  Separating environmental effects from those of biotic interactions on species
distributions has always been a central objective of ecology. Despite years of
effort in analysing patterns of species co-occurrences and communities and the
developments of sophisticated tools, we are still unable to address this major
objective. A key reason is that the wealth of ecological knowledge is not
sufficiently harnessed in current statistical models, notably the knowledge on
biotic interactions. Here, we develop ELGRIN, the first model that combines
simultaneously knowledge on species interactions (i.e. metanetwork),
environmental data and species occurrences to tease apart the relative effects
of abiotic factors and overall biotic interactions on species distributions.
Instead of focusing on single effects of pair-wise interactions, which have
little sense in complex communities, ELGRIN contrasts the overall effects of
biotic interactions to those of the environment. Using simulated and empirical
data, we demonstrate the suitability of ELGRIN to address the objectives for
various types of interactions like mutualism, competition and trophic
interactions. Data on ecological networks are everyday increasing and we
believe the time is ripe to mobilize these data to better understand
biodiversity patterns. We believe that ELGRIN will provide the unique
opportunity to unravel how biotic interactions truly influence species
distributions.
