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