MSB-ECA: Ecosystems in four dimensions: Measuring changes to forest structure and function in the Anthropocene

森林中的光合作用受到多种过程的控制，这些过程的作用范围从单个细胞、整片叶子、单个树木、整个森林冠层、区域和大陆。缺乏对这一系列复杂过程之间相互作用的理解是造成全球碳预算不确定性的一个重要因素。森林通过光合作用吸收了多少碳？他们损失了多少？随着干旱、洪水或其他极端事件频率的增加，这些速率如何变化？测量世界上每棵树上每片叶子上每个细胞的活动是不可能的，但最近的技术进步使我们比以前更接近这个理想。该合同使用了新兴的远程观测技术，包括光探测和测距（激光雷达）和成像光谱，现在可以从飞机和其他平台上绘制植物营养物质在景观中的二维分布，以及整个树冠的叶片垂直分布。这两种技术将结合起来开发森林养分的三维地图，然后，使用复杂的光和光合作用模型，随着时间的推移模拟森林的碳吸收。除了科学进步外，该奖项还将培养本科生和研究生，将产生改善生态和地理教育的教材，所有数据、计算机代码和教材将通过现有数据库公开。这项提案的首要目标是回答森林生态学中的两个关键问题。首先，更大的叶片特征多样性（更高的功能多样性）是否会增加区域的光合作用产量？第二，关于森林的物理结构和功能多样性的详细信息对于准确预测当前和未来的森林生产力是否必要？目前的植物生产力预测模型假定，对森林三维结构的了解对于估计其生产力并不重要。然而，实证研究表明，这种假设是不正确的。国家生态观测网？’s机载观测平台（NEON AOP）具有良好的空间分辨率，结合高保真成像光谱和激光雷达系统，为测量和了解跨越三维空间和时间的生态系统生产力提供了前所未有的机会。最终，该奖项将验证一个假设，即关于森林结构和功能多样性的详细信息对于预测森林光合作用生产的关键要素至关重要，包括温带森林生态系统的高峰生长季节和生产力的日常循环。为了验证这一假设，将使用基于NEON AOP数据的三维叶片结构和功能重建。能够模拟森林光照状况的现实模型将使用这种结构和功能重建，并将对研究地点的光合生产力进行详细预测。

Photosynthesis in forests is controlled by a variety of processes acting across scales ranging from individual cells, whole leaves, individual trees, entire forest canopies, regions, and continents. Lack of understanding of the interactions among this complex set of processes is a significant contributor to the uncertainty of global carbon budgets. How much carbon do forests take up via photosynthesis? How much do they lose? How do these rates change with an increasing frequency of droughts, floods, or other extreme events? It is impossible to measure the activity of every cell in every leaf on every tree around the world, but recent technological advances have made it possible to get much closer to this ideal than was previously possible. This award uses emerging remote observation technologies, including light detection and ranging (LiDAR) and imaging spectroscopy, which now make it possible to map the two-dimensional distribution of plant nutrients across landscapes and the vertical distribution of leaves throughout a canopy from aircraft and other platforms. These two technologies will be combined to develop three dimensional maps of forest nutrients, then, using sophisticated models of light and photosynthesis, forest carbon uptake will be simulated over time. In addition to scientific advances, this award will train undergraduate and graduate students, will result in educational materials to improve ecological and geographical education, and all data, computer code, and teaching materials will be made publicly available via existing databases.The overarching goal of this proposal is to answer two critical questions in forest ecology. First, does a greater diversity of leaf traits (higher functional diversity) increase the photosynthetic production of regions? And, second, is detailed information about the physical structure of the forest and functional diversity necessary to accurately predict current and future productivity of forests? Current predictive models of plant productivity assume that knowledge of the three-dimensional structure of forests is not essential to estimating their productivity. Yet empirical studies have shown that this assumption is incorrect. The National Ecological Observatory Network?s Airborne Observation Platform (NEON AOP), with its fine spatial resolution combined with high-fidelity imaging spectroscopy and LiDAR systems, offers an unprecedented opportunity to measure and understand ecosystem productivity across three-dimensional space and through time. Ultimately, this award will test the hypothesis that detailed information about forest structural and functional diversity is critical to predicting key elements of forest photosynthetic production, including peak growing season and daily cycles of productivity in temperate forest ecosystems. To test this hypothesis, 3-D leaf structure and function reconstructions based on NEON AOP data will be used. Realistic models capable of simulating the light regime in forests will use this structural and functional reconstruction and detailed predictions of the study sites' photosynthetic productivity will be made.