生态系统服务是自然环境与人类活动之间密切联系的纽带，且各项生态系统服务之间存在着复杂且多样的作用关系。生态系统服务的相关研究是实现人地耦合系统协调发展，科学管理生态系统的基础，对人类生存与可持续发展至关重要。陕西省作为“一带一路”的关键区域，在此背景下，揭示生态系统服务演变规律、权衡/协同关系及其驱动机制特征，具有重要战略意义。

本文以陕西省为研究区，通过InVEST模型，利用气温降水、土壤质地、土地利用类型与社会经济等数据，定量评估陕西省2000-2020年水源供给、碳储量、土壤保持和生境质量4项生态系统服务，探究在不同尺度下区域生态系统服务的时空演变规律及相互关系。借助相关分析法探讨4项生态系统服务的权衡/协同关系及其尺度效应；运用K均值聚类方法识别生态系统服务簇，厘清各服务簇内部生态系统服务的空间差异及主导服务的类型；最后，采用地理探测器定量识别各项生态系统服务的关键影响因素，深入分析陕西省各项生态系统服务空间异质性对自然和社会经济因素的响应特征。研究结论如下：

（1）生态系统服务时空格局。2000-2020年，水源供给服务、土壤保持量和生境质量在研究时段内均呈现“N”字型变化特征，碳储量呈先增后减的规律特征；从空间分布看，水源供给服务总体自北至南呈增长趋势，主要集中在建设用地和耕地上，汉中市供给量占比在全省最大，同时也是空间自相关的高-高聚集区。总碳储量高值集中于榆林市和延安市，林地在单位面积碳储量和总碳储量均最大。平均土壤保持量和土壤保持总量高值主要分布在安康市和汉中市南部，土壤保持量的低-低集聚区面积占比最高。生境质量高值区集中于宝鸡市和商洛市，该区域人类活动干扰小；空间自相关以高-高和低-低型集聚为主，二者共占研究区总面积的半数以上。

（2）生态系统服务权衡与协同关系。权衡与协同关系在全域和市域上尺度效应显著。水源供给与碳储量、土壤保持在全域尺度分别表现为权衡关系和不显著，但在市域上分别在榆林市为弱协同相关以及在铜川市等地区为弱权衡关系；碳储量和土壤保持在西安市表现为弱协同或中等协同关系；土壤保持和生境质量在全域上表现弱协同，在宝鸡市、西安市、渭南市和榆林市上升为中等协同关系。双变量自相关结果表明，生境质量和碳储量之间协同关系较突出，土壤保持和水源供给以及碳储量与土壤保持这两个组合的协同显著，低-低协同在每个成对生态系统服务中均有分布在榆林市西北部以及关中平原渭河以北地区，在所有生态系统服务对中，高-高协同在秦岭地区均有连续或零星分布。

（3）生态系统服务簇。从各项生态系统服务的贡献度来看，碳储量服务在各簇内表现均比较突出，尤其是服务簇3。服务簇1生境质量和碳储量为主导服务类型，主要分布陕北地区内大部分县区，且分布向北移动；服务簇2碳储量和生境质量服务贡献度较高；服务簇3主要分布在秦岭地带；服务簇4各项生态系统服务间存在较小程度的差异，贡献度都比较高，集中分布在安康市和汉中市南部的各县，并向西北方向延伸。

（4）生态系统服务驱动因素分析。对水源供给解释力最强的因子是年均降水量，达0.712。对碳储量服务和生境质量起决定作用的因子是土地利用类型，但土地利用类型对土壤保持服务空间分布格局的影响力不显著。土壤保持服务空间异质性主要由年均降水量、坡度、植被覆盖度和平均气温等因素所致。任意两个因子间的交互探测中双因子增强比非线性增强类型出现的频率高，且对生态系统服务空间分异的解释度均高于单个因子。

Ecosystem services act as the tight connection between the natural surroundings and human undertakings, and there exist intricate and multifaceted interactions among diverse ecosystem services. Research on ecosystem services serves as the foundation for the

harmonious advancement of the human-land interaction system and the evidence-based stewardship of the ecosystem, thereby playing a pivotal role in human existence and sustainable progress. As a key region of the "Belt and Road" initiative, Shaanxi Province has a large difference in natural endowments and strong spatial heterogeneity. In this context, it is of strategic significance to reveal the evolution rules, trade-offs/synergies and driving mechanisms of ecosystem services.

Taking Shaanxi Province as the research area, based on the InVEST model, combined with climate, soil texture, land use type and socio-economic data, this paper comprehensively analyzed four key ecosystem services—water supply, carbon storage, soil conservation, and habitat quality—in Shaanxi Province over a 20-year period from 2000 to 2020. Through quantitative evaluation, explored the spatial and temporal patterns of these services, as well as their regional variations across multiple scales. Using correlation analysis, the trade-off/synergy relationship between the four ecosystem services and their scale effects were explored; The K-means clustering method was employed to identify distinct clusters of ecosystem services, which also aided in elucidating the spatial disparities and prevalent service types of ecosystem services within each respective cluster.Utilizing the geographical detector, the ultimate achievement was the quantitative identification of the primary factors influencing ecosystem services, and the response characteristics of spatial heterogeneity of various ecosystem services in Shaanxi Province to natural and socio-economic factors were deeply analyzed. The conclusions drawn from the study are listed below:

（1）The comprehensive analysis reveals the spatiotemporal dynamics of various ecosystem services.Between 2000 and 2020, the water supply service, soil conservation, and habitat quality demonstrated an "N"-shaped trend, whereas carbon storage first rose and then fell. Regarding spatial distribution, the water supply service demonstrated a general northward-to-southward increase in its overall dispersal, mainly concentrated in construction land and cultivated land. The supply of Hanzhong accounted for the largest proportion in the province, and was also a high-high cluster area of spatial autocorrelation. The high value of total carbon storage was concentrated in Yulin and Yan'an, and forest land exhibited the highest carbon storage capacity both per unit area and in aggregate. The high value of average soil conservation and total soil conservation was mainly distributed in Ankang and the south of Hanzhong, and the low-low cluster area of soil conservation accounted for the highest proportion. The high value of habitat quality was concentrated in Baoji and Shangluo, which had little interference from human activities. The spatial autocorrelation predominantly exhibited high-high and low-low clustering, encompassing over half of the entire study area.

（2）The trade-off/synergy relationship of ecosystem services. Significant scale effects of the trade-off/synergy relationship were observed at both the global and municipal levels. From a provincial perspective, there are trade-off relationships as well as non-significant relationships among water supply, carbon storage, and soil conservation, but weak synergy correlation in Yulin City and weak trade-off relationship in Tongchuan City, respectively. Carbon storage and soil conservation showed weak synergy or moderate synergy relationship in Xi'an City. Soil conservation and habitat quality showed weak synergy relationship at the global scale, and moderate synergy relationship in Baoji City, Xi'an City, Weinan City and Yulin City. The results of the bivariate autocorrelation analysis demonstrated significant synergistic relationships between habitat quality and carbon storage, between soil conservation and water supply, as well as between carbon storage and soil conservation. Low-low synergy was distributed in the northwest of Yulin City and the north of Weihe River in the Guanzhong Plain in each pair of ecosystem services. In all ecosystem services, high-high synergy was continuous or sporadic in the Qinling Mountains.

（3）Cluster of ecosystem services. From the contribution of various ecosystem services, carbon storage services have shown outstanding performance within each cluster, especially in service cluster 3. The dominant service types are habitat quality and carbon storage in service cluster 1, mainly distributed in most counties and districts in northern Shaanxi, and moving northward; Service cluster 2 has a high contribution to carbon storage and habitat quality services; Service cluster 3 is mainly distributed in the Qinling Mountains region; There are relatively small differences and high contributions among the ecosystem services in service cluster 4, which are concentrated in various counties in the southern part of Ankang City and Hanzhong City, and extend northwestward.

（4）Analysis and research on driving factors of ecosystem services. The strongest explanatory power for water supply was annual precipitation, which reached 0.712. The determining factor for carbon storage service and habitat quality was land use type, but its influence on the spatial distribution pattern of soil conservation service was not significant. The main influencing factors of spatial heterogeneity of soil conservation service were annual precipitation, slope, vegetation coverage and average temperature. In the interactive detection between any two factors, the type of dual-factor enhancement occurs more frequently than nonlinear enhancement, and the degree of interpretation of spatial differentiation of ecosystem services is higher than that of a single factor.

[1]Daily GC, Polasky S, Goldstein J, et al. Ecosystem services in decision making: time to deliver[J]. Frontiers in Ecology and the Environment. 2009, 7: 21-28.

[2]Daily GC. Nature's Services: Societal Dependence on Natural Ecosystems. Washington DC: Island Press, 1997, 1-25.

[3]Costanza R, D'Arge R, Groot R, et al. The value of the world's ecosystem services and natural capital[J]. Nature, 1997, 387(15): 253-260.

[4]MEA(Millennium Ecosystem Assessment). Ecosystems and human Well-Being[M]. Washington, DC: Island Press, 2005.

[5]Paul R. J., Douglas B. T., Bennett E. M., et al. Trade-offs across Space, Time, and Ecosystem Services[J]. Ecology and Society, 2005, 11(1): 709-723.

[6]李文华, 张彪, 谢高地. 中国生态系统服务研究的回顾与展望[J].自然资源学报，2009, 24(1): 1-10．

[7]王晓峰, 马雪, 冯晓明, 等. 重点脆弱生态区生态系统服务权衡与协同关系时空特征[J].生态学报,2019,39(20):7344-7355.

[8]Fu B J, Zhang L W，Xu Z H, et al. Ecosystem services in changing land use[J]. Journal of Soils and Sediments, 2015, 15(4): 833-843.

[9]Jiang S, Wang J, Zhao Y, et al. Sustainability of water resources for agriculture considering grain production trade and consumption in China from 2004 to 2013[J]. Journal of Cleaner Production, 2017, 149: 1210-1218.

[10]Qing F, Zhao W W, Hu X P, et al. Trading-off ecosystem services for better ecological restoration:A case study in the Loess Plateau of China [J]. Journal of Cleaner Production, 2020, 257.120469.

[11]傅伯杰, 刘世梁, 马克明. 生态系统综合评价的内容与方法. 生态学报, 2001,(11): 1885-1892.

[12]Bennett E M, Balvanera P. The future of production systems in a globalized world[J]. Frontiers in Ecology and the Environment, 2007, 5(4): 191-198．

[13]Tallis H M, Kareiva P. Shaping global environmental decisions using socio-ecological models[J]. Trends in Ecology ＆ Evolution, 2006, 21(10): 562-568．

[14]傅伯杰, 周国逸, 白永飞, 等. 中国主要陆地生态系统服务功能与生态安全[J]．地球科学进展, 2009, 24(6): 571-576．

[15]《“十四五”生态保护监管规划》印发[J]. 中国环境监察, 2022, (4): 1-4.

[16]陕西省人民政府办公厅关于印发“十四五”生态环境保护规划的通知[J]. 陕西省人民政府公报, 2022, (2): 1-12.

[17]Liu L, Zhang H, Gao Y, et al. Hotspot identification and interaction analyses of the provisioning of multiple ecosystem services: Case study of Shaanxi Province, China[J]. Ecological indicators, 2019, 107: 1-13.

[18]Liang W, Bai D, Wang F, et al. Quantifying the impacts of climate change and ecological restoration on stream flow changes based on a Budyko hydrological model in China's Loess Plateau[J]. Water Resources Research, 2015, 51(8): 6500-6519.

[19]Li S, Liang W, Fu B, et al. Vegetation changes in recent large-scale ecological restoration projects and subsequent impact on water resources in China's Loess Plateau[J]. Science of the Total Environment, 2016, 569: 1032-1039.

[20]尤鑫. 西部地区城镇化水平与经济人口发展变化研究基于2000-2010年西部地区十二个省区面板数据[J]. 地理科学, 2015, 35(3): 268-274.

[21]李双成, 张才玉, 刘金龙, 等. 生态系统服务权衡与协同研究进展及地理学研究议题[J]. 地理研究, 2013, 32(8): 1379-1390.

[22]Tansley A G. The use and abuse of vegetational concepts and terms[J]. Ecology, 1935, 16(3): 284-307.

[23]Westman W E. How Much Are Nature's Services Worth?[J]. Science. 1977, 197(4307): 960-964.

[24]Ehrlich P R, Ehrlich A H. Extinction: The causes and consequences of the disappearance of species[M]. New York: Random House, 1981.

[25]Assessment M E. Ecosystems and Human Well-being: A Framework for Assessment [M]. Washington D. C.: Island Press, 2003.

[26]欧阳志云, 王如松, 赵景柱. 生态系统服务功能及其生态经济价值评价[J]. 应用生态学报, 1999, (5): 635-40.

[27]欧阳志云, 王效科, 苗鸿. 中国陆地生态系统服务功能及其生态经济价值的初步研究[J]. 生态学报. 1999, 19(5): 19-25.

[28]谢高地, 鲁春霞, 成升魁. 全球生态系统服务价值评估研究进展[J]. 资源科学. 2001, 23(6): 5-9.

[29]Wallace K J. Classification of ecosystem services: Problems and solutions[J]. Biological Conservation. 2007, 139(3-4): 235-246.

[30]Groot R D, Wilson M A, Boumans R M.A typology for the classification, description and valuation of ecosystem functions, goods and services[J]. Ecological Economics, 2002, 41(3): 393-408.

[31]谢高地, 张钮, 鲁春霞, 等. 中国自然草地生态系统服务价值[J]. 自然资源学报2001, 16(1): 47-53.

[32]李双成. 生态系统服务地理学[M]. 北京：科学出版社，2014.

[33]赵景柱, 肖寒, 吴刚. 生态系统服务的物质量与价值量评价方法的比较分析[J]. 应用生态学报, 2000, 11(2): 290-292.

[34]谢高地, 鲁春霞, 冷允法, 等. 青藏高原生态资产的价值评估[J]. 自然资源学报, 2003, 18(2): 189-196.

[35]Pagiola S. Payments for environmental services in Costa Rica[J]. Ecological Economics, 2008, 65(4): 712-724.

[36]Odum H, Odum E. The energetic basis for valuation of research in Latin America: The state of the art[J]. Ecosystem services, 2012, 41(1): 7-10.

[37]曹祺文, 卫晓梅, 吴健生. 生态系统服务权衡与协同研究进展[J]. 生态学杂志,2016, 35(11): 3102-3111.

[38]Brown T C, Bergstrp J C, Loomis J B. Defining, valuing, and providing ecosystem goods and services[J]. Natural Resources Journal. 2007, 47(2): 329-376.

[39]Raudsepp-Hearne C, Peterson G D, Bennett E M. Ecosystem service bundles for analyzing tradeoffs in diverse landscapes[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(11): 5242-5247.

[40]姚楠, 刘广全, 姚顺波, 等. 基于InVEST模型的黄土丘陵沟壑区退耕还林还草工程对生态系统碳储量的影响评估[J]. 水土保持通报, 2022, 42 (5): 329-336.

[41]Rodriguez J, Beard J, Bennett E, et al. Trade-offs across space, time, and ecosystem services[J]. Ecology and Society, 2006, 11(1): 28.

[42]彭建, 胡晓旭, 赵明月. 生态系统服务权衡研究进展: 从认知到决策[J]. 地理学报, 2017, 72 (6): 960-973.

[43]Lester S, Costello C, Halpern B, et al. Evaluating tradeoffs among ecosystem services to inform marine spatial planning[J]. Marine Policy, 2013, 38: 80-89.

[44]戴尔阜, 王晓莉, 朱建佳, 等. 生态系统服务权衡: 方法、模型与研究框架[J]. 地理研究. 2016, 35(6): 1005-1016.

[45]Maud A, Mouchet, et al. An interdisciplinary methodological guide for quantifying associations between ecosystem services-Science Direct[J]. Global Environmental Change, 2014, 28(1): 298-308.

[46]林泉, 吴秀芹. 生态系统服务冲突及权衡的研究进展[J]. 环境科学与技术, 2012, 35(6): 100-105.

[47]申嘉, 李双成, 梁泽. 生态系统服务供需关系研究进展与趋势展望[J]. 自然资源学报, 2021, 36(8): 1909-1922.

[48]吴健生, 钟晓红, 彭建, 等. 基于生态系统服务簇的小尺度区域生态用地功能分类——以重庆两江新区为例[J]. 生态学报, 2015, 35(11): 3808-3816.

[49]Kareiva P, Watts S, McDonald R, et al. Domesticated nature: Shaping landscapes and ecosystems for human welfare. Science, 2007, 316: 1866-1869.

[50]李慧蕾, 彭建, 胡熠娜, 等. 基于生态系统服务簇的内蒙古自治区生态功能分区[J]. 应用生态学报, 2017, 28 (8): 2657-2666.

[51]Liu Y X, Li T, Zhao W, et al. Landscape functional zoning at a county level based on ecosystem services bundle: methods comparison and management indication[J]. Journal of Environmental Management, 2019, 249: 109315.

[52]Orsi F, Cioli M, Primmer E, et al. Mapping hotspots and bundles of forest ecosystem services across the European Union[J]. Land Use Policy, 2020, 99: 104840.

[53]彭立, 邓伟, 黄佩, 等. 四川盆地多重生态系统服务景观指数评价与服务簇识别[J]. 生态学报, 2021, 41(23): 9328-9340.

[54]严恩萍, 林辉, 王广兴, 等. 1990—2011年三峡库区生态系统服务价值演变及驱动力[J]. 生态学报, 2014, 34(20): 5962-5973.

[55]Bennett E M, Peterson G D, Gordon L J. Understanding relationships among multiple ecosystem services[J]. Ecology Letters, 2009, 12: 1394-1404.

[56]戴尔阜, 王晓莉, 朱建佳, 等. 生态系统服务权衡/协同研究进展与趋势展望. 地球科学进展, 2015, 30(11): 1250-1259.

[57]Lyu R, Clarke K C, Zhang J, et al. Spatial correlations among ecosystem services and their socio-ecological driving factors: A case study in the city belt along the Yellow River in Ningxia.China[J]. Applied Geography, 2019, 108: 64-73.

[58]Chen T, Feng Z, Zhao H, et al. Identification of ecosystem service bundles and driving factors in Beijing and its surrounding areas[J]. Science of the Total Environment, 2020, 711134687.

[59]孙小银, 郭洪伟, 廉丽妹, 等. 南四湖流域产水量空间格局与驱动因素分析[J]. 自然资源学报2017, 32(4): 669-679.

[60]Gordon L G, Enfors E. Land degradation, ecosystem services, and resilience of smallholder farmersin Makanya catchment, Tanzania[J]. Conserving Land, Protecting Water, 2008, 33-50.

[61]Zhang L, Dawes WR, Walker GR. Response of mean annual evapotranspiration to vegetation changes at catchment scale[J]. Water Resources Research, 2001, 37(3): 701-708.

[62]Zhang L, Hickel K, Dawes W R, et al. A rational function approach for estimating mean annual evapotranspiration[J]. Water Resources Research. 2004, 40(2): 85-94.

[63]Goyal M K, Khan M. Assessment of spatially explicit annual water-balance model for Sutlej River Basin in eastern Himalayas and Tungabhadra River Basin in peninsular India[J]. Nordic Hydrology. 2017, 48(2): 542-558.

[64]刘洋, 毕军, 吕建树. 生态系统服务分类综述与流域尺度重分类研究[J]. 资源科学2019, 41(7): 1189-1200.

[65]周文佐, 刘高焕, 潘剑君. 土壤有效含水量的经验估算研究——以东北黑土为例[J]. 干旱区资源与环境. 2003, (4): 88-95.

[66]Canadell J, Jackson R B, Ehleringer J B, et al. Maximum rooting depth of vegetation types at the global scale[J]. Oecologia. 1996, 108(4): 583-595.

[67]潘韬, 吴绍洪, 戴尔阜, 等. 基于InVEST 模型的三江源区生态系统水源供给服务时空变化[J]. 应用生态学报, 2013, 24(1): 183-189.

[68]包玉斌, 李婷, 柳辉, 等. 基于InVEST模型的陕北黄高原水源涵养功能时空变化[J]. 地理研究. 2016, 35(4): 664-676.

[69]Fang Z, Ding T, Chen J, et al. Impacts of land use/land cover changes on ecosystem services in ecologically fagile regions[J]. Science of the Total Environment, 2022, 831: 154967.

[70]崔高阳, 陈云明, 曹扬, 等. 陕西省森林生态系统碳储量分布格局分析. 植物生态学报, 2015, 39(4): 333-342.

[71]徐丽, 于贵瑞, 何念鹏. 1980s-2010s中国陆地生态系统土壤碳储量的变化. 地理学报, 2018, 73(11): 2150-2167.

[72]刘冠, 李国庆, 李洁, 等. 基于InVEST模型的1999-2016年麻塔流域碳储量变化及空间格局研究[J]. 干旱区研究. 2021, 38(1): 267-274.

[73]Wraich J, Jnadelhoffer K. Belowground Carbon Allocation in Forest Ecosystems: Global Trends[J]. Ecology, 1989, 70(5): 1346-1354.

[74]Alam S A, Starr M, Clark B J F. Tree biomass and soil organic carbon densities across the Sudanese woodland savannah: A regional carbon sequestration study[J]. Journal of arid environments, 2013, 89: 67-76.

[75]Giardina C P , Ryan M G, Evidence that decomposition rates of organic carbon ineral soil do notvary with temperature[J]. Nature, 2000,404(6780): 61-85.

[76]陈光水, 杨玉盛, 刘乐中, 等. 森林地下碳分配（TBCA）研究进展. 亚热带资源与环境学报. 2007, (1): 34-42.

[77]Delaney M, Brown S, Lugo A E, et al. The quantity and turnover of dead wood in permanent forest plots in six life zones of Venezuela[J]. Biotropica, 1998, 30(1): 2-11.

[78]贾芳芳. 基于InVEST模型的赣江流域生态系统服务功能评估[D]. 中国地质大学（北京）, 2014.

[79]Wischmeier W H, Smith D D. Predicting rainfall erosion losses: A guide to conservation Planning[M]. Washington, DC: Department of Agriculture, Science and Education Administration 1978.

[80]翟子宁, 王克勤, 苏备, 等. 松华坝水源区不同土地利用类型土壤可蚀性研究[J]. 西南林业大学学报, 2016, (5): 118-124.

[81]Williams J R, Renard K G, Dyke PT. EPIC: A new method for assessing erosion's effecton soil productivity[J]. Journal of Soil & Water Conservation. 1983, 38(5): 381-383.

[82]张科利, 彭文英, 杨红丽. 中国土壤可蚀性值及其估算[J]. 土壤学报. 2007(1):7-13.

[83]蔡崇法, 丁树文, 史志华, 等. 应用USLE模型与地理信息系统IDRISI预测小流域土壤侵蚀量的研究. 水土保持学报, 2000, (2): 19-24.

[84]巩杰, 柳冬青, 高秉丽, 等. 西部山区流域生态系统服务权衡与协同关系——以甘肃白龙江流域为例[J]. 应用生态学报, 2020, 31(4): 1278-1288.

[85]程琳, 杨勤科, 谢红霞, 等. 基于GIS和CSLE的陕西省土壤侵蚀定量评价方法研究[J]. 土壤保持学报, 2009, 23(5):61-66.

[86]Sharp R, Tallis H T, Ricketts T, et al. InVEST 3.5.0 User's Guide[M]. The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy and World Wild life Fund. 2018.

[87]杨洁. 黄河流域草地生态系统服务功能及其权衡协同关系研究[D]. 甘肃农业大学, 2021.

[88]包玉斌, 刘康, 李婷, 等. 基于InVEST模型的土地利用变化对生境的影响-以陕西省黄河湿地自然保护区为例[J]. 干旱区研究, 2015, 32(3): 622-629.

[89]Kumari M, Sarma K, Sharma R. Using Moran's I and GIS to study the spatial pattern of land surface temperature in relation to land use/cover around a thermal power plant in Singrauli district, Madhya Pradesh, India[J]. Remote Sensing Applications: Society and Environment. 2019, 15: 100239.

[90]Cohen I A. A power primer[J]. Psychological Bulletin. 1992, 112(1): 155-159.

[91]王劲峰, 徐成东. 地理探测器: 原理与展望[J]. 地理学报, 2017, 72(1): 116-134.

[92]Qiao P W, Yang S C, Lei M, et al. Quantitative analysis of the factors influencing spatial distribution of soil heavy metals based on geographical detector[J]. The Science of the total environment, 2019, 664: 392-413.