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

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.

KEYWORDS:

• Bayesian methods
• Ecology
• Graph completion
• Latent factors
• Variable importance

Supplementary Materials

Code and data for replicating the study results are available at https://github.com/gpapadog/Bird_Plant_Interactions. An R package that implements the proposed method is available at https://github.com/gpapadog/BiasedNetwork. The supplementary materials include supporting information, mathematical derivations, and additional simulation and study results. Supplement A includes a glossary. Supplement B includes mathematical derivations of the observed data likelihood and prior distributions. In Supplement C we provide the details of our MCMC procedure. In Supplement D we describe the variable importance metric and corresponding computations in more detail. In Supplement E we describe all the alternative models that we consider in our simulations, and Supplement F includes additional simulation results. Supplement G includes a discussion about the impact of out-of-sample species. In Supplement H, we include additional results from our study on bird-plant interactions, MCMC convergence diagnostics are shown in Supplement I, and a list of all bird and plant species in our study is given in Supplement J.

Disclosure Statement

The authors report that there are no competing interests to declare.

Funding

H2020 European Research Council;H2020 European Research Council;

Additional information

Funding

This project has received funding from the European Research Council (ERC) under the European Union Horizon 2020 research and innovation programme (grant agreement No 856506; ERC-synergy project LIFEPLAN). Otso Ovaskainen was funded by Academy of Finland (grant no. 309581), Jane and Aatos Erkko Foundation, Research Council of Norway through its Centres of Excellence Funding Scheme (223257). Carolina Bello acknowledges funding support from the European Research Council (ERC) under the European union’s Horizon 2020 research and innovation programme (grant agreement No 787638) and the Swiss National Science Foundation (grant No. 173342), both granted to Catherine Graham.