Complexity and structural properties of food webs in the Barents Sea

A food web topology describes the diversity of species and their trophic interactions, i.e. who eats whom, and structuralanalysis of food web topologies can provide insight into ecosystem structure and function. It appears simple, at rst sight,to list all species and their trophic interactions. Howe...

Full description

Bibliographic Details
Published in:Oikos
Main Authors: Olivier, Pierre, Benjamin, Planque
Format: Article in Journal/Newspaper
Language:unknown
Published: 2017
Subjects:
Online Access:https://research.abo.fi/en/publications/cbf7885c-e515-471b-a587-1d2f5bb43560
https://doi.org/10.1111/oik.04138
Description
Summary:A food web topology describes the diversity of species and their trophic interactions, i.e. who eats whom, and structuralanalysis of food web topologies can provide insight into ecosystem structure and function. It appears simple, at rst sight,to list all species and their trophic interactions. However, the very large number of species at low trophic levels and theimpossibility to monitor all trophic interactions in the ocean makes it impossible to construct complete food web topolo-gies. In practice, food web topologies are simpli ed by aggregating species into groups termed trophospecies. It is not clearthough, how much simpli ed versions of food webs retain the structural properties of more detailed networks. Using themost comprehensive Barents Sea food web to date, we investigate the performance of methods to construct simpli ed foodwebs using three approaches: taxonomic, structural and regular clustering. We then evaluate how topological propertiesvary with the level of network simpli cation. Results show that alteration of food web structural properties due to aggrega-tion are highly sensitive to the methodology used for grouping species and trophic links. In the speci c case of the BarentsSea, we show that it is possible to preserve key structural properties of the original complex food web in simpli ed versionswhen using taxonomic or structural clustering combined with intermediate 25% linkage for trophic aggregation.