文献类型：期刊文献

中文题名：华北落叶松人工林林分枯损株数随机效应预测模型

英文题名：Prediction Model of Stand Mortality of Larix principis-rupprechtii Plantation in the Core Area of Winter Olympic Games

作者：周泽宇[1,2,4] 冯林艳[1,2] 闫星蓉[1,2,3] 张晓芳[1,2] 杨旭平[5] 符利勇[1,2] 张会儒[1,2,4]

第一作者：周泽宇

机构：[1]中国林业科学研究院资源信息研究所,北京100091;[2]国家林业和草原局森林经营与生长模拟重点实验室,北京100091;[3]山西农业大学林学院,晋中030801;[4]中国林业科学研究院华北林业实验中心,北京102300;[5]中国林业科学研究院,北京100091

年份：2022

卷号：58

期号：10

起止页码：67-78

中文期刊名：林业科学

外文期刊名：Scientia Silvae Sinicae

收录：CSTPCD;;EI(收录号：20232814385045);Scopus;北大核心:【北大核心2020】；CSCD:【CSCD2021_2022】；

基金：张家口市崇礼区森林防火综合体系建设无人机巡护监测系统(DA2020001);国家自然科学基金面上项目(31971653)。

语种：中文

中文关键词：枯死木数量;Poisson模型;负二项模型;零膨胀模型;Hurdle模型;随机效应

外文关键词：mortality trees number;Poisson model;negative binomial model;zero expansion model;Hurdle model;random effect

分类号：S757

摘要：【目的】构建能够准确预测华北落叶松林分枯死木的计数模型,探究影响华北落叶松林中林木枯死数量的主要原因,为冬奥会核心区的华北落叶松人工林科学经营与管理提供决策依据。【方法】以张家口市崇礼冬奥核心区45块华北落叶松人工林样地为研究对象,构建华北落叶松林分枯死数量Poisson回归模型、负二项回归模型、零膨胀Poisson回归模型和零膨胀负二项回归模型、Hurdle-Poisson回归模型、Hurdle负二项回归模型,根据AIC值选出最优计数模型。基于最优计数模型,考虑不同随机效应水平和作用在截距和协变量上的随机参数,根据模型收敛情况和AIC值确定最优的随机效应水平和随机参数组合,构建最优林分枯死数量混合效应模型。【结果】林分平均直径、林分优势木平均高、林龄、林分断面积和林分胸径Gini系数为影响林分枯死的林分因子,立地因子对林分枯死的影响并不大。未考虑零膨胀现象时,负二项回归模型拟合效果优于Poisson回归模型;考虑零膨胀现象后,Hurdle-Poisson回归模型拟合效果优于零膨胀Poisson回归模型。最终几种考虑零值过多的计数模型的拟合精度表现为:Hurdle负二项回归模型(HNB)≈零膨胀负二项回归模型(ZINB)>Hurdle-Poisson回归模型(HP)>零膨胀Poisson回归模型(ZIP)。选用HNB和ZINB模型进一步进行混合效应模型构建,当随机效应水平为样地时,随机参数个数为1个,作用在除截距以外的其他协变量上时,模型均不能收敛,只有当随机参数作用在样地水平下的截距上时,模型可以收敛且拟合精度进一步提升,采用留一法LOOCV进行模型拟合误差评价,HNB和ZINB模型的平均误差(ME)分别为14和11株·hm^(-2)。【结论】在冬奥核心区,影响华北落叶松枯死的主要因素为林分因子而非海拔、坡向等立地因子;负二项回归模型优于Poisson回归模型;在拟合零值过多的情况下,HNB回归模型优于ZINB回归模型;基于HNB回归模型优于ZINB回归模型,考虑随机截距效应的广义线性混合效应模型能够提高模型拟合精度,降低拟合误差,构建的华北落叶松林分枯死木数量预测模型可为研究区森林经营提供一定理论依据。
【Objective】This study aimed to develope counting model that can accurately predict the number of dead trees in Larix principis-rupprechtii plantation,explore the main reasons affecting the number of dead trees in L.principisrupprechtii plantation,and provide decision-making basis for the scientific management of L.principis-rupprechtii plantation in the core area of the Winter Olympic Games.【Method】45 sample plots of L.principis-rupprechtii plantation in the core area of Winter Olympic Games in Chongli District of Zhangjiakou City were chosen as the research material.Poisson regression model,negative binomial regression model,zero-inflated Poisson regression model,zero-inflated negative binomial regression model,hurdle passion regression model,and hurdle negative binomial regression model were used to develop the models of L.principis-rupprechtii plantation stand mortality number,and the optimal model would be selected in terms of the AIC value.Based on the optimal model,the random effect of different levels and the combination of various random parameters lying on the intercepts were taken into consideration,and the optimal mixed effect model of stand dead number was constructed.The best random effect level and the most optimal combination of random parameters would be determined according to the model convergence situation and AIC value,and the optimal mixed effect model of stand mortality number would be developed.【Result】Stand average diameter,mean height of dominant trees,stand age,stand basal area,stand diameter Gini coefficient were the stand factors affecting stand mortality numbers,and site factors had little effect on stand death.When too many zeros were not considered,the fitting effect of negative binomial regression model was much better than Poisson regression model.After considering the phenomenon of zero expansion,the zero expansion model and hurdle model were used for simulation.It was found that the fitting effect of hurdle negative binomial regression model(HNB)and zero expansion negative binomial regression model(ZINB)were better than other models.Finally,the order of goodness of fit of several counting models considering lots of zero values was HNB regression model≈ZINB regression model>HP regression model>ZIP regression model.HNB model and ZINB model were selected for further mixed effect model construction.When the random effect level was specified at plot,the number of random parameters had only one,and the model cannot converge when acting on other covariates except for intercept.Only when the random parameters acted on the intercept at plot level,the model could get to converge and the fitting accuracy was further improved,and the evaluation error ME of HNB model and ZINB model were 14 and 11 trees·hm^(-2) respectively.【Conclusion】In the core area of the Winter Olympic Games,the main factors affecting the death of L.principis-rupprechtii plantation were stand factors rather than site factors.HNB regression model and ZINB regression model had more advantages than Poisson regression model and negative binomial regression model when fitting too many zeros.The generalized linear mixed effect model considering random intercept effect can improve the fitting accuracy of the model and reduce fitting error.