Decoding information in multilayer ecological networks: The keystone species case

Autores
Huaylla, Claudia A.; Nacif, Marcos Ezequiel; Coulin, Carolina; Kuperman, Marcelo N.; Garibaldi, Lucas Alejandro
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fil: Huaylla, Claudia A. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Huaylla, Claudia A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Nacif, Marcos E. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Nacif, Marcos E. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Coulin, Carolina. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Coulin, Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Kuperman, Marcelo N. Centro Atómico Bariloche. Río Negro, Argentina.
Fil: Garibaldi, Lucas A. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Garibaldi, Lucas A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
The construction of a network capturing the topological structure linked to the interactions among species and the analysis of its properties constitutes a clarifying way to understand the functioning of an ecosystem at different scales of analysis. Here, we present a novel systematic procedure to profit from the enhanced information derived from considering its multiple levels and apply it to analyse the presence of keystone species. The proposed method presents a way to unveil the information stored in a network by comparing it to some randomised modification of itself. The randomising of the original network is done by swapping a controlled number of links while preserving the degree of the nodes. Then, we compare the modularity value of the original network with the randomised counterparts, which gives us a measure of the amount of relevant information stored in the first one. Once we have verified that the modularity value is meaningful, we use it to perform a community analysis and a characterisation of other topological properties in order to identify keystone species. We applied this method to a pollinator–plant–herbivore trophic network as a case study and we found that (a) the comparison between the modularity of the original and the randomised networks is a suitable tool to detect relevant information; and (b) identifying keystone species yields different results in bipartite networks from the ones obtained in networks of more than two trophic levels. We also analysed the effect of eliminating selected species from the system on the cohesion of the network. The selection of these species was made according to the centralities values, such as degree and betweenness, of the corresponding nodes. Our findings show that our analysis, mainly based on the measure of modularity is a reliable tool to characterise ecological networks. Additionally, we argue that since degree and betweenness are not always correlated, it is more reliable to measure both in an attempt to detect keystone species. The methodology proposed here to identify keystone species can be applied to other ecological networks currently available in the literature.
-
Materia
Ecología
Matemática Aplicada
Betweenness
Ecosystem
Modularity
Random Networks
Restoration
Trofic Networks
Ecología
Matemática Aplicada
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
RID-UNRN (UNRN)
Institución
Universidad Nacional de Río Negro
OAI Identificador
oai:rid.unrn.edu.ar:20.500.12049/7569

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repository_id_str 4369
network_name_str RID-UNRN (UNRN)
spelling Decoding information in multilayer ecological networks: The keystone species caseHuaylla, Claudia A.Nacif, Marcos EzequielCoulin, CarolinaKuperman, Marcelo N.Garibaldi, Lucas AlejandroEcologíaMatemática AplicadaBetweennessEcosystemModularityRandom NetworksRestorationTrofic NetworksEcologíaMatemática AplicadaFil: Huaylla, Claudia A. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.Fil: Huaylla, Claudia A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.Fil: Nacif, Marcos E. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.Fil: Nacif, Marcos E. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.Fil: Coulin, Carolina. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.Fil: Coulin, Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.Fil: Kuperman, Marcelo N. Centro Atómico Bariloche. Río Negro, Argentina.Fil: Garibaldi, Lucas A. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.Fil: Garibaldi, Lucas A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.The construction of a network capturing the topological structure linked to the interactions among species and the analysis of its properties constitutes a clarifying way to understand the functioning of an ecosystem at different scales of analysis. Here, we present a novel systematic procedure to profit from the enhanced information derived from considering its multiple levels and apply it to analyse the presence of keystone species. The proposed method presents a way to unveil the information stored in a network by comparing it to some randomised modification of itself. The randomising of the original network is done by swapping a controlled number of links while preserving the degree of the nodes. Then, we compare the modularity value of the original network with the randomised counterparts, which gives us a measure of the amount of relevant information stored in the first one. Once we have verified that the modularity value is meaningful, we use it to perform a community analysis and a characterisation of other topological properties in order to identify keystone species. We applied this method to a pollinator–plant–herbivore trophic network as a case study and we found that (a) the comparison between the modularity of the original and the randomised networks is a suitable tool to detect relevant information; and (b) identifying keystone species yields different results in bipartite networks from the ones obtained in networks of more than two trophic levels. We also analysed the effect of eliminating selected species from the system on the cohesion of the network. The selection of these species was made according to the centralities values, such as degree and betweenness, of the corresponding nodes. Our findings show that our analysis, mainly based on the measure of modularity is a reliable tool to characterise ecological networks. Additionally, we argue that since degree and betweenness are not always correlated, it is more reliable to measure both in an attempt to detect keystone species. The methodology proposed here to identify keystone species can be applied to other ecological networks currently available in the literature.-Elsevier2021-11-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfHuaylla C. A, Nacif M. E, Coulin C., Kuperman M. N. y Garibaldi L. A, et al. (2021) Decoding information in multilayer ecological networks: the keystone species case. Ecological Modelling; 460;109734.0304-3800https://www.sciencedirect.com/science/article/pii/S0304380021002842?via%3Dihubhttp://rid.unrn.edu.ar/handle/20.500.12049/7569https://doi.org/10.1016/j.ecolmodel.2021.109734enghttp://www.journals.elsevier.com/ecological-modelling/460Ecological Modellinginfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/4.0/reponame:RID-UNRN (UNRN)instname:Universidad Nacional de Río Negro2025-09-29T14:29:13Zoai:rid.unrn.edu.ar:20.500.12049/7569instacron:UNRNInstitucionalhttps://rid.unrn.edu.ar/jspui/Universidad públicaNo correspondehttps://rid.unrn.edu.ar/oai/snrdrid@unrn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:43692025-09-29 14:29:14.103RID-UNRN (UNRN) - Universidad Nacional de Río Negrofalse
dc.title.none.fl_str_mv Decoding information in multilayer ecological networks: The keystone species case
title Decoding information in multilayer ecological networks: The keystone species case
spellingShingle Decoding information in multilayer ecological networks: The keystone species case
Huaylla, Claudia A.
Ecología
Matemática Aplicada
Betweenness
Ecosystem
Modularity
Random Networks
Restoration
Trofic Networks
Ecología
Matemática Aplicada
title_short Decoding information in multilayer ecological networks: The keystone species case
title_full Decoding information in multilayer ecological networks: The keystone species case
title_fullStr Decoding information in multilayer ecological networks: The keystone species case
title_full_unstemmed Decoding information in multilayer ecological networks: The keystone species case
title_sort Decoding information in multilayer ecological networks: The keystone species case
dc.creator.none.fl_str_mv Huaylla, Claudia A.
Nacif, Marcos Ezequiel
Coulin, Carolina
Kuperman, Marcelo N.
Garibaldi, Lucas Alejandro
author Huaylla, Claudia A.
author_facet Huaylla, Claudia A.
Nacif, Marcos Ezequiel
Coulin, Carolina
Kuperman, Marcelo N.
Garibaldi, Lucas Alejandro
author_role author
author2 Nacif, Marcos Ezequiel
Coulin, Carolina
Kuperman, Marcelo N.
Garibaldi, Lucas Alejandro
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ecología
Matemática Aplicada
Betweenness
Ecosystem
Modularity
Random Networks
Restoration
Trofic Networks
Ecología
Matemática Aplicada
topic Ecología
Matemática Aplicada
Betweenness
Ecosystem
Modularity
Random Networks
Restoration
Trofic Networks
Ecología
Matemática Aplicada
dc.description.none.fl_txt_mv Fil: Huaylla, Claudia A. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Huaylla, Claudia A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Nacif, Marcos E. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Nacif, Marcos E. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Coulin, Carolina. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Coulin, Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Kuperman, Marcelo N. Centro Atómico Bariloche. Río Negro, Argentina.
Fil: Garibaldi, Lucas A. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
Fil: Garibaldi, Lucas A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
The construction of a network capturing the topological structure linked to the interactions among species and the analysis of its properties constitutes a clarifying way to understand the functioning of an ecosystem at different scales of analysis. Here, we present a novel systematic procedure to profit from the enhanced information derived from considering its multiple levels and apply it to analyse the presence of keystone species. The proposed method presents a way to unveil the information stored in a network by comparing it to some randomised modification of itself. The randomising of the original network is done by swapping a controlled number of links while preserving the degree of the nodes. Then, we compare the modularity value of the original network with the randomised counterparts, which gives us a measure of the amount of relevant information stored in the first one. Once we have verified that the modularity value is meaningful, we use it to perform a community analysis and a characterisation of other topological properties in order to identify keystone species. We applied this method to a pollinator–plant–herbivore trophic network as a case study and we found that (a) the comparison between the modularity of the original and the randomised networks is a suitable tool to detect relevant information; and (b) identifying keystone species yields different results in bipartite networks from the ones obtained in networks of more than two trophic levels. We also analysed the effect of eliminating selected species from the system on the cohesion of the network. The selection of these species was made according to the centralities values, such as degree and betweenness, of the corresponding nodes. Our findings show that our analysis, mainly based on the measure of modularity is a reliable tool to characterise ecological networks. Additionally, we argue that since degree and betweenness are not always correlated, it is more reliable to measure both in an attempt to detect keystone species. The methodology proposed here to identify keystone species can be applied to other ecological networks currently available in the literature.
-
description Fil: Huaylla, Claudia A. Universidad Nacional de Río Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. Río Negro, Argentina.
publishDate 2021
dc.date.none.fl_str_mv 2021-11-15
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv Huaylla C. A, Nacif M. E, Coulin C., Kuperman M. N. y Garibaldi L. A, et al. (2021) Decoding information in multilayer ecological networks: the keystone species case. Ecological Modelling; 460;109734.
0304-3800
https://www.sciencedirect.com/science/article/pii/S0304380021002842?via%3Dihub
http://rid.unrn.edu.ar/handle/20.500.12049/7569
https://doi.org/10.1016/j.ecolmodel.2021.109734
identifier_str_mv Huaylla C. A, Nacif M. E, Coulin C., Kuperman M. N. y Garibaldi L. A, et al. (2021) Decoding information in multilayer ecological networks: the keystone species case. Ecological Modelling; 460;109734.
0304-3800
url https://www.sciencedirect.com/science/article/pii/S0304380021002842?via%3Dihub
http://rid.unrn.edu.ar/handle/20.500.12049/7569
https://doi.org/10.1016/j.ecolmodel.2021.109734
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.journals.elsevier.com/ecological-modelling/
460
Ecological Modelling
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RID-UNRN (UNRN)
instname:Universidad Nacional de Río Negro
reponame_str RID-UNRN (UNRN)
collection RID-UNRN (UNRN)
instname_str Universidad Nacional de Río Negro
repository.name.fl_str_mv RID-UNRN (UNRN) - Universidad Nacional de Río Negro
repository.mail.fl_str_mv rid@unrn.edu.ar
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