Testing robustness of CPUE standardization and inclusion of environmental variables with simulated longline catch datasets

• Autor(a) principal: Forrestal, Francesca C.
• Data de Publicação: 2019
• Outros Autores: Schirripa, Michael, Goodyear, C. Phillip, Arrizabalaga, Haritz, Babcock, Elizabeth A., Coelho, Rui, Ingram, Walter, Lauretta, Matthew, Ortiz, Mauricio, Sharma, Rishi, Walter, John
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.1/14871
• topic: Catch/effort; Longline; Statistical models; Simulation; Stock assessment; Environmental effects

Environmental variability changes the distribution, migratory patterns, and susceptibility to various fishing gears for highly migratory marine fish. These changes become especially problematic when they affect the indices of abundance (such as those based on catch-per-unit-effort: CPUE) used to assess the status of fish stocks. The use of simulated CPUE data sets with known values of underlying population trends has been recommended by ICCAT (International Commission for the Conservation of Atlantic Tunas) to test the robustness of CPUE standardization methods. A longline CPUE data simulator was developed to meet this objective and simulate fisheries data from a population with distinct habitat preferences. The simulation was used to test several statistical hypotheses regarding best practices for index standardization aimed at accurate estimation of population trends. Effort data from the US pelagic longline fleet was paired with a volume-weighted habitat suitability model for blue marlin (Makaira nigricans) to derive a simulated time series of blue marlin catch and effort from 1986 to 2015 with four different underlying population trends. The simulated CPUE data were provided to stock assessment scientists to determine if the underlying population abundance trend could accurately be detected with different methods of CPUE standardization that did or did not incorporate environmental data. While the analysts’ approach to the data and the modeling structure differed, the underlying population trends were captured, some more successfully than others. In general, the inclusion of environmental and habitat variables aided the standardization process. However, differences in approaches highlight the importance of how explanatory variables are categorized and the criteria for including those variables. A set of lessons learned from this study was developed as recommendations for best practices for CPUE standardization.