Land surface elements, such as land use, are in constant change and dynamically balanced, driving changes in global ecological processes and forming the regional differentiation of surface landscapes, which causes many ecological risks under multiple sources of stress. The landscape pattern index can quickly identify the disturbance caused by the vulnerability of the ecosystem itself, thus providing an effective method to support the spatial heterogeneity of landscape ecological risk. A landscape ecological risk model based on the degree of interference and fragility was constructed and spatiotemporal differentiation of risk between 1980 and 2017 in Shaanxi Province was analyzed. The spatiotemporal migration of risk was demonstrated from the perspective of geomorphological regionalization and risk gravity. Several conclusions were drawn: The risk of Shaanxi Province first increased and then decreased, at the same time, the spatial differentiation of landscape ecological risk was very significant. The ecological risk presented a significant positive correlation but the degree of autocorrelation decreased. The risk of the Qinba Mountains was low and the risk of the Guanzhong Plain and Han River basin was high. The risk of Loess Plateau and sandstorm transition zone decreased greatly and their risk gravities shifted to the southwest. The gravity of the Guanzhong Plain and Qinling Mountains had a northward trend, while the gravity of the Han River basin and Daba Mountains shifted to the southeast. In the analysis of typical regions, there were different relationships between morphological indicators and risk indexes under different geomorphological features. The appropriate engineering measures and landscape management for different geomorphological regionalization were suggested for effective reduction of ecological risks.

Cultural ecosystem services (CESs) are an important part of ecosystem services (ESs). Correctly understanding the supply and demand relationship of CES is the premise of ES sustainable management and helps to improve human well-being. However, the evaluation and mapping of CES supply and demand represents a significant gap in ES research. Using the Shigou Township of Mizhi County in China as an example, in this study, we evaluated CES supply and demand at the village scale. We first considered three aspects of supply potential, accessibility and quality to construct an indicator system of six types of CES supply, including aesthetic (Aest), sense of place (SP), social relations (SR), cultural heritage (Cult), education (Edu) and recreation (Recr) and obtained demand data through a questionnaire. Then, we identified the imbalance in the supply and demand of CES by Z-score standardization based on the quantification of the CES supply and demand. Secondly, bivariate spatial autocorrelation analysis was used to identify tradeoffs/synergies on the CES supply side, and chi-square tests were used to identify CES demand differences between stakeholder groups. The results indicated that the supply–demand patterns of CES presented evident spatial differences. The low-supply–high-demand patterns of Aest, SR and Recr accounted for the largest proportions, with values of 33.33%, 33.33% and 30.95%, respectively. The low-supply–low-demand patterns of SP and Cult accounted for the largest proportions, with values of 30.95% and 38.10%, respectively. The low-supply–low-demand pattern of Edu accounted for the smallest proportion (21.43%) and was mainly located in the south of Shigou Township. The southwest, northeast and central areas of Shigou Township were the key regions of tradeoffs/synergies of CES supply. There were significant differences in CES demand for SR, Cult and Edu among stakeholder groups. The results could contribute to optimizing regional ecosystem management and provide effective information for improving the imbalance between the supply and demand of CES.

Social-ecological production landscape resilience (SELPR) is a significant representation of the continuous supply capacity of landscape services. It is a quantitative assessment of the spatial-temporal evolution of SELPR under internal and external disturbances that provides a scientific basis for regional ecological environments and socio–economic development. Taking Mizhi County for the study of the Loess Plateau region, a three-dimensional (social system, ecosystem, and production system) SELPR evaluation framework was constructed. Data integration was performed using the watershed as the evaluation unit. This study quantitatively evaluated the spatial–temporal differentiation of the social–ecological production landscape (SELPs) subsystem’s resilience and the total SELPR in the study area and classified the areas from the three-system resilience combination level to achieve regional development trade-offs. The results were as follows: (1) In 2009–2018, the change in the social–ecological production landscapes pattern in Mizhi County showed a significant reduction in agricultural production landscapes, relatively stable social living landscapes, and an increase in ecological landscapes; (2) in 2009–2018, the SELPR increased by 12.38%. The spatial distribution of resilience was significantly different, showing a distribution pattern of high central and low surrounding areas; (3) the county’s watershed development zones were divided into five partitions: synergistic promotion areas, ecological restoration areas, social development areas, production optimization areas, and comprehensive remediation areas. The five types of zones have a certain agglomeration effect. In addition, the main obstacle factors affecting the SELPR of each zone are quite different. The key issues and development directions of different types of watersheds are also proposed in this paper.

Exploring and analyzing the common demands and behavioral responses of different stakeholders is important for revealing the mediating mechanisms of ecosystem service (ES) and realizing the management and sustainable supply of ES. This study took Mizhi County, a poverty-stricken area on the Loess Plateau in China, as an example. First, the main stakeholders, common demands, and behavioral responses in the food provision services were identified. Second, the relationship among stakeholders was analyzed. Finally, this study summarized three types of mediating mechanisms of food provision services and analyzed the influence of the different types of mediating mechanisms. The main conclusions are as follows: (1) Five main stakeholders in the study area were identified: government, farmers, enterprises, cooperatives, and middlemen. (2) Increasing farmers’ income is the common demand of most stakeholders in the study area, and this common demand has different effects on the behavioral responses of different stakeholders. (3) There are three types of mediating mechanisms in the study area: government + farmers mediating corn and mutton, government + enterprises mediating millet, and government + cooperatives mediating apples. On this basis, the effects of the different types of mediating mechanisms on variations in food yield, and trade-offs and synergies in typical townships, were analyzed.