{"title":"Dual-zone framework for ecosystem service governance: linking functional gradients and ecosystem services bundles along an urbanization gradient","authors":"Xin Li, Xinjie Liu, Leping Liu, Pengfei Sun, Jiajing Li, Xiaoqian Li, Zhi Dong, Xinchuang Chen","doi":"10.1016/j.ecolind.2026.114707","DOIUrl":null,"url":null,"abstract":"<div><div>Urbanization reshapes interactions among ecosystem services (<em>ES</em>), making it necessary to clarify how urbanization gradients, ecological mechanisms, together with nonlinear driver responses, jointly regulate <em>ES</em> dynamics to support ecological zoning management, especially in mountainous and hilly regions. Focusing on the central-south Shandong hilly region, we delineated developed (<em>E</em>), developing (<em>I</em>), and rural (<em>R</em>) zones through multidimensional clustering, quantified six major <em>ES</em>, and examined their trade-offs, synergies, and nonlinear drivers using Geographically Weighted Regression and <em>XGBoost-SHAP. ES</em> bundles were further identified using a Self-Organizing Map, and a dual-zone framework was established by integrating urban functional zones and <em>ES</em> bundles, with spatially explicit driver threshold characteristics used to support interpretation and management diagnosis. Results reveal a distinct <em>R</em> > <em>I</em> > <em>E</em> gradient in <em>ES</em> supply and increasing spatial heterogeneity toward urban cores. <em>ES</em> interactions shift from synergy-dominated in <em>E</em> to increasingly complex trade-offs in <em>I</em> and <em>R</em>, with water conservation‑carbon storage being the most urbanization-sensitive pair. Driver regimes differ across zones: socioeconomic factors dominate in <em>E</em>, natural-anthropogenic interactions in <em>I</em>, and biophysical constraints in <em>R</em>. Key nonlinear thresholds include forest cover >10%, temperature ranging from 14 to 14.7 °C, and precipitation >900 mm. Linking these nonlinear response characteristics with <em>ES</em> bundles and functional zones enables precise identification of sensitive areas, providing a scientifically grounded pathway for differentiated and zone-specific <em>ES</em> management in urbanizing mountainous regions.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"184 ","pages":"Article 114707"},"PeriodicalIF":8.7000,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Indicators","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1470160X26001044","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/2/21 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Urbanization reshapes interactions among ecosystem services (ES), making it necessary to clarify how urbanization gradients, ecological mechanisms, together with nonlinear driver responses, jointly regulate ES dynamics to support ecological zoning management, especially in mountainous and hilly regions. Focusing on the central-south Shandong hilly region, we delineated developed (E), developing (I), and rural (R) zones through multidimensional clustering, quantified six major ES, and examined their trade-offs, synergies, and nonlinear drivers using Geographically Weighted Regression and XGBoost-SHAP. ES bundles were further identified using a Self-Organizing Map, and a dual-zone framework was established by integrating urban functional zones and ES bundles, with spatially explicit driver threshold characteristics used to support interpretation and management diagnosis. Results reveal a distinct R > I > E gradient in ES supply and increasing spatial heterogeneity toward urban cores. ES interactions shift from synergy-dominated in E to increasingly complex trade-offs in I and R, with water conservation‑carbon storage being the most urbanization-sensitive pair. Driver regimes differ across zones: socioeconomic factors dominate in E, natural-anthropogenic interactions in I, and biophysical constraints in R. Key nonlinear thresholds include forest cover >10%, temperature ranging from 14 to 14.7 °C, and precipitation >900 mm. Linking these nonlinear response characteristics with ES bundles and functional zones enables precise identification of sensitive areas, providing a scientifically grounded pathway for differentiated and zone-specific ES management in urbanizing mountainous regions.
期刊介绍:
The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published.
• All aspects of ecological and environmental indicators and indices.
• New indicators, and new approaches and methods for indicator development, testing and use.
• Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources.
• Analysis and research of resource, system- and scale-specific indicators.
• Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs.
• How research indicators can be transformed into direct application for management purposes.
• Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators.
• Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.