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    贾炜玮, 罗天泽, 李凤日. 基于抚育间伐效应的红松人工林枝条密度模型[J]. 北京林业大学学报, 2021, 43(2): 10-21. DOI: 10.12171/j.1000-1522.20200057
    引用本文: 贾炜玮, 罗天泽, 李凤日. 基于抚育间伐效应的红松人工林枝条密度模型[J]. 北京林业大学学报, 2021, 43(2): 10-21. DOI: 10.12171/j.1000-1522.20200057
    Jia Weiwei, Luo Tianze, Li Fengri. Branch density model for Pinus koraiensis plantation based on thinning effects[J]. Journal of Beijing Forestry University, 2021, 43(2): 10-21. DOI: 10.12171/j.1000-1522.20200057
    Citation: Jia Weiwei, Luo Tianze, Li Fengri. Branch density model for Pinus koraiensis plantation based on thinning effects[J]. Journal of Beijing Forestry University, 2021, 43(2): 10-21. DOI: 10.12171/j.1000-1522.20200057

    基于抚育间伐效应的红松人工林枝条密度模型

    Branch density model for Pinus koraiensis plantation based on thinning effects

    • 摘要:
        目的  分析抚育间伐对红松人工林枝条数量的影响,建立基于间伐效应的生物数学模型,为制定更加科学合理的间伐体制提供理论依据。
        方法  基于黑龙江省林口林业局和东京城林业局不同林分条件及抚育间伐强度下的红松人工林49株解析木4 370组枝解析数据,利用R语言的nlme包,建立了基于抚育间伐效应的枝条密度单水平非线性混合模型,并利用调整决定系数( R_\rma^2 )、赤池信息准则(AIC)、贝叶斯信息准则(BIC)、对数似然值(Log-likelihood)以及似然比检验(LRT)等评价指标对所收敛的模型进行评价。
        结果  当地位指数和树木等级相近时,抚育间伐强度和冠长越大,枝条密度越大;当抚育间伐强度和树木等级相近时,地位指数和冠长越大,枝条密度越大;而抚育间伐强度和地位指数相近时,树木胸径与枝条密度呈负相关。基于样地效应的混合模型模拟精度均高于基础模型和基于样木效应的混合模型,最终选用含有总着枝深度(DINC)、相对着枝深度的自然对数(lnRDINC)、相对着枝深度的平方(RDINC2)、胸径(DBH)、抚育间伐强度与间伐年龄的比值(TI/TA)这5个随机效应参数的非线性混合模型为枝条密度最优预测模型,其R_\rma^2为0.825 7,均方根误差(RMSE)为2.171 4。
        结论  基于抚育间伐效应的红松枝条密度最优非线性混合效应模型,不但能提高模型精度,还能更加准确地体现抚育间伐对林木枝条产生的影响。

       

      Abstract:
        Objective  In order to analyze the influence of thinning on the number of branches for Pinus koraiensis plantation, this study constructed a biological mathematic model based on the thinning effect, and provided a theoretical basis for developing a scientific and reasonable thinning program.
        Method  Based on the data of 4 370 branches from 49 sample trees in Pinus koraiensis plantation in Linkou and Dongjingcheng Forestry Bureau of Heilongjiang Province of northeastern China, this study established a single-level nonlinear mixed effect model of branch density with thinning effects using nlme package of R. The converged models were then evaluated by adjusted coefficient of determination ( R_\rma^2 ), Akaike information criterion AIC, Bayesian information criterion (BIC), log likelihood and likelihood ratio test (LRT).
        Result  When site index and tree size were similar, branch density increased with the increase of thinning intensity and crown length. When thinning intensity and tree size were similar, branch density increased with the increase of site index and crown length. However, when thinning density and site index were similar, branch density was negatively correlated with DBH. Nonlinear mixed effect model with plot effect had higher fitting precision than that with tree effect and corresponding fixed effect model. Finally, the nonlinear mixed model with five random coefficients, including DINC (depth into crown), lnRDINC (natural logarithm of relative depth into crown), RDINC2 (square of relative depth into crown), DBH and TI/TA (thinning intensity over thinning age) was selected as the most optimized model for predicting branch density, whose R_\rma^2 was 0.825 7 and RMSE was 2.171 4.
        Conclusion  The optimal nonlinear mixed effect model with thinning effect not only has higher precision, but also more accurately reflects the effect of thinning on tree branches than other models.

       

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