文献类型：期刊文献

英文题名：Multidimensional factors influencing ecosystem services and their relationships in alpine ecosystems: A case study of the Daxing'anling forest area, Inner Mongolia

作者：Xu, Laixian[1] He, Jiang[2] He, Youjun[1] Zhang, Liang[3] Xu, Hui[4] Tang, Chunwei[3]

第一作者：Xu, Laixian

通信作者：He, YJ[1]

机构：[1]Chinese Acad Forestry, Res Inst Forestry Policy & Informat, 1 Dongxiaofu Rd,Qinglongqiao St, Beijing 100091, Peoples R China;[2]Wuhan Opt Valley Ecol Garden Construction Investme, Wuhan 430000, Peoples R China;[3]Inner Mongolia Daxinganling Carbon Sinks Sci & Tec, Yakeshi 022150, Peoples R China;[4]Inner Mongolia Fengtian Forest & Grass Design Cons, Yakeshi 022150, Peoples R China

年份：2025

卷号：14

外文期刊名：FOREST ECOSYSTEMS

收录：;Scopus(收录号：2-s2.0-105017541144);WOS:【SCI-EXPANDED(收录号:WOS:001602733100001)】；

基金：This research was funded primarily by the Central Public Welfare Research Institutes Basic Research Business Funds to Support the Administration's Central Work Project (Grant No. CAFYBB2023ZA003-4), the National Natural Science Foundation of China (Grant Nos. 31170593 and 31570633), and National Forestry and Grassland Administration Forestry Under the Project "Forestry Major Issues Research" (Grant Nos. 500102-1776 and 500102-5110).

语种：英文

外文关键词：Trade-off intensity (TI); Coupling coordination degree (CCD); Influencing factor; Optimal parameter-based geographical detector model (OPGDM); Gradient boosting regression tree model (GBRTM); Quantile regression model (QRM); Trade-offs and synergies

摘要：Understanding the influencing factors of ecosystem services (ESs) and their relationships is essential for sustainable ecosystem management in degraded alpine ecosystems. There is a lack of integrated multi-model approaches to explore the multidimensional influences on ESs and their relationships in alpine ecosystems. Taking the Daxing'anling forest area, Inner Mongolia (DFAIM) as a case study, this study used the integrated valuation of ecosystem services and trade-offs (InVEST) model to quantify four ESs-soil conservation (SC), water yield (WY), carbon storage (CS), and habitat quality (HQ)-from 2013 to 2018. We adopted root mean square deviation (RMSD) and coupling coordination degree models (CCDM) to analyze their relationships, and integrated three complementary approaches-optimal parameter-based geographical detector model (OPGDM), gradient boosting regression tree model (GBRTM), and quantile regression model (QRM)-to reveal multidimensional influencing factors. Key findings include the following: (1) From 2013 to 2018, WY, SC, and HQ declined while CS increased. WY was primarily influenced by mean annual precipitation (MAP), forest ratio (RF), and soil bulk density (SBD); CS and HQ by RF and population density (PD); and SC by slope (S), RF, and MAP. Mean annual temperature (MAT), gross domestic product (GDP), and road network density (RND) showed increasing negative impacts. (2) Low trade-off intensity (TI < 0.15) dominated all ES pairs, with RF, MAP, PD, and normalized difference vegetation index (NDVI) being the dominant factors. The factor interactions primarily showed two-factor enhancement patterns. (3) The average coupling coordination degree (CCD) of the four ESs was low and declined over time, with low-CCD areas becoming increasingly prevalent. RF, S, SBD, and NDVI positively influenced CCD, while PD, MAT, GDP, and RND had increasing negative impacts, with over 62% of the factor interactions exceeding the individual factor effects. In summary, ES supply generally decreased. Local relationships showed moderate coordination, while overall relationships indicated primary dysfunction. Land use and natural factors primarily shaped these ES and their relationships, while climate and socioeconomic changes diminished ES supply and intensified competition. We recommend enhancing the resilience of natural systems rather than replacing them, establishing climate adaptation monitoring systems, and promoting conservation tillage and cross-departmental coordination mechanisms for collaborative ES optimization. These results provide valuable insights into the sustainable management of alpine ecosystems.