生物多样性 ›› 2025, Vol. 33 ›› Issue (3): 24346.  DOI: 10.17520/biods.2024346  cstr: 32101.14.biods.2024346

• 研究报告: 动物多样性 • 上一篇    下一篇

新疆野生动物通道空间布局优化

付梦娣1,朱彦鹏1,任月恒1,李爽1,秦乐1,谢正君2,王清春3,张立博1*   

  1. 1. 中国环境科学研究院, 环境基准标准与风险管控全国重点实验室, 生态环境部区域生态过程与功能评估重点实验室, 北京 100012; 2. 新疆罗布泊野骆驼国家级自然保护区管理局, 乌鲁木齐 830011; 3. 北京林业大学生态与自然保护学院, 北京 100083
  • 收稿日期:2024-07-30 修回日期:2024-09-30 出版日期:2025-03-20 发布日期:2025-02-22
  • 通讯作者: 张立博

Research on the optimization of wildlife passage spatial layout in Xinjiang

Mengdi Fu1, Yanpeng Zhu1, Yueheng Ren1, Shuang Li1, Le Qin1, Zhengjun Xie2, Qingchun Wang3, Libo Zhang1*   

  1. 1 State Key Laboratory of Environmental Criteria and Risk Assessment, Key Laboratory of Regional Eco-Process and Function Assessment of the Ministry of Ecology and Environment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China 

    2 Xinjiang Lop Nor Wild Camel National Nature Reserve Administration, Urumqi 830011, China 

    3 School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China

  • Received:2024-07-30 Revised:2024-09-30 Online:2025-03-20 Published:2025-02-22
  • Contact: Libo Zhang

摘要: 线性基础设施的不断扩张已成为威胁生物多样性的重要因素之一。探索大尺度的野生动物通道优化路径对于区域生物多样性保护至关重要。本研究以新疆代表性野生动物为研究对象, 采用最大熵模型预测其潜在栖息地, 并对栖息地重要性进行评估。基于最小累积阻力模型计算线性基础设施影响的最小累积阻力路径, 结合现有及规划的道路、围栏, 精准识别提高栖息地连通性所需的通道位置和数量, 形成野生动物通道空间布局优化方案。结果表明: 不同代表性野生动物的栖息地分布呈现显著的生态差异, 其中食肉目动物主要分布于高山和亚高山地区, 而大型食草动物则偏好干旱荒漠地区。高阻力区域主要位于铁路和高速公路穿越自然保护地的地段。研究识别出2,494.98 km的通道区域及4,314个通道。道路的技术等级越高, 通道区域的长度和通道数量越多。尽管重要区域的通道总长度大于关键区域, 但关键区域的通道密度显著高于重要区域。建议根据线性基础设施的技术等级实施差异化的通道建设, 同时加强保护地外的通道布局优化, 以有效维持区域生态连通性。

关键词: 野生动物通道, 道路, 围栏, MaxEnt模型, MCR模型

Abstract

Aims: The rapid expansion of linear infrastructure poses a significant threat to biodiversity. Developing large-scale optimization pathways for wildlife passages is crucial for regional biodiversity conservation. 

Method: We employ the Maximum Entropy (MaxEnt) model, with emphasis on representative wildlife species in Xinjiang, to predict potential habitats and assess the importance of each. The minimal cumulative resistance (MCR) model computes the least-cost paths influenced by linear infrastructure. We then accurately pinpoint the necessary locations and numbers of passages by integrating current and planned roads and fences to improve habitat connectivity, ultimately proposing an optimized spatial layout for wildlife passages. 

Results: Our findings reveal notable ecological differences in the habitat distributions of various representative species. Carnivores are predominantly found in alpine and subalpine regions, while large herbivores show a preference for arid desert areas. High-resistance zones are mainly located where railways and highways intersect protected areas. Our analysis identified 2,494.98 km of passage regions and 4,314 individual passages. Higher technical grades of road are associated with longer passage regions and a greater number of passages. The total length of passages in important regions surpasses that of key regions, but passage density is significantly higher in key regions. 

Conclusion: Given these findings we suggest implementing differentiated passage construction based on the technical grade of linear infrastructure. Further, enhancing passage layout optimization outside protected areas to effectively preserve regional ecological connectivity is necessary to maintain biodiversity.

Key words: wildlife passage, road, fence, MaxEnt model, MCR model