Link prediction in ecological networks under extreme taxonomic bias

TL;DR

This paper introduces COIL+, a novel framework for link prediction in ecological networks that effectively reduces taxonomic bias by leveraging species traits, phylogeny, and multiple studies, improving accuracy in biased data scenarios.

Contribution

The paper presents COIL+, a new latent factor model that incorporates species traits, phylogeny, and multiple data sources to enhance link prediction under taxonomic bias in ecological networks.

Findings

COIL+ significantly improves link prediction accuracy.

It uncovers 5637 likely unobserved interactions.

The method reduces sampling bias among poorly studied species.

Abstract

Ecological networks offer powerful insights into community function, but without first characterizing these networks accurately, our ability to detect and interpret changes under environmental stress is limited. We develop an approach to reduce bias in link prediction in the common scenario in which data are derived from studies focused on a small number of species. Our Extended Covariate-Informed Link Prediction (COIL+) framework employs a latent factor model that flexibly borrows information across species, incorporates species traits and phylogeny, and leverages information from multiple studies to address uncertainty in species occurrence. We also propose a trait-matching procedure that allows heterogeneity in species-level trait-interaction associations. We illustrate the approach with a literature-based dataset of 268 sources reporting Afrotropical frugivory and compare performance with and without correction for occurrence uncertainty. COIL+ substantially improves link prediction and reduces sampling bias, revealing 5637 likely but unobserved frugivory interactions (a median of nine additional interactions per frugivore). Newly predicted interactions are concentrated among poorly sampled frugivores, such as the water chevrotain (Hyemoschus aquaticus, a small forest-dwelling ungulate) and the rufous-bellied helmetshrike (Prionops rufiventris, a passerine bird of East African tropical forests). Additionally, the method improves model discrimination compared to existing methods under strong taxonomic bias and narrow study focus. This framework generalizes to diverse network contexts and provides a useful tool for link prediction in the face of biased interaction data.