Global models and predictions of plant diversity based on advanced machine learning techniques
Jazyk angličtina Země Anglie, Velká Británie Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
36375492
DOI
10.1111/nph.18533
Knihovny.cz E-zdroje
- Klíčová slova
- biodiversity, diversity-environment models, phylogenetic diversity, species richness, vascular plants,
- MeSH
- biodiverzita * MeSH
- ekosystém * MeSH
- fylogeneze MeSH
- lidé MeSH
- lineární modely MeSH
- podnebí MeSH
- rostliny MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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. 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. 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. 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.
Biodiversity Macroecology and Biogeography University of Göttingen 37077 Göttingen Germany
Bioinvasions Global Change Macroecology Group University of Vienna 1030 Vienna Austria
Biota of North America Program Chapel Hill NC 27516 USA
Campus Institut Data Science 37077 Göttingen Germany
Centre of Biodiversity and Sustainable Land Use University of Göttingen 37077 Göttingen Germany
Department of Ecology Faculty of Science Charles University 12844 Prague Czech Republic
Ecology Department of Biology University of Konstanz 78464 Konstanz Germany
German Centre for Integrative Biodiversity Research Halle Jena Leipzig 04103 Leipzig Germany
Naturalis Biodiversity Center 2333 CR Leiden the Netherlands
PAS Botanical Garden 02 973 Warszawa Poland
School of Biological Sciences University of Canterbury 8140 Christchurch New Zealand
School of Natural Sciences Macquarie University 2109 Sydney NSW Australia
Swiss Federal Institute for Forest Snow and Landscape Research WSL 8903 Birmensdorf Switzerland
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