Targeting Extreme Events: Complementing Near-Term Ecological Forecasting With Rapid Experiments and Regional Surveys
Targeting Extreme Events: Complementing Near-Term Ecological Forecasting With Rapid Experiments and Regional Surveys
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DOI：
10.3389/fenvs.2019.00183
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发表时间：
2019-11
影响因子：
4.6
通讯作者：
M. Redmond;D. Law;J. Field;Nashelly Meneses;C. J. Carroll;A. Wion;D. Breshears;N. Cobb;M. Dietze;R. Gallery
中科院分区：
文献类型：
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作者：
M. Redmond;D. Law;J. Field;Nashelly Meneses;C. J. Carroll;A. Wion;D. Breshears;N. Cobb;M. Dietze;R. Gallery
生态学家正在使用近期生态预测来提高预测能力，在这种预测中，预测是迭代地和公开地进行的，以增加透明度、学习速度和最大化效用。目前的生态预测工作主要集中在连续变量的长期数据集，如二氧化碳通量，或更突然的变量，如物候事件或藻华。这些预测工作通常缺乏与发展中的极端气候（如干旱）同时发生的短期机会性数据的整合。我们认为，将在极端事件发展过程中迅速实施的有针对性的实验和区域调查纳入当前的预测工作，最终将提高我们预测极端气候的生态反应的能力，预计极端气候的频率和强度都会增加。我们重点介绍了一个名为“追踪树木死亡”的项目，在该项目中，我们将一项实验与正在发展的严重干旱期间的区域尺度野外观测调查相结合，以测试和改进树木死亡的预测。采用这种方法需要考虑的一般见解包括：(1)近乎实时地跟踪发展中的极端气候，以有效地快速增加测量，如果可行，快速启动实验——包括资金和选址挑战；(2)接受预估极端气候事件的不确定性，并根据需要调整采样设计，特别是考虑到许多生态干扰的空间异质性；(3)及时迭代输出。总之，在发展中的极端气候事件中迅速实施的有针对性的实验和区域调查有望有效地（在财政和后勤上）提高我们预测极端气候的生态反应的能力。
Ecologists are improving predictive capability using near-term ecological forecasts, in which predictions are made iteratively and publically to increase transparency, rate of learning, and maximize utility. Ongoing ecological forecasting efforts focus mostly on long-term datasets of continuous variables, such as CO2 fluxes, or more abrupt variables, such as phenological events or algal blooms. Generally lacking from these forecasting efforts is the integration of short-term, opportunistic data concurrent with developing climate extremes such as drought. We posit that incorporating targeted experiments and regional surveys, implemented rapidly during developing extreme events, into current forecasting efforts will ultimately enhance our ability to forecast ecological responses to climate extremes, which are projected to increase in both frequency and intensity. We highlight a project “chasing tree die-off”, in which we coupled an experiment with regional-scale observational field surveys during a developing severe drought to test and improve forecasts of tree die-off. General insights to consider in incorporating this approach include: (1) tracking developing climate extremes in near-real time to efficiently ramp up measurements rapidly and, if feasible, initiate an experiment quickly—including funding and site selection challenges; (2) accepting uncertainty in projected extreme climatic events and adjusting sampling design over-time as needed, especially given the spatially heterogeneous nature of many ecological disturbances; and (3) producing timely and iterative output. In summary, targeted experiments and regional surveys implemented rapidly during developing extreme climatic events offer promise to efficiently (both financially and logistically) improve our ability to forecast ecological responses to climate extremes.