Weak climatic associations among British plant distributions

Aim Species distribution models (SDMs) are used to infer niche responses and predict climate change-induced range shifts. However, their power to distinguish real from chance associations between spatially autocorrelated distribution and environmental data at continental scales has been questioned. Here, this is investigated at a regional (10 km) scale by modelling the distributions of 100 plant species native to the United Kingdom. Location UK Methods SDMs fitted using real climate data were compared to those utilising simulated climate gradients. The simulated gradients preserve the exact values and spatial structure of the real ones, but have no causal relationships with any species and so represent an appropriate null model. SDMs were fitted as generalised linear models or by the Random Forest machine learning algorithm and were either non-spatial or included spatially-explicit trend surfaces or autocovariates as predictors. Results GLMs erroneously detected significant effects (P<0.05) for 86% of null gradient-species combinations, with the highest error for strongly autocorrelated species and gradients and when species occupied 50% of sites. Even more false effects were found when curvilinear responses were modelled, and this was not adequately mitigated in the spatially-explicit GLMs. Non-spatial SDMs based on simulated climate data suggested 70-80% of the apparent explanatory power of the real data could be attributable to its spatial structure. Furthermore, the niche component of spatially-explicit SDMs did not significantly contribute to model fit in most species. Main conclusions The spatial structure in the climate, rather than functional relationships with species’ distributions, may account for much of the apparent fit and predictive power of SDMs. Failure to account for this means that the evidence for climatic limitation of species’ distributions may have been overstated. As such, predicted regional and national-scale impacts of climate change based on the analysis of static distribution snapshots will require re-evaluation.