globalchange  > 气候减缓与适应
DOI: 10.3390/f10010071
WOS记录号: WOS:000458910800070
论文题名:
Exploring the Sensitivity of Subtropical Stand Aboveground Productivity to Local and Regional Climate Signals in South China
作者: Zhou, Hua1; Luo, Yang1; Zhou, Guang2; Yu, Jian2; Shah, Sher2; Meng, Shengwang3; Liu, Qijing2
通讯作者: Liu, Qijing
刊名: FORESTS
ISSN: 1999-4907
出版年: 2019
卷: 10, 期:1
语种: 英语
英文关键词: forests ; mixed forests ; climate change and disturbances ; forest aboveground productivity ; streamflow ; MEI
WOS关键词: TROPICAL RAIN-FOREST ; TREE GROWTH ; DRY FOREST ; TEMPERATURE-VARIATION ; ALTITUDINAL GRADIENT ; USE EFFICIENCY ; CARBON STOCKS ; PICEA-ABIES ; RING ; BIOMASS
WOS学科分类: Forestry
WOS研究方向: Forestry
英文摘要:

Subtropical forest productivity is significantly affected by both natural disturbances (local and regional climate changes) and anthropogenic activities (harvesting and planting). Monthly measures of forest aboveground productivity from natural forests (primary and secondary forests) and plantations (mixed and single-species forests) were developed to explore the sensitivity of subtropical mountain productivity to the fluctuating characteristics of climate change in South China, spanning the 35-year period from 1981 to 2015. Statistical analysis showed that climate regulation differed across different forest types. The monthly average maximum temperature, precipitation, and streamflow were positively correlated with primary and mixed-forest aboveground net primary productivity (ANPP) and its components: Wood productivity (WP) and canopy productivity (CP). However, the monthly average maximum temperature, precipitation, and streamflow were negatively correlated with secondary and single-species forest ANPP and its components. The number of dry days and minimum temperature were positively associated with secondary and single-species forest productivity, but inversely associated with primary and mixed forest productivity. The multivariate ENSO (EI Nino-Southern Oscillation) index (MEI), computed based on sea level pressure, surface temperature, surface air temperature, and cloudiness over the tropical Pacific Ocean, was significantly correlated with local monthly maximum and minimum temperatures (Tmax and Tmin), precipitation (PRE), streamflow (FLO), and the number of dry days (DD), as well as the monthly means of primary and mixed forest aboveground productivity. In particular, the mean maximum temperature increased by 2.5, 0.9, 6.5, and 0.9 degrees C, and the total forest aboveground productivity decreased by an average of 5.7%, 3.0%, 2.4%, and 7.8% in response to the increased extreme high temperatures and drought events during the 1986/1988, 1997/1998, 2006/2007, and 2009/2010 EI Nino periods, respectively. Subsequently, the total aboveground productivity values increased by an average of 1.1%, 3.0%, 0.3%, and 8.6% because of lagged effects after the wet La Nina periods. The main conclusions of this study demonstrated that the influence of local and regional climatic fluctuations on subtropical forest productivity significantly differed across different forests, and community position and plant diversity differences among different forest types may prevent the uniform response of subtropical mountain aboveground productivity to regional climate anomalies. Therefore, these findings may be useful for forecasting climate-induced variation in forest aboveground productivity as well as for selecting tree species for planting in reforestation practices.


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/126835
Appears in Collections:气候减缓与适应

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作者单位: 1.Guizhou Acad Forestry, Res Stn Ecol, 382 Fuyuan South Rd, Guiyang 550000, Guizhou, Peoples R China
2.Beijing Forestry Univ, Coll Forestry, 35 Tsinghua East Rd, Beijing 100089, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Qianyanzhou Ecol Res Stn, 11A Datun Rd, Beijing 100089, Peoples R China

Recommended Citation:
Zhou, Hua,Luo, Yang,Zhou, Guang,et al. Exploring the Sensitivity of Subtropical Stand Aboveground Productivity to Local and Regional Climate Signals in South China[J]. FORESTS,2019-01-01,10(1)
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