MCB-ECA: Assimilation of tree-ring and forest inventory data across the interior western U.S.: a hierarchical analysis of patterns and drivers to forecast forest productivity.

MCB-ECA：美国西部内陆树木年轮和森林库存数据的同化：对预测森林生产力的模式和驱动因素进行分层分析。

• 批准号: 1802893
• 负责人: Margaret Evans
• 金额: $ 29.7万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1802893&HistoricalAwards=false
• 关键词: MCB; ECA; Assimilation; tree; ring

Forests are influenced by climate. They also influence climate because they remove carbon dioxide from the atmosphere as they grow. This project will better quantify both the influence of climate variation on tree growth and how much carbon is sequestered each year by forests by using two complementary data sets, the national forest inventory and tree-ring data. These data come from the U. S. Forest Service Forest Inventory and Analysis Program. This inventory program evaluates the current state and future trajectory of the nation's forests from measurements in a network of permanent sample plots. The plots are revisited and measurements are made once each decade in the western US. A spatially complete set of tree cores collected in the forest inventory plots contains information on the relationship between year-to-year variation in climate and tree growth, recorded in annual growth rings. The combined data will be used to study forest productivity across broad spatial scales and make predictions of ecosystem state, ecosystem services, and natural capital. This study will produce ecological forecasts relevant to society and informing strategies for climate adaptation. The project will also provide research experience and professional development for a post-doctoral fellow and undergraduates, open-source code for reproducible science, and outreach to schools and the public at the Laboratory of Tree-Ring Research at the University of Arizona. There is considerable uncertainty surrounding the future functioning of forest ecosystems, especially carbon sequestration, in the face of rising temperatures and evaporative demand. The focus of this project is the particularly vulnerable forest macrosystem of the interior western United States. Forest ecosystem functioning at this large scale will be analyzed by leveraging an existing, continental-scale ecological observatory network (the permanent sample plot network of the U. S. Forest Service's Forest Inventory and Analysis Program) and assimilating into it a new data stream with annual-resolution information on individual tree growth from increment cores collected in the same forest plot network. A hierarchical Bayesian multiple regression approach will be used to quantify the drivers of forest productivity across spatial scales and explain the effects of individual tree size, forest stand structure, geophysical conditions, disturbances, climate and their cross-scale interactions on tree-level and forest stand-level above-ground woody biomass production. Estimates of the effects of these drivers and their interactions will be used to make ecological forecasts of future tree- and stand-level productivity under various scenarios of stand density and stand size structure, including estimates of forecasting uncertainty. Data assimilation on this large scale will be supported by the cyberinfrastructure of NSF's CyVerse.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.

森林受到气候的影响。它们还影响气候，因为它们在生长过程中从大气中去除二氧化碳。该项目将通过使用两个互补的数据集，即国家森林清单和树木年轮数据，更好地量化气候变化对树木生长的影响以及森林每年吸收的碳量。这些数据来自美国。S.林务局森林清查和分析方案。这一清单计划通过对永久性样地网络的测量来评估国家森林的现状和未来轨迹。在美国西部，每十年对这些地块进行一次重新审视和测量。在森林资源清查样地收集的一套空间上完整的树芯包含了关于气候逐年变化与树木生长之间关系的信息，这些信息记录在年轮中。综合数据将用于研究大空间尺度的森林生产力，并对生态系统状态、生态系统服务和自然资本进行预测。这项研究将产生与社会相关的生态预测，并为气候适应战略提供信息。该项目还将为博士后研究员和本科生提供研究经验和专业发展，为可再生科学提供开源代码，并在亚利桑那大学树木年轮研究实验室向学校和公众推广。在气温上升和蒸发需求的情况下，森林生态系统的未来功能，特别是碳固存，存在着相当大的不确定性。该项目的重点是美国西部内陆特别脆弱的森林宏观系统。利用现有的大陆尺度生态观测网络（美国国家森林管理局的永久样地网络），将分析森林生态系统在这一大尺度上的功能。S.林务局的森林清查和分析程序），并吸收到它的一个新的数据流与年度分辨率的信息，对个别树木的生长从增量核心收集在同一个森林小区网络。将采用分层贝叶斯多元回归方法来量化不同空间尺度的森林生产力驱动因素，并解释单棵树的大小、林分结构、地球物理条件、干扰、气候及其跨尺度相互作用对树木和林分地上木质生物量生产的影响。这些驱动因素及其相互作用的影响的估计将被用来作出生态预测的未来树木和林分水平的生产力在各种情景下的林分密度和林分规模结构，包括预测的不确定性估计。这种大规模的数据同化将得到NSF CyVerse网络基础设施的支持。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进行评估，被认为值得支持。

项目成果

The policy and ecology of forest-based climate mitigation: challenges, needs, and opportunities

• DOI: 10.1007/s11104-022-05315-6
• 发表时间: 2022-04-14
• 期刊: PLANT AND SOIL
• 影响因子: 4.9
• 作者: Giebink, Courtney L.;Domke, Grant M.;Evans, Margaret E. K.
• 通讯作者: Evans, Margaret E. K.

Continental‐scale tree‐ring‐based projection of Douglas‐fir growth: Testing the limits of space‐for‐time substitution

• DOI: 10.1111/gcb.15170
• 发表时间: 2020-05
• 期刊: Global Change Biology
• 影响因子: 11.6
• 作者: S. Klesse;R. DeRose;F. Babst;B. Black;L. Anderegg;J. Axelson;A. Ettinger;Hardy Griesbauer;Christopher H. Guiterman;G. Harley;Jill E Harvey;Yueh-Hsin Lo;A. Lynch;Christopher D. O’Connor;Christina M. Restaino;D. Sauchyn;J. Shaw;Dan Smith;Lisa J. Wood;J. Villanueva‐Díaz;M. Evans

Sampling bias overestimates climate change impacts on forest growth in the southwestern United States

• DOI: 10.1038/s41467-018-07800-y
• 发表时间: 2018-12-17
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Klesse, Stefan;DeRose, R. Justin;Evans, Margaret E. K.
• 通讯作者: Evans, Margaret E. K.