Uncovering bidirectional ecological associations from co-occurrence and
environmental data
Abstract
The interplay between environmental suitability, dispersal and biotic
interactions induces spatial patterns of species’ co-abundance. Existing
statistical frameworks that infer the underlying interactions from these
patterns either ignore the species response to the environment or they
fail to account for the asymmetric nature of interactions. Here, we
propose a framework that (a) models pair-wise associations as directed
influences from a source to a target species, parameterized with two
species-specific latent variables: the response of the target species to
the community, and the effect of the source species on the community;
and (b) jointly fits these associations with a habitat suitability model
through a conditional abundance model. Using both simulated and
empirical data, we demonstrate the ability of the framework to recover
known associations and highlight the properties of the learned
association networks. Our framework should now pave the way for getting
more accurate pictures of interspecific dependencies from empirical
data.