Collaborative Research: EAGER-NEON: Is Canopy Structural Complexity a Global Predictor of Primary Production?: Using NEON to Transform Understanding of Forest Structure-function

University Of Connecticut, Storrs CT

Investigators

Abstract

Forests of the United States take up and store in plant biomass an enormous amount of carbon emitted from human activities, thereby slowing the accumulation of atmospheric carbon dioxide, a greenhouse gas. Canopy structure, an ecosystem feature that can be broadly characterized using remote sensing technologies, is a well-established determinant of forest carbon storage, with the quantity of canopy leaves a universal predictor of carbon storage that is incorporated into models used to forecast how the Nation's dynamic and diverse forested landscape affects climate. Recent work from a limited number of sites shows that the arrangement of leaves within a volume of canopy may be as influential to forest carbon storage as leaf quantity. Results from these studies suggest that leaf quantity and arrangement provide unique, complementary information about the underlying biological controls on forest carbon storage. Thus, coordinated measurements of both leaf quantity and arrangement within the canopies of a diverse array of forests may lead to substantially improved modeled estimates of carbon storage by the Nation's forests. In support of this goal, work here uses sites from the National Ecological Observatory Network (NEON) to evaluate whether canopy structural complexity, or the spatial variability in leaf arrangement within a canopy, is a global predictor of forest carbon storage within and across sites varying in physical structure, species composition and diversity, and climate. NEON's standardized methods, systematic sampling design, breadth of data, wide geographic footprint, and built-in gradient of forest physical structure provide an unprecedented opportunity to determine whether carbon storage-canopy structural complexity relationships are broadly generalizable. Enhanced knowledge of the role forest canopy structural complexity plays in carbon storage could transform fundamental understanding of how ecosystem structure affects carbon uptake, leading to more accurate climate models for informing science-based policy. Additionally, the results of this study have broad implications for how forests of the United States are managed in support of greenhouse gas mitigation and will provide new information on how management practices that modify canopy structure broadly affect land carbon sequestration. This project will train undergraduate, graduate, and postdoctoral researchers from a diverse group of academic institutions, and form the basis for a new Biology course at Virginia Commonwealth University taught by the project's postdoctoral associate. The researchers, including students and a postdoctoral associate, will play key roles in an NSF-supported research network that aims to develop broadly applicable remote sensing tools for quantifying forest features relevant to land managers, foresters, policy makers, and ecosystem and climate modelers. Ecosystem structure-function relationships represent a long-standing research area of ecosystem science; yet, whether relationships between canopy structural complexity (CSC) and net primary production (NPP), characterized at present for only small number of sites, are conserved across eco-climatic boundaries is unknown. Although considerable work has focused on the global importance of leaf area index (LAI) as a predictor or NPP, similar analysis of CSC and NPP spanning eco-climatic domains has not been conducted. As a result, whether CSC is a global predictor of NPP that provides additional mechanistic insight beyond LAI is not known, though site-level analyses, including those conducted by the PIs, suggest CSC may be as important as LAI in explaining variation in NPP. The National Ecological Observatory Network (NEON), with standardized measurements and sampling design, offers an unprecedented platform to transform understanding of forest structure-function relationships on a broad spatial scale. The goals of the project are to use 10 NEON sites containing a total of 176 plots to test whether forest CSC predicts NPP within and across a diverse array of temperate forest types and eco-climatic domains, and to identify underlying mechanisms linking CSC with NPP. Several metrics of CSC will be derived for each NEON site and individual plots within a site using data collected with a portable canopy lidar (PCL). Structural metrics will be related to co-located measurements of wood NPP estimated from the incremental change in woody biomass calculated using tree allometries. An underlying mechanistic basis for global NPP-CSC linkages is hypothesized to include improved resource-use efficiency as CSC increases, which will be examined by correlating CSC with measures of light-use efficiency (wood NPP/fraction of absorbed photosynthetic radiation [fPAR]) and nitrogen-use efficiency (wood NPP/canopy nitrogen mass). Within- and among-site variation in wood NPP as a function of CSC, leaf area index (LAI), and canopy nitrogen mass will be examined using a multi-model inference framework. The PIs hypothesize that model rankings will show variation in wood NPP within and among sites is best explained by multivariate models that include CSC in addition to LAI and canopy nitrogen mass parameters because each canopy feature represents complementary but not redundant mechanistic information. Using NEON sites to advance understanding of how and why CSC affects forest NPP across a broad spatial dimension could transform mechanistic understanding of ecosystem structure-carbon cycling relationships, and greatly improve carbon cycling models and remote sensing applications, while providing a crucial linkage between the two. Broader impacts stem from three separate areas: enhanced participation in a funded NSF Research Coordination Network (RCN), postdoctoral training and career development, and undergraduate research training. The PIs will advise and co/author resulting project publications and presentations with a postdoctoral and student researchers, with the postdoc serving as instructor of record for a 1-credit graduate topics course on ecosystem structure-function relationships at Virginia Commowealth University.