气候变化领域集成服务门户

Aim Predicting future changes in species richness in response to climate change is one of the key challenges in biogeography and conservation ecology. Stacked species distribution models (S-SDMs) are a commonly used tool to predict current and future species richness. Macroecological models (MEMs), regression models with species richness as response variable, are a less computationally intensive alternative to S-SDMs. Here, we aim to compare the results of two model types (S-SDMS and MEMs), for the first time for more than 14,000 species across multiple taxa globally, and to trace the uncertainty in future predictions back to the input data and modelling approach used. Location Global land, excluding Antarctica. Taxon Amphibians, birds and mammals. Methods We fitted S-SDMs and MEMs using a consistent set of bioclimatic variables and model algorithms and conducted species richness predictions under current and future conditions. For the latter, we used four general circulation models (GCMs) under two representative concentration pathways (RCP2.6 and RCP6.0). Predicted species richness was compared between S-SDMs and MEMs and for current conditions also to extent-of-occurrence (EOO) species richness patterns. For future predictions, we quantified the variance in predicted species richness patterns explained by the choice of model type, model algorithm and GCM using hierarchical cluster analysis and variance partitioning. Results Under current conditions, species richness predictions from MEMs and S-SDMs were strongly correlated with EOO-based species richness. However, both model types over-predicted areas with low and under-predicted areas with high species richness. Outputs from MEMs and S-SDMs were also highly correlated among each other under current and future conditions. The variance between future predictions was mostly explained by model type. Main conclusions Both model types were able to reproduce EOO-based patterns in global terrestrial vertebrate richness, but produce less collinear predictions of future species richness. Model type by far contributes to most of the variation in the different future species richness predictions, indicating that the two model types should not be used interchangeably. Nevertheless, both model types have their justification, as MEMs can also include species with a restricted range, whereas S-SDMs are useful for looking at potential species-specific responses.