A hierarchical Bayesian model for predicting ecological interactions using scaled evolutionary relationships

TL;DR

This paper introduces a hierarchical Bayesian model that leverages scaled phylogenetic relationships and a latent score framework to predict species interactions, improving accuracy and interpretability over existing methods.

Contribution

It presents a novel hierarchical Bayesian approach combining phylogeny and latent scores for better ecological interaction predictions.

Findings

Phylogenetic information significantly improves prediction accuracy.

Combining phylogeny with latent scores yields better results than either alone.

The model effectively reduces uncertainty in unobserved interactions.

Abstract

Identifying undocumented or potential future interactions among species is a challenge facing modern ecologists. Recent link prediction methods rely on trait data, however large species interaction databases are typically sparse and covariates are limited to only a fraction of species. On the other hand, evolutionary relationships, encoded as phylogenetic trees, can act as proxies for underlying traits and historical patterns of parasite sharing among hosts. We show that using a network-based conditional model, phylogenetic information provides strong predictive power in a recently published global database of host-parasite interactions. By scaling the phylogeny using an evolutionary model, our method allows for biological interpretation often missing from latent variable models. To further improve on the phylogeny-only model, we combine a hierarchical Bayesian latent score framework for bipartite graphs that accounts for the number of interactions per species with the host dependence informed by phylogeny. Combining the two information sources yields significant improvement in predictive accuracy over each of the submodels alone. As many interaction networks are constructed from presence-only data, we extend the model by integrating a correction mechanism for missing interactions, which proves valuable in reducing uncertainty in unobserved interactions.