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dc.contributor.author
Benadi, Gita
dc.contributor.author
Dormann, Carsten
dc.contributor.author
Fründ, Jochen
dc.contributor.author
Stephan, Ruth
dc.contributor.author
Vazquez, Diego P.
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dc.date.available
2022-10-06T15:50:05Z
dc.date.issued
2021-03
dc.identifier.citation
Benadi, Gita; Dormann, Carsten; Fründ, Jochen; Stephan, Ruth; Vazquez, Diego P.; Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny; University of Chicago Press; American Naturalist; 199; 6; 3-2021; 841–854
dc.identifier.issn
0003-0147
dc.identifier.uri
http://hdl.handle.net/11336/172267
dc.description.abstract
Ecological interactions link species in networks. Loss of species from or introduction of new species into an existing network may have substantial effects for interaction patterns. Predicting changes in interaction frequency while allowing for rewiring of existing interactions—and hence estimating the consequences of community compositional changes—is thus a central challenge for network ecology. Interactions between species groups, such as pollinators and flowers or parasitoids and hosts, are moderated by matching morphological traits or sensory clues, most of which are unknown to us. If these traits are phylogenetically conserved, however, we can use phylogenetic distances to construct latent, surrogate traits and try to match those across groups, in addition to observed traits. Understanding how important traits and trait matching are, relative to abundances and chance, is crucial to estimating the fundamental predictability of network interactions. Here, we present a statistically sound approach (“tapnet”) to fitting abundances, traits, and phylogeny to observed network data to predict interaction frequencies. We thereby expand existing approaches to quantitative bipartite networks, which so far have failed to correctly represent the nonindepen-dence of network interactions. Furthermore, we use simulations and cross validation on independent data to evaluate the predictive power of the fit. Our results show that tapnet is on a par with abundance-only, matching centrality, and machine learning approaches. This approach also allows us to evaluate how well current concepts of trait matching work. On the basis of our results, we expect that interactions in well-sampled networks can be well predicted if traits and abundances are the main driver of interaction frequency.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
University of Chicago Press
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dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
COMMUNITY
dc.subject
MORPHOLOGICAL TRAIT
dc.subject
MUTUALIST NETWORK
dc.subject
PHYLOGENY
dc.subject
POLLINATION
dc.subject.classification
Ecología
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dc.subject.classification
Ciencias Biológicas
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-09-05T14:06:09Z
dc.journal.volume
199
dc.journal.number
6
dc.journal.pagination
841–854
dc.journal.pais
Estados Unidos
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dc.journal.ciudad
Chicago
dc.description.fil
Fil: Benadi, Gita. Albert Ludwigs University of Freiburg; Alemania
dc.description.fil
Fil: Dormann, Carsten. Albert Ludwigs University of Freiburg; Alemania
dc.description.fil
Fil: Fründ, Jochen. Albert Ludwigs University of Freiburg; Alemania
dc.description.fil
Fil: Stephan, Ruth. Albert Ludwigs University of Freiburg; Alemania
dc.description.fil
Fil: Vazquez, Diego P.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Provincia de Mendoza. Instituto Argentino de Investigaciones de las Zonas Áridas. Universidad Nacional de Cuyo. Instituto Argentino de Investigaciones de las Zonas Áridas; Argentina. Albert Ludwigs University of Freiburg; Alemania
dc.journal.title
American Naturalist
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.journals.uchicago.edu/doi/10.1086/714420
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1086/714420
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