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

2022-11-14 | journal article. A publication with affiliation to the University of Göttingen.

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​Global models and predictions of plant diversity based on advanced machine learning techniques​
Cai, L.; Kreft, H.; Taylor, A.; Denelle, P.; Schrader, J.; Essl, F. & van Kleunen, M. et al.​ (2022) 
The New Phytologist237(4) art. nph.18533​.​ DOI: https://doi.org/10.1111/nph.18533 

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Authors
Cai, Lirong; Kreft, Holger; Taylor, Amanda; Denelle, Pierre; Schrader, Julian; Essl, Franz; van Kleunen, Mark; Pergl, Jan; Pyšek, Petr; Stein, Anke; Winter, Marten; Barcelona, Julie F.; Fuentes, Nicol; Inderjit, I.; Karger, Dirk Nikolaus; Kartesz, John; Kuprijanov, Andreij; Nishino, Misako; Nickrent, Daniel; Nowak, Arkadiusz; Patzelt, Annette; Pelser, Pieter B.; Singh, Paramjit; Wieringa, Jan J.; Weigelt, Patrick
Abstract
Summary 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.
See also the Commentary on this article by Puglielli & Pärtel, 237: 1074–1077.
Issue Date
14-November-2022
Journal
The New Phytologist 
Organization
Fakultät für Forstwissenschaften und Waldökologie ; Burckhardt-Institut ; Abteilung Biodiversität, Makroökologie und Biogeographie 
ISSN
0028-646X
eISSN
1469-8137
Language
English
Sponsor
China Scholarship Council http://dx.doi.org/10.13039/501100004543
Czech Academy of Sciences http://dx.doi.org/10.13039/501100004240
Czech Science Foundation http://dx.doi.org/10.13039/501100001824
Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
Austrian Science Foundation

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