The potential effects of conservation actions on threatened species can be predicted using ensemble ecosystem models. 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. Now, for the first time, larger and more realistic networks can be practically simulated.

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。这些模型集成通常在没有可用数据的情况下假设物种稳定共存。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，阻碍了对更大规模网络的研究。我们提出了一种新颖的序贯蒙特卡洛采样方法用于集成生成，其速度比现有方法快数个数量级。我们通过一种新颖的敏感性分析方法证明了该方法可产生等效的参数推断、模型预测以及紧密约束的参数组合。在某案例研究中，我们在保持等效集成的同时，将计算时间从108天缩短至6小时。如今，更大规模且更贴近现实的网络首次能够被实际模拟。