Delineating Diversity‐Based Freshwater Bioregions by Integrating Fish and Macroinvertebrates With Species Distribution Models and Spatial Clustering

Delineating ecologically meaningful spatial units is fundamental for understanding biodiversity patterns and guiding effective conservation. Here, we delineated diversity-based freshwater bioregions across the Yangtze River Basin (YRB) by integrating multi-taxon biodiversity data with species distribution models and spatial clustering. We compiled distribution records of 391 fish species and 984 macroinvertebrate taxa across 13,716 hydrological units. MaxEnt models were used to predict species distributions based on key environmental drivers identified through principal component analysis and Mantel tests. Beta diversity was quantified using Jaccard dissimilarity, and non-metric multidimensional scaling (NMDS) ordination was applied to extract the first three ordination axes for each taxon, which were then used in spatial clustering to define bioregions. Our approach identified four distinct bioregions (PERMANOVA, <i>R</i> ² = 0.211, <i>p</i> < 0.001): Headwater and Western Sichuan Plateau (HWS), Hengduan Mountains-Sichuan Basin (HDS), Dabie-Qinling-Wuling Mountains (DQW), and Mid-lower Floodplain (MLF). Fish and macroinvertebrates exhibited complementary distribution patterns, with fish preferring mainstem and primary tributaries, while macroinvertebrates favored streams and lakes. Beta diversity effectively revealed core transition zones including the southern edge of the Hengduan Mountains, the Sichuan Basin, the southeastern YRB margin, the estuary, and major tributary confluences. Compared to the scattered and discontinuous patterns generated by UPGMA clustering, spatial clustering preserved spatial connectivity and produced ecologically coherent bioregions. Some bioregions with lower species richness harbored relatively high beta diversity and proportions of threatened species, highlighting their conservation priority. This framework demonstrates how integrating multi-taxon data with predictive modeling and spatial clustering can reduce data biases and provide an ecologically meaningful foundation for freshwater conservation planning, and it offers a practical tool for implementing bioregion-specific management strategies.