Journal of Forestry Research

, Volume 27, Issue 1, pp 119–131 | Cite as

Incorporating topographic factors in nonlinear mixed-effects models for aboveground biomass of natural Simao pine in Yunnan, China

  • Guanglong Ou
  • Junfeng Wang
  • Hui Xu
  • Keyi Chen
  • Haimei Zheng
  • Bo Zhang
  • Xuelian Sun
  • Tingting Xu
  • Yifa Xiao


A total of 128 Simao pine trees (Pinus kesiya var. langbianensis) from three regions of Pu’er City, Yunnan Province, People’s Republic of China, were destructively sampled to obtain tree aboveground biomass (AGB). Tree variables such as diameter at breast height and total height, and topographical factors such as altitude, aspect of slope, and degree of slope were recorded. We considered the region and site quality classes as the random-effects, and the topographic variables as the fixed-effects. We fitted a total of eight models as follows: least-squares nonlinear models (BM), least-squares nonlinear models with the topographic factors (BMT), nonlinear mixed-effects models with region as single random-effects (NLME-RE), nonlinear mixed-effects models with site as single random-effects (NLME-SE), nonlinear mixed-effects models with the two-level nested region and site random-effects (TLNLME), NLME-RE with the fixed-effects of topographic factors (NLMET-RE), NLME-SE with the fixed-effects of topographic factors (NLMET-SE), and TLNLME with the fixed-effects of topographic factors (TLNLMET). The eight models were compared by model fitting and prediction statistics. The results showed: model fitting was improved by considering random-effects of region or site, or both. The models with the fixed-effects of topographic factors had better model fitting. According to AIC and BIC, the model fitting was ranked as TLNLME > NLMET-RE > NLME-RE > NLMET-SE > TLNLMET > NLME-SE > BMT > BM. The differences among these models for model prediction were small. The model prediction was ranked as TLNLME > NLME-RE > NLME-SE > NLMET-RE > NLMET-SE > TLNLMET > BMT > BM. However, all eight models had relatively high prediction precision (>90 %). Thus, the best model should be chosen based on the available data when using the model to predict individual tree AGB.


Aboveground biomass Mixed-effects models Regional effect Site quality effect Topographic factors Pinus kesiya var. langbianensis 


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Copyright information

© Northeast Forestry University and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Guanglong Ou
    • 1
  • Junfeng Wang
    • 1
  • Hui Xu
    • 1
  • Keyi Chen
    • 1
  • Haimei Zheng
    • 1
  • Bo Zhang
    • 1
  • Xuelian Sun
    • 1
  • Tingting Xu
    • 1
  • Yifa Xiao
    • 1
  1. 1.Key Laboratory of Biodiversity Conservation in Southwest China of State Forest AdministrationSouthwest Forestry UniversityKunmingChina

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