Standardizing fishery-dependent catch-rate information across gears and data collection programs for Alaska sablefish (Anoplopoma fimbria)

Abstract

Indices of abundance used to inform stock assessment models are commonly derived from fishery-dependent data sources. However, fishery catch-per-unit-effort (CPUE) are often confounded by a myriad of factors for which corrections must be made using model-based standardization methods. The Alaska sablefish (Anoplopoma fimbria) fishery provides a fitting case study of such issues, wherein a regulatory change in 2017 disrupted historic fishery dynamics, promoting a rapid transition in use of pot gear over demersal hook-and-line gear in the Gulf of Alaska. To address this, we combined across both observer and logbook programs (data sources) and gear types to develop an intercalibrated abundance index. We first regressed observer records against vessel logbooks to understand potential biases that may arise from combining data sources during the CPUE standardization process. Here, we found that both data sources exhibited strong agreement in reported CPUEs when compared on a set-by-set basis. Therefore, we intercalibrated both CPUE data sources and developed an index of abundance that incorporated catch records from both demersal hook-and-line and pot gear fisheries for sablefish in Alaska, to account for the recent rapid change in gear use. This standardized index of abundance compared favourably with an index generated from a fishery-independent hook-and-line survey currently used in management, suggesting it is representative of sablefish population trends. Our findings not only represent a valuable contribution to the management of sablefish in Alaska, but also provide a widely applicable framework for standardizing fishery-dependent CPUE data to support the management of multi-gear fisheries.

Introducing the Ecosystem and Socioeconomic Profile, a Proving Ground for Next Generation Stock Assessments

“…Recently, an update of the fishery catch-per-unit-effort (CPUE) indicator was added to the sablefish ESP to demonstrate the performance of a standardized combined gear (pot and hook-and-line) model-based index of abundance (Goethel et al 2022, Cheng et al 2023. This standardized CPUE index is being tested in a research version of the sablefish stock assessment model.…”

Bringing in the experts: application of industry knowledge to advance catch rate standardization for northern shortfin squid (Illex illecebrosus)

“…Thus, it is essential to collaborate with the fishing industry to understand these data, inform analytical approaches, and interpret results (Steins et al, 2022;Calderwood et al, 2023). The statistical methods used for CPUE standardizations are well described (Maunder and Punt, 2004;Bishop et al, 2004;Bishop, 2006;Bentley et al, 2012;Cheng et al, 2023), however, the methods for effectively engaging with industry to identify relevant explanatory variables and interpret CPUE indices are rarely implemented and not well documented. Fishery data are used extensively in scientific research, but there is limited literature on the science-industry research collaborations that are key to informing the analysis and application of fishery data (Mangi et al, 2018;Steins et al, 2022;Calderwood et al, 2023).…”

Co-creating Knowledge with Fishers: Challenges and Lessons for Integrating Fishers' Knowledge Contributions into Marine Science in Well-Developed Scientific Advisory Systems

“…Thus, it is essential to collaborate with the fishing industry to understand these data, inform analytical approaches, and interpret results . The statistical methods used for CPUE standardizations are well described (Maunder and Punt, 2004;Bishop et al, 2004;Bishop, 2006;Bentley et al, 2012;Cheng et al, 2023), however, the methods for effectively engaging with industry to identify relevant explanatory variables and interpret CPUE indices are rarely implemented and not well documented. Fishery data are used extensively in scientific research, but there is limited literature on the science-industry research collaborations that are key to informing the analysis and application of fishery data .…”