Spatiotemporal evolution and driving factors of ecosystem service trade-offs and synergies in the upper reaches of the Yangtze River Basin
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摘要: 文章以长江上游流域为研究区，评估了2000—2020年间产水量（WY）、碳储量（CS）、土壤保持（SC）和生境质量（HQ）4项关键生态系统服务的时空演变特征，研究结果表明：（1）2000—2020年，土壤保持以外、产水量、碳储量和生境质量的平均物理量在整个研究区尺度上均呈现上升趋势。从空间分布来看，SC和WY自西向东逐渐增加，而CS和HQ的高值区主要集中在西南部和北部，中部和西北部则表现为较低值。（2）ESs之间的权衡与协同关系表现出显著的空间异质性。WY-SC、SC-HQ、SC-CS和HQ-CS之间主要表现为协同效应，而WY-HQ和WY-CS之间则表现为权衡效应。从空间分布来看，WY-SC、WY-CS、CS-HQ和SC-HQ的协同与权衡关系在西北部和中部地区表现为协同，而在西部和南部则表现为权衡；与之相反，WY-HQ和CS-SC的权衡关系主要集中在中部平原地带。（3）生态系统服务权衡与协同关系的空间异质性是由多种驱动因素共同作用的结果。其中，地形因子和气候因子是影响ESs权衡协同关系的关键驱动因素，而气候因子与地形因子的交互作用对空间异质性的解释力尤为显著。揭示了自然要素之间的非线性协同效应，凸显了复杂地理环境下多因子耦合驱动的重要性。ESs之间的相互作用不仅受其内在关联的影响，更受到气候类型与地形类型等外部因子的协同调控；揭示了自然因子（气候、地形）和人为活动对长江上游流域ESs权衡与协同时空异质性的影响机制，为区域生态系统的可持续管理提供了科学依据，同时也为决策者制定差异化生态保护政策提供了重要参考。Abstract:
Understanding the spatiotemporal dynamics of Ecosystem Services (ESs), uncovering the trade-offs and synergies among different services, and identifying their driving mechanisms are of great significances for the effective management of ecosystems and regional sustainable development. This study focuses on the upper reaches of the Yangtze River Basin, assessing the spatiotemporal evolution of four key ESs-Water Yield (WY), Carbon Storage (CS), Soil Conservation (SC), and Habitat Quality (HQ)-from 2000 to 2020. The Spearman correlation coefficient and bivariate spatial autocorrelation analysis were employed to quantify the trade-offs and synergies among ESs and their spatial distribution patterns, while the geographical detector model was used to reveal their driving factors. The results indicate: (1) From 2000 to 2020, the average WY, SC, CS, and HQ in the study area were 547.33 mm, 229.20 t·hm−2, 264.0 t·hm−2, and 0.66, respectively. Over this period, WY, CS, and HQ showed an increasing trend at the regional scale, while SC remained relatively stable. Spatially, SC and WY exhibited a gradual increase from west to east, whereas CS and HQ displayed higher values in the southwestern and northern regions, with lower values in the central and northwestern areas. (2) The trade-offs and synergies among ESs demonstrated significant spatial heterogeneity. Synergistic relationships were observed between WY-SC, SC-HQ, SC-CS, and HQ-CS, while trade-offs were identified between WY-HQ and WY-CS. Spatially, the synergistic and trade-off relationships of WY-SC, WY-CS, CS-HQ, and SC-HQ showed similar geographical patterns, with synergies dominant in the northwestern and central regions and trade-offs prevalent in the western and southern areas. In contrast, the trade-offs between WY-HQ and CS-SC were primarily concentrated in the central plain region. (3) The spatial heterogeneity of ES trade-offs and synergies was driven by multiple factors. Among these, topographic and climatic factors were identified as the key drivers, with the interaction between climate and topography exhibiting particularly significant explanatory power. This interaction not only reveals the nonlinear effects among natural elements, but also further highlights the importance of multi-factor coupling driving forces wthin complex geographic enbironments. The study reveals that the interactions among ESs are not only influenced by their intrinsic relationships, but also co-regulated by external factors such as climate and topography. This study elucidates the influencing mechanisms by natural factors (climate, topography) and anthropogenic activities to the spatiotemporal heterogeneity of ES trade-offs and synergies in the upper Yangtze River Basin. The findings provide a scientific basis for the sustainable management of regional ecosystems and will offer valuable insights for policymakers to formulate differentiated ecological conservation strategies. -
图 3 不同年份不同土地类型的生态系统服务功能
Figure 3. Ecosystem services functions of different land use types across years
图 4 2000—2020年ES之间的权衡和协同效应
Figure 4. Trade-offs and synergies among ESs from 2000 to 2020
图 5 ES之间的权衡和协同空间分布
Figure 5. Spatial distribution of trade-offs and synergies among ESs
图 7 驱动因子对ES之间的解释力（交互因子检测）
Figure 7. Explanatory power of driving factors on ESs (interaction factor detection)
表 1 本研究中使用的数据集
Table 1. Datasets used in this study
类型 数据 空间分辨率 时间分辨率/年 数据来源 气候数据 年平均气温 1 km 2000、2010、2020 国家青藏高原科学数据中心（ https://data.tpdc.ac.cn ）年总降水量 1 km 2000、2010、2020 年实际蒸发量 1 km 2000、2010、2020 地形数据 DEM 30 m 2000、2010、2020 地理空间数据云（ https://www.gscloud.cn ）坡度 30 m 2000、2010、2020 由DEM数据空间分析得到 土地利用数据 土地利用类型 1 km 2000、2010、2020 中国科学院资源与环境科学数据中心（ http://www.resdc.cn ）经济数据 人口密度 1 km 2000、2010、2020 中国科学院资源与环境科学数据中心（ http://www.resdc.cn ）GDP 1 km 2000、2010、2020 土壤数据 土壤数据集 1 km / 中国土壤数据库（ https://isric.org ）
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