• Bioclimatic models;
  • climate gradients;
  • land-use effects;
  • large-scale patterns;
  • species distributions


Aim  To assess the influence of land cover and climate on species distributions across Europe. To quantify the importance of land cover to describe and predict species distributions after using climate as the main driver.

Location  The study area is Europe.

Methods  (1) A multivariate analysis was applied to describe land-cover distribution across Europe and assess if the land cover is determined by climate at large spatial scales. (2) To evaluate the importance of land cover to predict species distributions, we implemented a spatially explicit iterative procedure to predict species distributions of plants (2603 species), mammals (186 species), breeding birds (440 species), amphibian and reptiles (143 species). First, we ran bioclimatic models using stepwise generalized additive models using bioclimatic variables. Secondly, we carried out a regression of land cover (LC) variables against residuals from the bioclimatic models to select the most relevant LC variables. Finally, we produced mixed models including climatic variables and those LC variables selected as decreasing the residual of bioclimatic models. Then we compared the explanatory and predictive power of the pure bioclimatic against the mixed model.

Results  (1) At the European coarse resolution, land cover is mainly driven by climate. Two bioclimatic axes representing a gradient of temperature and a gradient of precipitation explained most variation of land-cover distribution. (2) The inclusion of land cover improved significantly the explanatory power of bioclimatic models and the most relevant variables across groups were those not explained or poorly explained by climate. However, the predictive power of bioclimatic model was not improved by the inclusion of LC variables in the iterative model selection process.

Main conclusion  Climate is the major driver of both species and land-cover distributions over Europe. Yet, LC variables that are not explained or weakly associated with climate (inland water, sea or arable land) are interesting to describe particular mammal, bird and tree distributions. However, the addition of LC variables to pure bioclimatic models does not improve their predictive accuracy.