Threshold Effects of Biodiversity on Ecological Resilience: Evidence from Guangdong’s Prefecture-Level Cities

Xin Huang, Yiwen Chen, Chang Liu, Kailun Fang and Tingting Chen ()
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Xin Huang: Guangzhou Urban Planning and Design Co., Ltd., Guangzhou 510030, China
Yiwen Chen: Guangzhou Urban Planning and Design Co., Ltd., Guangzhou 510030, China
Chang Liu: Guangzhou Urban Planning and Design Co., Ltd., Guangzhou 510030, China
Kailun Fang: Guangzhou Urban Planning and Design Co., Ltd., Guangzhou 510030, China
Tingting Chen: School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510030, China

Land, 2025, vol. 14, issue 12, 1-21

Abstract: Understanding interactions between ecological resilience and biodiversity is critical for sustainable ecosystems and coordinated regional development. This study examines prefecture-level cities in Guangdong Province—characterized by diverse ecological conditions and rapid urbanization—to explore how ecological systems respond to biodiversity dynamics. We construct an ecological resilience framework based on resistance–adaptability–recoverability, quantify biodiversity using species occurrence data from the Global Biodiversity Information Facility, and apply a panel threshold model to detect nonlinear couplings. To identify key drivers of resilience, we employ XGBoost and SHAP analyses for interpretable machine learning insights. Results show clear threshold behavior: ecological resilience is weak or negative at low biodiversity and improves once biodiversity exceeds critical levels; in 2015 and 2020, thresholds were approximately 99.73 and 232.01 with a significant high-biodiversity effect. Machine learning results align with the threshold findings and indicate forest coverage ratio is the dominant driver of ecological resilience across years. The integrated findings highlight pronounced spatial heterogeneity in ecological resilience and identify critical biodiversity thresholds influencing ecosystem stability, providing targeted evidence for biodiversity conservation and resilience-oriented management. This study advances understanding of nonlinear ecological–biodiversity interactions and offers practical guidance for strengthening ecological security in rapidly developing regions.

Keywords: ecological resilience; biodiversity; threshold effects; machine learning; Guangdong; nonlinear coupling (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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