Probabilistic near-term forecasting facilitates evaluation of model predictions against observations and is of pressing need in ecology to inform environmental decision-making and effect societal change. Despite this imperative, many ecologists are unfamiliar with the widely used tools for evaluating probabilistic forecasts developed in other fields. We address this gap by reviewing the literature on probabilistic forecast evaluation from diverse fields including climatology, economics, and epidemiology. We present established practices for selecting evaluation data (end-sample hold out), graphical forecast evaluation (times-series plots with uncertainty, probability integral transform plots), quantitative evaluation using scoring rules (log, quadratic, spherical, and ranked probability scores), and comparing scores across models (skill score, Diebold–Mariano test). We cover common approaches, highlight mathematical concepts to follow, and note decision points to allow application of general principles to specific forecasting endeavors. We illustrate these approaches with an application to a long-term rodent population time series currently used for ecological forecasting and discuss how ecology can continue to learn from and drive the cross-disciplinary field of forecasting science.

中文翻译：

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评估概率生态预测

概率近期预测有助于根据观察评估模型预测，并且在生态学中迫切需要为环境决策提供信息并影响社会变化。尽管有这种必要性，但许多生态学家并不熟悉在其他领域开发的用于评估概率预测的广泛使用的工具。我们通过回顾气候学、经济学和流行病学等不同领域的概率预测评估文献来弥补这一差距。我们提出了用于选择评估数据（最终样本保留）、图形预测评估（具有不确定性的时间序列图、概率积分变换图）、使用评分规则进行定量评估（对数、二次、球形和排名概率分数）的既定做法，并比较模型之间的分数（技能分数，迪堡-马里亚诺检验）。我们涵盖了常见的方法，强调了要遵循的数学概念，并记录了决策点，以允许将一般原则应用于特定的预测工作。我们将这些方法应用于目前用于生态预测的长期啮齿动物种群时间序列，并讨论生态学如何继续学习和推动预测科学的跨学科领域。