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Specify the desired plant diversity predictions for plotting and download here:


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Citation

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. (2022) Global models and predictions of plant diversity based on advanced machine learning techniques. New Phytologist. DOI: 10.1111/nph.18533

Model predictions


Raster layers

Here, we offer rasterized versions of the richness predictions at 7,774 km² provided as polygon hexagon shape files under Richness. The layers indicated as rasterized are simply rasterized to a resolution of 30 arc seconds (~1 km² at the equator) still mirroring the shapes of the original hexagon grid cells. For the layers indicated as interpolated the rasterized layers have first been aggregated at a resolution of 20 arc minutes and then resampled back to 30 arc seconds resolution using cubic interpolation.

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. (2022). 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. (2022) 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. (2022) Global models and predictions of plant diversity based on advanced machine learning techniques. New Phytologist. 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