Liu Lanfang, Wang Jinliang, Li Jie, He Suling, Lan Yongcui, Liu Fang
Faculty of Geography Yunnan Normal University Kunming China.
Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan Kunming China.
Ecol Evol. 2025 Apr 11;15(4):e71222. doi: 10.1002/ece3.71222. eCollection 2025 Apr.
Understanding and quantifying the dynamic features of local ecosystem services (ESs) and integrating diverse ecosystem assessment results form crucial foundations for regional ES management. However, existing methods for integrating and objectively evaluating multiple ESs remain limited. Consequently, this research evaluates four key services based on the InVEST and RUSLE models in the Central Yunnan Province (CYP)-from 2000 to 2020: water yield (WY), carbon storage (CS), habitat quality (HQ), and soil conservation (SC). It then constructs an Integrated Ecosystem Service Index (IESI) using principal component analysis (PCA). Additionally, this study explores the factors driving the spatial divergence of ESs by employing the optimal parameter-based geographical detector model (OPGD) at the optimal spatial scale. The results indicated that (1) the IESI was effectively applied in the CYP and could quantitatively and comprehensively integrate the assessment results of the four key ESs. (2) During the study period, the ESs in the CYP showed increasing trends for WY, HQ, and SC, while CS showed a decreasing trend. (3) The IESI during the study period exhibited a trend of initially decreasing and then increasing. The average IESI values for CYP were 0.7338 in 2000, 0.6981 in 2005, 0.6947 in 2010, 0.6650 in 2015, and 0.6992 in 2020. (4) A 4500 m × 4500 m grid was identified as the optimal spatial scale for detecting the spatial divergence of comprehensive ecosystem service (CES) in CYP, and relief degree of land surface (RDLS), slope, and the NDVI were the top three drivers based on q-values. This study offers a more scientific and effective method for evaluating regional CES. It also provides a comprehensive analytical tool for balancing land use competition and assessing the effectiveness of policy implementation.
理解和量化当地生态系统服务（ESs）的动态特征，并整合多样的生态系统评估结果，是区域生态系统服务管理的关键基础。然而，现有的整合和客观评估多种生态系统服务的方法仍然有限。因此，本研究基于InVEST和RUSLE模型，对2000年至2020年的滇中地区（CYP）的四项关键服务进行了评估：产水量（WY）、碳储量（CS）、栖息地质量（HQ）和土壤保持（SC）。然后，利用主成分分析（PCA）构建了综合生态系统服务指数（IESI）。此外，本研究通过在最优空间尺度上采用基于最优参数的地理探测器模型（OPGD），探讨了驱动生态系统服务空间分异的因素。结果表明：（1）IESI在滇中地区得到了有效应用，能够定量、全面地整合四项关键生态系统服务的评估结果。（2）在研究期间，滇中地区的产水量、栖息地质量和土壤保持呈增加趋势，而碳储量呈下降趋势。（3）研究期间IESI呈现先下降后上升的趋势。滇中地区2000年、2005年、2010年、2015年和2020年的IESI平均值分别为0.7338、0.6981、0.6947、0.6650和0.6992。（4）4500 m×4500 m网格被确定为检测滇中地区综合生态系统服务（CES）空间分异的最优空间尺度，基于q值，地形起伏度（RDLS）、坡度和归一化植被指数（NDVI）是前三大驱动因素。本研究为评估区域CES提供了一种更科学有效的方法。它还为平衡土地利用竞争和评估政策实施效果提供了一个综合分析工具。
