Optimizing sampling and monitoring of species interactions within Biodiversity Observation Networks

Optimal monitoring strategies should be designed to efficiently monitor all essential facets of biodiversity. Yet, species interactions are often overlooked in monitoring designs compared to spatial coverage and species richness, partly due to the inherent difficulty of sampling and monitoring interactions compared to species distributions. Here, we used simulations to test the efficiency of monitoring species interactions within Biodiversity Observations Networks (BONs). We tested several methods for designing and optimizing BONs to monitor species interactions and examined efficiency when increasing the number of monitored sites. We showed that the required sampling effort is considerably higher for interactions, especially when considering that interaction realization and detection realistically depend on species abundance. Thus, expectations to monitor a relevant proportion of interactions within a BON should be lower than expectations to monitor its species. However, from a single-species perspective, optimizing monitoring designs based on a known species range proved an efficient strategy to retrieve its interactions, outperforming designs based on total species or interaction richness. Further efficiency gains required detailed knowledge of where realized interactions occurred, highlighting the need for better knowledge of where interactions are likely to take place and integration of factors influencing interaction realization, such as species abundances. Our results were consistent even when the total species range was over- or underestimated by 10%, which shows a tolerance in the precision of the information required for optimization. These findings highlight how attainable levels of information can lead to efficiency improvements when designing BONs. With the right target and optimization strategy, and readily available information, such as species ranges, we can therefore guide observation network designs to monitor species interactions as central biodiversity components.