ABSTRACT

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.

Please report errors in award information by writing to: awardsearch@nsf.gov.