  Spatial ecological networks are widely used to model interactions between
georeferenced biological entities (e.g., populations or communities). The
analysis of such data often leads to a two-step approach where groups
containing similar biological entities are firstly identified and the spatial
information is used afterwards to improve the ecological interpretation. We
develop an integrative approach to retrieve groups of nodes that are
geographically close and ecologically similar. Our model-based
spatially-constrained method embeds the geographical information within a
regularization framework by adding some constraints to the maximum likelihood
estimation of parameters. A simulation study and the analysis of real data
demonstrate that our approach is able to detect complex spatial patterns that
are ecologically meaningful. The model-based framework allows us to consider
external information (e.g., geographic proximities, covariates) in the analysis
of ecological networks and appears to be an appealing alternative to consider
such data.
