State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Lin'an, 311300, Zhejiang, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A & F University, Lin'an, 311300, Zhejiang, China; School of Environmental and Resources Science, Zhejiang A & F University, Lin'an, 311300, Zhejiang, China.
State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Lin'an, 311300, Zhejiang, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A & F University, Lin'an, 311300, Zhejiang, China; School of Environmental and Resources Science, Zhejiang A & F University, Lin'an, 311300, Zhejiang, China.
J Environ Manage. 2018 Oct 1;223:713-722. doi: 10.1016/j.jenvman.2018.06.046. Epub 2018 Jul 3.
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, 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.
雷竹（Phyllostachys praecox）广泛分布于中国东南部。我们采用涡度相关法分析了一片雷竹林（2011-2013 年）的碳固存能力，并确定了碳通量的季节生物和非生物决定因素。我们采用一种名为随机森林（RF）的机器学习算法来识别影响碳通量的因素。RF 模型很好地预测了总生态系统生产力（GEP）、生态系统呼吸（RE）和净生态系统交换（NEE），并显示了不同季节驱动因素的变化。年平均 NEE、RE 和 GEP 分别为-105.2±23.1、1264.5±45.2 和 1369.6±52.5 g C m。在水分不成为限制因素时，气候变暖对 RE 的促进作用大于 GEP。夏季干旱对 GEP 的抑制作用不大，但土壤水分含量较低会抑制 RE，并在夏季干旱时增加碳汇。NEE 的最重要驱动因素是春季、夏季和冬季的土壤温度，以及秋季的光合有效辐射。空气和土壤温度是所有季节 GEP 的重要驱动因素。
