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Context <p><i>Grus japonensis</i> (<i>G. japonensis</i>), an endangered species in East Asia, relies on large, continuous wetland landscapes for wintering. With ongoing habitat fragmentation and human disturbance, restoring its habitat at the landscape scale is crucial for maintaining ecological connectivity and population viability.</p> Objectives <p>This study aimed to analyze the spatiotemporal changes in the ecological network (EN) of <i>G. japonensis</i> habitat in the Yancheng coastal area from 1987 to 2021. Additionally, it evaluated the structural robustness of the EN to support restoration planning.</p> Methods <p>Using species distribution and environmental data, we applied the MaxEnt model, stepping-stone theory, and circuit theory to simulate habitat suitability and ecological network structure. The study assessed temporal changes in ecological sources, corridors, and connectivity over a 34-year period.</p> Results <p>Suitable habitats remained concentrated in coastal zones, but their area significantly decreased—from 56,528.59&#xa0;ha in 1987 to 14,040.20&#xa0;ha in 2021. Ecological corridors declined in number and length. Network structural indices (<i>α</i>, <i>β</i>, <i>γ</i>) showed marked declines, reflecting reduced connectivity and increased fragmentation. Network robustness also weakened, with lower collapse thresholds upon node removal. The loss of key nodes triggered cascading failures, threatening system stability.</p> Conclusions <p>This study proposes a landscape-scale restoration framework that integrates habitat prediction, corridor dynamic simulation, network structure assessment, and policy-based restoration validation. By identifying priority restoration areas and optimizing EN stability, our findings provide a scientific basis for enhancing <i>G. japonensis</i> habitat conservation and ecological network resilience in the Yancheng coastal wetlands.</p>

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Structural vulnerability analysis and systematic restoration framework of the wintering ecological network for Grus japonensis in Yancheng coastal wetlands (1987–2021)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月26日

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  • Chunqi Qiu,
  • Yong Zhou,
  • Cheng Wang,
  • Gang Wang,
  • Zhaoyuan Yu,
  • Yongqiang Zhao,
  • Yufeng Li,
  • Hongyu Liu

摘要

Context

Grus japonensis (G. japonensis), an endangered species in East Asia, relies on large, continuous wetland landscapes for wintering. With ongoing habitat fragmentation and human disturbance, restoring its habitat at the landscape scale is crucial for maintaining ecological connectivity and population viability.

Objectives

This study aimed to analyze the spatiotemporal changes in the ecological network (EN) of G. japonensis habitat in the Yancheng coastal area from 1987 to 2021. Additionally, it evaluated the structural robustness of the EN to support restoration planning.

Methods

Using species distribution and environmental data, we applied the MaxEnt model, stepping-stone theory, and circuit theory to simulate habitat suitability and ecological network structure. The study assessed temporal changes in ecological sources, corridors, and connectivity over a 34-year period.

Results

Suitable habitats remained concentrated in coastal zones, but their area significantly decreased—from 56,528.59 ha in 1987 to 14,040.20 ha in 2021. Ecological corridors declined in number and length. Network structural indices (α, β, γ) showed marked declines, reflecting reduced connectivity and increased fragmentation. Network robustness also weakened, with lower collapse thresholds upon node removal. The loss of key nodes triggered cascading failures, threatening system stability.

Conclusions

This study proposes a landscape-scale restoration framework that integrates habitat prediction, corridor dynamic simulation, network structure assessment, and policy-based restoration validation. By identifying priority restoration areas and optimizing EN stability, our findings provide a scientific basis for enhancing G. japonensis habitat conservation and ecological network resilience in the Yancheng coastal wetlands.