Revealing biases in the sampling of ecological interaction networks
Authorde Aguiar, Marcus A.M.
Newman, Erica A.
Pires, Mathias M.
Yeakel, Justin D.
Burkle, Laura A.
Guimarães, Paulo R.
O’Donnell, James L.
Hembry, David H.
AffiliationUniv Arizona, Dept Ecol & Evolutionary Biol
Species interaction networks
Field sampling design
MetadataShow full item record
Citationde Aguiar MAM, Newman EA, Pires MM, Yeakel JD, Boettiger C, Burkle LA, Gravel D, Guimarães PR Jr, O’Donnell JL, Poisot T, Fortin M, Hembry DH. 2019. Revealing biases in the sampling of ecological interaction networks. PeerJ 7:e7566 https://doi.org/10.7717/peerj.7566
RightsCopyright © 2019 de Aguiar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
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AbstractThe 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 EcoNetGen, which constructs and samples networks with predetermined topologies. These networks may represent a wide variety of communities that vary in size and types of ecological interactions. We sampled these networks with different mathematical sampling designs that correspond to methods used in field observations. The observed networks generated by each sampling process were then analyzed with respect to the number of components, size of components and other network metrics. We show that the sampling effort needed to estimate underlying network properties depends strongly both on the sampling design and on the underlying network topology. In particular, networks with random or scale-free modules require more complete sampling to reveal their structure, compared to networks whose modules are nested or bipartite. Overall, modules with nested structure were the easiest to detect, regardless of the sampling design used. Sampling a network starting with any species that had a high degree (e.g., abundant generalist species) was consistently found to be the most accurate strategy to estimate network structure. Because high-degree species tend to be generalists, abundant in natural communities relative to specialists, and connected to each other, sampling by degree may therefore be common but unintentional in empirical sampling of networks. Conversely, sampling according to module (representing different interaction types or taxa) results in a rather complete view of certain modules, but fails to provide a complete picture of the underlying network. To reduce biases introduced by sampling methods, we recommend that these findings be incorporated into field design considerations for projects aiming to characterize large species interaction networks.
NoteOpen access journal
VersionFinal published version
SponsorsNational Science Foundation through NSF [DBI-1300426]; University of Tennessee, Knoxville; International Centre for Theoretical Physics ICTP-SAIFR [2016/01343-7 FAPESP]; FAPESP [2016/06054-3, 2016/01343-7]; CNPq [302049/2015-0]; University of Arizona Bridging Biodiversity and Conservation Science program; UC Berkeley Library
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