Abstract There is growing interest in using mechanistic ecosystem models for ecosystem-based management, as they have the advantage of capturing both bottom-up and top-down processes as well as system interactions from food web structure, spatial constraints, and human activities. However, they have the disadvantage of requiring many parameters, many of which are unknown and must be estimated or calibrated to available data. Sensitivity analysis (SA) is an important part of simulation model development in order to understand model uncertainty and which parameters are more or less influential, but has been relatively neglected with Atlantis models due to the large number of parameters and long simulation run time. The Atlantis Eastern English Channel (Atlantis-EEC) model has been applied to investigate ecosystem dynamics and processes as well as fishery management scenarios. Here we present the results of a SA of growth, mortality, and recruitment parameters, which are parameters particularly difficult to measure and thus commonly tuned through model calibration. To manage the large number of parameters in the model, we used a Morris screening approach. This method can efficiently provide information on parameter main effects and interactions/non-linear effects with relatively few simulations. We performed an initial SA including all groups on 90 parameters, where we found that the most important drivers of system dynamics and biomass across groups were: (1) plankton growth and mortality rates and (2) top predator's fixed recruitment and juvenile mortality rates. We then performed a follow-up SA on a subset of 61 parameters, excluding top predators and plankton groups from the analysis. We found that all parameters were important for system stability, while individual groups’ biomass were generally most influenced by their own parameters and a subset of benthic invertebrates. Nonlinear/interaction effects were widespread, demonstrating the prevalence of feedback loops in the trophic structure, and the importance of bottom-up effects and, to a lesser extent, top-down effects. The information gained from this SA provided a better understanding of the model structure. It also allowed us to make recommendations on the general Atlantis model calibration process as well as suggesting which parameters may be most important for propagation of uncertainty in model scenarios.

中文翻译：

---

提高对复杂生态系统模型的信心：亚特兰蒂斯生态系统模型的敏感性分析

摘要 使用机械生态系统模型进行基于生态系统的管理越来越受到关注，因为它们具有捕获自下而上和自上而下过程以及来自食物网结构、空间约束和人类活动的系统相互作用的优势。然而，它们的缺点是需要许多参数，其中许多参数是未知的，必须根据可用数据进行估计或校准。敏感性分析 (SA) 是仿真模型开发的重要组成部分，用于了解模型的不确定性以及哪些参数或多或少具有影响，但由于参数众多且仿真运行时间长，Atlantis 模型一直相对被忽视。亚特兰蒂斯东部英吉利海峡 (Atlantis-EEC) 模型已应用于研究生态系统动态和过程以及渔业管理方案。在这里，我们展示了生长、死亡率和招募参数的 SA 结果，这些参数特别难以测量，因此通常通过模型校准进行调整。为了管理模型中的大量参数，我们使用了莫里斯筛选方法。这种方法可以通过相对较少的模拟有效地提供有关参数主效应和相互作用/非线性效应的信息。我们对所有组进行了 90 个参数的初始 SA，我们发现系统动力学和生物量的最重要驱动因素是：（1）浮游生物生长和死亡率和（2）顶级捕食者 s 固定招募率和青少年死亡率。然后，我们对 61 个参数的子集进行了后续 SA，从分析中排除了顶级捕食者和浮游生物群。我们发现所有参数对于系统稳定性都很重要，而个体群体的生物量通常最受其自身参数和底栖无脊椎动物子集的影响。非线性/相互作用效应很普遍，证明了营养结构中反馈回路的普遍性，以及自下而上效应以及在较小程度上自上而下效应的重要性。从这个 SA 中获得的信息提供了对模型结构的更好理解。