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The Impact of Blue-Green Space Landscape Patterns on Bird Richness in Southwest China
Xingru He1,2,3, Siyuan Li1,2,3, Ziling He1,2,3
1Chongqing Engineering Research Center for Remote Sensing Big Data Application, Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China.
Abstract:
With the accelerated pace of urbanization, the substantial reduction in natural vegetation, water bodies, and wetlands has disrupted ecosystem structures, leading to significant declines in biodiversity. Blue-green spaces play a crucial role in maintaining urban habitat quality and supporting species diversity. As a sensitive indicator group to changes in the ecological environment, spatial variations in bird richness can provide important insights into changes in urban ecosystems and habitats. Therefore, a systematic investigation of the relationship between the landscape patterns of blue-green spaces and bird richness in ecologically complex regions is of great significance for achieving sustainable urban development and biodiversity conservation. This study focuses on Southwest China, utilizing bird richness data and blue-green space landscape pattern indicators. By integrating Random Forest (RF) models with Shapley (SHAP) methods, it quantitatively analyzes the relationship between blue-green space landscape patterns and bird richness in typical complex ecological regions. Results indicate nonlinear associations between blue-green landscape patterns and bird richness, with green spaces exerting a stronger overall influence than blue spaces. Edge density (ED) in green spaces demonstrated markedly higher feature importance than other landscape indicators. Within green spaces, ED and class area (CA) showed stronger associations with bird richness, while within blue spaces, CA and Percentage of Landscape (PLAND) provided more prominent explanatory power for bird richness. By clarifying the nonlinear responses and differentiated roles of blue and green landscape patterns, this study provides quantitative evidence for optimizing blue-green spatial planning and promoting biodiversity conservation in ecologically complex regions.
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