Resolution in species distribution models shapes spatial patterns of plant multifaceted diversity

Species distribution models (SDMs) are statistical tools that relate species observations to environmental conditions to retrieve ecological niches and predict species' potential geographic distributions. The quality and robustness of SDMs clearly depend on good modelling practices including ascertaining the ecological relevance of predictors for the studied species and choosing an appropriate spatial resolution (or 'grain size'). While past studies showed improved model performance with increasing resolution for sessile organisms, there is still no consensus regarding how inappropriate resolution of predictors can impede understanding and mapping of multiple facets of diversity. Here, we modelled the distribution of 1180 plant species across the European Alps for two sets of predictors (climate and soil) at resolutions ranging from 100-m to 40-km. We assessed predictors' importance for each resolution, calculated taxonomic (TD), relative phylogenetic (rPD) and functional diversity (rFD) accordingly, and compared the resulting diversities across space. In accordance with previous studies, we found the predictive performance to generally decrease with decreasing predictor resolution. Overall, multifaceted diversity was found to be strongly affected by resolution, particularly rPD, as exhibited by weak to average linear relationships between 100-m and 1-km resolutions (0.13 <= R-2 <= 0.57). Our results demonstrate the necessity of using highly resolved predictors to explain and predict sessile species distributions, especially in mountain environments. Using coarser resolution predictors might cause multifaceted diversity to be strongly mispredicted, with important consequences for biodiversity management and conservation.

Automated summary

Species distribution models (SDMs) are statistical tools used to predict the geographic distribution of species. The resolution of predictors plays a significant role in model performance and diversity. Previous studies have shown that higher resolution predictors can improve model performance. Our study found that multifaceted diversity, especially relative phylogenetic diversity (rPD), is strongly affected by predictor resolution. Therefore, it is necessary to use higher resolution predictors to explain and predict the distribution of sessile species, particularly in mountain environments. Assessing species diversity is crucial for biodiversity management and conservation.