  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.
