Revealing biases in the sampling of ecological interaction networks

1. The structure of ecological interactions is commonly understood through analyses of interaction networks. However, these analyses may be sensitive to sampling biases in 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 issues may affect the accuracy of empirically constructed ecological networks. Yet statistical biases introduced by sampling error are difficult to quantify in the absence of full knowledge of the underlying ecological network’s structure. 2. We explore the properties of sampled ecological networks by simulating large-scale ecological networks with predetermined topologies, and sampling them with different mathematical procedures. Several types of modular networks were generated, intended to represent a wide variety of communities that vary in size and types of ecological interactions. We then sampled these networks with different sampling designs that may be encountered in field experiments. The observed networks generated by each sampling process were then analyzed with respect to number of components, size of components and other network metrics. 3. We show that the sampling effort needed to accurately estimate underlying network properties depends 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, the structure of nested modules was the easiest to detect, regardless of sampling design.