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

合作提案：重新定义森林生态系统多个时空尺度扰动的生态记忆

• 批准号: 1946007
• 负责人: Andrew Finley
• 金额: $ 19.58万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1946007&HistoricalAwards=false
• 关键词: Collaborative; Proposal; Redefining; ecological; memory

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.

大多数关于森林的研究都是在一片森林内进行的，或者只在几年内进行，或者很少在几十年内进行。人们通常认为，局部干扰只会影响那片森林中的树木。然而，最近的证据表明，干扰影响了数百英里以上的森林，原因是250年前发生的一系列干旱事件和一场严寒的春季霜冻。这些极端天气事件使该地区的许多古树有可能从小树苗生长到现在的树冠。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，地区性森林减少在未来可能变得更加重要。为了看看这是否真的有可能，该奖项将从美国东北部的老森林中产生新的数据，以调查树木和森林对与250年前类似的极端事件的反应。更广泛的影响将集中在招聘在科学和学术机构中没有很好代表的人。学生和项目参与者将接受关于如何谦虚地进行科学、尊重所有人以及打击制度性种族主义和性别偏见的培训。该团队将活跃在社交媒体上，并在科学会议、公开演讲和教育活动中发表演讲，分享我们从研究中学到的东西。森林生态系统对极端气候事件的生态记忆没有被扰动理论和植被模型充分捕捉到；生态研究的常见规模在空间上太小，在时间上太短，无法捕捉到长期的生态系统发展。该奖项将使用两个动态植被模型和贝叶斯分层建模来研究多个数据流，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？2)局部高频干扰(风暴、空隙动态等)之间的长期相互作用是什么？以及大规模、低频率的干扰(严重干旱)？通过这些问题，这项研究将通过将覆盖美国东北部40万平方公里的600年树木生长数据中的模型与现实的干扰情景对峙来严格测试理论，以确定极端气候事件在多大程度上同步空间尺度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会增加。通过从季节到世纪的扩展，该项目将短期和长期研究联系起来，以提供必要规模的信息，以指导气候变化下复杂系统中的土地使用决策。除了培训，该项目还将举办一个模型研讨会，通过邀请来自一系列学科和代表性不足的群体的专家和人员，在数据模型同化、贝叶斯统计和空间和时间分析的交叉点上制作一篇概念性论文，以推动森林科学的边缘。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。