Ecosystem models predict changes in productivity and status for multiple species, and are important for incorporating climate‐linked dynamics in ecosystem‐based fisheries management. However, fishery regulations are primarily informed by single‐species stock assessment models, which estimate unexplained variation in dynamics (e.g., recruitment, survival, fishery selectivity, etc) using random effects. We review the general benefits of estimating random effects in ecosystem models: (1) better representing biomass cycles and trends for focal species; (2) conditioning interactions upon observed biomass for predators and prey; (3) easier replication of model results using formal estimation rather than informal model “tuning;” and (4) attributing process errors via comparison amongst different models. We then demonstrate these by introducing a new state‐space model EcoState (and associated R‐package) that extends mass balance dynamics from Ecopath with Ecosim. This model estimates mass balance (Ecopath) and time‐dynamics (Ecosim) parameters directly via their fit to time‐series data (biomass indices and fisheries catches) while also estimating the magnitude of process errors using RTMB. A real‐world application involving Alaska pollock (Gadus chalcogrammus) in the eastern Bering Sea suggests that fluctuations in krill consumption are associated with cycles of increased and decreased pollock production. A self‐test simulation experiment confirms that estimating process errors can improve estimates of productivity (growth and mortality) rates. Overall, we show that state‐space mass‐balance models can be fitted to time‐series data (similar to surplus‐production stock assessment models), and can attribute time‐varying productivity to both bottom‐up and top‐down drivers including the contribution of individual predator and prey interactions.

中文翻译：

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分层生态系统模型的好处：使用新的状态空间质量平衡模型 EcoState 进行演示


生态系统模型可预测多个物种的生产力和状况变化，对于将气候相关动态纳入基于生态系统的渔业管理非常重要。然而，渔业法规主要由单一物种种群评估模型提供信息，该模型使用随机效应估计无法解释的动态变化（例如，补充、生存、渔业选择性等）。我们回顾了在生态系统模型中估计随机效应的一般好处：（1） 更好地代表重点物种的生物量周期和趋势;（2） 根据观察到的捕食者和猎物的生物量调节相互作用;（3） 使用正式估计而不是非正式的模型“调整”更容易复制模型结果;（4） 通过不同模型之间的比较来归因过程错误。然后，我们通过引入一个新的状态空间模型 EcoState（和相关的 R 包）来证明这些，该模型使用 Ecosim 扩展了 Ecopath 的质量平衡动力学。该模型通过与时间序列数据（生物量指数和渔业渔获量）的拟合直接估计质量平衡 （Ecopath） 和时间动力学 （Ecosim） 参数，同时还使用 RTMB 估计过程误差的大小。涉及白令海东部阿拉斯加狭鳕 （Gadus chalcogrammus） 的实际应用表明，磷虾消费量的波动与狭鳕产量增加和减少的周期有关。自测模拟实验证实，估计过程误差可以提高对生产率（生长和死亡率）的估计。 总体而言，我们表明状态空间质量平衡模型可以拟合到时间序列数据（类似于剩余生产存量评估模型），并且可以将时变生产力归因于自下而上和自上而下的驱动因素，包括个体捕食者和猎物相互作用的贡献。