phyloregion: R package for biogeographic regionalization and macroecology

Barnabas Daru*, Klaus Peter Schliep, Piyal Karunarathne

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract


1. Biogeographical regionalization is the classification of regions in terms of their biotas and is key to our understanding of the ecological and historical drivers affecting species distribution in macroecological or large‐scale conservation studies. However, despite the mass production of species distributions and phylogenetic data, statistical and computational infrastructure to successfully incorporate, manipulate and analyze such massive amounts of data had not been fully developed.
2. Here, we present phyloregion, a statistical package for the analysis of biogeographic regionalization and macroecology in the R computing environment, tailored for mega phylogenies and macroecological datasets of ever‐increasing size and complexity.
3. Compared to available packages, phyloregion is several times faster and allocates less memory than other packages for analysis of alpha diversity (including phylogenetic diversity, phylogenetic endemism, and evolutionary distinctiveness and global endangerment) and beta diversity (including cluster analysis, determining optimal number of clusters, and evolutionary distinctiveness of regions).
4. We demonstrate the scalability of the package to large datasets with comprehensive phylogenies and global distribution maps of squamate reptiles (amphisbaenians, lizards, and snakes), and show that different phyloregions differ strongly in evolutionary distinctiveness across scales. Visualization tools allow graphical exploration of the generated patterns of biogeographical regionalization and macroecology in geographic space.
5. Ultimately, phyloregion will facilitate rapid biogeographic analyses that will accommodate the ongoing mass‐production of species occurrence records and phylogenetic datasets at any scale and for any taxonomic group into completely reproducible R workflows.
Original languageEnglish
Number of pages26
JournalMethods in Ecology and Evolution
DOIs
Publication statusAccepted/In press - 2020

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