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Context <p>Ecological conservation and restoration (ECR) is a crucial pathway for protecting and rehabilitating terrestrial ecosystems, as highlighted in the United Nations Sustainable Development Goal 15 (SDG 15). However, existing studies often define restoration reference states based on historical states or adjacent natural areas, neglecting the upper limit of ecosystem development constrained by vegetation succession. In addition, restoration planning frequently overlooks cost–benefit trade-offs in ECR, which limits decision-making efficiency.</p> Objectives <p>This study proposed an ECR decision-making framework grounded in vegetation succession to identify ECR enhancement potential, determined the optimal ECR scale, and prioritized ECR areas.</p> Methods <p>We adopted Potential Natural Vegetation (PNV) as an ideal reference state. By comparing vegetation types and ecosystem services between the ideal and the current state, ECR potential areas were identified. We designed multi-level gradient scenarios across varying ECR proportions to identify the optimal implementation scale. Irreplaceability indicators were then introduced to spatially prioritize ECR areas.</p> Results <p>The empirical results for Fujian Triangle Urban Agglomeration indicate that approximately 60.45% of the study area has significant ecosystem service enhancement potential. When the ecological conservation and restoration target is set at 60%, ecological benefits can be maximized while effectively controlling restoration costs, with integrated ecosystem services increasing by 47.8%.</p> Conclusions <p>An ideal reference state defined by PNV facilitates a more objective identification of ecological conservation and restoration potential. Building on this, a unified analytical framework that integrates restoration scale optimization with spatial prioritization supports quantitative decision-making on both the spatial configuration and implementation scale of ECR.</p>

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An ecological conservation and restoration framework based on vegetation succession: cost–benefit trade-offs for enhancing ecosystem services

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月9日

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  • Ye Xu,
  • Yao Qian,
  • Chen Mo,
  • Lina Tang

摘要

Context

Ecological conservation and restoration (ECR) is a crucial pathway for protecting and rehabilitating terrestrial ecosystems, as highlighted in the United Nations Sustainable Development Goal 15 (SDG 15). However, existing studies often define restoration reference states based on historical states or adjacent natural areas, neglecting the upper limit of ecosystem development constrained by vegetation succession. In addition, restoration planning frequently overlooks cost–benefit trade-offs in ECR, which limits decision-making efficiency.

Objectives

This study proposed an ECR decision-making framework grounded in vegetation succession to identify ECR enhancement potential, determined the optimal ECR scale, and prioritized ECR areas.

Methods

We adopted Potential Natural Vegetation (PNV) as an ideal reference state. By comparing vegetation types and ecosystem services between the ideal and the current state, ECR potential areas were identified. We designed multi-level gradient scenarios across varying ECR proportions to identify the optimal implementation scale. Irreplaceability indicators were then introduced to spatially prioritize ECR areas.

Results

The empirical results for Fujian Triangle Urban Agglomeration indicate that approximately 60.45% of the study area has significant ecosystem service enhancement potential. When the ecological conservation and restoration target is set at 60%, ecological benefits can be maximized while effectively controlling restoration costs, with integrated ecosystem services increasing by 47.8%.

Conclusions

An ideal reference state defined by PNV facilitates a more objective identification of ecological conservation and restoration potential. Building on this, a unified analytical framework that integrates restoration scale optimization with spatial prioritization supports quantitative decision-making on both the spatial configuration and implementation scale of ECR.