  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). These issues may affect the accuracy of
empirically constructed ecological networks. 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 sampled these networks with different sampling
designs that may be encountered in field experiments. The observed networks
generated by each sampling process were analyzed with respect to number and
size of components. We show that the sampling effort needed to estimate
underlying network properties depends both on the sampling design and on
network topology. Networks with random or scale-free modules require more
complete sampling compared to networks whose modules are nested or bipartite.
Overall, the structure of nested modules was the easiest to detect, regardless
of sampling design. Sampling according to species degree was consistently found
to be the most accurate strategy to estimate network structure. Conversely,
sampling according to module results in an accurate view of certain modules,
but fails to provide a global picture of the underlying network. We recommend
that these findings are incorporated into the design of projects aiming to
characterize large networks of species interactions in the field, to reduce
sampling biases. The software scripts developed to construct and sample
networks are provided for further explorations of network structure and
comparisons to real interaction data.
