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Collaborative Research: Predicting ecosystem resilience to climate and disturbance events with a multi-scale hydraulic trait framework

合作研究：利用多尺度水力特征框架预测生态系统对气候和干扰事件的恢复力

基本信息

批准号：
2003017
负责人：
William Anderegg
金额：
$ 29.24万
依托单位：
University of Utah
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2025-09-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2003017&HistoricalAwards=false
关键词：
Collaborative Research Predicting ecosystem resilience

项目摘要

Forests provide numerous services to society and play a critical role in governing the flux of carbon between the atmosphere and the biosphere. Currently, there is considerable uncertainty about the capacity of forests to continue providing services under altered precipitation regimes. By making detailed measurements related to drought-induced mortality for multiple, widespread tree species, using a continental-scale forest composition data set to extrapolate these measurements across the United States, and by incorporating predictions of future rainfall patterns, this project aims to improve our ability to predict the susceptibility of forests throughout the United States to drought. The investigators plan to make new measurements related to how low water availability in soil reduces water flow through trees, reduces rates of photosynthesis, and causes trees to die. The planned measurements, which are associated with key traits that influence coupled carbon and water flow in trees, provide a new basis for understanding how environmental variability has given rise to the forests of today, and for predicting how the species composition of forests is likely to change in the future. Furthermore, comparing the trait combinations of individual trees, and average trait values across species, to optimal values derived from a model enables a deeper understanding of why some species are more susceptible to drought than others. The team, composed of plant physiologists, forest ecologists, and Earth system modelers, plans to develop new measurement and modeling techniques, and plans to publicly disseminate datasets that will be used to identify regions at particular risk to changing rainfall patterns. In the process, the team will conduct interdisciplinary undergraduate and graduate training to prepare diverse, next-generation scientists to tackle ecological and data science challenges.The project team will combine a simple mechanistic vegetation model that links plant hydraulic traits to plant fitness, given local environmental conditions, and a wide range of datasets including forest community surveys from the USDA Forest Service Forest Inventory and Analysis (FIA) program, hydraulic trait databases, and new measurements of within-species variation of plant hydraulic traits. The team will aim to predict observed patterns in forest mortality at FIA plots, and across multiple long-term forest demography networks, using observed community hydraulic trait distributions and mechanistic model simulations driven by local environmental conditions. The team will identify the extent to which mortality during extreme events is dictated by community-weighted mean hydraulic traits and ecosystem hydraulic diversity. Finally, the team will apply these concepts to understand the limits of plant physiological plasticity/acclimation within a species. The project team will generate new continental-scale datasets documenting community-weighted hydraulic traits, community physiological function and community resilience. The knowledge gained will inform ecosystem management and conservation efforts, as well as future Earth system model development. The research team plans to engage local land managers about the impacts of climate on forest resilience, to incorporate undergraduate and high school researchers from local communities and underrepresented groups in STEM in project activities, and to reach out to local prison populations through an annual lecture series focused on the effects of variation in environmental conditions and terrestrial ecosystem health.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.

森林为社会提供众多服务，并在控制大气和生物圈之间的碳通量方面发挥着关键作用。目前，森林在降水状况改变的情况下继续提供服务的能力存在很大的不确定性。通过对多种广泛分布的树种进行与干旱引起的死亡相关的详细测量，使用大陆范围的森林组成数据集来推断美国各地的这些测量结果，并结合对未来降雨模式的预测，该项目旨在提高我们预测美国各地森林对干旱的敏感性的能力。研究人员计划进行新的测量，以了解土壤中的可用水量过低如何减少流经树木的水流、降低光合作用速率并导致树木死亡。计划中的测量与影响树木碳流和水流耦合的关键特征相关，为了解环境变化如何导致当今的森林以及预测未来森林的物种组成可能如何变化提供了新的基础。此外，将单棵树的性状组合以及跨物种的平均性状值与模型得出的最佳值进行比较，可以更深入地了解为什么某些物种比其他物种更容易受到干旱的影响。该团队由植物生理学家、森林生态学家和地球系统建模师组成，计划开发新的测量和建模技术，并计划公开传播数据集，用于识别降雨模式变化的特定风险区域。在此过程中，该团队将进行跨学科的本科生和研究生培训，为多样化的下一代科学家做好准备，应对生态和数据科学挑战。该项目团队将结合一个简单的机械植被模型，该模型将植物水力特征与植物适应性联系起来，考虑到当地环境条件，以及广泛的数据集，包括美国农业部林务局森林调查和分析（FIA）计划的森林群落调查、水力特征数据库，以及植物水力性状种内变异的新测量。该团队的目标是利用观察到的群落水力特征分布和由当地环境条件驱动的机制模型模拟，预测 FIA 地块和多个长期森林人口统计网络中观察到的森林死亡率模式。该团队将确定极端事件期间的死亡率在多大程度上由群落加权平均水力特征和生态系统水力多样性决定。最后，该团队将应用这些概念来了解一个物种内植物生理可塑性/驯化的局限性。该项目团队将生成新的大陆规模数据集，记录群落加权水力特征、群落生理功能和群落恢复力。获得的知识将为生态系统管理和保护工作以及未来地球系统模型的开发提供信息。研究团队计划让当地土地管理者了解气候对森林复原力的影响，将当地社区的本科生和高中研究人员以及 STEM 中代表性不足的群体纳入项目活动中，并通过每年一次的系列讲座接触当地监狱人口，重点关注环境条件变化和陆地生态系统健康的影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Quantifying the drivers of ecosystem fluxes and water potential across the soil-plant-atmosphere continuum in an arid woodland

量化干旱林地土壤-植物-大气连续体中生态系统通量和水势的驱动因素

DOI：
10.1016/j.agrformet.2022.109269
发表时间：
2023
期刊：
Agricultural and Forest Meteorology
影响因子：
6.2
作者：
Kannenberg, Steven A.;Barnes, Mallory L.;Bowling, David R.;Driscoll, Avery W.;Guo, Jessica S.;Anderegg, William R.L.
通讯作者：
Anderegg, William R.L.

Large volcanic eruptions elucidate physiological controls of tree growth and photosynthesis