A unifying approach for food webs, phytogeny, social networks, and statistics

A food web consists of nodes, each consisting of one or more species. The role of each node as predator or prey determines the trophic relations that weave the web. Much effort in trophic food web research is given to understand the connectivity structure, or the nature and degree of dependence amon...

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Bibliographic Details
Published inProceedings of the National Academy of Sciences - PNAS Vol. 108; no. 38; pp. 15881 - 15886
Main Authors Chiu, Grace S., Westveld, Anton H.
Format Journal Article
LanguageEnglish
Published National Academy of Sciences 20.09.2011
Subjects
Animal feeding behavior
Food webs
Hyperlinks
Inference
Mathematical vectors
Modeling
Parametric models
Phylogenetics
Predators
Statistics
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Summary:A food web consists of nodes, each consisting of one or more species. The role of each node as predator or prey determines the trophic relations that weave the web. Much effort in trophic food web research is given to understand the connectivity structure, or the nature and degree of dependence among nodes. Social network analysis (SNA) techniques—quantitative methods commonly used in the social sciences to understand network relational structure—have been used for this purpose, although postanalysis effort or biological theory is still required to determine what natural factors contribute to the feeding behavior. Thus, a conventional SNA alone provides limited insight into trophic structure. Here we show that by using novel statistical modeling methodologies to express network links as the random response of withinand internode characteristics (predictors), we gain a much deeper understanding of food web structure and its contributing factors through a unified statistical SNA. We do so for eight empirical food webs: Phytogeny is shown to have nontrivial influence on trophic relations in many webs, and for each web trophic clustering based on feeding activity and on feeding preference can differ substantially. These and other conclusions about network features are purely empirical, based entirely on observed network attributes while accounting for biological information built directly into the model. Thus, statistical SNA techniques, through statistical inference for feeding activity and preference, provide an alternative perspective of trophic clustering to yield comprehensive insight into food web structure.
ISSN:0027-8424
1091-6490