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). Advances in ground-based remote sensing are revolutionizing how ecologists quantify CSC and, recently, strong linkages between CSC and NPP have been observed in individual forest ecosystems. Whether CSC-NPP relationships, and the mechanisms coupling structure and function, are conserved across an array of structurally variable forest ecosystems is unknown, but the universality of such relationships has implications for fundamental ecological understanding of structure-function interactions, and for remotely sensing and modeling the carbon cycle.
Our objectives are to use remote sensing 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 measures of ecosystem structure, including leaf quantity and biological diversity. We sampled National Ecological Observatory Network (NEON), Ameriflux, and field station sites spanning six eco-climatic domains within the continental United States to characterize forest CSC-canopy light absorption-NPP relationships, with canopy light absorption a putative mechanism linking structural complexity with NPP. Measures of CSC at plot and site scales were derived using portable under-canopy LiDAR (PCL), and canopy light absorption defined as the fraction of photosynthetically active radiation (fPAR) absorbed. NEON and Ameriflux have provided plant biomass data, which will be used to calculate NPP
We found that canopy structural complexity strongly predicts canopy light absorption, as fPAR, within and across sites, providing mechanistic information that is complementary to, but not redundant with, other ecosystem structural features related to NPP. Canopy structural complexity was extremely low in relatively homogenous Florida pine savannah sites and very high in heterogeneous mixed deciduous forests of the North Carolina Smoky Mountains, with greater than 60 % of the region-wide variation in fPAR explained by CSC. Multivariate models incorporating both leaf area index, a structural measure commonly used to estimate fPAR and NPP, and multiple metrics of CSC provide the greatest predictive power, demonstrating that the inclusion of structural complexity in carbon cycling models could greatly improve their performance. We conclude that CSC representation in carbon cycling models may enhance 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.