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

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 gradien...

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Bibliographic Details
Published in:	The New phytologist 2023-02, Vol.237 (4), p.1432-1445
Main Authors:	Cai, Lirong, Kreft, Holger, Taylor, Amanda, Denelle, Pierre, Schrader, Julian, Essl, Franz, Kleunen, Mark, Pergl, Jan, Pyšek, Petr, Stein, Anke, Winter, Marten, Barcelona, Julie F., Fuentes, Nicol, Inderjit, 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
Format:	Article
Language:	English
Subjects:	Additives Biodiversity biodiversity conservation Biogeochemical cycle Biogeochemical cycles Climate Conservation diversity–environment models Ecological function Ecosystem ecosystems Environment models Environmental conditions Flowers & plants Generalized linear models geographical distribution Grain size Heterogeneity Humans Learning algorithms Linear Models Machine learning Mathematical models Neural networks phylogenetic diversity Phylogenetics Phylogeny Plant diversity Plants Predictions social welfare Species richness Statistical analysis Statistical methods Statistical models vascular plants Wildlife conservation
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