Beyond similarity: A network approach for identifying and delimiting biogeographical regions

TL;DR

This paper introduces a network theory-based community detection method to identify biogeographical regions, overcoming biases of traditional similarity-based algorithms and effectively handling transition zones.

Contribution

The study presents a novel network approach for delimiting biogeographical regions, improving accuracy and objectivity over existing similarity and clustering methods.

Findings

Successfully applied to global amphibian and US plant datasets

Identifies more recognized biogeographical regions than traditional methods

Effectively handles transition zones in biogeographical delineation

Abstract

Biogeographical regions (geographically distinct assemblages of species and communities) constitute a cornerstone for ecology, biogeography, evolution and conservation biology. Species turnover measures are often used to quantify biodiversity patterns, but algorithms based on similarity and clustering are highly sensitive to common biases and intricacies of species distribution data. Here we apply a community detection approach from network theory that incorporates complex, higher order presence-absence patterns. We demonstrate the performance of the method by applying it to all amphibian species in the world (c. 6,100 species), all vascular plant species of the USA (c. 17,600), and a hypothetical dataset containing a zone of biotic transition. In comparison with current methods, our approach tackles the challenges posed by transition zones and succeeds in identifying a larger number of commonly recognised biogeographical regions. This method constitutes an important advance towards objective, data derived identification and delimitation of the world's biogeographical regions.