Machine Learning Predicts Large Scale Declines in Native Plant Phylogenetic Diversity.

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ID: 105032
2020
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Abstract
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 variables. We demonstrate differences in predicted patterns and potential drivers of native versus non-native biodiversity. In particular, native phylogenetic diversity is likely to decrease over the next half century despite increases in species richness. We also identify that patterns of taxonomic diversity can be incongruent with those of phylogenetic diversity. The combination of macro-environmental factors that determine diversity likely vary at continental scales; thus, as climate change alters the combinations of these factors across the landscape, the collective effect on regional diversity will also vary. Our study represents one of the most comprehensive examination of plant diversity patterns to date and demonstrates that our ability to predict future diversity may benefit tremendously from the application of machine learning.
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park2020machinethe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Park, Daniel S;Willis, Charles G;Xi, Zhenxiang;Kartesz, John T;Davis, Charles C;Worthington, Steven;
Journal The New phytologist
Year 2020
DOI
10.1111/nph.16621
URL
Keywords
artificial intelligence Machine learning Biodiversity climate change phylogenetic diversity vascular plants

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