Cokriging As a Method for Combining Resource Surveys, Decreasing Uncertainty, and Mitigating Bias

Fishery scientists may receive resource survey data from multiple sources. However, the different surveys may use different survey gear, not cover the entire resource, may survey different areas in different years, or not span enough years to be included as an independent survey index. In a fishery where the spatial distribution of biomass is reasonably conserved across surveys within a season, cokriging, a geostatistical technique, is a potential method for combining overlapping surveys or using high-resolution, small-scale surveys to improve lower-resolution, large scale surveys. For the sea scallop fishery in the Northeast US, industry-supported research by multiple research groups provides survey data that augment the annual federal survey. However, differences in spatial extent, survey methodology, and sampling gear complicate unifying the survey data into a single biomass estimate. We examine the use of cokriging for using the industry-supported survey data to improve the biomass estimates for the federal resource survey. We find that this method consistently decreases survey CVs, often dramatically, and helps to remove bias for areas where surveys are spatially incomplete. We conclude that co-kriging is a promising technique for better utilizing survey data in stock assessment when such data cannot be used as an independent survey index.