Patterns of Biomass and Carbon Distribution across a Chronosequence of Chinese Pine Pinus tabulaeformis ForestsReport as inadecuate

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Patterns of biomass and carbon C storage distribution across Chinese pine Pinus tabulaeformis natural secondary forests are poorly documented. The objectives of this study were to examine the biomass and C pools of the major ecosystem components in a replicated age sequence of P. tabulaeformis secondary forest stands in Northern China. Within each stand, biomass of above- and belowground tree, understory shrub and herb, and forest floor were determined from plot-level investigation and destructive sampling. Allometric equations using the diameter at breast height DBH were developed to quantify plant biomass. C stocks in the tree and understory biomass, forest floor, and mineral soil 0–100 cm were estimated by analyzing the C concentration of each component. The results showed that the tree biomass of P. tabulaeformis stands was ranged from 123.8 Mg·ha–1 for the young stand to 344.8 Mg·ha–1 for the mature stand. The understory biomass ranged from 1.8 Mg·ha–1 in the middle-aged stand to 3.5 Mg·ha–1 in the young stand. Forest floor biomass increased steady with stand age, ranging from 14.9 to 23.0 Mg·ha–1. The highest mean C concentration across the chronosequence was found in tree branch while the lowest mean C concentration was found in forest floor. The observed C stock of the aboveground tree, shrub, forest floor, and mineral soil increased with increasing stand age, whereas the herb C stock showed a decreasing trend with a sigmoid pattern. The C stock of forest ecosystem in young, middle-aged, immature, and mature stands were 178.1, 236.3, 297.7, and 359.8 Mg C ha–1, respectively, greater than those under similar aged P. tabulaeformis forests in China. These results are likely to be integrated into further forest management plans and generalized in other contexts to evaluate C stocks at the regional scale.

Author: Jinlong Zhao , Fengfeng Kang , Luoxin Wang, Xiaowen Yu, Weihong Zhao, Xiaoshuai Song, Yanlei Zhang, Feng Chen, Yu Sun, Tengfei He



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