| 嘉陵江流域生态系统健康时空演变及影响因素分析 |
| 摘要点击 469 全文点击 10 投稿时间：2025-06-19 修订日期：2025-09-15 |
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| 中文关键词 生态系统健康 VORS模型 地形梯度 XGBoost-SHAP模型 嘉陵江流域 |
| 英文关键词 ecosystem health VORS model topographic gradient XGBoost-SHAP model Jialing River Basin |
| DOI 10.13227/j.hjkx.202506233 |
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| 中文摘要 |
| 嘉陵江流域作为长江上游关键生态屏障区，探究其生态系统健康状况对协调长江经济带生态保护与区域发展具有重要意义. 基于“活力-组织力-弹性-服务（VORS）”模型，评估2000~2022年嘉陵江流域生态系统健康时空演变特征，并结合地形梯度分析、空间自相关分析和XGBoost-SHAP模型，进一步揭示生态系统健康的地形梯度分异特征及驱动机制. 结果表明：①2000~2022年，嘉陵江流域生态系统健康指数整体呈上升趋势，在水平空间上呈现“中部高、南北低”的分布格局，且具有显著的空间聚集性，主要表现为高-高与低-低型聚集. ②垂直空间上，嘉陵江流域生态系统健康呈现显著的地形梯度分异特征，低地形梯度区生态系统健康水平相对较低，高地形梯度区健康水平较高. ③XGBoost-SHAP模型结果表明，自然因素对嘉陵江流域生态系统健康的影响远大于社会经济因素，其中植被覆盖度为最关键的影响因子. ④各影响因子与生态系统健康之间普遍存在显著的非线性关系和阈值效应. |
| 英文摘要 |
| As a key ecological barrier in the upper reaches of the Yangtze River， the Jialing River Basin plays a crucial role in coordinating ecological protection and regional development within the Yangtze River Economic Belt. Based on the “vigor-organization-resilience-services （VORS）” model， this study assesses the spatiotemporal evolution of ecosystem health in the Jialing River Basin from 2000 to 2022. By integrating terrain gradient analysis， spatial autocorrelation analysis， and the XGBoost-SHAP model， the study further reveals the differentiation characteristics of ecosystem health along terrain gradients and its driving mechanisms. The results show that： ① From 2000 to 2022， the ecosystem health index in the Jialing River Basin showed an overall upward trend， with a spatial pattern of “high in the central region and low in the north and south，” and significant spatial clustering， mainly in the form of high-high and low-low clusters. ② In the vertical dimension， ecosystem health exhibited significant differentiation along terrain gradients， with lower health levels in low-gradient areas and higher health levels in high-gradient areas. ③ The results of the XGBoost-SHAP model indicate that natural factors had a far greater influence on ecosystem health in the Jialing River Basin than that of socioeconomic factors， with vegetation cover identified as the most critical factor. ④ There were generally significant nonlinear relationships and threshold effects between the influencing factors and ecosystem health. |
