phyloregion: R package for biogeographical regionalization and macroecology

Barnabas Daru*, Klaus Peter Schliep, Piyal Karunarathne

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Biogeographical regionalization is the classification of regions in terms of their biota 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 analyse such massive amounts of data had not been fully developed. Here, we present phyloregion, a statistical package for the analysis of biogeographical regionalization and macroecology in the R computing environment, tailored for mega phylogenies and macroecological datasets of ever-increasing size and complexity. 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). 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 geographical space. Ultimately, phyloregion will facilitate rapid biogeographical 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
    Pages (from-to)1483-1491
    Number of pages9
    JournalMethods in Ecology and Evolution
    Volume11
    Issue number11
    DOIs
    Publication statusPublished - 1 Nov 2020

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