Why finer spatial resolutions do not necessarily improve species distribution models

In recent years, there has been growing criticism of using too coarse environmental data in species distribution models (SDMs), especially for sessile organisms such as plants. However, we argue that coarse‐resolution environmental predictors are not only conceptually in line with the idea of SDMs, but may often also lead to more robust and realistic models than those with resolutions approaching the sizes of individuals. We explicitly demonstrate this seeming paradox using a virtual ecologist experiment. The main reason behind this is the disequilibrium between a species' fundamental niche and its realised distribution. While this disequilibrium is well‐recognised in biogeography and metapopulation ecology, its impact on fine‐scale SDMs remains rather neglected. Contrary to recent claims, we therefore suggest that the optimal spatial resolution of predictors in SDMs should be guided less by the size of individuals and rather by the spatial extent required to sustain minimum viable populations. Synthesis: Seeking the perfect spatial resolution should not be regarded as the holy grail of ecological modelling. In reality, no such universally optimal resolution exists. Progress is more likely to come from explicitly integrating processes operating across multiple resolutions. In particular, incorporating neighbourhood‐scale variables as predictors in SDMs represents an important step in this direction.