Mapping species distribution in space and time is a key challenge for con-
servation. Typically, such information is critical to identify essential habitats or
biodiversity hotspots and to design protected areas. Massive and heterogeneous
datasets are progressively becoming available to scientists to map species dis-
tribution, e.g., citizen science data, camera-trap data, catch declarations from
fishers, and hunting records. Combining all these datasets to infer species dis-
tribution at a fine spatio-temporal resolution could unravel huge possibilities for
ecology and conservation.
However, combining these data into a single spatio-temporal framework
raises strong methodological issues. Generally, these data are not sampled fol-
lowing a standardized sampling scheme (opportunistic sampling); they do not
have the same spatial and temporal resolution, and they can be aggregated at
a coarse resolution while the process under study needs to be inferred at a finer
scale (change of support problem). Last, all these data have to be integrated into
a single framework while ensuring consistency between the different datasets.
In this presentation, I will outline statistical developments that address these
different methodological challenges. All these developments are based on con-
crete ecological applications from fisheries science, marine ecology, and broadly,
species distribution modeling.