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Predicting Suitable Spatial Distribution Areas for Urban Trees Under Climate Change Scenarios Using Species Distribution Models: A Case Study of Michelia chapensis

Author

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  • Chenbin Shen

    (School of Landscape Architecture and Architecture, Zhejiang Agriculture and Forestry University, Linan, Hangzhou 311300, China)

  • Xi Chen

    (School of Landscape Architecture and Architecture, Zhejiang Agriculture and Forestry University, Linan, Hangzhou 311300, China)

  • Chao Zhou

    (School of Landscape Architecture and Architecture, Zhejiang Agriculture and Forestry University, Linan, Hangzhou 311300, China)

  • Lingzi Xu

    (School of Landscape Architecture and Architecture, Zhejiang Agriculture and Forestry University, Linan, Hangzhou 311300, China)

  • Mingyi Qian

    (School of Landscape Architecture and Architecture, Zhejiang Agriculture and Forestry University, Linan, Hangzhou 311300, China)

  • Hongbo Zhao

    (School of Landscape Architecture and Architecture, Zhejiang Agriculture and Forestry University, Linan, Hangzhou 311300, China)

  • Kun Li

    (School of Landscape Architecture and Architecture, Zhejiang Agriculture and Forestry University, Linan, Hangzhou 311300, China)

Abstract

Climate change has presented considerable challenges in the management of urban forests and trees. Varieties of studies have predicted the potential changes in species distribution by employing single-algorithm species distribution models (SDMs) to investigate the impacts of climate change on plant species. However, there is still limited quantitative research on the impacts of climate change on the suitable distribution ranges of commonly used urban tree species. Therefore, our study aims to optimize traditional SDMs by integrating multiple machine learning algorithms and to propose a framework for identifying suitable distribution ranges of urban trees under climate change. We took Michelia chapensis , a tree species of particular significance in southern China, as a pilot tree species to investigate the evolution of its suitable distribution range in the context of two future climate scenarios (SSP126 and SSP585) across four periods (2030s, 2050s, 2070s, and 2090s). The findings indicated that the ensemble SDM showed strong predictive capacity, with an area under the curve (AUC) value of 0.95. The suitable area for Michelia chapensis is estimated at 15.9 × 10 5 km 2 currently and it will expand in most areas under future climate scenarios according to the projection. However, it will contract in southeastern Yunnan, central Guangdong, the Sichuan Basin, northern Hubei, and Jiangxi, etc. The central location of the current suitable distribution area is located in Hengyang, Hunan (27.36° N, 112.34° E), and is projected to shift westward with climate change in the future. The migration magnitude is positively correlated with the intensity of climate change. These findings provide a scientific basis for the future landscape planning and management of Michelia chapensis . Furthermore, the proposed framework can be seen as a valuable tool for predicting the suitable distribution ranges of urban trees in response to climate change, providing insights for proactive adaptation to climate change in urban planning and landscape management.

Suggested Citation

  • Chenbin Shen & Xi Chen & Chao Zhou & Lingzi Xu & Mingyi Qian & Hongbo Zhao & Kun Li, 2025. "Predicting Suitable Spatial Distribution Areas for Urban Trees Under Climate Change Scenarios Using Species Distribution Models: A Case Study of Michelia chapensis," Land, MDPI, vol. 14(3), pages 1-19, March.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:3:p:638-:d:1614663
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    References listed on IDEAS

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    1. Lei Zhang & Zhinong Jing & Zuyao Li & Yang Liu & Shengzuo Fang, 2019. "Predictive Modeling of Suitable Habitats for Cinnamomum Camphora (L.) Presl Using Maxent Model under Climate Change in China," IJERPH, MDPI, vol. 16(17), pages 1-16, August.
    2. Guo, Chuanbo & Lek, Sovan & Ye, Shaowen & Li, Wei & Liu, Jiashou & Li, Zhongjie, 2015. "Uncertainty in ensemble modelling of large-scale species distribution: Effects from species characteristics and model techniques," Ecological Modelling, Elsevier, vol. 306(C), pages 67-75.
    3. Maleknia, Rahim & Enescu, Raluca Elena, 2025. "Does climate change stimulate citizens' responses to conserving urban forest? Insights from stimulus-organism-response theory," Ecological Modelling, Elsevier, vol. 501(C).
    4. H. Oğuz Çoban & Ömer K. Örücü & E. Seda Arslan, 2020. "MaxEnt Modeling for Predicting the Current and Future Potential Geographical Distribution of Quercus libani Olivier," Sustainability, MDPI, vol. 12(7), pages 1-17, March.
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