2020
DOI: 10.1111/2041-210x.13478
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phyloregion: R package for biogeographical regionalization and macroecology

Abstract: In biogeography, there is growing interest in the analysis of datasets of ever-increasing size and complexity to explain biodiversity patterns and underlying processes. A common approach is biogeographical regionalization, the grouping of organisms based on shared features and how they respond to past or current physical and biological de

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Cited by 98 publications
(107 citation statements)
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“…The optimal number of units was defined using the ‘elbow’ method, setting the maximum number of clusters to k = 30 (Daru et al , 2020). The ‘elbow’ method identifies the optimal number of units based on the range of explained variances (Daru et al , 2020). We additionally evaluated the sensitivity of the resulting regionalisation scheme by setting the number of units to match the number of previosly recognized floristic kingdoms: k = 3, 4, 5, 6.…”
Section: Methodsmentioning
confidence: 99%
“…The optimal number of units was defined using the ‘elbow’ method, setting the maximum number of clusters to k = 30 (Daru et al , 2020). The ‘elbow’ method identifies the optimal number of units based on the range of explained variances (Daru et al , 2020). We additionally evaluated the sensitivity of the resulting regionalisation scheme by setting the number of units to match the number of previosly recognized floristic kingdoms: k = 3, 4, 5, 6.…”
Section: Methodsmentioning
confidence: 99%
“…For instance, the US National Science Foundation-funded software BiotaPhy facilitates integration, data collection and analysis by connecting to existing data repositories such as the Open Tree of Life, iDigBio, and Lifemapper (BiotaPhy, 2020), whereas the open-source package sampbias allows quantification of geographic sampling biases in species distribution data (Zizka et al, 2020). The R software package phyloregion -designed for biogeographic regionalization and macroecology -can overcome some computational challenges (Daru et al, 2020b). It contains tools for biogeographical regionalization, macroecology, conservation, and visualizing biodiversity patterns, and has potential application in diverse fields including evolution, microbial diversity, systematics, ecology, phylogenetics, and many others (Daru et al, 2020b).…”
Section: Overcoming the Impedimentsmentioning
confidence: 99%
“…The R software package phyloregion -designed for biogeographic regionalization and macroecology -can overcome some computational challenges (Daru et al, 2020b). It contains tools for biogeographical regionalization, macroecology, conservation, and visualizing biodiversity patterns, and has potential application in diverse fields including evolution, microbial diversity, systematics, ecology, phylogenetics, and many others (Daru et al, 2020b). We expect that the proliferation of more open-source analytical tools to greatly facilitate comprehensive understanding of seagrass sensitivity to ecological change driven by anthropogenic causes.…”
Section: Overcoming the Impedimentsmentioning
confidence: 99%
“…The relative contribution of a species to PD can also be used to represent evolutionary distinctiveness (ED), which reflects the degree of phylogenetic isolation/uniqueness for a particular species (Pavoine et al 2005;Redding & Mooers 2006;Isaac et al 2007). When ED is combined with extinction risk derived from the International Union for Conservation of Nature (IUCN) Red List, the Evolutionary Distinctiveness and Global Endangerment (EDGE) index can be used to prioritize species for conservation not only based on their likelihood of extinction, but also on their irreplaceability (Redding & Mooers 2006;Isaac et al 2007;Daru et al 2017Daru et al , 2020. Each of these metrics is informative for a specific facet of biodiversity and when integrated within a unified framework across multiple taxonomic groups, can provide a more robust and comprehensive characterization of evolutionary history and biodiversity patterns that can be used to prioritize conservation initiatives (Posadas et al 2001;González-Orozco et al 2015).…”
Section: Introductionmentioning
confidence: 99%