Collaborative Research: MRA: Evaluating hypotheses of long-term woody carbon dynamics with empirical data

合作研究：MRA：用经验数据评估长期木本碳动态的假设

• 批准号: 2213579
• 负责人: Jason McLachlan
• 金额: $ 94.55万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213579&HistoricalAwards=false
• 关键词: Collaborative; Research; MRA; Evaluating; hypotheses

Changes in the vegetation of the American Upper Midwest over a ten-thousand year period, between the end of the last ice age and the 19th century are recorded in fossil pollen buried in the sediments of lakes, swamps and bogs. It is necessary to use this fossil record because written vegetation surveys did not begin until the industrial period. Past investigation of these pollen assemblages has revealed shifts in vegetation with climate change (such as post-glacial warming) and natural disturbance (such as the wildfires that prevented trees from invading prairies). This project takes advantage of new statistical and computer modeling approaches to quantify post-glacial changes in Midwestern vegetation and climate in terms that are useful to society (changes in the numbers and biomass of trees on the landscape over centuries). Better quantification of pre-industrial Midwestern vegetation is important for two reasons related to this project. First, the pre-industrial landscape is a key benchmark for: land managers (a baseline for conservation, timber management, etc.); global change researchers (changes in pre-industrial vegetation biomass are among the most uncertain components of the global carbon cycle); and the public (pre-industrial vegetation helps define the character of the land). This project will engage with each of these communities through: conferences, publicly-available reconstructions of thousands of years of vegetation change, and free, online education modules that will be taught to a broad array of students, from high-schoolers to professional resource managers. Second, better quantification of long-term vegetation fills a key gap in our basic understanding of the ecology of vegetation change. Researchers know that changing climate and disturbances, like fire, can change vegetation suddenly and irreversibly over large areas, but the absence of well-quantified records of long-term vegetation change has been an obstacle to ecological understanding of how and when such changes actually occur. This project uses an integrated modeling framework to ensure that the new reconstructions of vegetation changes, which span centuries of tree-lifetimes, will complement the existing understanding of vegetation change, which is primarily based on data only collected over recent decades. In addition to the conferences, public outreach and online training, this project will also educate and train 4 graduate students in quantitative paleo-ecology.The project builds on and improves a recently-developed statistical model of changes in aboveground woody biomass pools (AGWB) across the Midwest over the last 10,000 years. These reconstructions of AGWB over space and time are the data that test a set of hypotheses about Midwestern vegetation history centered on the overall question of whether internal feedbacks (for example between vegetation and wildfire) played important roles in stabilizing or destabilizing the response of AGWB to changing climates over the Holocene, and whether modern land-use has altered these feedbacks significantly. The project will make joint probabilistic inference about changing biomass pools, demographic rates in forests, and climate drivers constrained by the AGWB reconstructions, an ensemble of modeled Holocene climate trajectories, and alternative models of the ecological process of forest change. Nonlinear threshold responses of vegetation to climate are a special focus of these retrospective “hindcasts”. The quantitative tools developed in the project underlie education and outreach programs to students, land managers, and other researchers through exercises, modules, and curricula that the researchers will test-drive in a diverse set of short courses, then made available to the public online. Training of four graduate students will also be interwoven with the research and outreach objectives.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.

从最后一个冰河世纪末到19世纪，美国上中西部地区的植被在一万年的时间里发生了变化，这些变化记录在埋藏在湖泊、沼泽和沼泽沉积物中的花粉化石中。使用这种化石记录是必要的，因为书面植被调查直到工业化时期才开始。过去对这些花粉组合的调查揭示了植被随着气候变化（如冰川后变暖）和自然干扰（如阻止树木入侵草原的野火）而发生的变化。该项目利用新的统计和计算机建模方法来量化中西部植被和气候的冰川后变化，这些变化对社会有用（几个世纪以来景观中树木数量和生物量的变化）。更好地量化前工业中西部植被是很重要的，有两个原因与本项目有关。首先，工业化前的景观是一个关键的基准：土地管理者（保护，木材管理等的基线）;全球变化研究人员（工业化前植被生物量的变化是全球碳循环中最不确定的组成部分）;公众（工业化前植被有助于确定土地的特征）。该项目将通过以下方式与这些社区进行互动：会议，数千年植被变化的公开重建，以及免费的在线教育模块，这些模块将教授给从高中生到专业资源管理人员的广泛学生。其次，更好地量化长期植被填补了我们对植被变化生态学的基本理解的一个关键空白。研究人员知道，不断变化的气候和干扰，如火灾，可以在大面积上突然和不可逆转地改变植被，但缺乏长期植被变化的量化记录一直是生态学理解这种变化如何以及何时发生的障碍。该项目使用了一个综合建模框架，以确保对植被变化的新重建，这跨越了几个世纪的树木寿命，将补充现有的植被变化的理解，这主要是基于最近几十年收集的数据。除了会议、公众宣传和在线培训外，该项目还将教育和培训4名定量古生态学研究生。该项目建立在最近开发的中西部地上木质生物量库（AGWB）变化统计模型的基础上，并对其进行了改进。这些重建的AGWB在空间和时间上的数据，测试一组假设中西部植被的历史集中的整体问题，是否内部反馈（例如植被和野火之间）发挥了重要作用，稳定或不稳定的AGWB的响应，气候变化的全新世，以及现代土地利用是否改变了这些反馈显着。该项目将对变化的生物量池，森林中的人口增长率，以及AGWB重建所约束的气候驱动因素，建模的全新世气候轨迹的集合以及森林变化生态过程的替代模型进行联合概率推断。植被对气候的非线性阈值响应是这些回顾性“后报”的一个特别重点。该项目中开发的定量工具是通过练习、模块和课程向学生、土地管理者和其他研究人员提供教育和推广计划的基础，研究人员将在各种短期课程中进行测试，然后在网上向公众提供。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价值和更广泛的影响审查标准进行评估来支持。