Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study

Species distribution models (SDM) are widely used to describe and explain how species relate to their environment and predict their spatial distributions. As such, they are the cornerstone of most of spatial planning efforts worldwide. SDM can be implemented with a wide array of data types (presence-only, presence-absence, count...), which can either be point- or areal-based, and use a wide array of environmental conditions as predictor variables. The choice of the sampling type as well as the resolution of environmental conditions to be used are recognized as of crucial importance, yet we lack any quantification of the effects these decisions may have on SDM reliability. In the present work, we fill this gap with an unprecedented simulation procedure. We simulated 100 possible distributions of two different virtual species in two different regions. Species distribution were modelled using either segment- or areal-based sampling and five different spatial resolutions of environmental conditions. The SDM performances were inspected by statistical metrics, model composition, shapes of relationships and prediction quality. We provided clear evidence of stochasticity in the modelling process (particularly in the shapes of relationships): two dataset from the same survey, species and region could yield different results. Sampling type had stronger effects than spatial resolution on the final model relevance. The effect of coarsening the resolution was directly related to the resistance of the spatial features to changes of scale: SDM failed to adequately identify spatial distributions when the spatial features targeted by the species were diluted by resolution coarsening. These results have important implications for the SDM community, backing up some commonly accepted choices, but also by highlighting some up-to-now unexpected features of SDM (stochasticity). As a whole, this work calls for carefully weighted decisions in implementing models, and for caution in interpreting results.