期刊
JOURNAL OF ENVIRONMENTAL MANAGEMENT
卷 223, 期 -, 页码 713-722出版社
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jenvman.2018.06.046
关键词
Phyllostachys praecox; Net ecosystem exchange; Ecosystem respiration; Gross ecosystem productivity; Seasonal variation; Random forest
资金
- National Natural Science Foundation of China [31670644, 31370637]
- Natural Science Foundation of Zhejiang Province [LR14C160001]
- Zhejiang Provincial Collaborative Innovation Center for Bamboo Resources and High-efficiency Utilization
- Key Science and Technology Projects of Zhejiang Province [2015C03008]
- Key Discipline of Forestry of Creative Technology Project of Zhejiang Province [201510]
- Department of Forestry of Zhejiang Province [2017SY04]
- Chinese Academy of Forestry [2017SY04]
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.
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