作者机构：
[Tian, Dalun; Zhao, Meifang; Liu, Shaohui; Xiang, Wenhua; Peng, Changhui; Deng, Xiangwen] Cent S Univ Forestry & Technol, Fac Life Sci & Technol, Changsha 410004, Hunan, Peoples R China.;[Liu, Shaohui] China State Forestry Adm, Dept Dev Planning & Assets Management, Beijing 100714, Peoples R China.;[Shen, Aihua] Zhejiang Forestry Acad, Hangzhou 310023, Zhejiang, Peoples R China.;[Lei, Xiangdong] Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China.;[Peng, Changhui] Univ Quebec, Dept Biol Sci, Inst Environm Sci, Montreal, PQ H3C 3P8, Canada.
通讯机构：
[Xiang, Wenhua] C;Cent S Univ Forestry & Technol, Fac Life Sci & Technol, Changsha 410004, Hunan, Peoples R China.
关键词：
Pinus massoniana;Allometric equation;Tree biomass carbon;Regional scale;Southern China subtropical region
摘要：
Applying allometric equations in combination with forest inventory data is an effective approach to use when qualifying forest biomass and carbon storage on a regional scale. The objectives of this study were to (1) develop general allometric tree component biomass equations and (2) investigate tree biomass allocation patterns for Pinus massoniana, a principal tree species native to southern China, by applying 197 samples across 20 site locations. The additive allometric equations utilized to compute stem, branch, needle, root, aboveground, and total tree biomass were developed by nonlinear seemingly unrelated regression. Results show that the relative proportion of stem biomass to tree biomass increased while the contribution of canopy biomass to tree biomass decreased as trees continued to grow through time. Total root biomass was a large biomass pool in itself, and its relative proportion to tree biomass exhibited a slight increase with tree growth. Although equations employing stem diameter at breast height (dbh) alone as a predictor could accurately predict stem, aboveground, root, and total tree biomass, they were poorly fitted to predict the canopy biomass component. The inclusion of the tree height (H) variable either slightly improved or did not in any way increase model fitness. Validation results demonstrate that these equations are suitable to estimate stem, aboveground, and total tree biomass across a broad range of P. massoniana stands on a regional scale.
作者机构：
[彭长辉] 中南林业科技大学生命科学与技术学院,长沙,410004;[彭长辉] Institute of Environment Sciences,Department of Biology Sciences university of Quebec at Montreal,Case postale 8888,Succ Centre-Ville,Montreal(QC)H3C 3P8 Canada;[田大伦; 赵梅芳; 邓湘雯; 项文化; 刘泽麟] 中南林业科技大学
通讯机构：
[Peng ChangHui] C;Cent S Univ Forestry & Technol, Coll Life Sci & Technol, Changsha 410004, Hunan, Peoples R China.
关键词：
global change;ecology;artificial neural network;nonlinear problem
摘要：
Fields that employ artificial neural networks (ANNs) have developed and expanded continuously in recent years with the ongoing development of computer technology and artificial intelligence. ANN has been adopted widely and put into practice by researchers in light of increasing concerns over ecological issues such as global warming, frequent El Ni?o-Southern Oscillation (ENSO) events, and atmospheric circulation anomalies. Limitations exist and there is a potential risk for misuse in that ANN model parameters require typically higher overall sensitivity, and the chosen network structure is generally more dependent upon individual experience. ANNs, however, are relatively accurate when used for short-term predictions; despite global climate change research favoring the effects of interactions as the basis of study and the preference for long-term experimental research. ANNs remain a better choice than many traditional methods when dealing with nonlinear problems, and possesses great potential for the study of global climate change and ecological issues. ANNs can resolve problems that other methods cannot. This is especially true for situations in which measurements are difficult to conduct or when only incomplete data are available. It is anticipated that ANNs will be widely adopted and then further developed for global climate change and ecological research.
作者机构：
[Tian Da-Lun; Yan Wen-De; Fang Xi; Kang Wen-Xing; Deng Xiang-Wen; Wang Guang-Jun] Cent S Univ Forestry & Technol, Fac Life Sci & Technol, Forest Ecol Sect, Changsha 410006, Hunan, Peoples R China.
通讯机构：
[Tian, DL ] ;Cent S Univ Forestry & Technol, Fac Life Sci & Technol, Forest Ecol Sect, Changsha 410006, Hunan, Peoples R China.
关键词：
forest;soil moisture;soil properties;soil respiration;soil temperature
摘要：
Forest management is expected to influence soil CO2 efflux (FCO2) as a result of changes in microenvironmental conditions, soil microclimate, and root dynamics. Soil FCO2 rate was measured during the growing season of 2006 in both thinning and non-thinning locations within stands ranging from 0 to 8 years after the most recent thinning in Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) plantations in Huitong Ecosystem Research Station, Hunan, China. Soil temperature and moisture were also measured to examine relationships between FCO2 and soil properties. Forest thinning resulted in huge changes in FCO2 that varied with time since cutting. Immediately following harvest (year 0) FCO2 in thinning area increased by about 30%, declined to 20%–27% below pre-cutting levels during years 4–6, and recovered to pre-cutting levels at 8 years post-cutting. A similar temporal pattern, but with smaller changes, was found in non-thinning locations. The initial increase in FCO2 could be attributed to a combination of root decay, soil disturbance, and increased soil temperature in gaps, while the subsequent decrease and recovery to the death and gradual regrowth of active roots. Strong effects of soil temperature and soil water content on FCO2 were found. Forest thinning mainly influenced FCO2 through changes in tree root respiration, and the net result was a decrease in integrated FCO2 flux through the entire felling cycle.
