Data from a flexible framework to assess patterns and drivers of beta diversity across spatial scales

Abstract

<p><span>The patterns and underlying ecological (e.g., environmental filtering) and </span><span>historical</span><span> (e.g., priority effects) drivers of beta diversity are scale-dependent but generally difficult to distinguish</span> <span>and rarely explored with a sufficiently broad range of spatial scales. We propose a general scale-explicit framework to assess and contrast the patterns and drivers of beta diversity across hierarchical spatial scales ranging from within fine-scale ecoregion-scale to among broad-scale ecoregion-scale. By applying this framework to aquatic macroinvertebrate datasets, we show that beta diversity generally increases with spatial extent.</span> <span>With an increasing spatial extent, beta diversity shifts from being more influenced by environmental filtering to being more influenced by recent historical factors (i.e., past beta diversity). Such recent historical effects may result from past environmental variation rather than priority effects.</span><span> We also found that the small-scale and large-scale environmental drivers act differently on beta diversity across spatial extents. Our research reveals a complex spatial-scale dependence in beta diversity patterns and their drivers and provides a more holistic understanding of beta diversity dynamics. Our framework represents a flexible way to unravel the internal structure of beta diversity across scales by partitioning of entire beta diversity variation into scale-specific differences and may have broad application in community ecology, landscape planning and biodiversity conservation.</span></p><p>Data are compiled in different files containing the following information: 1) Presence-absence macroinvertebrate taxon dataset in the previous survey, 2) Presence-absence macroinvertebrate taxon dataset in the following survey, 3) dataset of explanatory variables.</p><p>Funding provided by: National Natural Science Foundation of China<br>Crossref Funder Registry ID: https://ror.org/01h0zpd94<br>Award Number: 32101270</p&gt