Optimization of sampling effort for a fishery-independent survey with multiple goals.

Fishery-independent surveys are essential for collecting high quality data to support fisheries management. For fish populations with low abundance and aggregated distribution in a coastal ecosystem, high intensity bottom trawl surveys may result in extra mortality and disturbance to benthic community, imposing unnecessarily large negative impacts on the populations and ecosystem. Optimization of sampling design is necessary to acquire cost-effective sampling efforts, which, however, may not be straightforward for a survey with multiple goals. We developed a simulation approach to evaluate and optimize sampling efforts for a stratified random survey with multiple goals including estimation of abundance indices of individual species and fish groups and species diversity indices. We compared the performances of different sampling efforts when the target estimation indices had different spatial variability over different survey seasons. This study suggests that sampling efforts in a stratified random survey can be reduced while still achieving relatively high precision and accuracy for most indices measuring abundance and biodiversity, which can reduce survey mortality. This study also shows that optimal sampling efforts for a stratified random design may vary with survey objectives. A postsurvey analysis, such as this study, can improve survey designs to achieve the most important survey goals.