Predictive Phylogeography and Random Forest Workshop SSB 2018


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The goal of this workshop is to present examples for how to use random forest when making phylogeographic and conservation status predictions. For more information on these topics, refer to the background reading below.

Background Reading

Breiman L. Random Forests. Machine Learning 2001, 45, 5-32. link

Csardi G, Nepusz T. The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006, igraph link

Espindola A, Ruffley M, Smith M, Carstens BC, Tank D, Sullivan J. 2016. Identifying cryptic diversity with predictive phylogeography. Proceedings of the Royal Society of London B 2016, 283, 1841. link

Liaw A, Wiener M. Classification and regression by randomForest. R News 2002, 2, 18-22. link

Thomas Lin Pedersen TL. ggraph: An Implementation of Grammar of Graphics for Graphs and Networks. R package version 1.0.1. 2018, ggraph

Pelletier TA, Carstens BC. Geographic range size and latitude predict population genetic structure in a global survey. Biology Letters 2018, 14, 20170566. link

Pelletier TA, Carstens BC, Tank D, Sullivan J, Esp?ndola A. Predicting plant conservation priorities on a global scale. In review.

Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2009.

Wickham H, Francois R, Henry L, M?ller K. dplyr: A Grammar of Data Manipulation. R package version 0.7.2. 2017, dplyr

Funding Sources

We would like to thank NSF (DEB1457519) for funding this research, workshop, and travel for some of the participants.