Revealing biases in the sampling of ecological interaction networks

PeerJ

article

Marcus A.M. de Aguiar¹  Erica A. Newman²  Mathias M. Pires³  Justin D. Yeakel⁴  Carl Boettiger⁶  Laura A. Burkle⁷  Dominique Gravel⁸  Paulo R. Guimarães Jr⁹  James L. O’Donnell^1,10  Timothée Poisot^1,11  Marie-Josée Fortin^1,13  David H. Hembry² 

[1] Instituto de Física “Gleb Wataghin”, Universidade Estadual de Campinas;Department of Ecology and Evolutionary Biology, University of Arizona;Departamento de Biologia Animal, Instituto de Biologia, Universidade Estadual de Campinas;School of Natural Sciences, University of California;Santa Fe Institute;Department of Environmental Science, Policy, and Management, University of California;Department of Ecology, Montana State University;Département de Biologie, Université de Sherbrooke;Departamento de Ecologia, Instituto de Biociências, Universidade de São Paulo;School of Marine and Environmental Affairs, University of Washington;Département de Sciences Biologiques, Université de Montréal;Québec Centre for Biodiversity Sciences;Department of Ecology and Evolutionary Biology, University of Toronto;Department of Entomology, Cornell University

关键词: Ecological networks;    Modularity;    Network metrics;    Species interaction networks;    Network topology;    Nestedness;    Food webs;    Field sampling design;   

DOI  :  10.7717/peerj.7566

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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 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.

【 授权许可】

CC BY