生态系统服务作为生态系统与社会经济系统之间的关键连接，在自然环境与人类活动的互动中发挥着不可替代的桥梁作用。在国家大力推进黄河流域生态环境保护和高质量发展的大背景下，本研究聚焦黄河一级支流窟野河，深入剖析窟野河流域生态系统服务间的权衡/协同关系，探究背后驱动因素的影响，以期为黄河流域中游生态系统服务提供支持。

本研究以2005年、2010年、2015年和2020年窟野河流域气温、降水、DEM、社会经济等数据为基础，运用InVEST模型、相关性分析、地理加权回归模型等方法，针对流域产水量、碳储量、土壤保持、生境质量4项生态系统服务功能，开展了生态系统服务评估、权衡/协同关系及驱动因素研究。研究结论如下：

（1）2005年、2010年、2015年、2020年窟野河流域产水总量分别为14.35×10⁸ m³、27.22×10⁸ m³、20.83×10⁸ m³、32.99×10⁸ m³，产水量在空间上呈东南高西北低的格局，流域整体产水功能有所提升；碳储量稳定在99.52×10⁶ t左右，碳储存能力良好，空间上高值区分布在流域东南部；土壤保持量整体波动上升，2020年最高为42.57×10⁷ t，高值区集中在流域东南部神木市内；生境质量指数先升后降，多年平均值仅有0.23，受人类活动影响较大，高值区主要集中在伊金霍洛旗境内。从土地利用与生态系统服务关系来看，草地对产水量和土壤保持量贡献最大，占比分别为64.03%、67.67%，林地碳储量平均值最高，林地、草地和水域的生境质量指数平均值较高。从空间相关性来看，生态系统服务之间均呈高度正相关，主要表现为高-高和低-低聚集。

（2）2005-2020年窟野河流域产水量、碳储量、土壤保持量和生境质量均呈协同关系，正相关性在0.006~0.579间变化。空间上，窟野河流域产水量与碳储量以协同关系为主，集中分布在神木市和伊金霍洛旗；产水量与生境质量协同和权衡关系比例相近，分别为24.97%、23.84%，其中协同关系分布区域集中在神木市、伊金霍洛旗、东胜区、府谷县等区域，权衡关系分布区域集中于伊金霍洛旗、东胜区及神木市；产水量与土壤保持的协同关系占比为29.30%，协同关系分布区集中在神木市、伊金霍洛旗；碳储量与生境质量之间的协同关系突出(占比34.17%)，协同关系分布区集中在伊金霍洛旗、神木市、东胜区；碳储量与土壤保持的协同与权衡关系接近，协同关系分布区集中在神木市、伊金霍洛旗、准格尔旗，而权衡关系分布区集中在伊金霍洛旗、准格尔旗、府谷县及神木市；土壤保持与生境质量的协同关系占比为28.31%，协同关系分布区集中在神木市、伊金霍洛旗、府谷县。流域生态系统服务簇变化中，碳储量主导簇面积呈波动变化，与综合供给簇相互转换有关；生态保育簇分布稳定，产水主导簇逐年增加，集中窟野河干流及乌兰木伦河、悖牛川河等水资源丰富区。

（3）2005-2020年窟野河流域产水量受年均降水量影响明显，与土地利用类型交互后，影响强度从0.3961增至0.4931。碳储量受土地利用类型影响最大(q=0.9946)，土地利用类型与植被覆盖度、夜间灯光等因子间的交互增强了碳储量的空间分异。生境质量主要受夜间灯光等社会经济因子的驱动，夜间灯光与年均降水、高程、坡度等因素间的非线性增强关系显著提高了其对生境质量的解释力。土壤保持受坡度影响较大(q=0.5149)，坡度与年均降水、潜在蒸散发量交互后q值增至0.6601、0.6482。在空间上，年均气温对产水服务存在正相关性，空间影响格局为北高南低；年均降水与产水量同样存在正相关性，影响强度则由东南向西北递减；而潜在蒸散发量与产水量间存在负相关性，影响系数从北到南递增。土地利用类型与碳储存间存在负相关性，影响系数由东南向西北递减；植被覆盖度与碳储存间为正相关，流域东南部强度高于西北部。人口密度、夜间灯光与生境质量间均存在微弱的负相关性，负相关驱动强度在空间上表现为东南高西北低的布局。年均气温与土壤保持在流域北部存在明显的负相关性，而在南部为正相关；年均降水与土壤保持存在明显正相关，个别地区为负相关，空间上表现为东南高西北低；潜在蒸散发量与土壤保持之间在流域西北部存在正相关性，而流域东南部的负相关性从北向南递减；坡度与土壤保持存在强烈的正相关性，强度从南向北递减。

As the key link between ecosystems and socio-economic systems, ecosystem services play an irreplaceable bridging role in the interaction between the natural environment and human activities. Against the backdrop of the country's vigorous efforts to promote ecological environmental protection and high-quality development in the Yellow River Basin, this study focused on the Kuye River, a first-level tributary of the Yellow River, and deeply analyzed the trade-offs/synergies between ecosystem services in the Kuye River Basin, exploring the impact of the driving factors behind it, in order to provide support for ecosystem services in the middle reaches of the Yellow River Basin.

Based on the temperature, precipitation, DEM, socio-economic data of the Kuye River Basin in 2005, 2010, 2015 and 2020, this study used the InVEST model, correlation analysis, geographically weighted regression model and other methods to conduct ecosystem service assessment, trade-off/synergy relationship and driving factor research on four ecosystem service functions: water yield, carbon storage, soil conservation and habitat quality. The research conclusions are as follows: 

(1) The total water yield of the Kuye River Basin in 2005, 2010, 2015 and 2020 was 14.35×10⁸ m³, 27.22×10⁸ m³, 20.83×10⁸ m³ and 32.99×10⁸ m³, respectively. The water yield showed a spatial pattern of high in the southeast and low in the northwest, and the overall water yield function of the basin has improved. The carbon storage has stabilized at around 99.52×10⁶ t, with good carbon storage capacity. The high-value areas are distributed in the southeast of the basin. The soil retention capacity has fluctuated upward as a whole, reaching a maximum of 42.57×10⁷ t in 2020, and the high-value areas are concentrated in Shenmu City in the southeast of the basin. The habitat quality index first increased and then decreased, with an average value of only 0.23 over the years. It is greatly affected by human activities, and the high-value areas are mainly concentrated in Ejin Horo Banner. From the perspective of the relationship between land use and ecosystem services, grassland contributes the most to water production and soil conservation, accounting for 64.03% and 67.67% respectively. The average carbon storage of forest land is the highest, and the average habitat quality index of forest land, grassland and water area is relatively high. From the perspective of spatial correlation, ecosystem services are highly positively correlated, mainly manifested in high-high and low-low clustering.

(2) From 2005 to 2020, water yield, carbon storage, soil conservation and habitat quality in the Kuye River Basin showed a synergistic relationship, with positive correlations ranging from 0.006 to 0.579. Spatially, the water yield and carbon storage in the Kuye River Basin were mainly synergistic, concentrated in Shenmu City and Ejin Horo Banner; the proportions of synergy and trade-off relationships between water yield and habitat quality were similar, at 24.97% and 23.84%, respectively. The synergistic relationship distribution areas were concentrated in Shenmu City, Ejin Horo Banner, Dongsheng District, Fugu County and other areas, while the trade-off relationship distribution areas were concentrated in Ejin Horo Banner, Dongsheng District and Shenmu City; the synergistic relationship between water yield and soil conservation accounted for 29.30%, and the synergistic relationship distribution areas were concentrated in Shenmu City, Ejin Horo Banner, Dongsheng District and Shenmu City. Banner; the synergistic relationship between carbon storage and habitat quality is prominent (accounting for 34.17%), and the distribution area of ​​the synergistic relationship is concentrated in Ejin Horo Banner, Shenmu City, and Dongsheng District; the synergistic and trade-off relationships between carbon storage and soil conservation are close, and the distribution area of ​​the synergistic relationship is concentrated in Shenmu City, Ejin Horo Banner, and Jungar Banner, while the distribution area of ​​the trade-off relationship is concentrated in Ejin Horo Banner, Jungar Banner, Fugu County and Shenmu City; the synergistic relationship between soil conservation and habitat quality accounts for 28.31%, and the distribution area of ​​the synergistic relationship is concentrated in Shenmu City, Ejin Horo Banner, and Fugu County. In the changes of the basin ecosystem service clusters, the area of ​​the carbon storage-dominated cluster fluctuated, which was related to the mutual conversion of the comprehensive supply cluster; the distribution of the ecological conservation cluster was stable, and the water production-dominated cluster increased year by year, concentrated in the main stream of Kuye River and the water-rich areas such as the Ulanmulun River and the Beiniuchuan River. 

(3) From 2005 to 2020, water yield in the Kuye River Basin was significantly affected by annual average precipitation. After interacting with land use type, the influence intensity increased from 0.3961 to 0.4931. Carbon storage was most affected by land use type (q=0.9946). The interaction between land use type and factors such as vegetation coverage and night light enhanced the spatial differentiation of carbon storage. Habitat quality was mainly driven by socioeconomic factors such as night light. The nonlinear enhancement relationship between night light and factors such as annual average precipitation, elevation, and slope significantly improved its explanatory power for habitat quality. Soil retention was greatly affected by slope (q=0.5149). After the slope interacted with annual average precipitation and potential evapotranspiration, the q value increased to 0.6601 and 0.6482. Spatially, the annual average temperature has a positive correlation with water production services, and the spatial impact pattern is high in the north and low in the south; the annual average precipitation also has a positive correlation with water production, and the impact intensity decreases from southeast to northwest; while there is a negative correlation between potential evapotranspiration and water production, and the impact coefficient increases from north to south. There is a negative correlation between land use type and carbon storage, and the impact coefficient decreases from southeast to northwest; vegetation coverage and carbon storage are positively correlated, and the intensity in the southeast of the basin is higher than that in the northwest. There is a weak negative correlation between population density, night lights and habitat quality, and the negative correlation driving intensity is spatially high in the southeast and low in the northwest. There is a significant negative correlation between the annual average temperature and soil retention in the north of the basin, and a positive correlation in the south; there is a significant positive correlation between the annual average precipitation and soil retention, and some areas are negatively correlated, and spatially it is high in the southeast and low in the northwest; there is a positive correlation between potential evapotranspiration and soil retention in the northwest of the basin, and the negative correlation in the southeast of the basin decreases from north to south; there is a strong positive correlation between slope and soil retention, and the intensity decreases from south to north.

[1]Costanza R. Valuing natural capital and ecosystem services toward the goals of efficiency, fairness, and sustainability [J]. Ecosystem Services, 2020, 43: 101096.

[2]Hernández‐Blanco M, Costanza R, Chen H, et al. Ecosystem health, ecosystem services, and the well‐being of humans and the rest of nature [J]. Global change biology, 2022, 28(17): 5027-5040.

[3]Faber J, Van Wensem J. Elaborations on the use of the ecosystem services concept for application in ecological risk assessment for soils [J]. Science of the total environment, 2012, 415: 3-8.

[4]Zeng S, Jiang C, Bai Y, et al. Understanding scale effects and differentiation mechanisms of ecosystem services tradeoffs and synergies relationship: A case study of the Lishui River Basin, China [J]. Ecological Indicators, 2024, 167: 112648.

[5]宋家鹏, 陈松林. 基于生态系统服务簇的福州市生态系统服务格局 [J]. 应用生态学报, 2021, 32(3): 1045-1053.

[6]傅伯杰, 张立伟. 土地利用变化与生态系统服务: 概念, 方法与进展 [J]. 地理科学进展, 2014, 33(4): 441-446.

[7]Liu C, Li W, Xu J, et al. Global trends and characteristics of ecological security research in the early 21st century: A literature review and bibliometric analysis [J]. Ecological Indicators, 2022, 137: 108734.

[8]Shifaw E, Sha J, Li X, et al. Ecosystem services dynamics and their influencing factors: Synergies/tradeoffs interactions and implications, the case of upper Blue Nile basin, Ethiopia [J]. Science of The Total Environment, 2024: 173524.

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

[10]刘洋. 黄河流域生态系统服务及其权衡协同关系特征 [D]. 咸阳: 西北农林科技大学, 2023.

[11]王鹏, 秦思彤, 胡慧蓉. 近30 a拉萨河流域土地利用变化和生境质量的时空演变特征 [J]. 干旱区研究, 2023, 40(3): 492-503.

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

[13]刘琳轲, 梁流涛, 高攀, 等. 黄河流域生态保护与高质量发展的耦合关系及交互响应 [J]. 自然资源学报, 2021, 36(1): 176-195.

[14]盛前丽. 香溪河流域土地利用变化径流效应研究 [D]. 北京: 北京林业大学, 2008.

[15]王亚迪. 变化环境下黄河源区水文气象要素特征分析及径流变化驱动研究 [D]. 西安: 西安理工大学, 2020.

[16]Zhang J, Gao G, Fu B, et al. Investigation of the relationship between precipitation extremes and sediment discharge production under extensive land cover change in the Chinese Loess Plateau [J]. Geomorphology, 2020, 361: 107176.

[17]李立缠. 窟野河流域下垫面变化对暴雨洪水的影响 [D]. 西安: 西北大学, 2022.

[18]余荔. 窟野河流域植被覆盖变化及其对径流影响 [D]. 武汉: 华中农业大学, 2011.

[19]魏雷晗冰, 时鹏, 魏勇, 等. 窟野河流域生态系统服务功能变化及其驱动因素分析 [J]. 水土保持学报, 2024, 38(4): 222-235.

[20]蒋晓辉, 谷晓伟, 何宏谋. 窟野河流域煤炭开采对水循环的影响研究 [J]. 自然资源学报, 2010, 25(2): 300-307.

[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]Mouchet M A, Lamarque P, Martín-López B, et al. An interdisciplinary methodological guide for quantifying associations between ecosystem services [J]. Global environmental change, 2014, 28: 298-308.

[23]Ehrlich P R, Mooney H A. Extinction, substitution, and ecosystem services [J]. BioScience, 1983, 33(4): 248-254.

[24]Costanza R, D'arge R, De Groot R, et al. The value of the world's ecosystem services and natural capital [J]. nature, 1997, 387(6630): 253-260.

[25]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: 635-640.

[26]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, 5: 607-613.

[27]Boumans R, Roman J, Altman I, et al. The Multiscale Integrated Model of Ecosystem Services (MIMES): Simulating the interactions of coupled human and natural systems [J]. Ecosystem services, 2015, 12: 30-41.

[28]De Groot R, Brander L, Van Der Ploeg S, et al. Global estimates of the value of ecosystems and their services in monetary units [J]. Ecosystem services, 2012, 1(1): 50-61.

[29]Hu X, Hou Y, Li D, et al. Changes in multiple ecosystem services and their influencing factors in Nordic countries [J]. Ecological Indicators, 2023, 146: 109847.

[30]Alam M, Dupras J, Messier C. A framework towards a composite indicator for urban ecosystem services [J]. Ecological indicators, 2016, 60: 38-44.

[31]Grillos T. Economic vs non-material incentives for participation in an in-kind payments for ecosystem services program in Bolivia [J]. Ecological Economics, 2017, 131: 178-190.

[32]Mao B, Wang X, Liao Z, et al. Spatiotemporal variations and tradeoff-synergy relations of ecosystem services under ecological water replenishment in Baiyangdian Lake, North China [J]. Journal of Environmental Management, 2023, 343: 118229.

[33]Sherrouse B C, Clement J M, Semmens D J. A GIS application for assessing, mapping, and quantifying the social values of ecosystem services [J]. Applied geography, 2011, 31(2): 748-760.

[34]Yang D, Liu W, Tang L, et al. Estimation of water provision service for monsoon catchments of South China: Applicability of the InVEST model [J]. Landscape and Urban Planning, 2019, 182: 133-143.

[35]Leh M D, Matlock M D, Cummings E C, et al. Quantifying and mapping multiple ecosystem services change in West Africa [J]. Agriculture, ecosystems & environment, 2013, 165: 6-18.

[36]Aitali R, Snoussi M, Kolker A S, et al. Effects of land use/land cover changes on carbon storage in North African Coastal Wetlands [J]. Journal of Marine Science and Engineering, 2022, 10(3): 364.

[37]Wang H, Wang W J, Liu Z, et al. Combined effects of multi-land use decisions and climate change on water-related ecosystem services in Northeast China [J]. Journal of Environmental Management, 2022, 315: 115131.

[38]赵筱青, 石小倩, 李驭豪, 等. 滇东南喀斯特山区生态系统服务时空格局及功能分区 [J]. 地理学报, 2022, 77(3): 736-756.

[39]Zhang Z, Tong Z, Zhang L, et al. What are the dominant factors and optimal driving threshold for the synergy and tradeoff between ecosystem services, from a nonlinear coupling perspective? [J]. Journal of Cleaner Production, 2023, 422: 138609.

[40]Yu C, Cao Y, Li G, et al. Linking ecosystem services trade-offs, bundles and hotspot identification with cropland management in the coastal Hangzhou Bay area of China [J]. Land Use Policy, 2020, 97: 104689.

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

[42]张碧天, 闵庆文, 焦雯珺, 等. 生态系统服务权衡研究进展 [J]. 生态学报, 2021, 41(14): 5517-5532.

[43]黄麟, 祝萍, 曹巍. 中国退耕还林还草对生态系统服务权衡与协同的影响 [J]. 生态学报, 2021, 41(3): 1178-1188.

[44]Xue C, Chen X, Xue L, et al. Modeling the spatially heterogeneous relationships between tradeoffs and synergies among ecosystem services and potential drivers considering geographic scale in Bairin Left Banner, China [J]. Science of the Total Environment, 2023, 855: 158834.

[45]Feng X, Fu B, Piao S, et al. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits [J]. Nature Climate Change, 2016, 6(11): 1019-1022.

[46]Jessop J, Spyreas G, Pociask G E, et al. Tradeoffs among ecosystem services in restored wetlands [J]. Biological Conservation, 2015, 191: 341-348.

[47]Xu Y, Tang H, Wang B, et al. Effects of land-use intensity on ecosystem services and human well-being: a case study in Huailai County, China [J]. Environmental Earth Sciences, 2016, 75: 1-11.

[48]Djanibekov U, Khamzina A, Djanibekov N, et al. How attractive are short-term CDM forestations in arid regions? The case of irrigated croplands in Uzbekistan [J]. Forest Policy and Economics, 2012, 21: 108-117.

[49]Gong J, Liu D, Zhang J, et al. Tradeoffs/synergies of multiple ecosystem services based on land use simulation in a mountain-basin area, western China [J]. Ecological Indicators, 2019, 99: 283-293.

[50]Liu Y, Liu X, Zhao C, et al. The trade-offs and synergies of the ecological-production-living functions of grassland in the Qilian mountains by ecological priority [J]. Journal of Environmental Management, 2023, 327: 116883.

[51]祝萍, 刘鑫, 郑瑜晗, 等. 北方重点生态功能区生态系统服务权衡与协同 [J]. 生态学报, 2020, 40(23): 8694-8706.

[52]Palacios-Agundez I, Onaindia M, Barraqueta P, et al. Provisioning ecosystem services supply and demand: The role of landscape management to reinforce supply and promote synergies with other ecosystem services [J]. Land Use Policy, 2015, 47: 145-155.

[53]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.

[54]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.

[55]Hauck J, Winkler K J, Priess J A. Reviewing drivers of ecosystem change as input for environmental and ecosystem services modelling [J]. Sustainability of Water Quality and Ecology, 2015, 5: 9-30.

[56]Dou H, Li X, Li S, et al. Mapping ecosystem services bundles for analyzing spatial trade-offs in inner Mongolia, China [J]. Journal of Cleaner Production, 2020, 256: 120444.

[57]Qian D, Cao G, Du Y, et al. Impacts of climate change and human factors on land cover change in inland mountain protected areas: A case study of the Qilian Mountain National Nature Reserve in China [J]. Environmental Monitoring and Assessment, 2019, 191: 1-21.

[58]陈田田, 黄强, 王强. 基于地理探测器的喀斯特山区生态系统服务关系分异特征及驱动力解析: 以贵州省为例 [J]. 生态学报, 2022, 42(17): 6959-6972.

[59]李素晓. 京津冀生态系统服务演变规律与驱动因素研究 [D]. 北京: 北京林业大学, 2019.

[60]Sannigrahi S, Zhang Q, Pilla F, et al. Responses of ecosystem services to natural and anthropogenic forcings: A spatial regression based assessment in the world's largest mangrove ecosystem [J]. Science of the Total Environment, 2020, 715: 137004.

[61]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, 711: 134687.

[62]黄欣, 彭双云, 王哲, 等. 基于地理探测器的云南省生态系统产水服务的空间异质性及驱动因素 [J]. 应用生态学报, 2022, 33(10): 2813-2821.

[63]高明惠, 李成, 赵虎. 江苏省生态系统服务供需格局与影响因素 [J]. 水土保持研究, 2023, 30(5): 315-324.

[64]王晓峰, 尧文洁, 冯晓明, 等. 青藏高原生态系统服务供需变化及其驱动因素 [J]. 生态学报, 2023, 43(17): 6968-6982.

[65]Mcvicar T R, Roderick M L, Donohue R J, et al. Less bluster ahead? Ecohydrological implications of global trends of terrestrial near‐surface wind speeds [J]. Ecohydrology, 2012, 5(4): 381-388.

[66]毕早莹, 李艳忠, 林依雪, 等. 基于Budyko理论定量分析窟野河流域植被变化对径流的影响 [J]. 北京林业大学学报, 2020, 42(8): 61-71.

[67]郭巧玲, 陈新华, 刘培旺, 等. 窟野河流域径流变化及人类活动对其的影响率 [J]. 水土保持通报, 2014, 34(4): 110-113.

[68]雷宇昕. 陕西窟野河流域煤炭开采对水资源的影响研究 [D]. 西安: 西北大学, 2021.

[69]李磊磊. 基于系统动力学的窟野河流域水沙变化的社会水文学分析 [D]. 西安: 西北大学, 2020.

[70]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]. Hydrology Research, 2017, 48(2): 542-558.

[71]Hamel P, Guswa A J. Uncertainty analysis of a spatially explicit annual water-balance model: Case study of the Cape Fear basin, North Carolina [J]. Hydrology and Earth System Sciences, 2015, 19(2): 839-853.

[72]潘丽娟. 未来土地利用情景下的南京市生态系统水质净化功能模拟 [D]. 南京: 南京信息工程大学, 2016.

[73]Lecina S, Martı́Nez-Cob A, Pérez P, et al. Fixed versus variable bulk canopy resistance for reference evapotranspiration estimation using the Penman–Monteith equation under semiarid conditions [J]. Agricultural water management, 2003, 60(3): 181-198.

[74]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.

[75]刘萌萌. 黄河流域（河南段）生态系统服务功能评估及其分区管控 [D]. 焦作: 河南理工大学刘营春, 2023.

[76]刘营春. 黄河流域山东段生态系统服务簇时空分异及其生态功能分区 [D]. 曲阜:曲阜师范大学, 2024.

[77]范昕. 黄河上游生态脆弱区土地利用/覆被变化及生态系统服务权衡与协同研究 [D]. 武汉: 中国地质大学, 2022.

[78]Terrado M, Sabater S, Chaplin-Kramer B, et al. Model development for the assessment of terrestrial and aquatic habitat quality in conservation planning [J]. Science of the total environment, 2016, 540: 63-70.

[79]Li S, Dong B, Gao X, et al. Study on spatio-temporal evolution of habitat quality based on land-use change in Chongming Dongtan, China [J]. Environmental Earth Sciences, 2022, 81(7): 220.

[80]Xu H, Dong B, Gao X, et al. Habitat quality assessment of wintering migratory birds in Poyang Lake National Nature Reserve based on InVEST model [J]. Environmental Science and Pollution Research, 2023, 30(11): 28847-28862.

[81]Zheng L, Wang Y, Li J. Quantifying the spatial impact of landscape fragmentation on habitat quality: A multi-temporal dimensional comparison between the Yangtze River Economic Belt and Yellow River Basin of China [J]. Land Use Policy, 2023, 125: 106463.

[82]圆圆, 盛艳, 刘林甫, 等. 窟野河流域生境质量时空演变特征及其影响机制研究 [J]. 林草资源研究, 2023, 6: 67-74.

[83]王绍业. 基于InVEST模型的黄河流域生态系统服务功能评估 [D]. 郑州: 华北水利水电大学, 2023.

[84]Williams J R, Arnold J G. A system of erosion—sediment yield models [J]. Soil Technology, 1997, 11(1): 43-55.

[85]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.

[86]Anselin L. Local indicators of spatial association—LISA [J]. Geographical analysis, 1995, 27(2): 93-115.

[87]侯建坤. 黄河源区生态系统服务评估及未来多情景模拟 [D]. 桂林: 桂林理工大学, 2023.

[88]王硕, 盛艳, 范淑花, 等. 基于InVEST模型的窟野河流域碳储量变化对土地利用格局的响应 [J]. 水土保持研究, 2025, 32(3): 149-158.

[89]张静静, 穆艳华. 伏牛山地区森林生态系统生境质量多视角评价 [J]. 生态科学, 2021, 40(5): 116-121.

[90]裴渊杰. 陕西省生态系统服务时空变化与权衡研究 [D]. 西安: 长安大学, 2023.

[91]龙宇. 基于InVEST模型的黄河流域生态系统服务评估及其驱动因子研究 [D]. 成都: 西南财经大学, 2022.

[92]张健康. 窟野河流域水沙演变归因与源汇景观功效评价 [D]. 北京: 中国地质科学院, 2024.

[93]Jiang L, Yao Z, Liu Z, et al. Estimation of soil erosion in some sections of Lower Jinsha River based on RUSLE [J]. Natural Hazards, 2015, 76: 1831-1847.

[94]Klein A M, Steffan‐Dewenter I, Tscharntke T. Pollination of Coffea canephora in relation to local and regional agroforestry management [J]. Journal of Applied Ecology, 2003, 40(5): 837-845.

[95]张甜, 彭建, 刘焱序, 等. 基于植被动态的黄土高原生态地理分区 [J]. 地理研究, 2015, 34(9): 1643-1661.

[96]López-Rubio E. Probabilistic self-organizing maps for continuous data [J]. IEEE Transactions on Neural Networks, 2010, 21(10): 1543-1554.

[97]赵婷, 潘竟虎. 讨赖河流域生态系统服务权衡与协同的多尺度测度 [J]. 生态与农村环境学报, 2022, 38(7): 839-850.

[98]蒋红波, 覃盟琳, 王政强, 等. 基于生态系统服务簇评价的长沙市生态修复优先区识别 [J]. 环境工程技术学报, 2023, 13(4): 1325-1333.

[99]黄贤凤, 勾容, 苏维词. 贵州省生态系统服务权衡/协同与簇情景模拟研究 [J]. 中国环境科学, 2024, 45(2): 966-980.

[100]蔡兴冉, 李忠勤, 张慧, 等. 中国天山冰川变化脆弱性研究 [J]. 地理学报, 2021, 76(9): 2253-2268.

[101]侯文娟, 高江波, 戴尔阜, 等. 基于 SWAT 模型模拟乌江三岔河生态系统产流服务及其空间变异 [J]. 地理学报, 2018, 73(7): 1268-1282.

[102]Guo Q, Lai X, Jia Y, et al. Spatiotemporal Pattern and Driving Factors of Carbon Emissions in Guangxi Based on Geographic Detectors [J]. Sustainability, 2023, 15(21): 15477.

[103]Chen M, Bai Z, Wang Q, et al. Habitat quality effect and driving mechanism of land use transitions: A case study of Henan water source area of the middle route of the south-to-north water transfer project [J]. Land, 2021, 10(8): 796.

[104]Liu Q, Yang Z, Han F, et al. Ecological environment assessment in world natural heritage site based on remote-sensing data. A case study from the Bayinbuluke [J]. Sustainability, 2019, 11(22): 6385.

[105]Zhang X, Lyu C, Fan X, et al. Spatiotemporal variation and influence factors of habitat quality in Loess Hilly and Gully Area of Yellow River Basin: A case study of Liulin County, China [J]. Land, 2022, 11(1): 127.

[106]邵明, 董宇翔, 林辰松. 基于GWR模型的成渝城市群生态系统服务时空演变及驱动因素研究 [J]. 北京林业大学学报, 2020, 42(11): 118-129.

[107]王浩, 吴吉林, 龚磊, 等. 武陵山片区生态系统服务供需耦合协调评价及其驱动因素分析 [J]. 长江流域资源与环境, 2024, 33(1): 114-125.

[108]耿甜伟, 陈海, 张行, 等. 基于GWR的陕西省生态系统服务价值时空演变特征及影响因素分析 [J]. 自然资源学报, 2020, 35(7): 1714-1727.

[109]Wang J-F, Zhang T-L, Fu B-J. A measure of spatial stratified heterogeneity [J]. Ecological indicators, 2016, 67: 250-256.

[110]Brunsdon C, Fotheringham S, Charlton M. Geographically weighted regression [J]. Journal of the Royal Statistical Society: Series D (The Statistician), 1998, 47(3): 431-443.

[111]林晨宇, 宋羽, 叶玲兰. 福建省生态系统服务价值时空演变及驱动因素 [J]. 自然保护地, 2024, 4(2): 81-94.

[112]方时姣, 张柯. 长江经济带城市蔓延对能源碳排放的影响研究——来自夜间灯光的经验证据 [J]. 学习与实践, 2022, 10: 30-39.