Harvard Forest Symposium Abstract 2017
- Title: Mechanisms linking forest canopy structural complexity with primary production: Evaluating the generality of ecosystem structure-production relationships at a continental scale
- Primary Author: Robert Fahey (University of Connecticut)
- Additional Authors: Jeff Atkins (Virginia Commonwealth University); Brady Hardiman (Boston University)
Ecosystem structure-carbon (C) cycling relationships represent a long-standing research area for ecosystem science. Canopy structural complexity (CSC) – determined from the arrangement of vegetation in canopy space – is linked with growth-limiting resource allocation and plant physiological functioning, and therefore may broadly relate to net primary production (NPP). Strong relationships between canopy structural complexity (CSC) and rates of C accumulation in plant biomass, or net primary productivity (NPP), have been demonstrated for a limited number of forest sites. Whether CSC-NPP relationships are broadly conserved across an array of structurally variable forest ecosystems is unknown, but the universality of this relationship has important implications for remotely sensing and modeling the terrestrial carbon cycle. We are using terrestrial LiDAR remote sensing across National Ecological Observatory Network (NEON), Ameriflux, and field station sites spanning six eco-climatic domains within the continental United States to determine whether CSC-NPP linkages and underlying mechanisms are widespread. We hypothesized that CSC provides mechanistic information relevant to carbon cycling that is not fully captured by other commonly quantified metrics of ecosystem structure, including leaf quantity (LAI) and biological diversity. We find that canopy structural complexity strongly predicts canopy light absorption, as fPAR, within and across sites, providing mechanistic information that is complementary to other ecosystem structural expressions used to estimate NPP. Multivariate models incorporating LAI and multiple metrics of CSC provided the greatest power in predicting fPAR, demonstrating that the inclusion of structural complexity in carbon cycling models could greatly improve their performance. We conclude that realistic CSC representation in carbon cycling models may enhance the prediction of canopy light capture and, therefore, improve large-scale carbon cycling simulations. Work is underway to couple CSC and fPAR with NPP, and to evaluate the scalability of CSC using satellite remote sensing products.
- Research Category: Group Projects; Physiological Ecology, Population Dynamics, and Species Interactions; Regional Studies
- Figures: Harvard Forest Abstract.pdf
FPAR vs Rugosity.jpg