Covariate-Informed Latent Interaction Models: Addressing Geographic & Taxonomic Bias in Predicting Bird–Plant Interactions

Georgia Papadogeorgou, Carolina Bello, Otso Ovaskainen and David B. Dunson

Journal of the American Statistical Association, 2023, vol. 118, issue 544, 2250-2261

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 dataset 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. Supplementary materials for this article are available online.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:118:y:2023:i:544:p:2250-2261

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DOI: 10.1080/01621459.2023.2208390

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