Unlocking ensemble ecosystem modelling for large and complex networks.

ABSTRACT: 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,

SUBMITTER: Vollert SA 

PROVIDER: S-EPMC10965070 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature