Simulation Comparison of Simple and Stratified Sampling Designs for Pacific Coast Rockfish Involving Bottom Trawl and Submersible Gears

Information regarding several intensively managed groundfish off the U.S. West Coast is obtained from a randomized bottom trawl survey.  However, bottom trawl tows are sometimes fouled by bottom structures that may be associated with higher densities of these target species, and trawl performance for non-fouled tows may also be affected by bottom structures.  Indices of abundance resulting from this bottom trawl may be suspect due to these trawl performance issues, which has prompted the development of visual sampling methods that can access untrawlable habitats.  In this study, we use a spatial simulation model representing habitat selection for individuals and shoals of a Pacific rockfish (Sebastes spp.) to evaluate different sampling designs that could combine data from visual and bottom trawl sampling gears into a single index of abundance.  Specifically, we explore simple and stratified sampling designs involving both gears.  We explore stratification based on an estimated index of untrawlability as well as different methods for calibrating data from the two different gear types.  As expected, we find that using trawl data alone can result in a biased index of abundance when the proportion of a stock in trawlable or untrawlable strata changes over time.  Given optimistic assumptions about constant gear performance among strata, we also find that bottom trawl and visual sampling data can provide an index of abundance with less bias.  However, the accuracy of an index combining trawl and visual gears is decreased when gear performance varies greatly for both trawl and visual gears between strata.  Thus, we also identify some situations in which an index combining both data types is unlikely to perform well.