This study proposes a simulation model that well reproduces the spawning stock biomass of Pacific bluefin tuna. Environmental factors were chosen to estimate the recruitment per spawning stock biomass, and a simulatio...This study proposes a simulation model that well reproduces the spawning stock biomass of Pacific bluefin tuna. Environmental factors were chosen to estimate the recruitment per spawning stock biomass, and a simulation model that well reproduced the spawning stock biomass was developed. Then, effects of various fisheries regulations were evaluated using the simulation study. The results were as follows: 1) arctic oscillations, Pacific decadal oscillations and the recruitment number of the Pacific stock of Japanese sardine were chosen as the environmental factors that determined the recruitment per spawning stock biomass;2) spawning stock biomass could be well reproduced using a model that reproduced the recruitment per spawning stock biomass and the survival process of the population that included the effect of fishing;and 3) the effects of various fisheries regulation could be evaluated using the simulation model mentioned above. The effective regulation in the simulations conducted in this paper was a prohibition of fishing for 0- and 1-year-old fish in terms of recovering the spawning stock biomass. The reduction of fishing mortality coefficients for all age fish to 50% of actual values also showed a good performance. The recent reductions of the recruitment and spawning stock biomass were likely caused by heavy harvesting, especially of immature fish, since 2004.展开更多
The modern fishery stock assessment could be conducted by various models,such as Stock Synthesis model with high data requirement and complicated model structure,and the basic surplus production model,which fails to i...The modern fishery stock assessment could be conducted by various models,such as Stock Synthesis model with high data requirement and complicated model structure,and the basic surplus production model,which fails to incorporate individual growth,maturity,and fishery selectivity,etc.In this study,the Just Another Bayesian Biomass Assessment(JABBA)Select which is relatively balanced between complex and simple models,was used to conduct stock assessment for yellowfin tuna(Thunnus albacares)in the Atlantic Ocean.Its population dynamics was evaluated,considering the influence of selectivity patterns and different catch per unit effort(CPUE)indices on the stock assessment results.The model with three joint longline standardized CPUE indices and logistic selectivity pattern performed well,without significant retrospective pattern.The results indicated that the stock is not overfished and not subject to overfishing in 2018.Sensitivity analyses indicated that stock assessment results are robust to natural mortality but sensitive to steepness of the stock-recruitment relationship and fishing selectivity.High steepness was revealed to be more appropriate for this stock,while the fishing selectivity has greater influence to the assessment results than life history parameters.Overall,JABBA-Select is suitable for the stock assessment of Atlantic yellowfin tuna with different selectivity patterns,and the assumptions of natural mortality and selectivity pattern should be improved to reduce uncertainties.展开更多
The majority of fishery stocks in the world are data limited,which limits formal stock assessments.Identifying the impacts of input data on stock assessment is critical for improving stock assessment and developing pr...The majority of fishery stocks in the world are data limited,which limits formal stock assessments.Identifying the impacts of input data on stock assessment is critical for improving stock assessment and developing precautionary management strategies.We compare catch advice obtained from applications of various datalimited methods(DLMs)with forecasted catch advice from existing data-rich stock assessment models for the Indian Ocean bigeye tuna(Thunnus obesus).Our goal was to evaluate the consistency of catch advice derived from data-rich methods and data-limited approaches when only a subset of data is available.The Stock Synthesis(SS)results were treated as benchmarks for comparison because they reflect the most comprehensive and best possible scientific information of the stock.This study indicated that although the DLMs examined appeared robust for the Indian Ocean bigeye tuna,the implied catch advice differed between data-limited approaches and the current assessment,due to different data inputs and model assumptions.Most DLMs tended to provide more optimistic catch advice compared with the SS,which was mostly influenced by historical catches,current abundance and depletion estimates,and natural mortality,but was less sensitive to life-history parameters(particularly those related to growth).This study highlights the utility of DLMs and their implications on catch advice for the management of tuna stocks.展开更多
文摘This study proposes a simulation model that well reproduces the spawning stock biomass of Pacific bluefin tuna. Environmental factors were chosen to estimate the recruitment per spawning stock biomass, and a simulation model that well reproduced the spawning stock biomass was developed. Then, effects of various fisheries regulations were evaluated using the simulation study. The results were as follows: 1) arctic oscillations, Pacific decadal oscillations and the recruitment number of the Pacific stock of Japanese sardine were chosen as the environmental factors that determined the recruitment per spawning stock biomass;2) spawning stock biomass could be well reproduced using a model that reproduced the recruitment per spawning stock biomass and the survival process of the population that included the effect of fishing;and 3) the effects of various fisheries regulation could be evaluated using the simulation model mentioned above. The effective regulation in the simulations conducted in this paper was a prohibition of fishing for 0- and 1-year-old fish in terms of recovering the spawning stock biomass. The reduction of fishing mortality coefficients for all age fish to 50% of actual values also showed a good performance. The recent reductions of the recruitment and spawning stock biomass were likely caused by heavy harvesting, especially of immature fish, since 2004.
基金The Fund of National Key R&D Programs of China under contract No.2019YFD0901404the China Postdoctoral Science Foundation under contract No.2019M651475。
文摘The modern fishery stock assessment could be conducted by various models,such as Stock Synthesis model with high data requirement and complicated model structure,and the basic surplus production model,which fails to incorporate individual growth,maturity,and fishery selectivity,etc.In this study,the Just Another Bayesian Biomass Assessment(JABBA)Select which is relatively balanced between complex and simple models,was used to conduct stock assessment for yellowfin tuna(Thunnus albacares)in the Atlantic Ocean.Its population dynamics was evaluated,considering the influence of selectivity patterns and different catch per unit effort(CPUE)indices on the stock assessment results.The model with three joint longline standardized CPUE indices and logistic selectivity pattern performed well,without significant retrospective pattern.The results indicated that the stock is not overfished and not subject to overfishing in 2018.Sensitivity analyses indicated that stock assessment results are robust to natural mortality but sensitive to steepness of the stock-recruitment relationship and fishing selectivity.High steepness was revealed to be more appropriate for this stock,while the fishing selectivity has greater influence to the assessment results than life history parameters.Overall,JABBA-Select is suitable for the stock assessment of Atlantic yellowfin tuna with different selectivity patterns,and the assumptions of natural mortality and selectivity pattern should be improved to reduce uncertainties.
基金The National Natural Science Foundation of China under contract No.41676120。
文摘The majority of fishery stocks in the world are data limited,which limits formal stock assessments.Identifying the impacts of input data on stock assessment is critical for improving stock assessment and developing precautionary management strategies.We compare catch advice obtained from applications of various datalimited methods(DLMs)with forecasted catch advice from existing data-rich stock assessment models for the Indian Ocean bigeye tuna(Thunnus obesus).Our goal was to evaluate the consistency of catch advice derived from data-rich methods and data-limited approaches when only a subset of data is available.The Stock Synthesis(SS)results were treated as benchmarks for comparison because they reflect the most comprehensive and best possible scientific information of the stock.This study indicated that although the DLMs examined appeared robust for the Indian Ocean bigeye tuna,the implied catch advice differed between data-limited approaches and the current assessment,due to different data inputs and model assumptions.Most DLMs tended to provide more optimistic catch advice compared with the SS,which was mostly influenced by historical catches,current abundance and depletion estimates,and natural mortality,but was less sensitive to life-history parameters(particularly those related to growth).This study highlights the utility of DLMs and their implications on catch advice for the management of tuna stocks.
