Independent Validation of the Accuracy of Yelloweye Rockfish Catch Estimates from the Canadian Groundfish Integration Pilot Project

Abstract

Abstract The British Columbia fishing industry and the Department of Fisheries and Oceans Canada introduced 100% monitoring of the commercial groundfish hook-and-line and trap fisheries in April 2006. The monitoring system includes cameras to capture video footage of hauling at the vessel's side, Global Positioning System-linked winch sensors on all boats, 100% dockside monitoring of piece counts and weights, and 100% retention of all rockfishes Sebastes spp. The system provides official estimates of total catch in pieces and weight (retained and discarded) through the fisher logs and dockside monitoring. Using catches of yelloweye rockfish S. ruberrimus as a test case, this study examined the accuracy of catch estimates produced during the third year of the program (April 2008 to March 2009). The analysis indicates that the overall monitoring produces accurate catch estimates of yelloweye rockfish. A key, and possibly unique, component of the catch verification was the derivation of an alternate estimate of total catch. This estimate was derived from the data that result from the video review of randomly selected fishing events. This review process randomly selects 10% of the events from each trip and enumerates the catch of each species during the entire event. Originally designed as a random check on the veracity of the fisher logs, these review data were used in this study to provide an unbiased estimate of mean catch per event and its variance; mean catch per event was then expanded to total catch by the total number of events. Since these data come from video footage collected at the moment of capture, the video estimate cannot be corrupted by misreporting of discards or by dumping fish after being retained. Thus, the video data provide an unbiased and virtually independent catch estimate—rare in fisheries monitoring—that captures the extent to which the official catch accounting systems might be biased.