Covariate-informed latent interaction models: Addressing geographic & taxonomic bias in predicting bird-plant interactions

TL;DR

This paper introduces a novel covariate-informed latent interaction model to predict bird-plant interactions, addressing geographic and taxonomic biases, and demonstrates its effectiveness using data from the Atlantic Forest.

Contribution

It develops a flexible, bias-aware modeling approach that incorporates covariates and latent factors, improving prediction of ecological interactions across diverse species.

Findings

Identified 5% of unrecorded interactions with high probability of being possible.

Found bird body mass and plant fruit diameter as key interaction drivers.

Demonstrated the model's ability to address geographic and taxonomic biases.

Abstract

Reductions in natural habitats urge that we better understand species' interconnection and how biological communities respond to environmental changes. However, ecological studies of species' interactions are limited by their geographic and taxonomic focus which can distort our understanding of interaction dynamics. We focus on bird-plant interactions that refer to situations of potential fruit consumption and seed dispersal. We develop an approach for predicting species' interactions that accounts for errors in the recorded interaction networks, addresses the geographic and taxonomic biases of existing studies, is based on latent factors to increase flexibility and borrow information across species, incorporates covariates in a flexible manner to inform the latent factors, and uses a meta-analysis data set from 85 individual studies. We focus on interactions among 232 birds and 511 plants in the 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 important in driving the presence of species interactions, with a multiplicative relationship that exhibits both a thresholding and a matching behavior.