SDM env predictor comparison dataset

Identifying the environmental drivers of the global distribution of
succulent plants using the crassulacean acid metabolism pathway of
photosynthesis has previously been investigated through ensemble-modelling
of species delimiting the realised niche of the natural succulent biome.
An alternative approach, which may provide further insight into the
fundamental niche of succulent plants in the absence of dispersal
limitation, is to model the distribution of selected species that are
globally widespread and have become naturalised far beyond their native
habitats.  This could be of interest, for example, in defining
areas that may be suitable for cultivation of alternative crops resilient
to future climate change. We therefore explored the performance of
climate-only species distribution models in predicting the drivers and
distribution of two widespread CAM plants, Opuntia ficus-indica and
Euphorbia tirucalli.  Using two different algorithms and five
predictor sets, we created distribution models for these examplar species
and produced an updated map of global inter-annual rainfall
predictability. No single predictor set produced markedly more accurate
models, with the basic bioclim-only predictor set marginally
out-performing combinations with additional predictors. Minimum
temperature of the coldest month was the single most important variable in
determining spatial distribution, but additional predictors such as
precipitation and inter-annual precipitation variability were also
important in explaining the differences in spatial predictions between
SDMs. When compared against previous projections, an a posteriori approach
correctly does not predict distributions in areas of ecophysiological
tolerance yet known absence (e.g. due to biotic competition). An updated
map of inter-annual rainfall predictability has successfully identified
regions known to be depauperate in succulent plants. High model
performance metrics suggest that the majority of potentially suitable
regions for these species are predicted by these models with a limited
number of climate predictors, and there is no benefit in expanding model
complexity and increasing the potential for overfitting.