trait<-cladogeneticTraitCont(taxa)<br />tree<-taxa2phylo(taxa)<br />plotTraitgram(trait,tree,conf.int=F)<br /><br />And we get:<br /><br /><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjYMfC9QSiWSN2XurhgnSmyn739MH4IipAWnPdbYNahflHiRL6wU3CdM-bbI4YB4OB7OrwR8VPFJRfKXkidE6B8eVfBtqxwU5i7thsp8tOQu65bCD_uZW2cHozsN3s-qKKavO_0BCKR2c1q/s1600/speciational+trait+change+SIM.jpg"><img style="display:block; margin:0px auto 10px; text-align:center;cursor:pointer; cursor:hand;width: 400px; height: 375px;" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjYMfC9QSiWSN2XurhgnSmyn739MH4IipAWnPdbYNahflHiRL6wU3CdM-bbI4YB4OB7OrwR8VPFJRfKXkidE6B8eVfBtqxwU5i7thsp8tOQu65bCD_uZW2cHozsN3s-qKKavO_0BCKR2c1q/s400/speciational+trait+change+SIM.jpg" alt="" id="BLOGGER_PHOTO_ID_5717656565751118786" border="0" /></a>dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-28923972550723616702012-03-06T11:11:00.002-08:002012-03-06T12:35:26.371-08:00Presenting: paleotree v1.2Hello all,<br />After more than month of having my library up on CRAN and finally feeling satisfied that I've worked out enough of the bugs, I'd like to introduce you to my R library, paleotree! It does many things, but if you like phylogenetics, fossils and particularly the overlap of those two, you may find something in interest! With paleotree, you can time-scale cladograms of fossil taxa, estimate sampling rates from the ranges of fossil taxa, plot diversity curves and simulate diversification and sampling in the fossil record. So, go download version 1.2 and check it out! The entire library will be described in a paper I submitted a week and a half ago to Methods in Ecology and Evolution and apparently is now in review. I'll add a citation file to paleotree when that gets more definite. Until then, cite Bapst, D. (in review)!<br /><br />http://cran.r-project.org/web/packages/paleotree/index.html<br /><br />Now, I would like to note that some of the included functions, such as the sampling-rate conditioned time-scaling methods, are not described yet in the primary literature. The methods are included in the current release and you can use them if you wish, but Buyer Beware and all that. The algorithm behind these methods will be described in more detail in a future. For the moment, I would actually suggest you use the more typical time-scaling methods, applicable with timePaleoPhy and bin_timePaleoPhy.<br /><br />Let me know if you run into any bugs! You can find my email address in the help files for R if you don't know it already. Also, I have tried to be very meticulous with regards to the help files and tried to make them as clear as possible, but I am sure my general muddled-ness will show through. So, if you don't understand anything, let me know so I can clarify the help files in the next release!<br /><br />So far, in terms of potential errors and bugs in version 1.2, I have discovered that simFossilTaxa can enter an infinite loop under some parameter choices if you try a pure-birth model. I don't know why you'd want to do a pure-birth simulation of the fossil record, but this will be fixed in the next release. Then you can simulate your extinction-less fossil record to your heart's content!<br /><br />Finally, let me know if you get any good ideas of what I could add that would make analyses in phylogenetic paleobiology easier for you!<br /><br />Cheers,<br />-Dave B.dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0tag:blogger.com,1999:blog-497393262310058111.post-87384148280310025492011-12-18T14:33:00.000-08:002011-12-18T15:04:12.952-08:00Model-based Phylogenetics and MorphologyHello listeners,<br /><br />(Sorry for the long hiatus; I'm not much for soapboxes.)<br /><br />A while ago I found myself in discussions relating to how one should do phylogenetics. Now, one could have arguments for many lifetimes on all the details of how to make a tree, many of which I have no opinion on. One that I do feel strongly about is that more people should at least consider using model-based phylogenetics in morphological systematics; this is in contrast to the more classic use of parsimony-based phylogenetics. For those of you unfamiliar with this distinction, all you need to know that there are different ways biologists use to reconstruct phylogenies based on data about the characters that are shared or differ between lineages. Maximum parsimony just tries to find the tree(s) that have the fewest character changes along the branches (the most parsimonious tree; it infers the least complicated scenario of evolution). Model-based methods take some model of how a trait should change over time and calculates the likelihood/posterior probability of the characters being at their observed states, given a phylogenetic hypothesis. This is commonly in either a maximum likelihood or Bayesian analysis and more commonly with molecular character (ex. DNA) than morphological characters (distinct features of bones/shells/etc). Of course, if you're a paleontologist, you probably deal with morphological characters.<br /><br />Now, I should point out that even the most simple model of evolution for morphological characteristics has only been around in the published literature for about ten years. This model is Lewis's (2001) Mkv model, which is a description of the number of changes we expect to see in all characters where we see any change at all.<br /><br />Now, I could (and have) given long arguments about why we should use model-based approaches in morphological phylogenetics, even if they are relatively simple models. But I don't really care and anyway its mostly my opinion versus someone else's opinion. So who cares?<br /><br />I'd rather talk about something with data. A particular point that came up a month or two ago in a discussion, where a friend claimed that although models of morphological phylogenetics existed, no one used them. I thought he was mostly right at the time. Later, I decided to go see just how many papers I could find where a morphological dataset had been analyzed with model-based phylogenetics. The answer? I found about 50 papers. That's way more than I expected. I also happily saw that a number of them applied them to paleontological datasets. Of course, this would be insignificant compared to the number of parsimony-based morphological studies over the past 10 years, which surely in the hundreds, if not a few thousand.<br /><br />I sent this list of papers that use the Mkv model or a variant for morphological phylogenetics to a few people but recently decided that people might find it useful in general, so here it is below!<br /><br />Cheers!<br />-Dave<br /><br />Morphological Phylogenetic Analyses that Used the <span class="il">Mkv</span> model or a variant:<br />Ayache, N. C., and T. J. Near. 2009. The Utility of Morphological Data in Resolving Phylogenetic Relationships of Darters as Exemplified with Etheostoma (Teleostei: Percidae). Bulletin of the Peabody Museum of Natural History 50(2):327-346.<br />Bergmann, P. J., and A. P. Russell. 2007. Systematics and biogeography of the widespread Neotropical gekkonid genus Thecadactylus (Squamata), with the description of a new cryptic species. Zoological Journal of the Linnean Society 149(3):339-370.<br />Bergsten, J., and K. B. Miller. 2007. Phylogeny of Diving Beetles Reveals a Coevolutionary Arms Race between the Sexes. PLoS ONE 2(6):e522.<br />Beutel, R. G., F. Friedrich, T. Hörnschemeyer, H. Pohl, F. Hünefeld, F. Beckmann, R. Meier, B. Misof, M. F. Whiting, and L. Vilhelmsen. 2011. Morphological and molecular evidence converge upon a robust phylogeny of the megadiverse Holometabola. Cladistics 27(4):341-355.<br />Brandley, M. C., and K. d. Queiroz. 2004. Phylogeny, Ecomorphological Evolution, and Historical Biogeography of the Anolis cristatellus Series. Herpetological Monographs 18:90-126.<br />Bybee, S. M., T. H. Ogden, M. A. Branham, and M. F. Whiting. 2008. Molecules, morphology and fossils: a comprehensive approach to odonate phylogeny and the evolution of the odonate wing. Cladistics 24(4):477-514.<br />Cabrero-Sanudo, F. J. 2007. The phylogeny of Iberian Aphodiini species (Coleoptera, Scarabaeoidea, Scarabaeidae, Aphodiinae) based on morphology. Systematic Entomology 32(1):156-175.<br />Cabrero-Sañudo, F.-J., and R. Zardoya. 2004. Phylogenetic relationships of Iberian Aphodiini (Coleoptera: Scarabaeidae) based on morphological and molecular data. Molecular Phylogenetics and Evolution 31(3):1084-1100.<br />Ceotto, P., and T. Bourgoin. 2008. Insights into the phylogenetic relationships within Cixiidae (Hemiptera: Fulgoromorpha): cladistic analysis of a morphological dataset. Systematic Entomology 33(3):484-500.<br />Clarke, J. A., and K. M. Middleton. 2008. Mosaicism, Modules, and the Evolution of Birds: Results from a Bayesian Approach to the Study of Morphological Evolution Using Discrete Character Data. Systematic Biology 57(2):185-201.<br />Druckenmiller, P. S., and A. P. Russell. 2008. A phylogeny of Plesiosauria (Sauropterygia) and its bearing on the systematic status of Leptocleidus Andrews, 1922. Zootaxa 1863:1–120.<br />Egge, J. J. D., and A. M. Simons. 2009. Molecules, morphology, missing data and the phylogenetic position of a recently extinct madtom catfish (Actinopterygii: Ictaluridae). Zoological Journal of the Linnean Society 155(1):60-75.<br />Eklöf, J., F. Pleijel, and P. Sundberg. 2007. Phylogeny of benthic Phyllodocidae (Polychaeta) based on morphological and molecular data. Molecular Phylogenetics and Evolution 45(1):261-271.<br />Feng, C.-M., S. R. Manchester, and Q.-Y. Xiang. 2009. Phylogeny and biogeography of Alangiaceae (Cornales) inferred from DNA sequences, morphology, and fossils. Molecular Phylogenetics and Evolution 51(2):201-214.<br />Friedrich, F., B. D. Farrell, and R. G. Beutel. 2009. The thoracic morphology of Archostemata and the relationships of the extant suborders of Coleoptera (Hexapoda). Cladistics 25(1):1-37.<br />Fröbisch, N. B., and R. R. Schoch. 2009. Testing the Impact of Miniaturization on Phylogeny: Paleozoic Dissorophoid Amphibians. Systematic Biology 58(3):312-327.<br />Gernandt, D. S., S. Magallon, G. Geada Lopez, O. Zeron Flores, A. Willyard, and A. Liston. 2008. Use of Simultaneous Analyses to Guide Fossil-Based Calibrations of Pinaceae Phylogeny. International Journal of Plant Sciences 169(8):1086-1099.<br />Giusti, F., V. Fiorentino, A. Benocci, and G. Manganelli. 2011. A Survey of Vitrinid Land Snails (Gastropoda: Pulmonata: Limacoidea). Malacologia 53(2):279-363.<br />Glenner, H., A. J. Hansen, M. V. Sørensen, F. Ronquist, J. P. Huelsenbeck, and E. Willerslev. 2004. Bayesian Inference of the Metazoan Phylogeny: A Combined Molecular and Morphological Approach. Current Biology 14(18):1644-1649.<br />Heikkilä, M., L. Kaila, M. Mutanen, C. Peña, and N. Wahlberg. 2011. Cretaceous origin and repeated tertiary diversification of the redefined butterflies. Proceedings of the Royal Society B: Biological Sciences.<br />Hultgren, K. M., and J. E. Duffy. 2011. Multi-Locus Phylogeny of Sponge-Dwelling Snapping Shrimp (Caridea: Alpheidae: Synalpheus) Supports Morphology-Based Species Concepts. Journal of Crustacean Biology 31(2):352-360.<br />Jenner, R., C. Dhubhghaill, M. Ferla, and M. Wills. 2009. Eumalacostracan phylogeny and total evidence: limitations of the usual suspects. BMC Evolutionary Biology 9(1):21.<br />Keck, B. P., and T. J. Near. 2008. Assessing phylogenetic resolution among mitochondrial, nuclear, and morphological datasets in Nothonotus darters (Teleostei: Percidae). Molecular Phylogenetics and Evolution 46(2):708-720.<br />Lee, M. S. Y., and A. B. Camens. 2009. Strong morphological support for the molecular evolutionary tree of placental mammals. Journal of Evolutionary Biology 22(11):2243-2257.<br />Lee, M. S. Y., A. F. Hugall, R. Lawson, and J. D. Scanlon. 2007. Phylogeny of snakes (Serpentes): combining morphological and molecular data in likelihood, Bayesian and parsimony analyses. Systematics and Biodiversity 5(04):371-389.<br />Lee, M. S. Y., and T. H. Worthy. In Press. Likelihood reinstates Archaeopteryx as a primitive bird. Biology Letters.<br />Muller, J., and R. R. Reisz. 2006. The Phylogeny of Early Eureptiles: Comparing Parsimony and Bayesian Approaches in the Investigation of a Basal Fossil Clade. Systematic Biology 55(3):503-511.<br />Near, T. J. 2009. Conflict and resolution between phylogenies inferred from molecular and phenotypic data sets for hagfish, lampreys, and gnathostomes. Journal of Experimental Zoology Part B: Molecular and Developmental Evolution 312B(7):749-761.<br />Nylander, J. A. A., F. Ronquist, J. P. Huelsenbeck, and J. Nieves-Aldrey. 2004. Bayesian Phylogenetic Analysis of Combined Data. Systematic Biology 53(1):47-67.<br />Ogden, T. H., J. L. Gattolliat, M. Sartori, A. H. Staniczek, T. SoldÁN, and M. F. Whiting. 2009. Towards a new paradigm in mayfly phylogeny (Ephemeroptera): combined analysis of morphological and molecular data. Systematic Entomology 34(4):616-634.<br />Organ, C., C. L. Nunn, Z. Machanda, and R. W. Wrangham. 2011. Phylogenetic rate shifts in feeding time during the evolution of Homo. Proceedings of the National Academy of Sciences 108(35):14555-14559.<br />Pérez-Losada, M., M. Harp, J. T. Høeg, Y. Achituv, D. Jones, H. Watanabe, and K. A. Crandall. 2008. The tempo and mode of barnacle evolution. Molecular Phylogenetics and Evolution 46(1):328-346.<br />Pollitt, J. R., R. A. Fortey, and M. A. Wills. 2005. Systematics of the trilobite families Lichidae Hawle & Corda, 1847 and Lichakephalidae Tripp, 1957: The application of bayesian inference to morphological data. Journal of Systematic Palaeontology 3(3):225-241.<br />Pyron, R. A. 2011. Divergence Time Estimation Using Fossils as Terminal Taxa and the Origins of Lissamphibia. Systematic Biology 60(4):466-481.<br />Ravara, A., H. Wiklund, M. R. Cunha, and F. Pleijel. 2010. Phylogenetic relationships within Nephtyidae (Polychaeta, Annelida). Zoologica Scripta 39(4):394-405.<br />Robovský, J., V. ŘIčánková, and J. Zrzavý. 2008. Phylogeny of Arvicolinae (Mammalia, Cricetidae): utility of morphological and molecular data sets in a recently radiating clade. Zoologica Scripta 37(6):571-590.<br />Schneider, H., A. R. Smith, and K. M. Pryer. 2009. Is Morphology Really at Odds with Molecules in Estimating Fern Phylogeny? Systematic Botany 34(3):455-475.<br />Schneider, S. A., and J. S. LaPolla. 2011. Systematics of the mealybug tribe Xenococcini (Hemiptera: Coccoidea: Pseudococcidae), with a discussion of trophobiotic associations with Acropyga Roger ants. Systematic Entomology 36(1):57-82.<br />Shimizu, A., M. Wasbauer, and Y. Takami. 2010. Phylogeny and the evolution of nesting behaviour in the tribe Ageniellini (Insecta: Hymenoptera: Pompilidae). Zoological Journal of the Linnean Society 160(1):88-117.<br />Sikes, D. S., R. B. Madge, and S. T. Trumbo. 2006. Revision of Nicrophorus in part: new species and inferred phylogeny of the nepalensis-group based on evidence from morphology and mitochondrial DNA (Coleoptera : Silphidae : <div id=":ij"><wbr>Nicrophorinae). Invertebrate Systematics 20(3):305-365.<br />Sikes, D. S., S. M. Vamosi, S. T. Trumbo, M. Ricketts, and C. Venables. 2008. Molecular systematics and biogeography of Nicrophorus in part—The investigator species group (Coleoptera: Silphidae) using mixture model MCMC. Molecular Phylogenetics and Evolution 48(2):646-666.<br />Snively, E., A. P. Russell, and G. L. Powell. 2004. Evolutionary morphology of the coelurosaurian arctometatarsus: descriptive, morphometric and phylogenetic approaches. Zoological Journal of the Linnean Society 142(4):525-553.<br />Straka, J., and P. Bogusch. 2007. Phylogeny of the bees of the family Apidae based on larval characters with focus on the origin of cleptoparasitism (Hymenoptera: Apiformes). Systematic Entomology 32(4):700-711.<br />Tippery, N. P., C. T. Philbrick, C. P. Bove, and D. H. Les. 2011. Systematics and Phylogeny of Neotropical Riverweeds (Podostemaceae: Podostemoideae). Systematic Botany 36(1):105-118.<br />Torres-Carvajal, O. 2007. Phylogeny and biogeography of a large radiation of Andean lizards (Iguania, Stenocercus). Zoologica Scripta 36(4):311-326.<br />Voss, R. S., and S. A. Jansa. 2009. Phylogenetic Relationships and Classification of Didelphid Marsupials, an Extant Radiation of New World Metatherian Mammals. Bulletin of the American Museum of Natural History:1-177.<br />Wahlberg, N., M. F. Braby, A. V. Z. Brower, R. de Jong, M.-M. Lee, S. Nylin, N. E. Pierce, F. A. H. Sperling, R. Vila, A. D. Warren, and E. Zakharov. 2005. Synergistic effects of combining morphological and molecular data in resolving the phylogeny of butterflies and skippers. Proceedings of the Royal Society B: Biological Sciences 272(1572):1577-1586.<br />Wiens, J. J., C. A. Kuczynski, T. Townsend, T. W. Reeder, D. G. Mulcahy, and J. W. Sites. 2010. Combining Phylogenomics and Fossils in Higher-Level Squamate Reptile Phylogeny: Molecular Data Change the Placement of Fossil Taxa. Systematic Biology 59(6):674-688.<br />Winterton, S. L., N. B. Hardy, and B. M. Wiegmann. 2010. On wings of lace: phylogeny and Bayesian divergence time estimates of Neuropterida (Insecta) based on morphological and molecular data. Systematic Entomology 35(3):349-378.<br />Zaldivar-Riverón, A., M. Mori, and D. L. J. Quicke. 2006. Systematics of the cyclostome subfamilies of braconid parasitic wasps (Hymenoptera: Ichneumonoidea): A simultaneous molecular and morphological Bayesian approach. Molecular Phylogenetics and Evolution 38(1):130-145.<br /><br />Introducing or examining aspects of <span class="il">Mkv</span>:<br /> Lewis, P. O. 2001. A Likelihood Approach to Estimating Phylogeny from Discrete Morphological Character Data. Systematic Biology 50(6):913-925.<br />Allman, E. S., M. T. Holder, and J. A. Rhodes. 2010. Estimating trees from filtered data: Identifiability of models for morphological phylogenetics. Journal of Theoretical Biology 263(1):108-119.<br />Springer, M. S., A. Burk-Herrick, R. Meredith, E. Eizirik, E. Teeling, S. J. O'Brien, and W. J. Murphy. 2007. The Adequacy of Morphology for Reconstructing the Early History of Placental Mammals. Systematic Biology 56(4):673-684.</div>dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com9tag:blogger.com,1999:blog-497393262310058111.post-63418384412956943952011-06-23T16:11:00.001-07:002011-06-23T16:23:22.574-07:00On the Use of Terms 'Phylogenetic Comparative Methods' and 'Neontologist'Some boring discussion of terminology.<br /><br />I've heard the phrase "Phylogenetic Comparative Methods" or "Comparative Methods" get used for everything from just analyses for assessing evolutionary correlations (independent contrasts or phylogenetic general least squares), to all phylogeny-based analyses of trait evolution, to even include birth-death modelling of lineage diversification and phylogenetic community analyses. I'm actually a bit surprised that things in the past, like Pete Wagner's work on evolutionary rates, hasn't been subsumed under this phrase yet. I think using this phrase so vaguely, to refer to anything in which a phylogeny is somehow involved, contributes to some confusion among newcomers and increasingly removes any ability of ours to refer to a cohesive distinction among analytical methods.<br /><br /> It would be better and clearer if we used PCM to only refer to studies of evolutionary correlation. That's what comparative methods meant, prior to Felsenstein (1985), from what I understand. If I can be so forward, why not the term 'macroevolutionary analysis'? Of course, that term would also include many of the analyses done in evolutionary paleobiology, but well, as I was stating earlier, these methods largely differ in data used, not the question addressed. Maybe we can differentiate with 'phylogenetic macroevolutionary analysis' if we really want.<br /><br />To finally shut my trap on things that Evolution 2011 has made me think about, I'd like to mention that the meeting caused me to have mixed feelings about the use of the word neontologist. I never really used it until I came to Chicago, it has become a useful addition to my lexicon to make the distinction between those using fossil data and those not using fossil data. However, biologists don't know they are 'neontologists' and in trying to explain why we would give such a label when I accidentally say it, I feel like I am just perpetuating bad feelings from several decades ago. But if neontologist is a poor word, than maybe paleontologist is too. Hrm.dwbapsthttp://www.blogger.com/profile/17606476387441191531noreply@blogger.com0