Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny

FRÜND, JOCHEN; BENADI, GITA; STEPHAN, RUTH; DORMANN, CARSTEN; VÁZQUEZ, DIEGO P

AMERICAN NATURALIST

UNIV CHICAGO PRESS

Año: 2021

0003-0147

Ecological interactions link species in networks. Loss of species from, or introduction of new species into, an existing network may have substantial e?ects for interaction pa?erns. Predicting changes in interaction frequency while allowing for rewiring of existing interactions, and hence estimating the consequences of community compositional changes is thus a central challenge for network ecology. Interactions between species groups, such as pollinators and ?owers or parasitoids and hosts, are moderated by matching morphological traits or sensory clues, most of which are unknown to us. If these traits are phylogenetically conserved, however, we can use phylogenetic distances to construct latent, surrogate traits and try to match those across groups, in addition to observed traits. Understanding how important traits and trait-matching are, relative to abundances and chance, is crucial to estimate the fundamental predictability of network interactions. Here we present a statistically sound approach (?tapnet?) to fitting abundances, traits and phylogeny to observed network data in order to predict interaction frequencies. We thereby expand existing approaches to quantitative bipartite networks, which so far failed to correctly represent the non-independence of network interactions. Furthermore, we use simulations and cross-validation on independent data to evaluate the predictive power of the fit. Our results show that tapnet is on a par with abundance-only, matching-centrality and machine learning approaches. ?is approach also allows us to evaluate how well current concepts of trait matching work. Based on our results, we expect that interactions in well-sampled networks can be well predicted if traits and abundances are the main driver of interaction frequency.