Food web topology and nested keystone species complexes

Daniele Capocefalo, Juliana Pereira, Tommaso Mazza, Ferenc Jordán

Research output: Contribution to journalArticle

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Abstract

Important species may be in critically central network positions in ecological interaction networks. Beyond quantifying which one is the most central species in a food web, a multinode approach can identify the key sets of the most central n species as well. However, for sets of different size n, these structural keystone species complexes may differ in their composition. If larger sets contain smaller sets, higher nestedness may be a proxy for predictive ecology and efficient management of ecosystems. On the contrary, lower nestedness makes the identification of keystones more complicated. Our question here is how the topology of a network can influence nestedness as an architectural constraint. Here, we study the role of keystone species complexes in 27 real food webs and quantify their nestedness. After quantifying their topology properties, we determine their keystone species complexes, calculate their nestedness, and statistically analyze the relationship between topological indices and nestedness. A better understanding of the cores of ecosystems is crucial for efficient conservation efforts, and to know which networks will have more nested keystone species complexes would be a great help for prioritizing species that could preserve the ecosystem's structural integrity.

Original languageEnglish
Article number1979214
JournalComplexity
Volume2018
DOIs
Publication statusPublished - Jan 1 2018

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nestedness
keystone species
species complex
topology
food web
ecosystem
ecology

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Food web topology and nested keystone species complexes. / Capocefalo, Daniele; Pereira, Juliana; Mazza, Tommaso; Jordán, Ferenc.

In: Complexity, Vol. 2018, 1979214, 01.01.2018.

Research output: Contribution to journalArticle

Capocefalo, Daniele ; Pereira, Juliana ; Mazza, Tommaso ; Jordán, Ferenc. / Food web topology and nested keystone species complexes. In: Complexity. 2018 ; Vol. 2018.
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