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.
Highlights
Network theory provides an efficient way to represent and characterize the structure of ecological systems by organizing the complex relationships between species as graphs, where nodes represent the species, and links represent their interactions (Pascual & Dunne, 2006)
With EcoNetGen, we examined how the interplay between network structure and sampling design alters our inference on the among-module connectedness in networks, and were able to draw conclusions about the sampling design that captures the most accurate picture of the complete network for a given topology
In the modeled ecological interaction network, inter-module interactions are those that indirectly connect a module or guild with another, or interactions of a species that inhabits two habitat types over its lifetime, or are species interactions involving species that connect different interaction modules
Summary
Network theory provides an efficient way to represent and characterize the structure of ecological systems by organizing the complex relationships between species as graphs, where nodes represent the species, and links represent their interactions (Pascual & Dunne, 2006). Modularity (or compartmentalization) is a tendency of networks to form clusters of highly connected nodes, with weaker connections among clusters, which can be revealed using analytical methods (Marquitti et al, 2014). These clusters are referred to as modules or compartments. Modularity may arise within communities of co-occurring species or describe clustering in space, may or may not correspond to taxonomic or functional groups of species, and may arise in intraspecific interactions (e.g., family groups). We primarily focus on modular networks that represent the clustering of species and the interactions between them
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