Trophic groups and modules: two levels of group detection in food webs

TL;DR

This paper introduces a new algorithm for detecting trophic groups in food webs, revealing a hierarchical structure with modules and trophic groups that simplifies food web analysis while preserving key information.

Contribution

The study develops a novel trophic group detection method and demonstrates its application, linking trophic groups and modules to enhance food web analysis.

Findings

Trophic groups simplify food webs while retaining information.

A hierarchical structure with modules and trophic groups was identified.

Trophic groups provide a meaningful ecological classification.

Abstract

Within food webs, species can be partitioned into groups according to various criteria. Two notions have received particular attention: trophic groups, which have been used for decades in the ecological literature, and more recently, modules. The relationship between these two group definitions remains unknown in empirical food webs because they have so far been studied separately. While recent developments in network theory have led to efficient methods for detecting modules in food webs, the determination of trophic groups (sets of species that are functionally similar) is based on subjective expert knowledge. Here, we develop a novel algorithm for trophic group detection. We apply this method to several well-resolved empirical food webs, and show that aggregation into trophic groups allows the simplification of food webs while preserving their information content. Furthermore, we reveal a 2-level hierarchical structure where modules partition food webs into large bottom-top trophic pathways whereas trophic groups further partition these pathways into sets of species with similar trophic connections. Bringing together trophic groups and modules provides new perspectives to the study of dynamical and functional consequences of food-web structure, bridging topological analysis and dynamical systems. Trophic groups have a clear ecological meaning in terms of trophic similarity, and are found to provide a trade-off between network complexity and information loss.