CN 11-5366/S     ISSN 1673-1530
“风景园林,不只是一本期刊。”

南京市鸟类生境识别与网络构建

Avian Habitat Identification and Network Construction in Nanjing

  • 摘要:
    目的 城市化进程的加快严重威胁鸟类多样性,恢复城市生境、维护生态系统健康与稳定是保护城市生物多样性的关键。
    方法 按照从“单一生境”到“完整网络”的步骤完善南京市鸟类生境网络体系,使用最大熵模型(MaxEnt)识别生境,生成城市鸟类生境适宜区,并选取预测结果值大于0.9的核心区域确定为鸟类生境源地;运用形态学空间格局分析法(morphological spatial pattern analysis, MSPA)识别城市边缘生境斑块,利用最小累积阻力(minimum cumulative resistance, MCR)模型和电路理论模型完成源地与斑块间的生境网络构建及优化。
    结果 1)识别出钟山风景区内水陆相交及林地区域、城市边缘大型森林公园为南京市鸟类高适宜区生境,最湿月降水量、土地利用类型和与水域的距离是影响鸟类生境适宜性的主要因子;2)构建了钟山风景区的生境源地与南京城市范围内生境斑块的鸟类生境网络,其中长江及滁河沿线较城中公园绿地的丰富性和连通性更高。
    结论 形成鸟类生境识别和网络构建的系统方法,为城市生物多样性保护提供科学依据与参考。发现提升网络途经的中小型绿地的适宜度可使连通性更高,应发展这些绿地成为促进生物多样性的缓冲空间。

     

    Abstract:
    Objective The accelerating pace of urbanization presents an escalating challenge to avian biodiversity, especially in densely populated regions where habitat loss, fragmentation, and environmental degradation have become pervasive. As urban areas expand, the displacement and transformation of natural habitats vital to bird species have led to a marked decline in species diversity and disrupted ecological equilibrium. Thus, the restoration of urban habitats and the maintenance of healthy and stable avian ecosystems are imperative for safeguarding biodiversity in cities. This research aims to devise an optimized strategy for constructing urban avian habitat networks, transforming isolated habitats into an interconnected and cohesive network that fosters ecological resilience. The overarching objective is to furnish urban planners and conservationists with a robust framework to enhance urban avian biodiversity through integrated and scientifically grounded habitat restoration and management initiatives.
    Methods A systematic approach is adopted to optimize urban avian habitat networks, following a structured process from the identification of “single habitats” to the assembly of a “comprehensive network”. This research employs the Maximum Entropy Model (MaxEnt) to forecast and identify suitable avian habitats across the urban landscape of Nanjing, China. MaxEnt, known for its effectiveness in modeling species distribution based on environmental variables and occurrence data, is utilized to generate habitat suitability maps. These maps identify zones with suitability values exceeding 0.9, which are subsequently designated as core habitat areas, or “avian habitat sources”. These sources represent the most ecologically favorable environments for bird species and serve as focal nodes in the network. To enhance spatial analysis, the Morphological Spatial Pattern Analysis (MSPA) is applied to delineate ecological patches at the urban periphery. MSPA, a landscape-level analysis tool, can enable the identification of key structural elements such as corridors, islets, and core patches. Further, the research employs both the Minimum Cumulative Resistance (MCR) model and Circuit Theory to construct the habitat network, evaluating connectivity between the identified habitat sources and the peripheral ecological patches. The MCR model facilitates the identification of least-cost pathways for species movement by incorporating landscape resistance, while Circuit Theory introduces a probabilistic dimension, allowing for multiple potential routes and accounting for the stochastic nature of species dispersal. By integrating these models, the research aims to develop a robust and optimized avian habitat network that improves connectivity and mitigates habitat fragmentation.
    Results The MaxEnt model reveals several habitats highly suitable for avian species in Nanjing, particularly in regions where aquatic and terrestrial ecosystems intersect. Notably, Zhongshan Mountain Scenic Area, and large forest parks at the urban fringe are identified as key habitats with high suitability that can offer favorable environmental conditions conducive to sustaining a rich avian community. The principal determinants of habitat suitability are identified as precipitation during the wettest month, land use classification, and proximity to water body. Precipitation, in particular, plays a crucial role in supporting vegetation density and water resources, which directly impact food availability and nesting opportunities. The constructed avian habitat network successfully links central habitat sources, such as Zhongshan Mountain Scenic Area, with smaller ecological patches scattered throughout the urban landscape. The analysis demonstrates that ecological connectivity between these core areas and patches along the Yangtze and Chuhe rivers is significantly higher than that observed in centrally located urban green spaces, such as parks. This highlights the importance of riparian corridors, which serve as vital conduits for species movement and refuge, and can enhance avian biodiversity within urban ecosystems. Moreover, the research underscores the importance of small and medium-sized green spaces within the urban fabric as integral components of the avian habitat network. While these green spaces may not individually sustain large bird populations, they contribute to network cohesion by acting as “stepping stones” or buffer zones. Our findings suggest that improving the ecological quality of these green spaces — through targeted vegetation management, water resource enhancement, and habitat restoration — could significantly bolster the efficacy of habitat corridors.
    Conclusion This research introduces a comprehensive and methodologically rigorous framework for identifying and constructing urban avian habitat networks. By integrating deterministic (least-cost) and stochastic (random-walk) models of species movement, the research achieves a more nuanced representation of avian dispersal patterns. This dual-model approach may facilitate the creation of a habitat network that optimally supports species connectivity, thus mitigating the effects of habitat fragmentation. The research further highlights the critical role of small and medium-sized green spaces in enhancing the connectivity of urban avian habitats. Urban policymakers and planners should prioritize the restoration and ecological management of these areas to maximize their contribution to urban biodiversity conservation. Future work should incorporate emerging technologies, such as passive acoustic monitoring (PAM), to acquire more precise data on avian diversity and movement patterns. Additionally, incorporating three-dimensional ecological resistance modeling will provide a more comprehensive understanding of how birds navigate urban landscape, given their distinct vertical movement patterns compared to terrestrial species. These advancements will offer crucial insights for the continued optimization of urban avian habitat networks. In conclusion, this research provides a valuable reference for urban biodiversity conservation, particularly concerning avian species, and underscores the need for sustained efforts to restore and interconnect urban habitats to support ecological resilience.

     

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