The potential effects of conservation actions on threatened species can be predicted using ensemble ecosystem models by forecasting populations with and without intervention. These model ensembles commonly assume stable coexistence of species in the absence of available data. However, existing ensemble-generation methods become computationally inefficient as the size of the ecosystem network increases, preventing larger networks from being studied. We present a novel sequential Monte Carlo sampling approach for ensemble generation that is orders of magnitude faster than existing approaches. We demonstrate that the methods produce equivalent parameter inferences, model predictions, and tightly constrained parameter combinations using a novel sensitivity analysis method. For one case study, we demonstrate a speed-up from 108 days to 6 hours, while maintaining equivalent ensembles. Additionally, we demonstrate how to identify the parameter combinations that strongly drive feasibility and stability, drawing ecological insight from the ensembles. Now, for the first time, larger and more realistic networks can be practically simulated and analysed.

翻译：保护行动对受威胁物种的潜在影响可通过集成生态系统模型进行预测，具体方法是在有干预和无干预两种情况下进行种群数量预测。这些模型集成通常假设在缺乏可用数据时物种能够稳定共存。然而，随着生态系统网络规模增大，现有集成生成方法的计算效率逐渐降低，导致无法对更大规模的网络开展研究。我们提出了一种新型序贯蒙特卡洛采样方法用于集成生成，其计算速度比现有方法快数个数量级。通过一种新的敏感性分析方法，我们证明该方法能产生等效的参数推断、模型预测，以及高度约束的参数组合。在某个案例研究中，我们实现了计算时间从108天缩短至6小时的加速效果，同时保持了集成结果的等效性。此外，我们还展示了如何识别强烈驱动可行性与稳定性的参数组合，从而从集成结果中提取生态学见解。至此，更大规模且更符合实际的网络首次得以被实际模拟与分析。