IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v483y2023ics0304380023001874.html
   My bibliography  Save this article

Machine-learning modeling on tree mortality and growth reduction of temperate forests with climatic and ecophysiological parameters

Author

Listed:
  • Cho, Nanghyun
  • Agossou, Casimir
  • Kim, Eunsook
  • Lim, Jong-Hwan
  • Seo, Jeong-Wook
  • Kang, Sinkyu

Abstract

Massive tree diebacks and abnormal growth reduction have been more reported across continents, which calls an attention for methods that can assess the physiological stress of tree growth. This study aims to develop machine learning models (MLM) to explain tree mortality and growth reduction in terms of the key parameters of climate and tree ecophysiology in temperate forests of Korea. For this, we produced various ecophysiological parameters with a process-based vegetation model that was used as inputs to multiple machine learning algorithms and the conventional multiple linear regression (MLR). As a result, the MLMs outperformed the MLR. Among the MLMs, random forest (RF) showed the highest overall accuracy and AUC for both tree mortality (79% and 0.84±0.09, respectively) and growth reduction (61% and 0.63 ± 0.09). Winter temperature along with previous-year autumn soluble sugar content and precipitation were the most important in determining the tree mortality, while the growth reduction was largely regulated by current-year conditions such spring and autumn precipitation and summer starch content. Also, in the proportion of variance (communalities) of PCA, the precipitation and soluble sugar showed low values 0.15 and 0.29, respectively, which low communalities (<0.3) indicate that these variables have limited shared variance with other variables in the analysis. This study indicates the combined use of vegetation model and MLM can give reciprocal benefits to each other by providing ecophysiological parameters for MLM, otherwise hard to get, and coping with convoluted process of tree vitality undescribed yet in the vegetation model, respectively.

Suggested Citation

  • Cho, Nanghyun & Agossou, Casimir & Kim, Eunsook & Lim, Jong-Hwan & Seo, Jeong-Wook & Kang, Sinkyu, 2023. "Machine-learning modeling on tree mortality and growth reduction of temperate forests with climatic and ecophysiological parameters," Ecological Modelling, Elsevier, vol. 483(C).
  • Handle: RePEc:eee:ecomod:v:483:y:2023:i:c:s0304380023001874
    DOI: 10.1016/j.ecolmodel.2023.110456
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380023001874
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2023.110456?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Marjan Goodarzi & Mehdi Pourhashemi & Zahra Azizi, 2019. "Investigation on Zagros forests cover changes under the recent droughts using satellite imagery," Journal of Forest Science, Czech Academy of Agricultural Sciences, vol. 65(1), pages 9-17.
    2. L. Rowland & A. C. L. da Costa & D. R. Galbraith & R. S. Oliveira & O. J. Binks & A. A. R. Oliveira & A. M. Pullen & C. E. Doughty & D. B. Metcalfe & S. S. Vasconcelos & L. V. Ferreira & Y. Malhi & J., 2015. "Death from drought in tropical forests is triggered by hydraulics not carbon starvation," Nature, Nature, vol. 528(7580), pages 119-122, December.
    3. Sun, Qingling & Li, Baolin & Zhang, Tao & Yuan, Yecheng & Gao, Xizhang & Ge, Jinsong & Li, Fei & Zhang, Zhijun, 2017. "An improved Biome-BGC model for estimating net primary productivity of alpine meadow on the Qinghai-Tibet Plateau," Ecological Modelling, Elsevier, vol. 350(C), pages 55-68.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Heidarlou, Hadi Beygi & Mirshekarlou, Asma Karamat & Lopez-Carr, David & Borz, Stelian Alexandru, 2024. "Conservation policy and forest transition in Zagros forests: Statistical analysis of human welfare, biophysical, and climate drivers," Forest Policy and Economics, Elsevier, vol. 161(C).
    2. William M. Hammond & A. Park Williams & John T. Abatzoglou & Henry D. Adams & Tamir Klein & Rosana López & Cuauhtémoc Sáenz-Romero & Henrik Hartmann & David D. Breshears & Craig D. Allen, 2022. "Global field observations of tree die-off reveal hotter-drought fingerprint for Earth’s forests," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Chaobin Zhang & Ying Zhang & Jianlong Li, 2019. "Grassland Productivity Response to Climate Change in the Hulunbuir Steppes of China," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    4. Junyi Liu & Zhixiang Wu & Siqi Yang & Chuan Yang, 2022. "Sensitivity Analysis of Biome-BGC for Gross Primary Production of a Rubber Plantation Ecosystem: A Case Study of Hainan Island, China," IJERPH, MDPI, vol. 19(21), pages 1-13, October.
    5. Qifei Han & Geping Luo & Chaofan Li & Shoubo Li, 2018. "Response of Carbon Dynamics to Climate Change Varied among Different Vegetation Types in Central Asia," Sustainability, MDPI, vol. 10(9), pages 1-15, September.
    6. de Assis Prado, Carlos Henrique Britto & de Brito Melo Trovão, Dilma Maria, 2023. "The woody crown network model incorporates maximum height," Ecological Modelling, Elsevier, vol. 481(C).
    7. Rius, Bianca Fazio & Filho, João Paulo Darela & Fleischer, Katrin & Hofhansl, Florian & Blanco, Carolina Casagrande & Rammig, Anja & Domingues, Tomas Ferreira & Lapola, David Montenegro, 2023. "Higher functional diversity improves modeling of Amazon forest carbon storage," Ecological Modelling, Elsevier, vol. 481(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:483:y:2023:i:c:s0304380023001874. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.