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

气候变化使树木面临新的环境条件，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(树冠内树叶的排列和方向)是树木适应和适应环境变化的重要方式，但还没有成熟的理论或技术来衡量树冠结构。这项研究测试了新的理论和应用了新的技术来测量树冠结构，包括安装在美国东部九个国家生态观测网络(NEON)塔上的自动相机，以及来自NEON空中观测平台(AOP)的伴随激光测距(LiDAR)的测量。通过测试树冠结构的测量差异与一致的空中和卫星测量树木生产力的关系，这项研究可以为旨在最大限度地实现可持续森林增长的生态模型和森林管理技术提供参考。研究团队包括来自西弗吉尼亚大学(WVU)参加本科生研究体验(REU)项目的学生，以及数百名六年级学生和他们的老师，他们利用西弗吉尼亚大学科学冒险学校使用的“可持续树屋”测量树木结构和生产力。这项研究补充了叶经济学的成功理论，开发了基于特征的树冠经济学理论。首先，通过测量安装在九座霓虹塔和可持续树屋上的新延时相机的平均树叶角度特征，以及来自霓虹灯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导致这三个树冠特征的协变的理论。其次，研究人员考察了树冠特征在植被近红外反射率(NIRv)驱动模式中的作用，通过以下测量：(1)塔式安装的酚类相机，(2)霓虹灯AOP光谱数据，(3)陆地卫星峰值NIRv反射率和生长季内NIRv“绿城”速率的陆地卫星物候指标。研究小组使用站点内和站点之间的空间变异性来测试NIRV(以及水、碳和反照率的相关通量)的年度峰值在密度较大但皱纹较少的树冠中更高，这些树冠具有更多水平叶角度。而且，该团队使用时间变异性来衡量在整个生长季节，NIRV Greendown的速率在多大程度上受到越来越垂直的叶片角度的影响。因此，通过分析树冠特征和NIRV之间的这些关系，这项研究可以展示树冠经济理论的预测如何解释控制局部到大陆尺度的森林对气候变化的反应的新机制。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.