Revealing biases in the sampling of ecological interaction networks.
Ecological networks
Field sampling design
Food webs
Modularity
Nestedness
Network metrics
Network topology
Species interaction networks
Journal
PeerJ
ISSN: 2167-8359
Titre abrégé: PeerJ
Pays: United States
ID NLM: 101603425
Informations de publication
Date de publication:
2019
2019
Historique:
received:
23
04
2019
accepted:
29
07
2019
entrez:
20
9
2019
pubmed:
20
9
2019
medline:
20
9
2019
Statut:
epublish
Résumé
The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases with respect to both the interactors (the nodes of the network) and interactions (the links between nodes), because the detectability of species and their interactions is highly heterogeneous. These ecological and statistical issues directly affect ecologists' abilities to accurately construct ecological networks. However, statistical biases introduced by sampling are difficult to quantify in the absence of full knowledge of the underlying ecological network's structure. To explore properties of large-scale ecological networks, we developed the software
Identifiants
pubmed: 31534845
doi: 10.7717/peerj.7566
pii: 7566
pmc: PMC6727833
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e7566Déclaration de conflit d'intérêts
The authors declare that they have no competing interests.
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