摘要
为了研究渔业数据失真对两种非平衡剩余产量模型评估结果的影响,以南大西洋长鳍金枪鱼渔业产量和单位捕捞努力量渔获量(CPUE)数据作为基础数据,加入5种不同程度[变异系数(CV)=1%、5%、10%、20%和30%]的随机误差,模拟了(1)无数据失真,(2)仅产量数据失真,(3)仅CPUE数据失真,(4)产量和CPUE数据均失真等4种情况。利用基于ASPIC的非平衡剩余产量模型(ASM)和基于贝叶斯状态空间建模方法的非平衡剩余产量模型(BSM)分别评估了最大可持续产量(MSY)、B_(MSY)、F_(MSY)、B_(2011)/B_(MSY)、F2011/F_(MSY)等5种生物学参考点和管理指标。结果显示,在无数据失真情况下,ASM和BSM评估的MSY分别为2.866×10~4 t和2.836×10~4 t,B_(2011)/B_(MSY)分别为1.366和1.324,F2011/F_(MSY)分别为0.627和0.667,均相差不大,表明该渔业目前状态良好,ASM得到了较大的B_(MSY)(31.48×10~4 t)和较小的F_(MSY)(0.091);数据失真对ASM评估的B_(MSY)和F_(MSY)分别产生了严重的过低估计和过高估计,且CPUE数据失真产生的影响要比产量数据失真大;随着随机误差的增大,BSM评估的生物学参考点和管理指标的绝对百分比偏差有增大趋势;与ASM相比,BSM能够更好地处理渔业数据中存在的随机误差,除了MSY以外,BSM评估的生物学参考点和管理指标绝对百分比偏差均要比ASM的评估结果低,尤其是B_(MSY)和F_(MSY)。因此,在使用存在较大随机误差的渔业数据进行资源评估时,BSM具有一定的优势。
Marine fisheries provide a major source of food and livelihoods for people worldwide. Fishery management plays an important role in achieving sustainable fisheries. Catch per unit effort(CPUE) data from either fishery independent or-dependent surveys are the most informative for variations in population size over time, meanwhile catches from the fishery-dependent survey are also required to assess fishing. If these data are inaccurate, the statistical analyses would be biased, leading to mismanagement of fishery resources. However, systematic distortions appeared in world fisheries catch trends. Moreover, due to lack of fishery scientific investigation, CPUE data were mainly from commercial fishing, and influenced by spatial-temporal factors, environmental factors and also spatial autocorrelation problem. Therefore, it is important to understand the impacts of distorted fishery data on stock assessments. This study used catch and CPUE data of the albacore(Thunnus alalunga) fishery in the South Atlantic. Simulations were conducted to estimate biological reference points(BRPs), i.e., maximum sustainable yield(MSY), B_(MSY), F_(MSY), B_(2011)/B_(MSY), and F2011/F_(MSY) using non-equilibrium surplus production models based on ASPIC(ASM) and Bayesian state-space modelling(BSM). Simulations were conducted under the following scenarios: both catch and CPUE data are accurate; only catch data is misreporting; only CPUE data is misreporting,and both catch and CPUE data are misreporting. Five levels(coefficient of variation, CV=1%, 5%, 10%, 20%, and30%) of stochastic errors were superimposed on catch and CPUE data. The estimated MSYs were 2.866×10~4 t and2.836×10~4 t, B_(2011)/B_(MSY) were 1.366 and 1.324, F2011/F_(MSY) were 0.627 and 0.667 by ASM and BSM, respectively,for the first scenario. Larger B_(MSY)(31.48×10~4 t) and smaller F_(MSY)(0.091) were obtained by ASM. These results indicate that this fishery was in a good condition in 2011. Overestimate B_(MSY) and underestimate F_(MSY) were obtained using distorted catch and CPUE data by ASM, and distorted CPUE data made more impact than distorted catch data. Absolute percentage bias of estimated BRPs by BSM had a tendency to increase with the stochastic error increasing, and smaller than those by ASM, especially B_(MSY) and F_(MSY). BSM can deal with the stochastic errors better than ASM. Therefore, BSM is suggested to be applied in fishery stock assessment when the fishery data include stochastic error.
引文
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