Diagnosing irreducible uncertainty for adaptive environmental management: A transferable framework from wetland...

Environmental decision-makers increasingly confront non-stationary systems where predictive models fail, yet management actions remain urgent. Conventional approaches assume sufficient data and stability for forecasting, but these assumptions are often violated in data-limited contexts of the Global South. We present a diagnostic forecasting framework that acknowledges irreducible uncertainty and provides decision-support tools for adaptive governance. Rather than pursuing complex models that may produce misleading precision under irreducible uncertainty, our framework emphasizes diagnostic capacity: understanding system state, identifying stressors, decomposing uncertainty, and preparing for plausible futures. Applied to a 35-year wetland fisheries dataset from Bangladesh, model selection uncertainty contributed 40% of forecast variance, with prediction intervals exceeding historical variability. While trends in climate variables were significant, climate-ecology relationships became nonsignificant when controlling for shared temporal trends, indicating dominance of local anthropogenic stressors. The framework delivers three management-ready outputs: diagnostic monitoring, stressor-led intervention pathways, and scenario-based decision rules transferable across socioecological contexts worldwide.