About
Here we provide global predictions of vascular plant
species and phylogenetic richness based on
machine learning and conventional statistical
models as described in Cai
et al. (2023).
As input data we used species inventories from
the
GIFT database (Global Inventory of Floras and Traits,
Weigelt
et al. 2020) and a set of past and present
environmental predictor variables. See Cai
et al.
(2023) for an assessesment of
the predictive performance of the different modelling
techniques applied and a detailed description of the
methods. Please cite the paper in case you use the
predictions provided here.
Cai, L., Kreft, H., Taylor, A., Denelle, P., Schrader,
J., Essl, F., van Kleunen, M., Pergl, J., Pyšek, P.,
Stein, A., Winter, M., Barcelona, J.F., Fuentes, N.,
Inderjit, Karger, D.N., Kartesz, J., Kuprijanov, A.,
Nishino, M., Nickrent, D., Nowak, A., Patzelt, A.,
Pelser, P.B., Singh, P., Wieringa, J.J. & Weigelt, P.
(2023)
Global models and predictions of plant
diversity based on advanced machine learning techniques.
New Phytologist, 237, 1432-1445.
DOI:
10.1111/nph.18533
Weigelt, P., König, C. & Kreft, H. (2020)
GIFT - A
Global Inventory of Floras and Traits for macroecology
and biogeography. Journal of Biogeography, 47,
16-43. DOI:
10.1111/jbi.13623