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Evaluating the convergence between eddy-covariance and biometric methods for assessing carbon budgets of forests

Author

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  • M. Campioli

    (Centre of Excellence PLECO (Plant and Vegetation Ecology), University of Antwerp)

  • Y. Malhi

    (Environmental Change Institute, School of Geography and the Environment, University of Oxford)

  • S. Vicca

    (Centre of Excellence PLECO (Plant and Vegetation Ecology), University of Antwerp)

  • S. Luyssaert

    (LSCE CEA-CNRS-UVSQ, Orme des Merisiers
    Present address: Department of Ecological Science, VU University Amsterdam, 1081 HV Amsterdam, The Netherlands)

  • D. Papale

    (DIBAF, University of Tuscia
    Euro-Mediterranean Center on Climate Change)

  • J. Peñuelas

    (CSIC, Global Ecology Unit, CREAF-CEAB-CSIC-UAB, Cerdanyola del Vallès
    CREAF, Cerdanyola del Vallès)

  • M. Reichstein

    (Max Planck Institute for Biogeochemistry)

  • M. Migliavacca

    (Max Planck Institute for Biogeochemistry)

  • M. A. Arain

    (School of Geography & Earth Sciences, McMaster University)

  • I. A. Janssens

    (Centre of Excellence PLECO (Plant and Vegetation Ecology), University of Antwerp)

Abstract

The eddy-covariance (EC) micro-meteorological technique and the ecology-based biometric methods (BM) are the primary methodologies to quantify CO2 exchange between terrestrial ecosystems and the atmosphere (net ecosystem production, NEP) and its two components, ecosystem respiration and gross primary production. Here we show that EC and BM provide different estimates of NEP, but comparable ecosystem respiration and gross primary production for forest ecosystems globally. Discrepancies between methods are not related to environmental or stand variables, but are consistently more pronounced for boreal forests where carbon fluxes are smaller. BM estimates are prone to underestimation of net primary production and overestimation of leaf respiration. EC biases are not apparent across sites, suggesting the effectiveness of standard post-processing procedures. Our results increase confidence in EC, show in which conditions EC and BM estimates can be integrated, and which methodological aspects can improve the convergence between EC and BM.

Suggested Citation

  • M. Campioli & Y. Malhi & S. Vicca & S. Luyssaert & D. Papale & J. Peñuelas & M. Reichstein & M. Migliavacca & M. A. Arain & I. A. Janssens, 2016. "Evaluating the convergence between eddy-covariance and biometric methods for assessing carbon budgets of forests," Nature Communications, Nature, vol. 7(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms13717
    DOI: 10.1038/ncomms13717
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    Cited by:

    1. Pengyuan Wang & Shaoqiang Wang & Bin Chen & Muhammad Amir & Lei Wang & Jinghua Chen & Li Ma & Xiaobo Wang & Yuanyuan Liu & Kai Zhu, 2022. "Light and Water Conditions Co-Regulated Stomata and Leaf Relative Uptake Rate (LRU) during Photosynthesis and COS Assimilation: A Meta-Analysis," Sustainability, MDPI, vol. 14(5), pages 1-26, February.
    2. Xiaofei Yan & Qinxin Guo & Yajie Zhao & Yandong Zhao & Jianhui Lin, 2021. "Evaluation of Five Gas Diffusion Models Used in the Gradient Method for Estimating CO 2 Flux with Changing Soil Properties," Sustainability, MDPI, vol. 13(19), pages 1-13, September.
    3. Dan Bruhn & Freya Newman & Mathilda Hancock & Peter Povlsen & Martijn Slot & Stephen Sitch & John Drake & Graham P. Weedon & Douglas B. Clark & Majken Pagter & Richard J. Ellis & Mark G. Tjoelker & Ke, 2022. "Nocturnal plant respiration is under strong non-temperature control," Nature Communications, Nature, vol. 13(1), pages 1-10, December.

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