Sampling and asymptotic network properties of spatial multi‐trophic networks

Collecting well‐resolved empirical trophic networks requires significant time, money and expertise, yet we are still lacking knowledge on how sampling effort and bias impact the estimation of network structure. Filling this gap is a critical first step towards creating accurate representations of ecological networks and for teasing apart the impact of sampling compared to ecological and evolutionary processes that are known to create spatio‐temporal variation in network structure. We use a well‐sampled spatial dataset of lake food webs to examine how sample effort influences network structure. Specifically, we predict asymptotic network properties (ANPs) for our dataset by comparing lake‐specific network metrics with increasing sampling effort. We then contrast three sampling strategies – random, smallest lake to largest lake or largest lake to smallest lake – to assess which strategy best captures the regional metaweb (i.e. network of all potential interactions) network properties. We demonstrate metric‐specific relationships between sample effort and network metrics, often diverging from the ANPs. For example, low sample effort can contribute to much lower and poorer estimates of closeness centralization, as compared to approximations of modularity with similar sample efforts. In fact, many network metrics (e.g. connectance) have a quadratic relationship with sample effort indicating a sampling ‘sweet spot', which represents optimal sample effort for a close approximation of the ANP. Further, we find that sampling larger lakes followed by smaller lakes is a more optimal sampling strategy for capturing metaweb properties in this lentic ecosystem. Overall, we provide clear ways to better understand the impacts of sampling bias in food‐web studies which may be particularly critical given the rapid increase in studies comparing food webs across space and time.