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Disentangling the co-structure of multilayer interaction networks: degree distribution and module composition in two-layer bipartit networks


Astegiano, Julia; Altermatt, Florian; Massol, François (2017). Disentangling the co-structure of multilayer interaction networks: degree distribution and module composition in two-layer bipartit networks. Scientific Reports, 7:15465.

Abstract

Species establish di erent interactions (e.g. antagonistic, mutualistic) with multiple species, forming multilayer ecological networks. Disentangling network co-structure in multilayer networks is crucial to predict how biodiversity loss may a ect the persistence of multispecies assemblages. Existing methods to analyse multilayer networks often fail to consider network co-structure. We present a new method to evaluate the modular co-structure of multilayer networks through the assessment of species degree co-distribution and network module composition. We focus on modular structure because of its high prevalence among ecological networks. We apply our method to two Lepidoptera-plant networks,
one describing caterpillar-plant herbivory interactions and one representing adult Lepidoptera nectaring on owers, thereby possibly pollinating them. More than 50% of the species established either herbivory or visitation interactions, but not both. These species were over-represented among plants and lepidopterans, and were present in most modules in both networks. Similarity in module composition between networks was high but not di erent from random expectations. Our method clearly delineates the importance of interpreting multilayer module composition similarity in the light of the constraints imposed by network structure to predict the potential indirect e ects of species loss through interconnected modular networks.

Abstract

Species establish di erent interactions (e.g. antagonistic, mutualistic) with multiple species, forming multilayer ecological networks. Disentangling network co-structure in multilayer networks is crucial to predict how biodiversity loss may a ect the persistence of multispecies assemblages. Existing methods to analyse multilayer networks often fail to consider network co-structure. We present a new method to evaluate the modular co-structure of multilayer networks through the assessment of species degree co-distribution and network module composition. We focus on modular structure because of its high prevalence among ecological networks. We apply our method to two Lepidoptera-plant networks,
one describing caterpillar-plant herbivory interactions and one representing adult Lepidoptera nectaring on owers, thereby possibly pollinating them. More than 50% of the species established either herbivory or visitation interactions, but not both. These species were over-represented among plants and lepidopterans, and were present in most modules in both networks. Similarity in module composition between networks was high but not di erent from random expectations. Our method clearly delineates the importance of interpreting multilayer module composition similarity in the light of the constraints imposed by network structure to predict the potential indirect e ects of species loss through interconnected modular networks.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Evolutionary Biology and Environmental Studies
Dewey Decimal Classification:570 Life sciences; biology
590 Animals (Zoology)
Language:English
Date:13 November 2017
Deposited On:05 Jan 2018 20:35
Last Modified:19 Feb 2018 10:03
Publisher:Nature Publishing Group
ISSN:2045-2322
Funders:A thanks FAPESP for financial support (Grant 2011/09951-2 and Grant 2012/04941-1), FA thanks the Swiss National Science Foundation for financial support (grant no. PP00P3_150698), FM thanks the CNRS and the ANR (ARSENIC project, grant no. 14-CE02-0012) for financial support., JA is a researcher of CONICET (Argentina)
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1038/s41598-017-15811-w
PubMed ID:29133886

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