The ecological forecast limit revisited: Potential, absolute and relative system predictability

Abstract Ecological forecasts are model‐based statements about currently unknown ecosystem states in time or space. For a forecast to be useful to inform decision makers, model validation and verification determine adequacy. The measure of forecast goodness that can be translated into a limit up to which a forecast is acceptable is known as the ‘forecast limit’. While verification in weather forecasting follows strict criteria with established metrics and forecast limits, assessments of ecological forecasts still remain experiment‐specific and forecast limits are rarely reported. As such, users of ecological forecasts remain uninformed of how far into the future statements can be trusted. In this work, we synthesise existing approaches to define empirical forecast limits in a unified framework for assessing ecological predictability and offer recipes for their computation. We distinguish the model's potential and absolute forecast limits, and show how a benchmark model can help determine its relative forecast limit. The approaches are demonstrated with three case studies from population, ecosystem and Earth system research. We found that forecast limits can be computed with three requirements: Verification data, a scoring function and a reference for predictive error tolerance. Within our framework, forecast limits are defined for practically any ecological forecast and support research on ecological predictability analysis.