文献类型：期刊文献

英文题名：Multilevel Nonlinear Mixed-Effect Crown Ratio Models for Individual Trees of Mongolian Oak (Quercus mongolica) in Northeast China

作者：Fu, Liyong[1] Zhang, Huiru[1] Lu, Jun[1] Zang, Hao[1] Lou, Minghua[1] Wang, Guangxing[2,3]

第一作者：符利勇

通信作者：Wang, GX[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China;[3]So Illinois Univ, Dept Geog & Environm Resources, Carbondale, IL 62901 USA

年份：2015

卷号：10

期号：8

外文期刊名：PLOS ONE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000358942700012)】；

基金：The authors acknowledge the National Science and Technology Support Project of the Twelfth Five-year Plan of China (Nos. 2012BAD22B02), the Chinese National Natural Science Foundation (Nos. 31300534), and the Central South University of Forestry and Technology (Nos. 112-0990) for financial support of this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

语种：英文

摘要：In this study, an individual tree crown ratio (CR) model was developed with a data set from a total of 3134 Mongolian oak (Quercus mongolica) trees within 112 sample plots allocated in Wangqing Forest Bureau of northeast China. Because of high correlation among the observations taken from the same sampling plots, the random effects at levels of both blocks defined as stands that have different site conditions and plots were taken into account to develop a nested two-level nonlinear mixed-effect model. Various stand and tree characteristics were assessed to explore their contributions to improvement of model prediction. Diameter at breast height, plot dominant tree height and plot dominant tree diameter were found to be significant predictors. Exponential model with plot dominant tree height as a predictor had a stronger ability to account for the heteroskedasticity. When random effects were modeled at block level alone, the correlations among the residuals remained significant. These correlations were successfully reduced when random effects were modeled at both block and plot levels. The random effects from the interaction of blocks and sample plots on tree CR were substantially large. The model that took into account both the block effect and the interaction of blocks and sample plots had higher prediction accuracy than the one with the block effect and population average considered alone. Introducing stand density into the model through dummy variables could further improve its prediction. This implied that the developed method for developing tree CR models of Mongolian oak is promising and can be applied to similar studies for other tree species.