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多模型融合下成都市生态系统服务空间格局、权衡协同关系及驱动机制

The spatial pattern, trade-off and synergy relationship, and driving mechanism of ecosystem services in Chengdu under multi-model fusion

  • 摘要: 在快速城市化背景下,人类活动强度持续增强,显著改变土地利用格局与生态过程运行方式,进而引发生境退化、碳储量下降及水土流失等生态安全问题。以成都市为研究区,基于2000—2020年多源时空数据,综合运用 InVEST 模型、XGBoost 机器学习算法、SHAP 可解释性分析方法及基于主成分的间接梯度分析,对生境质量、碳储量、产水量和土壤保持四类关键生态系统服务的空间格局、时序演变特征及其驱动机制进行系统解析。结果表明,成都市生态系统服务整体呈现显著的“西高东低”空间分异特征,其中生境质量、碳储量与土壤保持在空间分布上高度耦合,高值区主要集中于西部山地区域,而低值区集中于东部平原及中心城区;产水量空间格局相对破碎,主要受降水条件控制。间接梯度分析结果揭示,生境质量、碳储量与土壤保持之间表现出显著协同关系,而产水量与上述调节类生态系统服务之间普遍存在权衡。XGBoost-SHAP 分析进一步表明,自然地形因子(高程、坡度)在四类生态系统服务中均占据主导地位,年降水量是产水量变化的核心控制因子,而人口密度、GDP 与夜间灯光等人类活动因子对生境质量和碳储量具有显著负向影响。研究结果为揭示城市化背景下生态系统服务协同—权衡关系及其非线性驱动机制提供了科学依据。

     

    Abstract: Against the background of rapid urbanization, the intensity of human activities has continued to increase, significantly reshaping land-use patterns and ecological processes, and consequently leading to a series of ecological security issues, including habitat degradation, carbon storage loss, and intensified soil erosion. Taking Chengdu City as the study area, this study integrates multi-source spatiotemporal data from 2000 to 2020 and applies the InVEST model, the XGBoost machine learning algorithm, SHAP-based explainable analysis, and principal component–based indirect gradient analysis to systematically investigate the spatial patterns, temporal dynamics, and driving mechanisms of four key ecosystem services: habitat quality, carbon storage, water yield, and soil retention. The results indicate that ecosystem services in Chengdu exhibit a pronounced “high-in-the-west and low-in-the-east” spatial differentiation pattern. Habitat quality, carbon storage, and soil retention show strong spatial coupling, with high-value areas mainly concentrated in the western mountainous regions, while low-value areas are predominantly distributed in the eastern plains and central urban areas. In contrast, the spatial pattern of water yield is relatively fragmented and is primarily controlled by precipitation conditions. The indirect gradient analysis reveals significant synergies among habitat quality, carbon storage, and soil retention, whereas water yield generally shows trade-offs with these regulating ecosystem services. Further XGBoost-SHAP analysis demonstrates that natural topographic factors (elevation and slope) dominate the variation in all four ecosystem services, precipitation is the key controlling factor for water yield, and human activity indicators such as population density, GDP, and nighttime light intensity exert significant negative effects on habitat quality and carbon storage. These findings provide a scientific basis for understanding the synergy–trade-off relationships among ecosystem services and their nonlinear driving mechanisms under rapid urbanization.

     

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