San Francisco Estuary and Watershed Science

Monitoring in the San Francisco Estuary has fluctuated in sampling effort over time with changes to resources, objectives, and unforeseen events. I designed an approach to evaluate how reduced sampling would alter our ability to describe the status and trends of key species. This approach can evaluate the sensitivity of the estuary monitoring program to disruptions in sampling and whether sampling effort could be reduced without compromising the information provided by these surveys. I simulated reduced sampling on top of the historical data record (1985 – 2018) by selectively removing data and evaluating the impact on model inference. The same model structure is fit to the full dataset and several reduced datasets representing simulations of reduced sampling effort. Model predictions from reduced models are then compared to those from the full model to evaluate how reduced sampling may have affected our ability to detect key patterns in the data. In a case study, I applied this approach to Sacramento Splittail abundance trends from the Bay Study and the Suisun Marsh Fish Study otter trawls. Sampling reductions of 10 and 20% had fairly low impacts on the overlap of reduced model predictions with those from the full model. These results demonstrate the utility of my approach, but they are not generalizable beyond our ability to detect trends in Splittail abundance from Bay Study and Suisun Marsh Fish Study otter trawl data. A thorough analysis should run these simulations on multiple species and multiple parameters (e.g., abundance, distribution, length). By simulating sampling reductions on top of historical conditions, this approach could evaluate differential impacts in varying environmental or historical conditions (e.g., droughts, species declines, invasions). In addition, it can easily be extended to other functional groups (e.g., zooplankton, phytoplankton) as well as physical parameters (e.g., temperature, salinity, Secchi depth).