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

由于干旱、气温升高、害虫和病原体的影响，树木死亡正在全球范围内发生。这些灭绝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通量的变化以及生态系统服务的损失。预测森林如何对干旱作出反应的能力是采取快速土地管理行动以减轻不良后果的关键。这项研究的一个目标是提高pi？松树（Pinus edulis）对干旱的反应。这将通过在美国西南部出现的雪旱期间进行浇水实验和区域调查来实现。多次预测圆周率？关于死亡率对干旱的反应已经发展起来。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的预测将每隔一周到一年进行一次。这个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。本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要预测生态对趋势的反应，还要预测极端生态事件对极端气候事件的反应。在这些事件中，最重要和最广泛的是树木因干旱、变暖以及相关的害虫和病原体而死亡。尽管许多研究只关注树木死亡率超过阈值的原因，产生了许多潜在的预测关系，但这些关系都没有经过近期生态预测的检验。此外，由于难以有效地模拟严重干旱或处理和对照地块同时死亡的挑战，试图在很大程度上施加干旱的田间试验在改进预测或测试近期预测方面是无效的。因此，迫切需要一项实验，利用正在发展的极端干旱来测试近期生态预测背景下的多种替代假设。该项目将在美国西南部出现严重雪旱的同时，迅速实施实验和监测测量，以测试pi？松树（Pinus edulis）是研究干旱导致死亡率最多的树种。近期生态预报将根据可用输入数据的频率，在整个干旱期间每隔1周至1年更新一次。通过对死亡率预测的改进和近期生态预测（包括极端生态事件）的进步，这项研究将是发展预测的关键一步，这些预测将提供快速土地管理行动所需的信息，并考虑到碳管理等长期影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.