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Estimating dynamics of central hardwood forests using random forests

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  • Ma, Wu
  • Lin, Guang
  • Liang, Jingjing

Abstract

Estimation of forest population dynamics is critical for forest management decisions making. In this study, we developed an innovative climate-sensitive matrix model using random forests (RF) algorithm to estimate tree diameter growth, tree mortality, and stand recruitment and consequently predict population dynamics of the central hardwood forests under four climate scenarios (i.e. Representative Concentration Pathway [RCP]2.6, 4.5, 6.0, and 8.5). Based on post-sample validation, this RF matrix (RFMatrix) model was more accurate than the traditional climate-sensitive matrix model and Landis pro 7.0. According to the importance values of all predicted variables, the variability in tree diameter growth, tree mortality, and stand recruitment was mainly explained by local tree and stand-level factors, followed by climatic and anthropogenic factors, and soil factors were the least important for all the species. Additionally, our model predictedthat climate change could substantially reduce total stand basal area. The RFMatrix model and its prediction results could assist future forest population dynamics studies on the central hardwood region under changing climate.

Suggested Citation

  • Ma, Wu & Lin, Guang & Liang, Jingjing, 2020. "Estimating dynamics of central hardwood forests using random forests," Ecological Modelling, Elsevier, vol. 419(C).
    • Handle: RePEc:eee:ecomod:v:419:y:2020:i:c:s0304380020300193
    DOI: 10.1016/j.ecolmodel.2020.108947
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    References listed on IDEAS

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    1. Duncan MacMichael & Dong Si, 2018. "Machine Learning Classification of Tree Cover Type and Application to Forest Management," International Journal of Multimedia Data Engineering and Management (IJMDEM), IGI Global, vol. 9(1), pages 1-21, January.
    2. Ma, Wu & Liang, Jingjing & Cumming, Jonathan R. & Lee, Eungul & Welsh, Amy B. & Watson, James V. & Zhou, Mo, 2016. "Fundamental shifts of central hardwood forests under climate change," Ecological Modelling, Elsevier, vol. 332(C), pages 28-41.
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