Climatic Drivers of Plant Species Distributions Across Spatial Grains in Southern Africa Tropical Forests

Understanding the importance of climate in determining species distribution and how it might change as a function of spatial grain size is a vital issue for species distribution modeling (SDM), yet it is often not accounted for in models and has not been extensively addressed in under sampled areas in tropical forests. Using extensive field sampled vegetation plots data on species occurrences and current climate conditions we modeled 150 vascular plant species in the Okavango River Basin, to map their current projected suitable climate space at 2, 5, 10, 20, and 50 km2 pixel resolution. Relationships between the variable importance scores and variable identity and their interaction with predictor spatial grain were investigated using Generalized Linear Models and post-hoc analysis. We found variation in the relative influence of temperature and precipitation variables across the spatial grains. The importance of the determinants of species distribution may change between species but such changes are less determined by the predictor's spatial grain. Potential evapotranspiration consistently exhibited the greatest influence in determining species and richness distribution across fine to coarse spatial grains. We found that the spatial grain of predictors had no effect on the model predictive power and that varying predictor spatial grains had only negligible effects on the model performance measured by AUC and Kappa statistics. The spatial grains of the climatic predictors used showed no effect on species richness pattern either. Our results indicate that in areas with relatively low topographic variation, modeling at coarse spatial grain for conservation purposes can be acceptable. Moreover, we show that in tropical areas that have comparatively homogeneous climatic conditions along large spatial extents the variable importance is not influenced by predictor spatial grain. For projections of contemporary species suitable climate space in relatively flat and topographically homogeneous areas which often have a climatically homogenous landscape, more attention must be given to the identity of the selected predictor variables for modeling the species distributions than to their spatial grain size. We suggest that in species distribution modeling for conservation planning, assessment of the input datasets spatial grain should be informed and guided by knowledge of the landscape level topographic conditions, as protocol.