Collaborative Research: MSA: Tree crown economics: testing and scaling a functional trait-based theory

气候变化使树木受到新的环境条件的影响，了解树木如何应对这些变化变得越来越重要。虽然生态学家早就知道树冠结构（树冠内叶子的排列和方向）是树木适应和适应环境变化的重要方式，但没有完善的理论或技术来测量树冠结构。这项研究测试了新的理论，并应用了测量树冠结构的新技术，包括安装在美国东部9个国家生态观测网络（NEON）塔上的自动摄像机，以及NEON机载观测平台（AOP）的伴随激光测距（LiDAR）测量。通过测试测量到的树冠结构差异如何与树木生产力的同步机载和卫星测量相关联，该研究可以为旨在最大化可持续森林生长的生态模型和森林管理技术提供信息。研究团队包括来自西弗吉尼亚大学（WVU）的学生，他们参加了本科生（REU）项目的研究体验，以及数百名六年级学生和他们的老师，他们在西弗吉尼亚大学科学冒险学校使用的“可持续发展树屋”中制作树木建筑和生产力测量。本研究补充了成功的叶片经济学理论，发展了基于性状的皇冠经济学理论。首先，通过测量安装在九个NEON塔和可持续树屋上的新延时相机的平均叶片角度特征，以及NEON AOP激光雷达数据的树冠密度和粗糙度特征，研究人员评估了假设经济权衡驱动这三个树冠特征共变的理论。其次，研究人员研究了树冠性状在植被近红外反射率（NIRv）驱动模式中的作用，测量方法包括：(1)塔式物候相机，(2)NEON AOP光谱数据，以及(3)Landsat物候指标的峰值NIRv反射率和生长季节内NIRv“绿倒”率。研究小组利用这些站点内部和站点之间的空间变异性来测试NIRv的年峰值（以及水、碳和反照率通量的相关率）在密度更大、但皱褶较少、叶片水平角度更大的树冠中更高。并且，该团队使用时间变异性来衡量NIRv绿降率在生长季节中由越来越垂直的叶片角度驱动的程度。因此，通过分析林冠性状与NIRv之间的关系，研究可以证明林冠经济理论的预测如何能够阐明控制森林对气候变化的局地到大陆尺度响应的新机制。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。

Climate change is subjecting trees to new environmental conditions and it is increasingly important to understand how trees respond to these changes. While ecologists have long known that tree crown architecture (the arrangement and orientation of leaves within a tree crown) can be an important way that trees adapt and acclimate to environmental change, there is not well-established theory or techniques for measuring crown architecture. This study tests novel theory and applies new technologies for measuring crown architecture, including from automated cameras mounted to nine National Ecological Observation Network (NEON) towers in the eastern USA, and from accompanying laser ranging (LiDAR) measurements from the NEON Airborne Observation Platform (AOP). By testing how measured differences in crown architecture relate to coincident airborne and satellite measurements of tree productivity, the research can inform ecological models and forest management techniques aimed at maximizing sustainable forest growth. The research team includes students from West Virginia University (WVU) participating in a research experience for undergraduates (REU) program, as well as hundreds of sixth-graders and their teachers making tree architecture and productivity measurements from a “Sustainability Treehouse” used by the WVU Science Adventure School.Complementing the successful theory of leaf economics, the research develops a trait-based theory of crown economics. First, by measuring the trait of mean leaf angle from new time-lapse cameras mounted to nine NEON towers and the Sustainability Treehouse, and traits of crown density and rugosity from NEON AOP LiDAR data, the researchers assess theory positing that economic tradeoffs drive co-variation in these three crown traits. Second, the researchers examine the role of crown traits in driving patterns of near-infrared reflectance from vegetation (NIRv), as measured by: (1) tower-mounted phenocams, (2) NEON AOP spectral data, and (3) Landsat phenology metrics of peak NIRv reflectance and the rate of NIRv “greendown” within a growing season. The research team uses spatial variability within and across the sites to test that the annual peak of NIRv (and correlated rates of water, carbon, and albedo fluxes) is higher in denser, but less rugose, crowns that have more horizontal leaf angles. And, the team uses temporal variability to gauge the degree to which rates of NIRv greendown are driven by increasingly vertical leaf angles through the growing season. Thus, by analyzing these relationships among crown traits and NIRv, the research can demonstrate how predictions of crown economic theory can elucidate new mechanisms controlling local- to continental-scale responses of forests to climate change.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.