Robust, data-driven bioregionalizations emerge from diversity concordance

Abstract

AbstractAimDespite the increasing interest in developing new bioregionalizations and assessing the most widely accepted biogeographic frameworks, no study to date has sought to systematically define a system of small bioregions nested within larger ones that better reflect the distribution and patterns of biodiversity. Here, we examine how an algorithmic, data-driven model of diversity patterns can lead to an ecologically interpretable hierarchy of bioregions.LocationAustralia.Time periodPresent.Major taxa studiedTerrestrial vertebrates and vascular plants.MethodsWe compiled information on the biophysical characteristics and species occupancy of Australia’s geographic conservation units (bioregions). Then, using cluster analysis to identify groupings of bioregions representing optimal discrete-species areas, we evaluated what a hierarchical bioregionalization system would look like when based empirically on the within-and between-site diversity patterns across taxa. Within an information-analytical framework, we then assessed the degree to which the World Wildlife Fund’s (WWF) biomes and ecoregions and our suite of discrete-species areas are spatially associated and compared those results among bioregionalization scenarios.ResultsInformation on biodiversity patterns captured was moderate for WWF’s biomes (50– 58% for birds’ beta, and plants’ alpha and beta diversity, of optimal discrete areas, respectively) and ecoregions (additional 4–25%). Our plants and vertebrate optimal areas retained more information on alpha and beta diversity across taxa, with the two algorithmically derived biogeographic scenarios sharing 86.5% of their within- and between-site diversity information. Notably, discrete-species areas for beta diversity were parsimonious with respect to those for alpha diversity.Main conclusionsNested systems of bioregions must systematically account for the variation of species diversity across taxa if biodiversity research and conservation action are to be most effective across multiple spatial or temporal planning scales. By demonstrating an algorithmic rather than subjective method for defining bioregionalizations using species-diversity concordances, which reliably reflects the distributional patterns of multiple taxa, this work offers a valuable new tool for systematic conservation planning.