  Climate change and reductions in natural habitats necessitate that we better
understand species' interactivity and how biological communities respond to
environmental changes. However, ecological studies of species' interactions are
limited by geographic and taxonomic bias which can lead to severe
under-representation of certain species and distort our understanding of
inter-species interactions. We illustrate that ignoring these biases can result
in poor performance. We develop a model for predicting species' interactions
that (a) accounts for errors in the recorded interaction networks, (b)
addresses the geographic and taxonomic bias of existing studies, (c) is based
on latent factors to increase flexibility and borrow information across
species, (d) incorporates covariates in a flexible manner to inform the latent
factors, and (e) uses a meta-analysis data set from 166 individual studies. We
focus on interactions among 242 birds and 511 plants in the Brazilian Atlantic
Forest, and identify 5% of pairs of species with an unrecorded interaction, but
posterior probability of existing that is over 80%. Finally, we develop a
permutation-based variable importance procedure and identify that a bird's body
mass and a plant's fruit diameter are most important in driving the presence
and detection of species interactions, with a multiplicative relationship.
