Decision makers are increasingly requesting that environmental and climate drivers be included in stock assessments and subsequent projections that provide managers with advice on the consequences of applying harvest control rules. Another key direction in stock assessment science is to capture the full range of uncertainty (model, process, and estimation). However, multiple sources of uncertainty are rarely accounted for when conducting projections based on environmental and climate drivers. We describe a framework for conducting projections that allows for structural model uncertainty (in the structure of the population dynamics model on which the assessment is based, and the Earth System Models and emission scenarios used to drive future recruitment and growth), for process error in future recruitment, and for uncertainty in the parameter estimates of the population dynamics model. We then apply the framework to data for Pacific cod, Gadus macrocephalus, in the eastern Bering Sea, with projections based on a harvest control rule that attempts to maximize the difference between revenue and variable costs based on the current growth and recruitment dynamics of the stock. Increases in temperature are found to increase weight-at-age but reduce recruitment. However, the negative effects on recruitment outweigh the positive effects on weight-at-age. In many cases, the harvest control rules considered in this paper, particularly those based on the assumption of no future environmental effects on population parameters, fail to conserve the stock if the inferred catch limits are taken, which suggests that declines in biomass and catch will take place if the current harvest control rules continue to be used. The strategies that lead to reductions in catch and biomass also lead to much lower profits for the fishery, particularly over the long-term. However, basing future catches on the environmental scenario that leads to the poorest outcomes (GFDL ssp585) generally keeps the stock above the threshold of 20 % of unfished spawning biomass under most climate scenarios and also achieves long-term profits at or greater than those expected in the next ten years.

决策者越来越多地要求将环境和气候驱动因素纳入库存评估和后续预测中，为管理者提供有关应用收获控制规则的后果的建议。库存评估科学的另一个关键方向是捕捉全方位的不确定性（模型、过程和估计）。然而，在根据环境和气候驱动因素进行预测时，很少考虑多种不确定性来源。我们描述了一个进行预测的框架，该框架允许结构模型的不确定性（在评估所依据的人口动态模型的结构中，以及用于驱动未来招募和增长的地球系统模型和排放情景中），以解决过程错误未来的招募，以及种群动态模型参数估计的不确定性。然后，我们将该框架应用于白令海东部太平洋鳕鱼（Gadus macrocephalus）的数据，并根据捕捞控制规则进行预测，该规则试图根据种群当前的增长和补充动态，最大化收入和可变成本之间的差异。研究发现，温度升高会增加年龄体重，但会减少募集。然而，对招募的负面影响超过了对年龄体重的正面影响。在许多情况下，本文考虑的捕捞控制规则，特别是基于未来环境对种群参数没有影响的假设的规则，如果采用推断的捕获量限制，则无法保护种群，这表明生物量和捕获量的下降将导致种群数量减少。如果继续使用现行的收获控制规则，就会发生这种情况。导致捕捞量和生物量减少的策略也会导致渔业利润大幅下降，特别是从长期来看。然而，根据导致最差结果的环境情景（GFDL ssp585）来确定未来的捕捞量，在大多数气候情景下，通常可以使种群数量保持在未捕捞产卵生物量的 20% 的阈值之上，并且还可以实现等于或高于预期的长期利润未来十年。