Dataset Information

Revealing biases in the sampling of ecological interaction networks.

ABSTRACT: 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.

SUBMITTER: de Aguiar MAM

PROVIDER: S-EPMC6727833 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Revealing biases in the sampling of ecological interaction networks.

de Aguiar Marcus A M MAM Newman Erica A EA Pires Mathias M MM Yeakel Justin D JD Boettiger Carl C Burkle Laura A LA Gravel Dominique D Guimarães Paulo R PR O'Donnell James L JL Poisot Timothée T Fortin Marie-Josée MJ Hembry David H DH

PeerJ 20190902

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, statistic ...[more]

PMID: 31534845

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Project description:The analysis of transcriptomes is well-established and increasingly affordable in studies at the interface of ecology and evolution. Expression analysis of thousands of genes in parallel reveals functions and pathways involved in relevant phenotypic differentiation. The application of such methods typically involves the sacrifice of the analysed organisms, which is potentially subject to ethical and legal constraints. As an alternative to lethal sampling, transcriptome analyses can be performed using small biopsies of dispensable tissues. It has to be verified, however, to what extent such results are representative of the whole organism. Here, we use a custom microarray to compare transcriptomes of tail-clip samples with those of the remaining whole-body of fire salamander larvae (Salamandra salamandra). The microarray was calibrated using target RNA to validate the performance of each probe. We varied water temperature to test whether the thermal response in gene expression can be characterized in both types of sample. A large fraction (51 %) of the differentially expressed genes showed parallel changes for both tail clips and whole bodies in response to temperature. While sets of differentially expressed were not identical, they largely belonged to the same functional categories. The gene functions thus revealed a common thermal response of larvae irrespective of the sampled tissue. This included an overexpression of mitochondrial transcripts, an expected thermal acclimatization response of ectotherms. Hence, ecological transcriptomics based on small biopsies represent an alternative to the analysis of lethally sampled tissues in situations where the sacrifice of individuals is not an option. Larvae of European Fire salamander were exposed to two different temperatures (9°C & 17°C; n(cold) = 6, n(warm) = 6, n(total) = 12). The transcriptome response to temperature was assessed based on RNA extracted from tail tips, which can be sampled without sacrificing the individual, and based on RNA extracted from the remaining whole body. Results from the analysis of both tissues were compared.

Dataset Information

Revealing biases in the sampling of ecological interaction networks.

Publications

Revealing biases in the sampling of ecological interaction networks.

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