Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models

Abstract

AbstractAimModelling species distributions at the community level is required to make effective forecasts of global change impacts on diversity and ecosystem functioning. Community predictions may be achieved using macroecological properties of communities (macroecological models, MEM), or by stacking of individual species distribution models (stacked species distribution models, S‐SDMs). To obtain more realistic predictions of species assemblages, the SESAM (spatially explicit species assemblage modelling) framework suggests applying successive filters to the initial species source pool, by combining different modelling approaches and rules. Here we provide a first test of this framework in mountain grassland communities.LocationThe western Swiss Alps.MethodsTwo implementations of the SESAM framework were tested: a ‘probability ranking’ rule based on species richness predictions and rough probabilities from SDMs, and a ‘trait range’ rule that uses the predicted upper and lower bound of community‐level distribution of three different functional traits (vegetative height, specific leaf area, and seed mass) to constrain a pool of species from binary SDMs predictions.ResultsWe showed that all independent constraints contributed to reduce species richness overprediction. Only the ‘probability ranking’ rule allowed slight but significant improvements in the predictions of community composition.Main conclusionsWe tested various implementations of the SESAM framework by integrating macroecological constraints into S‐SDM predictions, and report one that is able to improve compositional predictions. We discuss possible improvements, such as further understanding the causality and precision of environmental predictors, using other assembly rules and testing other types of ecological or functional constraints.