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|Table of Contents|

Modelling the vertical variation in the number of second order branches of Pinus koraiensis plantation trees through GLMM(PDF)

Journal of Nanjing Forestry University(Natural Science Edition)[ISSN:1000-2006/CN:32-1161/S]

Issue:
2017 04
Page:
121-128
Column:
lw
publishdate:
2017-07-31

Article Info:/Info

Title:
Modelling the vertical variation in the number of second order branches of Pinus koraiensis plantation trees through GLMM
Article ID:
1000-2006(2017)04-0121-08
Author(s):
MIAO Zheng DONG Lihu LI Fengri* BAI Dongxue WANG Jiahui
School of Forestry, Northeast Forestry University, Harbin 150040, China
Keywords:
Korean pine(Pinus koraiensis) number of second order branches Poisson regression model generalized linear mixed model(GLMM)
Classification number :
S757.1
DOI:
10.3969/j.issn.1000-2006.201604066
Document Code:
A
Abstract:
【Objective】Establish a method for estimating the spatial distribution of branch and foliage biomass within individual Korean pine(Pinus koraiensis)crowns,the aim of the present study was to develop a predictive model for the vertical variation in number of second-order branches in farmed Korean pines.【Method】Using count data from a total of 955 branches sampled from 65 Korean pines in the Mengjiagang Forest Farm, the number of second-order branches was modeled as a function of the relative distance into the crown(RDINC), crown length(CL), diameter(DBH)and height/diameter ratio(HDR),based on a previously developed model. Subject-specific variation was captured using tree-level random coefficients, and the auto correlation among the branches sampled in consecutive whorls of the same crown were taken into account using a first-order auto regressive correlation structure AR(1) in the generalized linear mixed models. The predictive accuracy of the random-coefficient models were compared with that of the fixed-effects model using common methods for validating forest models.【Result】All of the converged models with random coefficients provided better fits than the fixed-effect model,and the model with four random coefficients(intercept, lnRDINC, R2DINC and CL)and the first-order auto regressive correlation structure AR(1) proved to be the optimum mixed model. In the fixed-effect part of this model,the parameter estimates for lnRDINC,CL and DBH were positive, whereas those for R2DINC and HDR were negative.Consequently there was a peak in the number of predicted second-order branches as RDINC increased. The Pseudo-R2, RMSE,MAE and MAE% of the optimal model were 0.896 1,5.15, 3.83, and 23.25%, respectively.【Conclusion】The generalized linear mixed models with random coefficients had greater precision than the previously developed fixed-effect model since they delineated both the mean trend of vertical variation in number of second-order branches and tree-specific deviation from the mean trend.

References

[1] 张小全, 徐德应, 赵茂盛. 林冠结构、辐射传输与冠层光合作用研究综述[J]. 林业科学研究,1999,12(4):411-421.DOI:10. 13275/j.cnki.lykxyj.1999.04.014. ZHANG X Q, XU D Y, ZHAO M S. Review on forest canopy structure, radiation transfer and canopy photosynthesis[J]. Forest Research, 1999,12(4): 411-421.
[2] 张智昌. 落叶松人工林枝条生长与节子大小预测模型的研究[D]. 哈尔滨:东北林业大学, 2010. ZHANG Z C. Predicting models of branch growth and knot properties for larch plantation [D]. Harbin: Northeast Forestry University, 2010.
[3] NEWTON M, LACHENBRUCH B, ROBBINS J M,et al. Branch diameter and longevity linked to plantation spacing and rectangularity in young Douglas-fir [J]. Fuel & Energy Abstracts, 2012, 266:75-82. DOI:10.1016/j.foreco.2011.11.009.
[4] 郑杨, 董利虎, 李凤日. 黑龙江省红松人工林枝条分布数量模拟[J]. 应用生态学报, 2016, 27(7):2172-2180. DOI:10.13287/j.1001-9332.201607.21. ZHENG Y, DONG L H, LI F R. Branch quantity distribution simulation for Pinus koraiensis plantation in Heilongjiang Province, China[J]. Chinese Journal of Applied Ecology, 2016, 27(7):2172-2180.
[5] DAVIDIAN M, GILTINAN D. Nonlinear models for repeated measurement data: an overview and update [J]. Journal of Agricultural Biological & Environmental Statistics, 2003, 8(4):387-419. DOI: 10.1198/1085711032697.
[6] FANG Z, BAILEY R L, SHIVER B D. A multivariate simultaneous prediction system for stand growth and yield with fixed and random effects [J]. Forest Science, 2001, 47(4): 550-562.
[7] 李春明, 唐守正. 基于非线性混合模型的落叶松云冷杉林分断面积模型[J].林业科学,2010,46(7):106-113. DOI:10.11707//j.1001-7488.2100716. LI C M, TANG S Z. The basal area model of mixed stands of Larix olgensis,Abies nephrolepis and Picea jezoensis based on nonlinear mixed model [J]. Scientia Silvae Sinicae, 2010,46(7): 106-113.
[8] 杨志雄, 袁岱菁. 非线性混合效应模型和广义线性模型拟合随机效应logistic回归的应用比较[J]. 中国卫生统计, 2011(3):321-323. DOI:10.3969/j.issn.1002-3674.2011.03.038.
[9] HEIN S, MäKINEN H, YUE C, et al. Modelling branch characteristics of Norway spruce from wide spacings in Germany [J]. Forest Ecology and Management, 2007, 242(2-3):155-164. DOI:10.1016/j.foreco.2007.01.014.
[10] NEMEC A F L, GOUDIE J W, PARISH R. A Gamma-Poisson model for vertical location and frequency of buds on lodgepole pine(Pinus contorta)leaders [J]. Canadian Journal of Forest Research, 2010, 40(10):2049-2058. DOI:10.1016/j.foreco.2007.01.0140
[11] KINT V, HEIN S, CAMPIOLI M, et al. Modelling self-pruning and branch attributes for young Quercus robur L. and Fagus sylvatica L. trees [J]. Forest Ecology and Management, 2010, 260(11):2023-2034. DOI:10.1016/j.foreco.2010.09.008.
[12] NEMEC A F L, PARISH R, GOUDIE J W. Modelling number, vertical distribution, and size of live branches on coniferous tree species in British Columbia [J]. Canadian Journal of Forest Research, 2012, 42(42): 1072-1090. DOI:10.1139/X2012-06.
[13] SATTLER D F, COMEAU P G, ACHIM A. Branch models for white spruce (Picea glauca(Moench)Voss)in naturally regenerated stands [J]. Forest Ecology and Management, 2014, 325: 74-89. DOI:http://dx.doi.org/10.1016/j.foreco.2014.03.051.
[14] 贺宝龙. 广义线性混合模型在精算分析中的应用[D]. 武汉:武汉理工大学, 2008. HE B L. The application of generalized linear mix models in actuarial analysis [D]. Wuhan: Wuhan University of Technology, 2008.
[15] 陈丹萍. 广义线性混合效应模型(GLMM)与复杂抽样的logistics回归模型在分层整群抽样数据分析中的比较[D]. 上海:复旦大学, 2010. CHEN D P. Comparison of generalized linear mixed models(GLMM)and logistic regression in complex sampling for analyzing data obtain from stratified cluster random sampling [D]. Shanghai: Fudan University, 2010.
[16] WOLFINFER R, O'CONNELL M. Generalized linear mixed model: a pseudo-likelihood approach [J].Journal of Statistical Computation and Simulation, 1993, 48(3):233-243. DOI: 10.1080/00949659308811554.
[17] 杨肇, 朱凯旋. Logistic回归分析中的过度离散现象及纠正[J].中国卫生统计, 2003(4):48-49. DOI:10.3969/j.issn.1002-3674.2003.04.016.
[18] MAGUIRE D A, MOEUR M, BENNETT W S. Models for describing basal diameter and vertical distribution of primary branches in young Douglas-fir [J]. Forest Ecology and Management, 1994, 63(1):23-55.
[19] ISHII H, MCDOWELL N. Age-related development of crown structure in coastal Douglas-fir trees [J]. Forest Ecology and Management, 2002, 169(3):257-270.
[20] WEISKITTEL A R, MAGUIRE D A, MONSERUD R A. Response of branch growth and mortality to silvicultural treatments in coastal Douglas-fir planations: implications for predicting tree growth [J]. Forest Ecology and Management, 2007, 251(3):182-194. DOI:10.1016/j.foreco.2007.06.007.
[21] MAKINEN H, OJANSUU R, SAIRANEN P, et al. Predicting branch characteristics of Norway spruce(Picea abies(L.)Karst.)from simple stand and tree measurements [J]. The Journal of the Society of Foresters of Great Britain, 2003, 76(5):525-546.
[22] 康萌萌. 广义线性混合模型及其SAS实现[J]. 统计教育, 2009(10):50-54. KANG M M. Generalized linear mixed models and implementation with SAS [J] Statistical Thinktank, 2009(10):50-54.

Last Update: 1900-01-01