phyr: Anrpackage for phylogenetic species-distribution modelling in ecological communities

Daijiang Li, Russell Dinnage, Lucas A. Nell, Matthew R. Helmus, Anthony R. Ives

Research output: Contribution to journalOther Journal Articlepeer-review

83 Citations (Scopus)


Model-based approaches are increasingly popular in ecological studies. A good example of this trend is the use of joint species distribution models to ask questions about ecological communities. However, most current applications of model-based methods do not include phylogenies despite the well-known importance of phylogenetic relationships in shaping species distributions and community composition. In part, this is due to a lack of accessible tools allowing ecologists to fit phylogenetic species distribution models easily. To fill this gap, therpackagephyr(pronounced fire) implements a suite of metrics, comparative methods and mixed models that use phylogenies to understand and predict community composition and other ecological and evolutionary phenomena. Thephyrworkhorse functions are implemented in C++ making all calculations and model estimations fast. phyrcan fit a variety of models such as phylogenetic joint-species distribution models, spatiotemporal-phylogenetic autocorrelation models, and phylogenetic trait-based bipartite network models.phyralso estimates phylogenetically independent trait correlations with measurement error to test for adaptive syndromes and performs fast calculations of common alpha and beta phylogenetic diversity metrics. Allphyrmethods are united under Brownian motion or Ornstein-Uhlenbeck models of evolution, and phylogenetic terms are modelled as phylogenetic covariance matrices. The functions and model formula syntax we propose inphyrprovide an easy-to-use collection of tools that we hope will ignite the use of phylogenies to address a variety of ecological questions.
Original languageEnglish
Pages (from-to)1455-1463
Number of pages9
JournalMethods in Ecology and Evolution
Issue number11
Publication statusPublished - 1 Nov 2020


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