2018 ESA Annual Meeting (August 5 -- 10)

OOS 23-7 - Coupling ecosystem complexity with leaf to canopy light and carbon cycling dynamics

Wednesday, August 8, 2018: 3:40 PM
348-349, New Orleans Ernest N. Morial Convention Center
Jeffrey Atkins1, Robert Fahey2, Brady Hardiman3 and Christopher Gough1, (1)Department of Biology, Virginia Commonwealth University, Richmond, VA, (2)Natural Resources and the Environment, University of Connecticut, Storrs, CT, (3)Forestry and Natural Resources, Purdue University, West Lafayette, IN
Background/Question/Methods

Understanding the relationships between structure and function of forest canopies is a long-standing area of inquiry fundamental to advancing understanding of many areas of ecology, including the modeling and interpretation of biogeochemical cycles. Novel, multi-dimensional measures of canopy structural complexity (CSC) that describe the arrangement and position of vegetation are now possible because of technological advances in remote sensing, and may improve modeled estimates of productivity and resource use efficiencies. However, it is important to establish broad scale relationships of remotely sensed measures of CSC with empirical field data. During 2016 and 2017, we surveyed forests across the eastern, southern, and midwestern United States using portable canopy LiDAR (PCL). This survey included 14 National Ecological Observation Network (NEON), Long-Term Ecological Research Network (LTER,) Ameriflux, US Forest Service (USFS) and University affiliated sites.

Results/Conclusions

Our findings show that the inclusion of CSC parameters into models of light acquisition explains 89% of the variance across all sites, an improvement over an LAI only model. Preliminary findings also show strong correlations of CSC with net primary productivity, measures of diversity, and light and nitrogen use efficiencies. We conclude that scalable estimates of CSC metrics may improve continent-wide estimates of canopy light absorption and carbon uptake, with implications for remote sensing and earth system modeling.