RAPID: Collaborative Research: Testing Near-term Ecological Forecasting Throughout Emerging Extreme Drought

树木因干旱、气温升高、虫害和病原体而死亡的现象正在全球范围内发生。这些死亡事件改变了森林和林地生态系统。这包括许多地区水和能量流动的变化以及生态系统服务的损失。预测森林如何应对干旱的能力是采取快速土地管理行动以减轻不良后果的关键。这项研究的目标之一是提高死亡率预测pi？在松树（Pinus edulis）对干旱的反应。这将通过美国西南部新出现的雪旱期间的浇水实验和区域调查来实现。π的多种预测？关于干旱造成的死亡率的研究已经展开。这项研究的另一个目标是使用近期生态预测来测试这些不同的预测。在整个干旱期间，基于这些不同预测的树木死亡可能性预测将每隔1周至1年进行一次。该项目将通过一个每周更新树木死亡率预测的网站吸引公众、土地管理者和科学家。该项目将开发和实施一个教学模块，以加强本科课程，并为博士后研究人员提供教育培训。 面对环境变化，生态系统科学的一个新兴前沿是不仅预测对趋势的生态反应，而且预测极端气候事件的极端生态事件。这些事件中最重要和最广泛的是树木因干旱、变暖和相关的害虫和病原体而死亡。虽然许多研究只是集中在什么超过阈值的树木死亡率，产生许多潜在的预测关系，这些关系都没有被测试与近期的生态预测。此外，由于有效模拟严重干旱或处理和对照地块同时死亡的挑战，试图施加干旱的实地实验在很大程度上对改善预测或测试近期预测无效。因此，迫切需要进行一项实验，利用正在发生的极端干旱，在短期生态预测的背景下测试多种替代假设。该项目将迅速实施实验和监测措施，同时出现在美国西南部的严重雪旱，以测试多个预测树木死亡率的pi？对松树（Pinus edulis），最深入研究的树种干旱引起的死亡率。在整个干旱期间，将根据现有输入数据的频率，每隔一周到一年更新一次近期生态预报。通过改进死亡率预测和在可涵盖极端生态事件的近期生态预测方面取得进展，这项研究将是制定预测的关键一步，为迅速采取土地管理行动提供所需的信息，并考虑到更长的时间-该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

Tree die-off in response to drought, warmer temperatures, and pests and pathogens is occurring globally. These die-off events have altered forest and woodland ecosystems. This includes changes in water and energy fluxes and losses in ecosystem services in many areas. The ability to predict how forests will respond to drought is key for applying rapid land management actions to mitigate undesired outcomes. One goal of this research is to improve mortality predictions for pi?on pine (Pinus edulis) in response to drought. This will be achieved through a watering experiment and a regional survey during an emerging snow drought in the US Southwest. Multiple predictions of pi?on mortality in response to drought have already been developed. Another goal of this research is to use near-term ecological forecasting to test these different predictions. Throughout the drought, forecasts of the likelihood of tree mortality based on these different predictions will be done at intervals of 1 week to 1 year. This project will engage the public, land managers, and scientists through a website that is updated weekly with tree mortality forecasts. This project will develop and implement a teaching module to enhance undergraduate curricula and provide education training for a postdoctoral researcher. An emerging frontier of ecosystem science in the face of environmental change is to predict not just ecological responses to trends but to predict extreme ecological events to extreme climate events. Among the most important and extensive of such events is tree die-off in response to drought, warming and associated pests and pathogens. Although much research has focused simply on what exceeds thresholds for tree mortality, yielding numerous potential predictive relationships, none of these relationships have been tested with near-term ecological forecasting. Further, field experiments that have attempted to impose drought largely have been ineffective at improving predictions or testing near-term forecasts due to the challenges of effectively mimicking a severe drought or simultaneous mortality of treated and controls plots. There is thus a critical need for an experiment that takes advantage of a developing extreme drought to test multiple alternate hypotheses in a near-term ecological forecasting context. This project will rapidly implement experimental and monitoring measurements concurrent with the severe snow drought emerging across the southwestern US to test multiple predictions of tree mortality for pi?on pine (Pinus edulis), the most intensively studied tree species for drought-induced mortality. Near-term ecological forecasting will be updated at intervals of 1 week to 1 year throughout the drought, based on the frequency of available input data. Through improved mortality predictions and advancements in near-term ecological forecasting that can encompass extreme ecological events, this research will be a critical step towards developing forecasts that provide the information needed for rapid land management actions and account for longer-term implications such as carbon management.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.