Ecosystem knowledge should replace coexistence and stability assumptions in ecological network modelling

Abstract: Quantitative population modelling is an invaluable tool for identifying the cascading effects of ecosystem management and interventions. Ecosystem models are often constructed by assuming stability and coexistence in ecological communities as a proxy for abundance data when monitoring programs are not available. However, a growing body of literature suggests that these assumptions are inappropriate for modelling conservation outcomes. In this work, we develop an alternative for dataless population modelling that instead relies on expert-elicited knowledge of species abundances. While time series abundance data is often not available for ecosystems of interest, these systems may still be highly studied or observed in an informal capacity. In particular, limits on population sizes and their capacity to rapidly change during an observation period can be reasonably elicited for many species. We propose a robust framework for generating an ensemble of ecosystem models whose population predictions match the expected population dynamics, as defined by experts. Our new Bayesian algorithm systematically removes model parameters that lead to unreasonable population predictions without incurring excessive computational costs. Our results demonstrate that models constructed using expert-elicited information, rather than stability and coexistence assumptions, can dramatically impact population predictions, expected responses to management, conservation decision-making, and long-term ecosystem behaviour. In the absence of data, we argue that field observations and expert knowledge are preferred for representing ecosystems observed in nature instead of theoretical assumptions of coexistence and stability.