Evaluating probabilistic ecological forecasts
Evaluating probabilistic ecological forecasts
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评估概率生态预测
DOI：
10.1002/ecy.3431
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发表时间：
2021
期刊：
影响因子：
4.8
通讯作者：
Ernest, S. K. Morgan
中科院分区：
文献类型：
--
作者：
Simonis, Juniper L.;White, Ethan P.;Ernest, S. K. Morgan
概率短期预报有助于根据观测结果评估模型预测，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革。尽管这是必要的，许多生态学家不熟悉广泛使用的工具，用于评估在其他领域开发的概率预测。我们通过回顾来自不同领域（包括气候学、经济学和流行病学）的概率预测评估文献来解决这一差距。我们提出了选择评估数据（最终样本保持），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（对数，二次，球形和排名概率得分）进行定量评估以及比较模型之间的得分（技能得分，Diebold-Mariano测试）的既定做法。我们涵盖了常见的方法，突出数学概念遵循，并注意决策点，使一般原则的应用，以具体的预测工作。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。
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.