Reducing Bias and Filling in Spatial Gaps in Fishery‐Dependent Catch‐per‐Unit‐Effort Data by Geostatistical Prediction, II. Application to a Scallop Fishery

Abstract

AbstractFishery‐dependent catch per unit effort (CPUE) comprises critical input for many stock assessments. Construction of CPUE indices usually employs some method of data standardization. However, conventional methods based on linear models do not effectively deal with the fact that samples are collected with a selection bias or with the problem of filling spatial gaps. Geostatistical interpolation methods can ameliorate some of the biases caused by both of these problems while remaining complementary to traditional linear model‐based CPUE standardization. In this paper we present geostatistical estimates of sea scallop Placopecten magellanicus CPUE from tows recorded by onboard observers during an opening of Georges Bank Closed Area II in 1999. By selecting tows for which there was little prior effort (on the basis of accumulated effort measured by vessel monitoring systems), we obtained tows that reflected initial abundance as closely as possible. These tows were used to obtain a variogram which was used in geostatistical prediction of sea scallop CPUE. The kriged mean was substantially lower than the arithmetic sample mean, indicating that a geostatistical approach reduced the influence of repeated sampling in locations of extremely high CPUE and increased the weight of isolated observations in areas of low CPUE. The results produced a map that was qualitatively similar to that obtained from a preseason fishery‐independent survey. Overall differences between the two approaches were driven by the extension of predictions into areas at the edges of spatial autocorrelation where kriging predictions approached the grand mean of the data set.Received July 26, 2013; accepted June 2, 2014