Treffer: Unlocking ensemble ecosystem modelling for large and complex networks.

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. Author summary: Mathematical models can be used to predict the potential effects of human actions on an ecosystem. Even without data, information from food webs and ecological theory has been used to build ecosystem models; but the current methods for generating them are slow, making analysis only practical for small, simple, food webs. We used a statistical method from the field of approximate Bayesian inference to speed up the process of model generation, so that we can study larger and more complex food webs. Using ecosystem case studies and randomly generated food webs, we show that our method can produce equivalent models and prediction in a fraction of the time. When tested on a large reef food web, the existing method was not fast enough to generate models within a reasonable time, but this is now possible with our new method. Hence, we can now analyse the large and complex ecosystems that exist in nature without needing to simplify our knowledge to save computation time. [ABSTRACT FROM AUTHOR]

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