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Exploring the regime shifts of dryland vegetation under climate change: A case study of the Altay, Xinjiang

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  • Liu, Chen
  • Pang, Yi-Zhi
  • Xue, Qiang
  • Li, Li
  • Luo, Xiaofeng

Abstract

In dryland ecosystems, broader spatial patterns not only directly depict the structure of vegetation distribution within the study area, but also indirectly reflect the resilience of the ecosystem. Climate change threatens the evolution of these spatial patterns, highlighting the urgent need to integrate climate change into research efforts. However, few spatiotemporal models couple vegetation with climate factors to reveal their impacts. We constructed a climate–vegetation coupling dynamical model with nonlocal delay effects, focusing on natural vegetation patterns. Using climate data from the Altay region, our model reproduced features consistent with actual patterns, specifically a labyrinth structure. Variations in climate parameters significantly alter vegetation pattern structures and average biomass. Our simplified model successfully predicts the actual spatial structure of the Altay region, and responds reasonably to potential regional climate changes. Further development of our model could provide valuable tools for formulating strategies to protect these ecosystems and guide site selection for conservation areas that support vegetation patterns under future climate scenarios.

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  • Liu, Chen & Pang, Yi-Zhi & Xue, Qiang & Li, Li & Luo, Xiaofeng, 2025. "Exploring the regime shifts of dryland vegetation under climate change: A case study of the Altay, Xinjiang," Chaos, Solitons & Fractals, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:chsofr:v:191:y:2025:i:c:s096007792401419x
    DOI: 10.1016/j.chaos.2024.115867
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    1. Zhang, Yajun & Li, Li & Zhang, Zhenwei & Li, Bin, 2025. "Multimodal learning for vegetation patterns classification in global arid and semi-arid regions," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).

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