Iterative near-term ecological forecasting: Needs, opportunities, and challenges
Iterative near-term ecological forecasting: Needs, opportunities, and challenges
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DOI：
10.1073/pnas.1710231115
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发表时间：
2018-02-13
影响因子：
11.1
通讯作者：
White, Ethan P.
中科院分区：
文献类型：
--
作者：
Dietze, Michael C.;Fox, Andrew;White, Ethan P.
关于可持续性的两个基本问题是“生态系统及其提供的服务在未来将如何变化？人类的决定如何影响这些轨迹？“解决这些问题需要预测生态过程的能力。不幸的是，大多数生态预测都集中在百年尺度的气候响应上，因此既不能满足近期（每日到十年）环境决策的需要，也不允许将具体的定量预测与新的观测数据进行比较，而这是最强的观测数据之一。科学理论的测试。短期预报提供了一个机会，可以根据新的证据在执行分析和更新预测之间反复循环。这种获得反馈、积累经验、修正模型和方法的迭代过程对于改进预测至关重要。迭代的短期预测将加速生态研究，使其与社会更加相关，并为高度不确定性和适应性管理下的可持续决策提供信息。在这里，我们确定了迭代短期生态预测的直接科学和社会需求、机遇和挑战。在过去的十年中，数据量、种类和可访问性都大大增加，但在互操作性、延迟和不确定性量化方面仍然存在挑战。同样，生态学家在应用计算、信息和统计方法方面取得了相当大的进展，但仍有机会改进预测特定的理论、方法和网络基础设施。有效的预测还需要科学培训、文化和机构的变革。现在就需要开始预测;现在是让生态学更具预测性的时候了，边做边学是推动科学向前发展的最快途径。
Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.