Integrating the flow of supply and demand of ecosystem services (ESs) into the ecological security pattern (ESP) of coastal ecosystems with extremely fragile ecological backgrounds and contradictory human–land relationships is beneficial to the coordinated development of human–land systems. However, existing studies ignore the issue of scales of supply–demand linkages, making the ESP not properly guide sustainable development. Based on ESs delivery chain theory and landscape ecology approaches, we developed a sustainable development framework consisting of coupled microscopic natural–social systems. The method was tested using data from the Liao River Delta. In this study area, the natural supply potential and demand mapping distribution of key ESs were assessed to identify ecological sources in the Liao River Delta, a typical coastal zone in northern China. The resistance surface based on land use type assignment was modified using hydrological connectivity frequency and nighttime light intensity. Ecological corridors were extracted and optimized using a minimum cumulative resistance model and connectivity evaluation. The study found that the high supply area and the high demand reflection area are not consistent in location and supply level. Ecological source areas are evenly distributed, accounting for 12% of the total area. The ecological corridors are mainly concentrated in the west and southeast and do not cross the built-up areas in the east. This ESP framework safeguards the local demand for natural products and the natural potential to maintain services over the longer term and to a larger scale while informing the development of environmental management measures.

Human demand for natural resources has grown, leading to ecological debasement and related ecological system administration. Using Dalian as an example, we estimated the changes in the ecosystem service value (ESV) in 2005 and 2020. We used ArcGIS and spatial statistics to conduct estimations and change analyses of the ESV. Based on the results of the ESV, the geographical detector and geographically weighted regression (GWR) elucidated the contributions of different driving factors of the ESV in a 2 km grid. In summary, these results indicated that: (1) from a holistic perspective, the ESV of Dalian fell by 206.8009 billion CNY over 15 years, and the hot spots were concentrated in both the northern and the western parts, whereas the cold spots were distributed in the central part; (2) according to the results from the geographical detector, land use structure factors influenced the ESV most significantly, followed by socio-economic factors, and the impact of natural factors was relatively small; and (3) according to the results of the GWR, land use structure factors negatively affected the ESV, and the positive impact of the proportion of the natural land area was the most obvious. We conclude that the decline in the ESV reflects the impact of human activities on the ecosystem in the studied landscape. Understanding ESV changes should be made a priority in ecosystem management, and evaluating ESV drivers can contribute to developing land use strategies for policy-making.

Developing effective methods to coordinate the trade-offs among ecosystem services (ES) is important for achieving inclusive growth and sustainable development, and has been the focus of scholars and ecosystem managers globally. Using remote sensing and geographic information system (GIS) data, our study examined Wafangdian County of Liaoning Province as a case study to reveal the spatiotemporal evolution of four ES (food supply [FS], net primary productivity [NPP], water yield [WY], and soil conservation [SC]) and changes among their interactions. Then, an ordered weighted averaging model was introduced to simulate the optimal scenario of ES allocation. Results showed that: (1) the spatial and temporal changes in ES were significant over 14 years. All ES presented an inverted U-shaped growth curve from 2000–2014. (2) Synergies were observed within provisioning services, and there were trade-offs between provisioning services and regulating services, as well as provisioning services and supporting services. (3) The optimal scenario for Wafangdian was scenario 5 (trade-off coefficient, 0.68). The allocation of FS, NPP, WY, and SC in scenario 5 were 0.187, 0.427, 0.131, and 0.063, respectively. Implementing each ES weight of optimal scenario in land use management contributed to achieving intercoordination of ES. We propose to coordinate land and sea management to restore natural habitats that were expanded into in the high ES area. It is our anticipation that this study could provide a scientific basis for optimizing the allocation of ES and improving land use structure of coastal zones in the future.

Ecosystem service (ES) bundles can be defined as the temporal and spatial co-occurrence of ESs. ES bundles are jointly driven by socio-ecological factors and form at different scales. However, in recent research, a few studies have analyzed the dynamic evolution and driving mechanisms of ES bundles at different scales. Therefore, this study explored the spatial patterns of six ESs supplied in Dalian (China) from 2005 to 2015 at three spatial scales, determining the distribution and evolution patterns of ES bundles and their responses to socio-ecological driving factors. Our results are as follows: (1) We identified four ES bundles representing ecological conservation, water conservation, ecological depletion, and food supply. The developmental trajectory of each ES bundle could be attributed to the combined effects of environmental conditions and urban expansion. In particular, the water conservation bundle and food supply bundle were changed to the ecological depletion bundle. Given the ongoing urbanization, the conflict between ESs has intensified. (2) The impact of socio-ecological driving factors on ES bundles vary with scale. At three spatial scales, the digital elevation model (DEM) and normalized difference vegetation index (NDVI) had a great impact on ES bundles. Urbanization indicators also strongly explain the spatial distribution of ES bundles at the county and grid scales. The interaction factor detector shows that there is no combination of mutual weakening, indicating that the formation of ES bundles is driven by multiple factors in Dalian. Overall, this study used a more holistic approach to manage the ecosystem by studying the temporal-spatial dynamics of the multiple ESs.