The sensitivity of fisheries-independent survey indices to decisions of sampling design and intensity and the mitigation of biased precision estimators for systematic sampling

Fisheries-independent surveys are used to track trends in fish stocks globally. Survey alterations occur for a myriad of reasons (e.g., vessel availability, changes in fish distribution, funding shortfalls). Understanding the sensitivity of survey estimates to changes in sampling is pivotal to sustainable management. We present a case-study of an annual multispecies survey. Simulating distributions for four species with a spatiotemporal model, we evaluated simple random, stratified random, and systematic grid sampling designs across four sampling intensities. The systematic design yielded higher-precision estimates at all sampling intensities. However, the commonly used standard error estimator thereof resulted in mean biases from 24% to 63%. We evaluated two alternative standard error estimators that successfully mitigated these biases. Our results indicate that estimates from systematic survey designs may be robust to decreases in sampling intensity, and that analyses such as integrated stock assessments which use these estimates may mitigate model misspecification by applying alternative variance estimators.