Using data on biomass and fishing mortality in stock production modelling of flatfish |
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| Affiliation: | 1. Korean Ocean Research and Development Institute, Ansan, P.O. Box 29, Seoul 425-600, Korea;22. Fisheries Research Institute, University of Washington, Seattle, WA 98195, USA;3. International Pacific Halibut Commission, P.O. Box 95009, Seattle, WA 98195, USA;1. CONACYT - Centro de Investigaciones Biológicas del Noroeste, S.C., Av. Instituto Politécnico Nacional 195, Playa Palo de Santa Rita Sur, La Paz, B.C.S. 23096, Mexico;2. Centro Interdisciplinario de Ciencias Marinas-Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional s/n, Playa Palo de Santa Rita, La Paz, BCS 23096, Mexico;1. State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China;2. Dongfeng Nissan Passenger Vehicle Company, Guangzhou 510800, China;3. School of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China;1. Department of Chemical and Process Engineering, University of Surrey, Guildford, GU2 7XH, United Kingdom;2. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China;3. Laizhou Mingbo Aquatic Products Co., Ltd., Shandong, 261418, China;4. Business School, University of Bedfordshire, Luton, LU1 3BE, UK;1. Institute of Biology, Laboratory of Eco-Epidemiology of Parasites, Emile-Argand 11, 2000 Neuchâtel, Switzerland;2. Rue Louis Ruchonnet 14, 1337 Vallorbe, Switzerland;3. Department of Mammalogy and Ornithology, Natural History Museum of Geneva, Geneva, Switzerland;4. Jaman''s Group of Faunal Studies, Lausanne, Switzerland;5. Swiss Ornithological Institute, Seerose 1, 6204 Sempach, Switzerland;1. WiscSIMS, Department of Geoscience, University of Wisconsin-Madison, 1215 W. Dayton St., Madison, WI 53706, USA;2. Chemistry Division, Nuclear and Radiochemistry, Los Alamos National Laboratory, MSJ514, Los Alamos, NM 87545, USA;3. National Institute of Polar Research, Tokyo 190-8518, Japan;4. Kochi Institute for Core Sample Research, JAMSTEC, 200 Monobe-otsu, Nankoku, Kochi 783-8502, Japan;5. Fi Group, Direction Scientifique, 14 terrasse Bellini, 92800 Puteaux, France;6. Institute of Earth Sciences, Heidelberg University, Im Neuenheimer Feld 236, 69120 Heidelberg, Germany |
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| Abstract: | Stock production modelling was used to estimate population parameters such as the carrying capacity (B∞), as well as management parameters such as maximum sustainable yield (MSY), the instantaneous rate of fishing mortality at MSY (FMSY) and the sustainable biomass at MSY (BMSY). The input data were not catch and effort data, which usually require adjustments for changes in catchability, but biomass and catch (or fishing mortality), which are frequently available from cohort analysis or direct surveys. The model does not require the assumption of stock equilibrium for estimating parameters.The model was applied to data from the Alaska plaice, Pleuronectes quadrituberculatus, and yellowfin sole, Limanda aspera stocks in the eastern Bering Sea, and the Pacific halibut, Hippoglossus stenolepis, stock in the Gulf of Alaska and Bering Sea. All three stocks are characterized by separation of nursery area and exploitable population. There are at least five age groups present in nursery areas and ten or more in the exploitable stock so that recruitment levels and exploitable stock sizes are well-buffered.Predictions from the surplus production model provided reasonable fits to the biomass time series for all three stocks examined, given the sources of uncertainty in the biomass estimates available. It appears that the stock dynamics for the three species can be described by a relatively simple density-dependent model assuming instantaneous responses in stock biomass via recruitment and growth. |
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