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A categorization of recent network approaches to analyse trophic interactions
Author(s) -
Miranda María,
Parrini Francesca,
Dalerum Fredrik
Publication year - 2013
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12092
Subject(s) - trophic level , categorization , ecological network , computer science , network analysis , ecology , set (abstract data type) , modularity (biology) , data science , ecosystem , artificial intelligence , biology , evolutionary biology , physics , quantum mechanics , programming language
Summary Network analysis, a branch of discrete mathematics that quantifies the structure of links between a set of nodes, is emerging as a powerful methodology to approach complex ecological interactions. There has been a rapidly increasing body of research targeting the topological description of trophic interactions. Here we categorize studies applying a topological approach to empirical trophic networks, with the aim of identifying recent trends and deficiencies in this approach to analyse trophic interactions. There were biases in the taxonomic groups addressed and in the methodological approaches used for data collection and analyses. Studies on antagonistic interactions were generally focused on a single kind of bipartite interaction, and most studies compared network attributes across time, ecosystems or treatments. We encourage a broader use of both interlinked and unipartite networks that would allow for describing indirect effects of trophic interactions, as well as time series of networks across seasons and phenological states of organisms. We also noted shortages of studies identifying interaction structures across levels of organization, on the correspondence between network structure and function, and particularly of studies on the behaviour of trophic networks in the face of environmental perturbations, which could provide guidance for preserving ecological interactions.

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