Range‐weighted metrics of species and phylogenetic turnover can better resolve biogeographic transition zones

Abstract

Summary Understanding changes of biodiversity across the landscape underlies biogeography and ecology and is important in land management and conservation. Measures of species and phylogenetic turnover used to estimate the rate of change of assemblages between sets of locations are more often influenced by wide‐ranging taxa. Transition zones between regions that are associated with range‐restricted taxa can be obscured by wide‐ranging taxa that span them. We present a set of new range‐weighted metrics of taxon and phylogenetic turnover, as modifications of conventional metrics, where the range‐restricted components of the assemblages are assigned greater weight in the calculations. We show how these metrics are derived from weighted endemism and phylogenetic endemism and demonstrate their properties using a continent‐wide data set of Australian Acacia. The range‐weighted metrics result in better delineated transition zones between regions, in that the rate of turnover is steeper than with conventional turnover measures. These metrics provide important complementary information for the interpretation of spatial turnover patterns derived from conventional turnover metrics. Additionally, the phylogenetic variant incorporates information about phylogenetic relatedness while also not saturating at high values of turnover, thus remaining useful for comparisons over greater distances than conventional turnover metrics.

Contrasting patterns of diversification in a bird family (Aves: Gruiformes: Rallidae) are revealed by analysis of geospatial distribution of species and phylogenetic diversity

“…In most cases neo-endemism involves rapid phenotypic (e.g. (A) WE is the fraction of the range of each taxon occurring across the sampling area (Crisp et al 2001, Laffan et al 2016. Several species of Gallirallus have independently evolved flightless insular endemics.…”

“…SR is the absolute number of species and WE is the sum of the proportions of species ranges represented in each equalarea square grid cells (Crisp et al 2001, Laffan and Crisp 2003, Lee and Mishler 2014, Laffan et al 2016. Species richness (SR) and weighted endemism (WE) were used to evaluate the distribution of taxonomic diversity (richness).…”

Congruent biogeographical disjunctions at a continent-wide scale: Quantifying and clarifying the role of biogeographic barriers in the Australian tropics

“…The methods used in this study are time consuming and analog, but more or less reliable, providing hypotheses for cross-validation using more advanced statistical tools. Recent developments in computational spatial analyses for identifying zones of biotic turnover [3,81] will be important in further quantifying and refining the extent of barriers recognized here and elsewhere.…”

A comparison of network and clustering methods to detect biogeographical regions

“…From these grid cells several measures of biodiversity can be calculated, such as richness (the total number of taxa within the cell), weighted endemism (WE) (the summed inverse range of all taxa, generally species, in a cell) and corrected weighted endemism (CWE) (the averaged inverse range of all taxa in a cell (Laffan et al 2016)). From these grid cells several measures of biodiversity can be calculated, such as richness (the total number of taxa within the cell), weighted endemism (WE) (the summed inverse range of all taxa, generally species, in a cell) and corrected weighted endemism (CWE) (the averaged inverse range of all taxa in a cell (Laffan et al 2016)).…”

“…In the WPGMA (weighted pair-group method using arithmetic averages) clustering algorithm used in the Biodiverse software package (Laffan et al 2010), a distance or turnover metric compares the composition of taxa between cells to create a similarity tree or dendrogram, with cells that are grouped within the same branch being more similar. Three commonly used similarity metrics include the Jaccard, Sorenson and Beta Simpson, and each have their advantages (Laffan et al 2016). Three commonly used similarity metrics include the Jaccard, Sorenson and Beta Simpson, and each have their advantages (Laffan et al 2016).…”