Abstract
The construction of ecological networks within the context of urbanization is an effective approach to cope with the challenges of urban biodiversity decline, representing a crucial goal in urban planning and development. However, existing studies often overlook the richness and uniqueness within species communities by homogenizing traits of species in the same class. This study proposes a framework for constructing and optimizing ecological networks focused on differential conservation within the same class. By classifying birds into three groups (specialists of water, forest or urban areas) based on their ecological requirements and urbanization tolerance, we constructed an ecological network tailored to their distinct migratory dispersal patterns. We then identified strategic areas including pinch points, barriers, and breakpoints specific to each bird group. Our findings reveal notable variations in suitable habitat distribution among different bird groups in urban environments. Corridor layouts varied according to habitat preferences and migratory dispersal patterns. Despite these differences, urban built-up areas persist as central hubs for the distribution of suitable habitats for 75% of bird species, with peripheral mountain-plain transition areas constituting 63% of crucial dispersal corridors. This emphasizes the critical role of urban built-up areas in maintaining biodiversity and ecological connectivity. Prioritizing connectivity between central urban areas and distant natural spaces is imperative. Our approach innovatively classifies and constructs networks to identify strategic areas with diverse species-specific attributes, providing valuable spatial information for land planning and guiding solutions to enhance target species. While the primary focus is on bird conservation in Beijing, our framework is broadly applicable to global biodiversity management and green planning under urbanization challenges. Overall, this study offers innovative insights for urban planning development and serves as decision support for prioritizing urban actions.
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