###### Abstract
The temporal dynamics of ESs, including their fluctuations and trends, reveal how ecosystems respond to external pressures [7]. While many studies have concentrated on assessments at specific time points or over short-term time scales, understanding long-term continuous changes is essential [8]. Spatial variation in ESs is typically influenced by a combination of geographic, ecological, and socioeconomic factors, exhibiting high heterogeneity, which poses challenges for research at large scales [9; 10]. For instance, forests and wetlands with greater vegetation provide critical ESs, such as water retention and carbon storage, whereas agricultural and urbanized areas primarily support food production and infrastructure. Human activities and land use changes alter the spatial distribution of ESs at local and regional scales, enhancing some while risking degradation in others [11]. Therefore, cross-scale management and coordination are of particular importance. In summary, investigating the long-term dynamics of ESs is vital for optimizing their spatial allocation and promoting sustainable ecosystem development. Trade-offs and synergies are critical issues in ES research. ESs, such as climate regulation, water supply, and food production, often compete for limited resources [12], resulting in complex trade-offs. Concurrently, different services may also demonstrate mutually reinforcing synergies under specific conditions [13]. Understanding the trade-offs and synergies among ESs is essential for optimizing management strategies that balance ecological conservation with economic growth [14]. However, the existing studies exhibit numerous shortcomings in elucidating these complex relationships. Specifically, there is a lack of systematic assessment of the dynamics among different ESs, their scale dependencies, and spatiotemporal variations in trade-offs/synergies [12; 15; 16]. Some researchers have investigated the relationships between ESs at the same spatial scale but across different time points [17; 18], which helps to understand the variations in ecosystem services over relatively short time spans. In specific regions or at a single time scale, systematically capturing the changing patterns of trade-offs and synergies across various spatiotemporal scales is challenging [19; 20]. In particular, in-depth analysis of mountainous regions remains relatively scarce, hindering the optimization of integrated ecosystem service management strategies [21]. Therefore, comprehensively revealing the trade-offs and synergies of ecosystem services in mountainous areas is a critical direction that requires attention. Analyzing the drivers of ESs is crucial for understanding their dynamic changes and underlying mechanisms. Various forces influence the supply and quality of ESs, including natural, social, and economic factors [2]. The driving factors are often multi-scale and complex, involving interactions among climate, topography, and human activities [22]. Identifying and analyzing these primary drivers, along with the relationships between them, can illuminate the fundamental causes of changes in ESs, providing valuable insights for effective policy making [23]. Additionally, clustering similar ESs offers an empirical basis for ecological management. Through the identification of service clusters, researchers can reveal the relationships among different services and the predominant types within regions [24; 25]. This cluster analysis supports regional development planning, ecological compensation mechanisms, and the evaluation of ecological restoration projects while enhancing our understanding of ES dynamics [26; 27]. Such analysis helps to identify key ES supply areas for targeted management and protection, thereby enhancing the sustainability and resilience of ecosystems. It also facilitates the targeting of potential gaps and risks in services, thereby improving ecosystem resilience to environmental change which, in turn, supports scientific ecological management.