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Community Ecology

, Volume 11, Issue 1, pp 59–67 | Cite as

Relationships between centrality indices and trophic levels in food webs

  • M. Scotti
  • F. JordánEmail author
Article

Abstract

In the last decades, many works investigated the trophic structure of communities stressing, in particular, the role played by species in food webs (e.g., their trophic level and, more recently, their centrality). There exist some encouraging applications, but few details are known about the relationships between centrality measurements and trophic levels. In addition, these studies almost refer to unweighted trophic networks, despite the acknowledged need of investigating weighted webs. Here we aim to contribute to the synthetic treatment of these complementary issues by analyzing several indices of centrality and trophic level. Studying 19 ecosystems, we ranked the nodes according to their positional importance values (based on various centrality indices) and we compared the rank order of coefficients with unweighted or weighted trophic levels. Our goal was revealing potential biases in finding high centrality nodes among basal, intermediate and top species. We found that key species occupy intermediate positions of the trophic hierarchy. In case of unweighted data, trophic levels of key nodes do not deviate from trends displayed by the whole dataset. Significant differences were observed when using weighted data. These results contradict the common belief of many ecologists that identified top-predators and charismatic megafauna as main targets of conservation policies. We discuss the potential consequences of the observed features on ecosystem dynamics.

Keywords

Key species Trophic networks Trophic level 

Abbreviation

TL

Trophic Level

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© Akadémiai Kiadó, Budapest 2010

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.The Microsoft Research - University of TrentoCentre for Computational and Systems BiologyPovo (Trento)Italy
  2. 2.Center for Network ScienceCentral European UniversityBudapestHungary

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