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

森林中的光合作用是由各种过程控制的，这些过程跨越不同的尺度，从单个细胞、整个树叶、单个树木、整个林冠、地区和大陆。对这一复杂过程之间的相互作用缺乏了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？他们损失了多少钱？随着干旱、洪水或其他极端事件频率的增加，这些比率是如何变化的？测量世界各地每棵树的每一片叶子中的每一个细胞的活动是不可能的，但最近的技术进步使人们有可能比以前更接近这一理想。该奖项使用了新兴的远程观测技术，包括光探测和测距(LiDAR)和成像光谱学，现在可以从飞机和其他平台上绘制出植物养分在景观中的二维分布，以及树叶在树冠上的垂直分布。这两项技术将结合起来绘制森林养分的三维地图，然后使用复杂的光和光合作用模型，模拟随着时间的推移森林的碳吸收。除了科学进步，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。这项提议的总体目标是回答森林生态学中的两个关键问题。首先，叶片特征的更大多样性(更高的功能多样性)是否会增加地区的光合作用生产？其次，关于森林的物理结构和功能多样性的详细信息是否对于准确预测森林当前和未来的生产力是必要的？目前的植物生产力预测模型假定，森林的三维结构知识并不是估计其生产力所必需的。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航空观测平台以其良好的空间分辨率，结合高保真成像光谱和激光雷达系统，为跨三维空间和跨时间测量和了解生态系统生产力提供了前所未有的机会。最终，该奖项将检验这样一个假设，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中的生长旺季和生产力的每日周期。为了验证这一假设，将使用基于霓虹灯AOP数据的3-D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作用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.