Soil respiration dynamics in Cinnamomum camphora forest and a nearby Liquidambar formosana forest in Subtropical China - Tian, DaLun; Wang, GuangJun; Yan, WenDe; Xiang, WenHua; Peng, ChangHui (Chinese Science Bulletin, 2010)

Application of artificial neural networks in global climate change and ecological research: An overview - Liu, ZeLin; Peng, ChangHui; Xiang, WenHua; Tian, DaLun; Deng, XiangWen; Zhao, MeiFang (Chinese Science Bulletin, 2010)

Simulations of runoff and evapotranspiration in Chinese fir plantation ecosystems using artificial neural networks - Liu, Zelin; Peng, Changhui; Xiang, Wenhua; Deng, Xiangwen; Tian, DaLun; Zhao, Meifang; Yu, Guirui (Ecological Modelling, 2012)

Effects of Increased Nitrogen Deposition and Rotation Length on Long-Term Productivity of Cunninghamia lanceolata Plantation in Southern China - Zhao, Meifang; Xiang, Wenhua; Tian, Dalun; Deng, Xiangwen; Huang, Zhihong; Zhou, Xiaolu; Peng, Changhui (PLoS ONE, 2013)

General allometric equations and biomass allocation of Pinus massoniana trees on a regional scale in southern China - Xiang, Wenhua; Liu, Shaohui; Deng, Xiangwen; Shen, Aihua; Lei, Xiangdong; Tian, Dalun; Zhao, Meifang; Peng, Changhui (Ecological Research, 2011)
Abstract 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.

Applying an artificial neural network to simulate and predict Chinese fir (Cunninghamia lanceolata) plantation carbon flux in subtropical China - Wen, Xuding; Zhao, Zhonghui; Deng, Xiangwen; Xiang, Wenhua; Tian, Dalun; Yan, Wende; Zhou, Xiaolu; Peng, Changhui (Ecological Modelling, 2014)

Plant phenological modeling and its application in global climate change research: overview and future challenges - Zhao, Meifang; Peng, Changhui; Xiang, Wenhua; Deng, Xiangwen; Tian, Dalun; Zhou, Xiaolu; Yu, Guirui; He, Honglin; Zhao, Zhonghui (Environmental Reviews, 2013)
Plants interact to the seasonality of their environments, and changes in plant phenology have long been regarded as sensitive indicators of climatic change. Plant phenology modeling has been shown to be the simplest and most useful tool to assess phenol–climate shifts. Temperature, solar radiation, and water availability are assumed to be the key factors that control plant phenology. Statistical, mechanistic, and theoretical approaches have often been used for the parameterization of plant phenology models. The statistical approaches correlate the timing of phenological events to environmental factors or heat unit accumulations. The approaches have the simplified calculation procedures, correct phenological mechanism assumptions, but limited applications and predictive abilities. The mechanistic approaches describe plant phenology with the known or assumed “cause–effect relationships” between biological processes and key driving variables. The mechanistic approaches have the improved parameter processes, realistic assumptions, broad applications, and effective predictions. The theoretical approaches assume cost–benefit tradeoff strategies in trees. These methods are capable of capturing and quantifying the potential impacts and consequences of global climate change and human activity. However, certain limitations still exist related to our understanding of phenological mechanisms in relation to (1) interactions between plants and their specific climates, (2) the integration of both field observational and remote sensing data with plant phenology models across taxa and ecosystem type, (3) amplitude clarification of scale-related sensitivity to global climate change, and (4) improvements in parameterization processes and the overall reduction of modeling uncertainties to forecast impacts of future climate change on plant phenological dynamics. To improve our capacity in the prediction of the amplitude of plant phenological responses with regard to both structural and functional sensitivity to future global climate change, it is important to refine modeling methodologies by applying long-term and large-scale observational data. It is equally important to consider other less used but critical factors (such as heredity, pests, and anthropogenic drivers), apply advanced model parameterization and data assimilation techniques, incorporate process-based plant phenology models as a dynamic component into global vegetation dynamic models, and test plant phenology models against long-term ground observations and high-resolution satellite data across different spatial and temporal scales.

Application of TRIPLEX model for predicting Cunninghamia lanceolata and Pinus massoniana forest stand production in Hunan Province, southern China - Zhao, Meifang; Xiang, Wenhua; Deng, Xiangwen; Tian, Dalun; Huang, Zhihong; Zhou, Xiaolu; Yu, Guirui; He, Honglin; Peng, Changhui (Ecological Modelling, 2013)