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Global models and predictions of plant diversity based on advanced machine learning techniques

Global models and predictions of plant diversity based on advanced machine learning techniques

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CAI, Lirong, Holger KREFT, Amanda TAYLOR, Pierre DENELLE, Julian SCHRADER, Franz ESSL, Mark VAN KLEUNEN, Jan PERGL, Anke STEIN, Patrick WEIGELT, 2022. Global models and predictions of plant diversity based on advanced machine learning techniques. In: The New Phytologist. Wiley. ISSN 0028-646X. eISSN 1469-8137. Available under: doi: 10.1111/nph.18533

@article{Cai2022-11-14Globa-59170, title={Global models and predictions of plant diversity based on advanced machine learning techniques}, year={2022}, doi={10.1111/nph.18533}, issn={0028-646X}, journal={The New Phytologist}, author={Cai, Lirong and Kreft, Holger and Taylor, Amanda and Denelle, Pierre and Schrader, Julian and Essl, Franz and van Kleunen, Mark and Pergl, Jan and Stein, Anke and Weigelt, Patrick} }

Pergl, Jan Weigelt, Patrick 2022-11-14 Schrader, Julian 2022-11-16T11:40:46Z van Kleunen, Mark Cai, Lirong Weigelt, Patrick 2022-11-16T11:40:46Z Essl, Franz Taylor, Amanda Despite the paramount role of plant diversity for ecosystem functioning, biogeochemical cycles, and human welfare, knowledge of its global distribution is still incomplete, hampering basic research and biodiversity conservation.<br /><br />Here, we used machine learning (random forests, extreme gradient boosting, and neural networks) and conventional statistical methods (generalized linear models and generalized additive models) to test environment-related hypotheses of broad-scale vascular plant diversity gradients and to model and predict species richness and phylogenetic richness worldwide. To this end, we used 830 regional plant inventories including c. 300 000 species and predictors of past and present environmental conditions.<br /><br />Machine learning showed a superior performance, explaining up to 80.9% of species richness and 83.3% of phylogenetic richness, illustrating the great potential of such techniques for disentangling complex and interacting associations between the environment and plant diversity. Current climate and environmental heterogeneity emerged as the primary drivers, while past environmental conditions left only small but detectable imprints on plant diversity.<br /><br />Finally, we combined predictions from multiple modeling techniques (ensemble predictions) to reveal global patterns and centers of plant diversity at multiple resolutions down to 7774 km2 . Our predictive maps provide accurate estimates of global plant diversity available at grain sizes relevant for conservation and macroecology. van Kleunen, Mark Stein, Anke Cai, Lirong Attribution 4.0 International eng Kreft, Holger Essl, Franz Stein, Anke Kreft, Holger Taylor, Amanda Denelle, Pierre Schrader, Julian Denelle, Pierre Pergl, Jan Global models and predictions of plant diversity based on advanced machine learning techniques

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