Ecological forecasts are model-based statements about currently unknown ecosystem states in time or space. For a model forecast to be useful to inform decision-makers, model validation and verification determine adequateness. The measure of forecast goodness that can be translated into a limit up to which a forecast is acceptable is known as the `forecast horizon'. While verification of meteorological models follows strict criteria with established metrics and forecast horizons, assessments of ecological forecasting models still remain experiment-specific and forecast horizons 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, define empirical forecast horizons in a unified framework for assessing ecological predictability and offer recipes on their computation. We distinguish upper and lower boundary estimates of predictability limits, reflecting the model's potential and actual forecast horizon, and show how a benchmark model can help determine its relative forecast horizon. The approaches are demonstrated with four case studies from population, ecosystem, and earth system research.

翻译：生态预测是基于模型对未来或空间上未知生态系统状态的陈述。为使模型预测能为决策者提供有用信息，需通过模型验证与确认来确定其适用性。衡量预测质量的指标可转化为预测可接受性的上限，这一概念被称为"预测视野"。气象模型的验证遵循严格标准，采用既定指标和预测视野，而生态预测模型的评估仍局限于具体实验，且鲜少报告预测视野。因此，生态预测用户无法确知预测结论在未来多长时间内可信。本研究综合现有方法，在评估生态可预测性的统一框架中定义经验性预测视野，并提供计算方法指南。我们区分了可预测性界限的上限与下限估计，分别反映模型的潜在预测视野和实际预测视野，并展示基准模型如何帮助确定相对预测视野。这些方法通过种群生态学、生态系统科学和地球系统研究领域的四个案例研究进行实证演示。