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Assessment and Management of American Lobster Fisheries
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Development of a user-friendly stock assessment model for the American lobster Yuying Zhang1, Yong Chen1, Carl Wilson2 and Minoru Kanaiwa3
1School of Marine Science, University of Maine, Orono ME, 04469 2Maine Department of Marine Resources, West Boothbay Harbor, ME 3Tokyo University of Agriculture, Aquatic Resource Lab, Hokkaido Japan 099-2493 Abstract The American lobster (Homarus americanus) supports one of the most valuable commercial fisheries in the United States. A Bayesian size-structured stock assessment model has recently been developed. The model is sex-specific and uses season as time step. It can generate estimates of various key fisheries parameters such as legal biomass, fishing mortality, and recruitment and their associated uncertainty, and can also project the dynamics of the lobster population under different levels of catch or fishing mortality. The model has been tested extensively and adopted by the Atlantic States Marine Fisheries Commission for the assessment of American lobster in the northeastern United States. As a model of high complexity, this new model requires a large amount of input data and generates a large quantity of outputs, which becomes an obstacle for the model being used by biologists and managers. In an attempt to overcome this problem, we design and develop a user-friendly version of the model and test its performance. We apply the user-friendly model to assess the lobster stock in the Gulf of Maine. This new software provides a new tool to lobster biologists and managers in their assessment of lobster population dynamic.
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Recruitment dynamic of American lobster and its social and economic implication in the Gulf of Maine Jui-Han Chang (1) develop a comprehensive lobster recruitment dynamic model to quantify and evaluate recruitment dynamics for different life history stages including the egg-settlement stage and settlement-fishery recruitment stage by incorporating environmental, anthropogenic and ecological forces; (2) develop a bioeconomic model to estimate the price elasticity of demand for herring baits and evaluate the strength between different markets and its impact to the lobster fishery; (3) develop and evaluate management strategies for a lobster-herring linked system; and (4) quantify and evaluate the value of the estimation of future recruitment to the lobster industry and management agency and conduct a cost-benefit analysis of improving recruitment forecasting capacity.
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Developing and evaluating biological reference points for the American lobster fishery management Yuying Zhang, Yong Chen, and Minoru Kanaiwa School of Marine Sciences, University of Maine, Orono, ME 04469
Carl Wilson Maine Department of Marine Resources, West Boothbay Harbor, ME 04575
The American lobster (Homarus americanus) supports one of the most valuable commercial fisheries in the United States. There is a great controversy in the biological reference point (BRP) used in assessing the lobster stock status over the last two decades. The status of the lobster stock determined based on the current BRP F10% is considered inconsistent with reality of the lobster fishery in the Gulf of Maine (GOM), calling for the evaluation of the current BRP and development of new BRPs. In this study we simulate a lobster fishery based on the data collected from the GOM lobster fishery from 1981 to 2003 and apply different BRPs in managing the simulated fishery. The BRPs considered in the evaluation include biomass-based and fishing mortality-based BRPs, and survey abundance index-based and fishery CPUE-based references points. Different scenarios are considered in the simulation, including the length of testing period (5 and 25 years), temporal variations in recruitment, natural mortality, growth rates, and gear selectivity. We then compare the performance of different BPRs in managing the simulated lobster fisheries, and identify the effectiveness of each BRP in managing the GOM lobster stock.
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Copyright ©2003 All Rights Reserved webmaster Yuying Zhang Email: Yuying_zhang@umit.maine.edu Last updated by Xiaoyan Liu Email: xiaoyan.liu@maine.edu |
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