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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：
2003205
负责人：
Anna Trugman
金额：
$ 114.57万
依托单位：
University of California-Santa Barbara
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 2026-03-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2003205&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.

森林为社会提供多种服务，并在控制大气和生物圈之间的碳通量方面发挥关键作用。目前，森林在降雨量变化情况下继续提供服务的能力存在很大的不确定性。通过对干旱引起的多个分布广泛的树种的死亡率进行详细的测量，使用大陆尺度的森林组成数据集来推断美国各地的这些测量结果，并通过纳入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，以了解土壤中的低水可用性如何减少通过树木的水流，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中碳和水耦合流动的关键特征有关，为了解环境变化如何导致今天的森林以及预测森林物种组成未来可能如何变化提供了新的基础。此外，将个别树木的性状组合和各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，这些数据集将用于识别降雨模式变化特别危险的地区。在此过程中，该团队将进行跨学科的本科生和研究生培训，以培养多样化的下一代科学家，以应对生态和数据科学挑战。该项目团队将结合联合收割机一个简单的机械植被模型，该模型将植物水力性状与植物适应性联系起来，考虑到当地的环境条件，以及广泛的数据集，包括美国农业部林务局森林清查和分析（FIA）计划的森林群落调查，水力特性数据库，和植物水力性状种内变异的新测量。该团队的目标是预测在FIA地块和多个长期森林人口网络中观察到的森林死亡率模式，使用观察到的社区水力特征分布和由当地环境条件驱动的机械模型模拟。该小组将确定极端事件期间死亡率受社区加权平均水力特征和生态系统水力多样性影响的程度。最后，该团队将应用这些概念来了解植物生理可塑性/适应性在一个物种内的限制。项目小组将生成新的大陆尺度数据集，记录社区加权水力特征、社区生理功能和社区复原力。所获得的知识将为生态系统管理和保护工作以及未来的地球系统模型开发提供信息。该研究小组计划让当地土地管理者参与气候对森林复原力的影响，将来自当地社区和STEM中代表性不足的群体的本科和高中研究人员纳入项目活动，并通过每年一次的系列讲座向当地监狱人口伸出援手，重点是环境条件和陆地生态系统健康变化的影响。该奖项反映了NSF的法定使命，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。

项目成果

期刊论文数量（26）

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 within‐species trait variation in space and time reveals limits to trait‐mediated drought response

量化物种内性状在空间和时间上的变化揭示了性状介导的干旱反应的局限性

DOI：
10.1111/1365-2435.14112
发表时间：
2022
期刊：
Functional Ecology
影响因子：
5.2
作者：
Kerr, Kelly L.;Anderegg, Leander D. L.;Zenes, Nicole;Anderegg, William R. L.
通讯作者：
Anderegg, William R. L.

Evapotranspiration regulates leaf temperature and respiration in dryland vegetation

蒸散量调节旱地植被的叶温和呼吸

DOI：
10.1016/j.agrformet.2023.109560
发表时间：
2023
期刊：
Agricultural and Forest Meteorology
影响因子：
6.2
作者：
Kibler, Christopher L.;Trugman, Anna T.;Roberts, Dar A.;Still, Christopher J.;Scott, Russell L.;Caylor, Kelly K.;Stella, John C.;Singer, Michael Bliss
通讯作者：
Singer, Michael Bliss

Understanding and predicting forest mortality in the western United States using long‐term forest inventory data and modeled hydraulic damage

使用长期森林清查数据和模拟水力损害了解和预测美国西部的森林死亡率