Covariate-informed latent interaction models: Addressing geographic taxonomic bias in predicting bird-plant interactions by Georgia Papadogeorgou
Date: October 12, 2022
Speaker: Georgia Papadogeorgou
Title: Covariate-informed latent interaction models: Addressing geographic taxonomic bias in predicting bird-plant interactions
Abstract: 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 their geographic and taxonomic focus which can lead to severe under-representation of certain species and distort our understanding of inter-species interactions. We illustrate that ignoring the studies’ focus 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 biases of existing studies, (c) is based on latent factors to increase flexibility and borrow information across species, (d) incorpo- rates 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 that the interaction is possible over 80%. Finally, we develop a permutation- based variable importance procedure for latent factor network models 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.