Accounting for niche truncation to improve spatial and temporal predictions of species distributions - Chevalier, Mathieu; Zarzo-Arias, Alejandra; Guélat, Jérôme; Mateo, Rubén G.; Guisan, Antoine (Frontiers in Ecology and Evolution, 2022)
Species Distribution Models (SDMs) are essential tools for predicting climate change impact on species’ distributions, commonly employed as an informative tool on which to base management and conservation actions. Focusing only on a part of the entire distribution of a species for fitting SDMs is a common approach. Yet, geographically restricting their range can result in considering only a subset of the species’ ecological niche (i.e. niche truncation) which could lead to biased spatial predictions of future climate change effects. The integration of large-scale distribution data encompassing the whole species range with more regional data can improve future predictions, but comes along with challenges owing to the broader scale and/or lower quality usually associated with these data. Here we compare future predictions obtained from a traditional SDM fitted on a regional dataset (Switzerland) to predictions obtained from data integration methods that combine regional and European datasets for several bird species breeding in Switzerland. Three models were fitted: a traditional SDM based only on regional data and thus not accounting for niche truncation (benchmark model), a data pooling model where the two datasets are merged without considering differences in extent or resolution, and a downscaling hierarchical approach that accounts for differences in extent and resolution. Results show that the benchmark model leads to much larger predicted range shifts (either positively or negatively) under climate change than both data integration methods. They also identify different variables as main drivers of species’ distribution compared to data-integration approaches owing to mis-estimated response curves. In conclusion, we showed that (i) using regional datasets where species niches are truncated can lead to incorrect future predictions, and (ii) using data integration methods leads to more accurate predictions and more nuanced range changes than regional SDMs through a better characterization of species' entire realized niches.