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.

R code and additional raster datasets attached for recreating the data, models and results presented in Buckland et al. (2022) - Ecol & Evolution.

Save the additional raster datasets to the working directory, and update the file pathways in the R code to the relevant directory locations. Please refer to README file for futher information, or contact the corresponding author: catherine.buckland@ouce.ox.ac.uk