• Scopus
  • Chinese Science Citation Database (CSCD)
  • A Guide to the Core Journal of China
  • CSTPCD
  • F5000 Frontrunner
  • RCCSE
Advanced search
Yao Dandan, Xu Qigang, Yan Xiaowang, Li Yutang. Individual-tree mortality model of Mongolian oak forests based on Bayesian method[J]. Journal of Beijing Forestry University, 2019, 41(9): 1-8. DOI: 10.13332/j.1000-1522.20180260
Citation: Yao Dandan, Xu Qigang, Yan Xiaowang, Li Yutang. Individual-tree mortality model of Mongolian oak forests based on Bayesian method[J]. Journal of Beijing Forestry University, 2019, 41(9): 1-8. DOI: 10.13332/j.1000-1522.20180260

Individual-tree mortality model of Mongolian oak forests based on Bayesian method

More Information
  • Received Date: August 06, 2018
  • Revised Date: January 24, 2019
  • Available Online: September 08, 2019
  • Published Date: August 31, 2019
  • ObjectiveThe Bayesian method is preponderant on improving the stability of model parameters. This paper explores the application of Bayesian method in the individual-tree mortality model and the improvement of estimation method of model parameters to provide reference for the growth and yield of Mongolian oak natural forests.
    MethodWith the data of 202 Mongolian oak forest permanent sample plots, we developed individual-tree mortality model based on logistic model using classical method, Bayesian method and hierarchical Bayesian method. A random sample of 80% data was used for model calibration, and the remaining 20% was used for model validation. We developed individual-tree mortality model based on logistic model using classical method, Bayesian method and hierarchical Bayesian method, Bayesian statistics with prior and hierarchical Bayesian method with uninformative prior. Models were evaluated by calculating AUC (area under ROC curve) and Pearson-χ2 test.
    ResultThe results showed that: (1) the parameter estimated values of classical method and Bayesian method were similar, and the standard deviation of Bayesian statistics was smaller than classical method. (2) The confidence intervals of the 3 parameter estimation methods had a large coincidence. Bayesian method with informative prior had the smallest confidence interval, which was 6.0%−31.8% smaller than confidence interval of classical method. The confidence interval of hierarchical Bayesian method was more dispersed, which was 11.2%−185.0% larger than classical method. (3) The model of hierarchical Bayesian method had the best goodness of fit. The values of AUC of classical method and Bayesian method were 0.73, and the AUC value of hierarchical Bayesian method was 0.83. It is indicated that the results of the three methods are statistically significant.
    ConclusionThe hierarchical Bayesian method has obvious advantages in fitting the individual-tree mortality model, whose performance is the best, and the model has the highest prediction accuracy.
  • [1]
    刘平, 马履一, 贾黎明, 等. 油松林木枯损率模型研究[J]. 林业资源管理, 2008(2):51−56. doi: 10.3969/j.issn.1002-6622.2008.02.012

    Liu P, Ma L Y, Jia L M, et al. Study on tree mortality model for Pinus tabulaeformis plantation[J]. Forest Resources Management, 2008(2): 51−56. doi: 10.3969/j.issn.1002-6622.2008.02.012
    [2]
    Fahey T J, Battles J J, Wilson G F. Responses of early successional northern hardwood forests to changes in nutrient availability[J]. Ecological Monographs, 1998, 68: 183−212. doi: 10.1890/0012-9615(1998)068[0183:ROESNH]2.0.CO;2
    [3]
    Kobe R K. Intraspecific variation in sapling mortality and growth predicts geographic variation in forest composition[J]. Ecological Monograph, 1996, 66: 181−201. doi: 10.2307/2963474
    [4]
    Hamilton D. A logistic model of mortality in thinned andunthinned mixed conifer stands of northern Idaho[J]. Forest Science, 1986, 32: 989−1000.
    [5]
    Wyckoff P H, Clark J S. Predicting tree mortality from diameter growth: a comparison of maximum likelihood and Bayesian approaches[J]. Canadian Journal of Forest Research, 2000, 30(1): 156−167. doi: 10.1139/x99-198
    [6]
    Metcalf C J E, McMahon S M, Clark J S. Overcoming data sparseness and parametric constraints in modeling of tree mortality: a new nonparametric Bayesian model[J]. Canadian Journal of Forest Research, 2009, 39(9): 1677−1687. doi: 10.1139/X09-083
    [7]
    Lu L, Wang H, Chhin S, et al. A Bayesian model averaging approach for modelling tree mortality in relation to site, competition and climatic factors for Chinese fir plantations[J]. Forest Ecology and Management, 2019, 440: 169−177.
    [8]
    Green E J, Roesch F A, Smith A F M, et al. Bayesian estimation for the three-parameter Weibull distribution with tree diameter data[M]. Washington: International Biometric Society, 1994.
    [9]
    Bullock B P, Boone E L. Deriving tree diameter distributions using Bayesian model averaging[J]. Forest Ecology and Management, 2007, 242(2−3): 127−132. doi: 10.1016/j.foreco.2007.01.024
    [10]
    Li R X, Stewart B, Weiskittel A. A Bayesian approach for modelling non-linear longitudinal/hierarchical data with random effects in forestry[J]. Forestry, 2012, 85(1): 17−25. doi: 10.1093/forestry/cpr050
    [11]
    Green E J, Strawderman W E. A Bayesian growth and yield model for slash pine plantations[J]. Journal of Applied Statistics, 1996, 23(2−3): 285−300. doi: 10.1080/02664769624251
    [12]
    Nystrom K, Stahl G. Forecasting probability distributions of forest yield allowing for a Bayesian approach to management planning[J]. Silva Fennica, 2001, 35(2): 185−201.
    [13]
    张雄清, 张建国, 段爱国. 基于贝叶斯法估计杉木人工林树高生长模型[J]. 林业科学, 2014, 50(3):69−75.

    Zhang X Q, Zhang J G, Duan A G. Tree-height growth model for Chinese fir plantation based on Bayesian method[J]. Scientia Silvae Sinicae, 2014, 50(3): 69−75.
    [14]
    Zhang X Q, Duan A G, Zhang J G. Estimating tree height-diameter models with the Bayesian method[J/OL]. The Scientific World Journal, 2014: 1−9 [2018−01−06]. https://www.ncbi.nlm.nih.gov/pubmed/24711733.
    [15]
    Tatsumi S, Owari T. Bayesian modeling of neighborhood competition in uneven-aged mixed-species stands[J]. Formath, 2013, 12: 191−209. doi: 10.15684/formath.12.191
    [16]
    马武. 蒙古栎林单木生长模型系研究[D]. 北京: 中国林业科学研究院, 2012.

    Ma W. Growth model for individual-tree in natural Quercus mongolica forests[D]. Beijing: Chinese Academy of Forestry, 2012.
    [17]
    Fang Z, Bailey R L. Nonlinear mixed effects modeling for slash pine dominant height growth following intensive silvicultural treatments[J]. Forest Science, 2001, 47(3): 287−300.
    [18]
    Calegario N, Daniels R F, Maestri R, et al. Modeling dominant height growth based on nonlinear mixed-effects model: a clonal eucalyptus plantation case study[J]. Forest Ecology and Management, 2005, 204(1): 11−21. doi: 10.1016/j.foreco.2004.07.051
    [19]
    Patenaud G, Milne R, Van Oijen M, et al. Integrating remote sensing datasets into ecological modelling: a Bayesian approach[J]. International Journal of Remote Sensing, 2008, 29(5): 1295−1315. doi: 10.1080/01431160701736414
    [20]
    Klemedtsson L, Jansson P E, Gustafsson D, et al. Bayesian calibration method used to elucidate carbon turnover in forest on drained organic soil[J]. Biogeochemistry, 2008, 89(1): 61−79. doi: 10.1007/s10533-007-9169-0
    [21]
    Russell M B. Influence to prior distributions and random effects on count regression models: implications for estimating standing dead tree abundance[J]. Environment and Ecological Statistics, 2015, 22: 145−160. doi: 10.1007/s10651-014-0290-7
  • Cited by

    Periodical cited type(5)

    1. 郎博帅,刘叶凡,韩阳媚,欧阳嗣航,李玉灵,程顺. 林内色彩斑块分布格局对秋季生态景观林美景度的影响——以塞罕坝机械林场为例. 林业与生态科学. 2023(01): 98-105 .
    2. 孙广鹏,章志都,刘海轩,朱济友,徐程扬. 基于树冠生长和空间竞争指数的油松风景林经营密度表编制. 中南林业科技大学学报. 2022(02): 17-26+54 .
    3. 刘格言,王与茜,黄尹姝,盛志祎,黄笑,陈其兵,江明艳. 西南地区风景游憩竹林林内景观评价与改造策略研究. 竹子学报. 2020(02): 66-73 .
    4. 崔义,刘海轩,吕娇,吴鞠,许丽娟,韦柳端,余玉磊,徐程扬. 城市森林林内景观质量定量通用判别技术研究. 北京林业大学学报. 2020(12): 9-23 . 本站查看
    5. 金雅庆,张瀚元. 浅谈城镇化建设中景观色彩设计的布局形式. 北方建筑. 2019(01): 29-32 .

    Other cited types(6)

Catalog

    Article views (2045) PDF downloads (120) Cited by(11)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return