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MODELING INDIVIDUAL TREE MORTALITY RATES USING MARGINAL AND RANDOM EFFECTS REGRESSION MODELS

Authors

  • ZHIHAI MA,

    1. Départment des Sciences Biologiques, Institut des Sciences de l’environnement, Université du Québec à Montréal (UQAM), Montréal, H3C3P8, Canada
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  • CHANGHUI PENG,

    Corresponding author
    1. Départment des Sciences Biologiques, Institut des Sciences de l’environnement, Université du Québec à Montréal (UQAM), Montréal, H3C3P8, Canada; Laboratory for Ecological Forecasting and Global Change, College of Forestry, Northwest A & F University, Yangling, Shaanxi, 712100, P. R. China
      Dr. Changhui Peng, Department of Biology Sciences, Institute of Environment Sciences, University of Quebec at Montreal, C.P. 8888, Succ. Centre-Ville, Montreal H3C 3P8, Canada, E-mail: peng.changhui@uqam.ca
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  • WEIZHONG LI,

    1. Laboratory for Ecological Forecasting and Global Change, College of Forestry, Northwest A & F University, Yangling, Shaanxi, 712100, P. R. China
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  • QIUAN ZHU,

    1. Laboratory for Ecological Forecasting and Global Change, College of Forestry, Northwest A & F University, Yangling, Shaanxi 712100, China
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  • WEIFENG WANG,

    1. Départment des Sciences Biologiques, Institut des Sciences de l’environnement, Université du Québec à Montréal (UQAM), Montréal, H3C3P8, Canada
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  • XINZHANG SONG,

    1. Départment des Sciences Biologiques, Institut des Sciences de l’environnement, Université du Québec à Montréal (UQAM), Montréal, H3C3P8, Canada; Zhejiang Provincial Key Laboratory of Carbon Cycling and Carbon Sequestration in Forest Ecosystems, Zhejiang Agriculture and Forestry University, Lin’an, 311300, China
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  • JIANWEI LIU

    1. Forestry Branch, Manitoba Conservation, Box 70, 200 Saulteaux Cres, Winnipeg, MB R3J 3W3, Canada
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Dr. Changhui Peng, Department of Biology Sciences, Institute of Environment Sciences, University of Quebec at Montreal, C.P. 8888, Succ. Centre-Ville, Montreal H3C 3P8, Canada, E-mail: peng.changhui@uqam.ca

Abstract

Abstract Developing models to predict tree mortality using data from long-term repeated measurement data sets can be difficult and challenging due to the nature of mortality as well as the effects of dependence on observations. Marginal (population-averaged) generalized estimating equations (GEE) and random effects (subject-specific) models offer two possible ways to overcome these effects. For this study, standard logistic, marginal logistic based on the GEE approach, and random logistic regression models were fitted and compared. In addition, four model evaluation statistics were calculated by means of K-fold cross-valuation. They include the mean prediction error, the mean absolute prediction error, the variance of prediction error, and the mean square error. Results from this study suggest that the random effects model produced the smallest evaluation statistics among the three models. Although marginal logistic regression accommodated for correlations between observations, it did not provide noticeable improvements of model performance compared to the standard logistic regression model that assumed impendence. This study indicates that the random effects model was able to increase the overall accuracy of mortality modeling. Moreover, it was able to ascertain correlation derived from the hierarchal data structure as well as serial correlation generated through repeated measurements.

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