A hierarchical Bayesian model for predicting ecological interactions using evolutionary relationships

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 covariables are limited to only a fraction of species. On the other hand, evolutionary relationships among species, 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 significant predictive power in a recently pubilshed global database of host-parasite interactions. Drawing from evolutionary biology, we find that applying alternative evolutionary models to the phylogeny greatly improves it. To further improve on the phylogeny-only model, we use a hierarchical Bayesian latent score framework for bipartite graphs that accounts for the number of interactions per species, as well as 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.