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Though substantial effort has gone into predicting how global climate change will impact biodiversity patterns, the scarcity of taxon-specific information has hampered the efficacy of these endeavors. Further, most studies analyzing spatiotemporal patterns of biodiversity focus narrowly on species richness. We apply machine learning approaches to a comprehensive vascular plant database for the United States and generate predictive models of regional plant taxonomic and phylogenetic diversity in response to a wide range of environmental
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