The clustering of spatially associated species unravels patterns in Bornean tree species distributions

作者：Sean E. H. Pang, Ferry Slik, Damaris Zurell, Edward L. Webb · 发表于：bioRxiv (Cold Spring Harbor Laboratory) · 年份：2022 · DOI：10.1101/2022.09.13.507725 · 被引用次数：4 · 研究领域：Species Distribution and Climate Change、Ecology and Vegetation Dynamics Studies、Plant and animal studies

Abstract Complex distribution data can be summarised by grouping species with similar or overlapping distributions to unravel patterns in species distributions and separate trends (e.g., of habitat loss) among spatially unique groups. However, such classifications are often heuristic, lacking the transparency, objectivity, and data-driven rigour of quantitative methods, which limits their interpretability and utility. Here, we develop and illustrate the clustering of spatially associated species, a methodological framework aimed at statistically classifying species using explicit measures of interspecific spatial association. We investigate several association indices and clustering algorithms and show how these methodological choices engender substantial variations in clustering outcome and performance. To facilitate robust decision making, we provide guidance on choosing methods appropriate to the study objective(s). As a case study, we apply the framework to model tree distributions in Borneo to evaluate the impact of land-cover change on separate species groupings. We identified 11 distinct clusters that unravelled ecologically meaningful patterns in Bornean tree distributions. These clusters then enabled us to quantify trends of habitat loss tied to each of those specific clusters, allowing us to discern particularly vulnerable species clusters and their distributions. This study demonstrates the advantages of adopting quantitatively derived clusters of spatially associa...