Collaborative Proposal: Redefining the ecological memory of disturbance over multiple temporal and spatial scales in forest ecosystems

大多数森林研究都是在一个森林内进行的，或仅在几年内进行，有时甚至是几十年。人们通常认为，局部干扰只影响该森林中的树木。然而，最近的证据表明，250年前发生的一系列干旱事件和严重的春季霜冻影响了数百英里以上的森林。这些极端天气事件使该地区的许多古树从小树苗成长为现在的树冠树。这些大规模但非常短暂的事件，如干旱和霜冻，预计将更频繁地发生，并在未来变得更糟。如果发生这种情况，区域森林减少在未来可能会变得更加重要。为了验证这是否是真实的可能性，该奖项将从美国东北部的古老森林中获得新的数据，以调查树木和森林如何应对类似于250年前的极端事件。更广泛的影响将集中在招聘那些在科学和学术机构中没有充分代表的人。学生和项目参与者将接受培训，学习如何谦逊地开展科学研究，尊重所有人，打击制度性种族主义和性别偏见。该团队将活跃在社交媒体上，并在科学会议、公开讲座和教育活动中发表演讲，分享我们的研究成果。森林生态系统对极端气候事件的生态记忆没有被干扰理论和植被模型充分捕捉;生态研究的共同尺度在空间上太小，在时间上太短，无法捕捉长期的生态系统发展。该奖项将通过两个动态植被模型和贝叶斯分层模型来研究多个数据流，以回答几个问题，包括：1）极端气候事件如何影响生态系统的发展和生态过程？和2）什么是长期的相互作用之间的本地，高频干扰（风暴，间隙动力学，等等）大规模、低频率的干扰（严重干旱）？通过这些问题，该研究将严格测试理论，通过从覆盖美国东北部400，000平方公里的600年树木生长数据中获得的现实干扰情景来对抗模型，以确定极端气候事件在多大程度上同步了空间尺度上的干扰及其潜在的长期遗产。获奖结果将有助于预测气候-森林相互作用，因为预计未来极端事件将增加。通过从季节到世纪的缩放，该项目将短期和长期研究连接起来，以提供必要的信息，指导气候变化下复杂系统中的土地使用决策。除了培训外，该项目还将举办一个建模讲习班，邀请来自一系列学科和代表性不足的群体的专家和人员编写一份数据模型同化、贝叶斯统计、该奖项反映了NSF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。

Most research on forests occurs within a single forest or only over a few years or, less often, a few decades. It is typically believed that local disturbances affect trees only in that forest. However, recent evidence suggests that disturbances affected forests over hundreds of miles and more by the same series of drought events and a hard spring frost that occurred 250 years ago. These extreme weather events made it possible for many ancient trees within this region to grow from small saplings into the canopy trees they are now. These large-scale but very short-lived events like drought and frost are predicted to occur more often and become worse in the future. If that happens, regional forest declines could become more important in the future. To see if this is a real possibility, this award will yield new data from old forests throughout the Northeastern U.S. to investigate how trees and forests respond to extreme events similar to those from 250 years ago. The broader impacts will focus on recruiting people who are not well represented in scientific and academic institutions. Students and project participants will be trained on how to conduct science with humility, have respect for all people, and to combat institutional racism and gender bias. The team will be active on social media and make presentations at scientific meetings, public lectures, and educational events to share what is learned through our study.The ecological memory of forested ecosystems to extreme climatic events is not adequately captured by theories of disturbance and vegetation models; the common scale of ecological research is spatially too small and temporally too short to capture long-term ecosystem development. This award will investigate multiple data streams with two dynamic vegetation models and Bayes hierarchical modelling to answer several questions, including: 1) How do extreme climatic events impact ecosystem development and ecological processes? and 2) What are the long-term interactions between local, high-frequency disturbance (windstorms, gap dynamics, etc.) and large-scale, low-frequency disturbance (severe drought)? Through these questions, the research will rigorously test theory by confronting models with realistic disturbance scenarios from 600 years of tree-growth data covering 400,000 km2 of the northeastern US to determine to what extent extreme climatic events synchronize disturbance across spatial scales and their potential long-term legacies. The award outcomes will be useful in forecasting climate-forest interactions, as extreme events are expected to increase in the future. By scaling from seasons to centuries, this project bridges short- and long-term studies to provide information at the scales necessary to guide land use decisions in complex systems under a changing climate. In addition to training, the project will conduct a modelling workshop to push the margins of forest science by inviting experts and people from a range of disciplines and underrepresented groups to produce a conceptual paper at the intersection of data-model assimilation, Bayesian statistics, and spatial and temporal analyses.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.