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Response of carbon uptake to abiotic and biotic drivers in an intensively managed Lei bamboo forest

Lei bamboo (Phyllostachys praecox) is widely distributed in southeastern China. We used eddy covariance to analyze carbon sequestration capacity of a Lei bamboo forest (2011–2013) and to identify the seasonal biotic and abiotic determinants of carbon fluxes. A machine learning algorithm called rando...

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Bibliographic Details
Published in:Journal of environmental management 2018-10, Vol.223, p.713-722
Main Authors: Liu, Yuli, Zhou, Guomo, Du, Huaqiang, Berninger, Frank, Mao, Fangjie, Li, Xuejian, Chen, Liang, Cui, Lu, Li, Yangguang, Zhu, Di'en, Xu, Lin
Format: Article
Language:English
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Summary:Lei bamboo (Phyllostachys praecox) is widely distributed in southeastern China. We used eddy covariance to analyze carbon sequestration capacity of a Lei bamboo forest (2011–2013) and to identify the seasonal biotic and abiotic determinants of carbon fluxes. A machine learning algorithm called random forest (RF) was used to identify factors that affected carbon fluxes. The RF model predicted well the gross ecosystem productivity (GEP), ecosystem respiration (RE) and net ecosystem exchange (NEE), and displayed variations in the drivers between different seasons. Mean annual NEE, RE, and GEP were −105.2 ± 23.1, 1264.5 ± 45.2, and 1369.6 ± 52.5 g C m−2, respectively. Climate warming increased RE more than GEP when water inputs were not limiting. Summer drought played little role in suppressing GEP, but low soil moisture contents suppressed RE and increased the carbon sink during drought in the summer. The most important drivers of NEE were soil temperature in spring, summer, and winter, and photosynthetically active radiation in autumn. Air and soil temperature were important drivers of GEP in all seasons. [Display omitted] •The managed Lei bamboo forest was a carbon sink annually and a weak carbon source in certain month.•Dynamics of carbon fluxes was closely associated with phenology of Lei bamboo.•Seasonal drivers of carbon fluxes were identified by random forest.•The results can provide a reference to simulate and optimize ecosystem carbon cycle.
ISSN:0301-4797
1095-8630
DOI:10.1016/j.jenvman.2018.06.046