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

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Journal of Forestry Research 2016年第1期27卷 119-131页

作者： Guanglong Ou Junfeng Wang Hui Xu Keyi Chen Haimei Zheng Bo Zhang Xuelian Sun Tingting Xu Yifa Xiao Key Laboratory of Biodiversity Conservation in Southwest China of State Forest Administration Southwest Forestry University

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 var.ables 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 ran- dom-effects, and the topographic var.ables 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-ef- fects models with the two-level nested region and site random-effects （TLNLME）, NLME-RE with the fixed-ef- fects 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 modelfitting 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 pre- diction 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.

关键词： Aboveground biomass Mixed-effectsmodels Regional effect Site quality effect Topographicfactors pinus kesiya var. langbianensis

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Genetic Analysis of Superior Clones and Identification of Specific Marker of Elite Clones of pinus kesiya

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Agricultural Science & Technology 2017年第12期18卷 2524-2527页

作者： yunfeng zhu shaoyu chen jiabo hao tao wu yunnan academy of forestry Kunming 650201China yunnan provincial key laboratory of cultivation and exploitation of forest plants/yunnan laboratory for conservation of rare endangered&endemic forest plants state forestry administrationKunming 650201China

In the present study, the genetic distances between 85 superior clones from seven natural populations of pinus kesiya var. langbianensis were analyzed by RAPD-PCR assays, and then, a UPGMA cluster diagram was established based on the genetic distances to reveal their genetic relationship. In addition, specific markers of elite clones were identified by carefully comparing the fingerprints of the 85 clones. The results showed that the 85 P. kesiya clones were divided into three groups at a genetic distance of 0.59. Two specific markers that were able to distinguish some elite clones from others were identified. The results may provide a molecular basis for the identification and selection of elite clones of pinus kesiya var. langbianensis.

关键词： pinus kesiya var. langbianensis Superior clones Genetic diversity Specific marker RAPD UPGMA

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