In the context of global biodiversity loss, more and more surveys are done at a broad spatial extent and during a long time period, which is done in order to understand processes driving the distribution, the abundance and the trends of populations at the relevant biological scales. These studies allow then defining more precise conservation status for species and establish pertinent conservation measures. However, the statistical analysis of such datasets leads some concerns. Usually, generalized linear models (GLM) are used, trying to link the variable of interest (e.g. presence/absence or abundance) with some external variables suspected to influence it (e.g. climatic and habitat variables). The main unresolved concern is about the selection of these external variables from a spatial dataset. This thesis details several possibilities and proposes a widely usable method based on a cross-validation procedure accounting for spatial dependencies. The method is evaluated through simulations and applied on several case studies, including datasets with higher than expected variability (overdispersion). A focus is also done for methods accounting for an excess of zeros (zeroinflation). The last part of this manuscript applies these methodological developments for modelling the distribution, abundance and trend of raptors breeding in France.
