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.

森林受气候的影响。它们还影响气候，因为它们在生长过程中会从大气中清除二氧化碳。该项目将通过使用两个互补的数据集，即国家森林清查和树木年轮数据，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。这些数据来自美国林务局森林清查和分析项目。该清单计划通过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，这些地块被重新审视，每十年进行一次测量。在森林清查样地收集的一套空间完整的树芯包含了记录在年轮上的气候年际变化与树木生长之间关系的信息。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轮研究实验室向学校和公众推广。面对不断上升的温度和蒸发需求，森林生态系统的未来功能，特别是碳封存功能存在相当大的不确定性。这个项目的重点是美国西部内陆特别脆弱的森林宏观系统。这种大规模的森林生态系统功能将通过利用现有的大陆尺度生态观测站网络（美国林业局森林清查和分析计划的永久样地网络）进行分析，并将从同一森林样地网络中收集的增量核心中收集的单个树木生长的年度分辨率信息吸收到新的数据流中。分层贝叶斯多元回归方法将用于量化森林生产力在空间尺度上的驱动因素，并解释单株大小、林分结构、地球物理条件、干扰、气候及其跨尺度相互作用对林分水平和林分水平地上木质生物量生产的影响。对这些驱动因素的影响及其相互作用的估计将用于在不同林分密度和林分大小结构情景下对未来乔木和林分水平生产力的生态预测，包括预测不确定性的估计。如此大规模的数据同化将由NSF的CyVerse网络基础设施提供支持。该奖项反映了美国国家科学基金会的法定使命，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。

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.