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 language | English |
|---|---|
| Article number | 1979214 |
| Journal | Complexity |
| Volume | 2018 |
| DOIs | |
| Publication status | Published - Jan 1 2018 |
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ASJC Scopus subject areas
- General
Cite this
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 journal › Article
}
TY - JOUR
T1 - Food web topology and nested keystone species complexes
AU - Capocefalo, Daniele
AU - Pereira, Juliana
AU - Mazza, Tommaso
AU - Jordán, Ferenc
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85060345503&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060345503&partnerID=8YFLogxK
U2 - 10.1155/2018/1979214
DO - 10.1155/2018/1979214
M3 - Article
AN - SCOPUS:85060345503
VL - 2018
JO - Complexity
JF - Complexity
SN - 1076-2787
M1 - 1979214
ER -