Collaborative Research: EAGER-NEON: Is Canopy Structural Complexity a Global Predictor of Primary Production?: Using NEON to Transform Understanding of Forest Structure-function

合作研究：EAGER-NEON：树冠结构复杂性是初级生产的全球预测因子吗？：利用 NEON 转变对森林结构功能的理解

• 批准号: 1560944
• 负责人: Robert Fahey
• 金额: $ 5.99万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-12-01 至 2018-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1560944&HistoricalAwards=false
• 关键词: Collaborative; Research; EAGER; NEON; Canopy

Forests of the United States take up and store in plant biomass an enormous amount of carbon emitted from human activities, thereby slowing the accumulation of atmospheric carbon dioxide, a greenhouse gas. Canopy structure, an ecosystem feature that can be broadly characterized using remote sensing technologies, is a well-established determinant of forest carbon storage, with the quantity of canopy leaves a universal predictor of carbon storage that is incorporated into models used to forecast how the Nation's dynamic and diverse forested landscape affects climate. Recent work from a limited number of sites shows that the arrangement of leaves within a volume of canopy may be as influential to forest carbon storage as leaf quantity. Results from these studies suggest that leaf quantity and arrangement provide unique, complementary information about the underlying biological controls on forest carbon storage. Thus, coordinated measurements of both leaf quantity and arrangement within the canopies of a diverse array of forests may lead to substantially improved modeled estimates of carbon storage by the Nation's forests. In support of this goal, work here uses sites from the National Ecological Observatory Network (NEON) to evaluate whether canopy structural complexity, or the spatial variability in leaf arrangement within a canopy, is a global predictor of forest carbon storage within and across sites varying in physical structure, species composition and diversity, and climate. NEON's standardized methods, systematic sampling design, breadth of data, wide geographic footprint, and built-in gradient of forest physical structure provide an unprecedented opportunity to determine whether carbon storage-canopy structural complexity relationships are broadly generalizable. Enhanced knowledge of the role forest canopy structural complexity plays in carbon storage could transform fundamental understanding of how ecosystem structure affects carbon uptake, leading to more accurate climate models for informing science-based policy. Additionally, the results of this study have broad implications for how forests of the United States are managed in support of greenhouse gas mitigation and will provide new information on how management practices that modify canopy structure broadly affect land carbon sequestration. This project will train undergraduate, graduate, and postdoctoral researchers from a diverse group of academic institutions, and form the basis for a new Biology course at Virginia Commonwealth University taught by the project's postdoctoral associate. The researchers, including students and a postdoctoral associate, will play key roles in an NSF-supported research network that aims to develop broadly applicable remote sensing tools for quantifying forest features relevant to land managers, foresters, policy makers, and ecosystem and climate modelers. Ecosystem structure-function relationships represent a long-standing research area of ecosystem science; yet, whether relationships between canopy structural complexity (CSC) and net primary production (NPP), characterized at present for only small number of sites, are conserved across eco-climatic boundaries is unknown. Although considerable work has focused on the global importance of leaf area index (LAI) as a predictor or NPP, similar analysis of CSC and NPP spanning eco-climatic domains has not been conducted. As a result, whether CSC is a global predictor of NPP that provides additional mechanistic insight beyond LAI is not known, though site-level analyses, including those conducted by the PIs, suggest CSC may be as important as LAI in explaining variation in NPP. The National Ecological Observatory Network (NEON), with standardized measurements and sampling design, offers an unprecedented platform to transform understanding of forest structure-function relationships on a broad spatial scale. The goals of the project are to use 10 NEON sites containing a total of 176 plots to test whether forest CSC predicts NPP within and across a diverse array of temperate forest types and eco-climatic domains, and to identify underlying mechanisms linking CSC with NPP. Several metrics of CSC will be derived for each NEON site and individual plots within a site using data collected with a portable canopy lidar (PCL). Structural metrics will be related to co-located measurements of wood NPP estimated from the incremental change in woody biomass calculated using tree allometries. An underlying mechanistic basis for global NPP-CSC linkages is hypothesized to include improved resource-use efficiency as CSC increases, which will be examined by correlating CSC with measures of light-use efficiency (wood NPP/fraction of absorbed photosynthetic radiation [fPAR]) and nitrogen-use efficiency (wood NPP/canopy nitrogen mass). Within- and among-site variation in wood NPP as a function of CSC, leaf area index (LAI), and canopy nitrogen mass will be examined using a multi-model inference framework. The PIs hypothesize that model rankings will show variation in wood NPP within and among sites is best explained by multivariate models that include CSC in addition to LAI and canopy nitrogen mass parameters because each canopy feature represents complementary but not redundant mechanistic information. Using NEON sites to advance understanding of how and why CSC affects forest NPP across a broad spatial dimension could transform mechanistic understanding of ecosystem structure-carbon cycling relationships, and greatly improve carbon cycling models and remote sensing applications, while providing a crucial linkage between the two. Broader impacts stem from three separate areas: enhanced participation in a funded NSF Research Coordination Network (RCN), postdoctoral training and career development, and undergraduate research training. The PIs will advise and co/author resulting project publications and presentations with a postdoctoral and student researchers, with the postdoc serving as instructor of record for a 1-credit graduate topics course on ecosystem structure-function relationships at Virginia Commowealth University.

美国的森林吸收并储存了人类活动排放的大量碳，从而减缓了大气中二氧化碳（一种温室气体）的积累。冠层结构是一种可以使用遥感技术广泛表征的生态系统特征，是森林碳储存的一个公认的决定因素，冠层叶子的数量是碳储存的通用预测因子，被纳入用于预测国家碳储存的模型中。动态和多样化的森林景观影响着气候。最近在有限数量的地点进行的研究表明，一定范围内的树叶排列对森林碳储存的影响可能与树叶数量一样。这些研究的结果表明，叶子的数量和排列提供了有关森林碳储存的潜在生物控制的独特的补充信息。因此，对不同森林树冠内的叶子数量和排列进行协调测量可能会大大改进对国家森林碳储存的模型估计。为了支持这一目标，这里的工作使用国家生态观测站网络（NEON）的站点来评估冠层结构复杂性或冠层内叶子排列的空间变异性是否是不同站点内和跨站点森林碳储存的全球预测因子。物理结构、物种组成和多样性以及气候。 NEON 的标准化方法、系统抽样设计、数据广度、广泛的地理足迹和森林物理结构的内置梯度为确定碳储存-冠层结构复杂性关系是否可以广泛推广提供了前所未有的机会。增强对森林冠层结构复杂性在碳储存中所起作用的了解，可以改变对生态系统结构如何影响碳吸收的基本理解，从而产生更准确的气候模型，为基于科学的政策提供信息。此外，这项研究的结果对如何管理美国森林以支持温室气体减排具有广泛的影响，并将提供有关改变树冠结构的管理实践如何广泛影响土地碳固存的新信息。该项目将培训来自不同学术机构的本科生、研究生和博士后研究人员，并为弗吉尼亚联邦大学由该项目的博士后助理教授的新生物学课程奠定基础。研究人员，包括学生和博士后助理，将在国家科学基金会支持的研究网络中发挥关键作用，该网络旨在开发广泛适用的遥感工具，用于量化与土地管理者、林务人员、政策制定者以及生态系统和气候建模者相关的森林特征。生态系统结构-功能关系是生态系统科学的一个长期研究领域；然而，目前仅在少数地点表征的冠层结构复杂性（CSC）和净初级生产力（NPP）之间的关系是否在生态气候边界上保持不变尚不清楚。尽管大量工作集中在叶面积指数 (LAI) 作为预测因子或 NPP 的全球重要性上，但尚未对跨越生态气候领域的 CSC 和 NPP 进行类似的分析。因此，CSC 是否是 NPP 的全局预测因子，能够提供 LAI 之外的额外机制见解尚不清楚，尽管现场分析（包括 PI 进行的分析）表明，在解释 NPP 变化方面，CSC 可能与 LAI 一样重要。国家生态观测站网络（NEON）具有标准化的测量和抽样设计，提供了一个前所未有的平台，以转变对广泛空间尺度上森林结构与功能关系的理解。该项目的目标是使用包含总共 176 个地块的 10 个 NEON 站点来测试森林 CSC 是否可以预测各种温带森林类型和生态气候域内部和之间的 NPP，并确定 CSC 与 NPP 之间的联系的潜在机制。将使用便携式冠层激光雷达 (PCL) 收集的数据为每个 NEON 站点和站点内的各个地块导出 CSC 的几个指标。结构指标将与木材 NPP 的同位测量相关，该测量是通过使用树木异速生长计算的木质生物量增量变化来估计的。假设全球 NPP-CSC 联系的潜在机制基础包括随着 CSC 的增加而提高的资源利用效率，这将通过将 CSC 与光利用效率指标（木材 NPP/吸收光合辐射分数 [fPAR]）相关联来进行检查和氮利用效率（木材 NPP/冠层氮质量）。将使用多模型推理框架检查木材 NPP 的场内和场间变化作为 CSC、叶面积指数 (LAI) 和冠层氮质量的函数。 PI 假设模型排名将显示站点内部和站点之间木材 NPP 的变化，这可以通过多元模型得到最好的解释，其中包括 CSC、LAI 和冠层氮质量参数，因为每个冠层特征代表互补但非冗余的机械信息。使用 NEON 站点来加深对 CSC 如何以及为何在广泛的空间维度上影响森林 NPP 的理解，可以改变对生态系统结构-碳循环关系的机制理解，并极大地改进碳循环模型和遥感应用，同时提供两者之间的重要联系。更广泛的影响源于三个不同的领域：加强对受资助的 NSF 研究协调网络 (RCN) 的参与、博士后培训和职业发展以及本科生研究培训。 PI 将与博士后和学生研究人员一起提供建议并共同撰写项目出版物和演示文稿，博士后将担任弗吉尼亚联邦大学关于生态系统结构-功能关系的 1 学分研究生主题课程的记录讲师。