Estimating heterotrophic respiration at large scales: challenges, approaches, and next steps

Journal Article · 27 June 2016 · Ecosphere

DOI: https://doi.org/10.1002/ecs2.1380 · OSTI ID:1259577

Bond‐Lamberty, Ben ^[1]; Epron, Daniel ^[2]; Harden, Jennifer ^[3]; Harmon, Mark E. ^[4]; Hoffman, Forrest ^[5]; Kumar, Jitendra ^[5]; David McGuire, Anthony ^[6]; Vargas, Rodrigo ^[7]

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1. Joint Global Change Research Institute Pacific Northwest National Laboratory 5825 University Research Court College Park Maryland 20740 USA
2. Université de Lorraine UMR INRA‐UL 1137 Ecologie et Ecophysiologie Forestières Vandoeuvre‐les‐Nancy F54500 France
3. United States Geological Survey Menlo Park California 94025 USA
4. Department of Forest Ecosystems and Society Oregon State University Corvallis Oregon 97331 USA
5. Oak Ridge National Laboratory Climate Change Science Institute Oak Ridge Tennessee 37831 USA
6. United States Geological Survey Alaska Cooperative Fish and Wildlife Research Unit University of Alaska Fairbanks Fairbanks Alaska 99775 USA
7. Department of Plant and Soil Sciences University of Delaware Newark Delaware 19716 USA

Abstract Heterotrophic respiration (HR), the aerobic and anaerobic processes mineralizing organic matter, is a key carbon flux but one impossible to measure at scales significantly larger than small experimental plots. This impedes our ability to understand carbon and nutrient cycles, benchmark models, or reliably upscale point measurements. Given that a new generation of highly mechanistic, genomic‐specific global models is not imminent, we suggest that a useful step to improve this situation would be the development of “Decomposition Functional Types” (DFTs). Analogous to plant functional types (PFTs), DFTs would abstract and capture important differences in HR metabolism and flux dynamics, allowing modelers and experimentalists to efficiently group and vary these characteristics across space and time. We argue that DFTs should be initially informed by top‐down expert opinion, but ultimately developed using bottom‐up, data‐driven analyses, and provide specific examples of potential dependent and independent variables that could be used. We present an example clustering analysis to show how annual HR can be broken into distinct groups associated with global variability in biotic and abiotic factors, and demonstrate that these groups are distinct from (but complementary to) already‐existing PFTs. A similar analysis incorporating observational data could form the basis for future DFTs. Finally, we suggest next steps and critical priorities: collection and synthesis of existing data; more in‐depth analyses combining open data with rigorous testing of analytical results; using point measurements and realistic forcing variables to constrain process‐based models; and planning by the global modeling community for decoupling decomposition from fixed site data. These are all critical steps to build a foundation for DFTs in global models, thus providing the ecological and climate change communities with robust, scalable estimates of HR.

• Sponsoring Organization: USDOE
• OSTI ID: 1259577
• Journal Information: Ecosphere, Journal Name: Ecosphere Journal Issue: 6 Vol. 7; ISSN 2150-8925
• Publisher: Wiley Blackwell (John Wiley & Sons)Copyright Statement
• Country of Publication: United States
• Language: English