The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is rich in natural and marine resources, and it is scientifically valuable to study the evolution patterns and driving mechanisms of the ecosystem service values (ESVs) of the GBA for the governance and conservation of its ecosystems. Based on the land use changes in the GBA from 2000 to 2020, the ESVs in the GBA were measured at the grid scale, and the Markov model was used to predict the ESVs in 2030; the calculated results were used to analyze the spatial and temporal variation characteristics of the ESVs during the 30-year period, while the driving role of the topographic factors on the ESVs is revealed through the construction of the geographically weighted regression model (GWR). The results show the following: (1) During the 20-year period, the area of arable land and water in the GBA fluctuated greatly, with the area decreasing year by year and shifting mainly into construction land; in terms of shifting the center of gravity of the land, and the center of gravity of the grassland and unused land shifted the greatest distance due to the expansion of construction land, with the center of gravity shifting westward as a whole. (2) The ecosystem services (ESs) in the GBA show obvious aggregation in the spatial distribution, with the total ESVs decreasing year by year. Among them, the areas with an increasing total value are mainly located in the cities of Zhaoqing and Huizhou in the GBA, accounting for 27%, and the areas with a decreasing total value year by year are mainly located in the dense urban areas in the central part of the GBA, accounting for 35%, and the area is increasing, indicating that the habitat quality is deteriorating, and the model prediction shows that the value of ecosystem services in 2030 have a decreasing trend under the development of the natural state. (3) Topographic factors have a significant influence on the ESVs, and in terms of spatial distribution, the areas with the strongest effect are distributed in the northwestern and northeastern parts of the GBA, and the main uses for the land are wood land, arable land, water and the area of the water–land intersection near the sea.

Ancient trees play an important ecosystem service role in high-density cities, revealing the zonal distribution characteristics of vegetation under climate influence. The ancient trees in Guangzhou and Foshan in 2018 and 2023 were taken as study objects to explore the evolution of their spatio-temporal patterns and to analyze the spatial differentiation characteristics of their driving factors using the geographical weighted regression (GWR) model. The results showed the following: (1) The ancient trees in Guangzhou and Foshan were composed of typical subtropical evergreen broad-leaved forest communities, mainly represented by broad-leaved species of evergreen dicotyledonous plants. The dominant species mainly included Litchi chinensis, Ficus microcarpa, Canarium pimela, Ficus virens, and Dimocarpus longan. However, there was a significant difference between Guangzhou and Foshan. (2) The number of ancient trees in Guangzhou showed negative growth, while Foshan saw a significant increase. However, species diversity in both areas increased, with the highest diversity in the northeast, higher diversity in the south-central part, and lower diversity in the western and northwestern parts. (3) The maximum kernel density of ancient trees in Guangzhou and Foshan differed 22-fold, indicating a spatial distribution pattern of multiple clusters. (4) The GWR model effectively explained the driving factors of the heterogeneity of the spatial distribution of ancient trees. The results showed that artificial disturbance was the most important factor affecting the spatial distribution of ancient trees in high-density urban agglomerations in the same vegetation zone. The study clarified the characteristics of the spatial distribution and species diversity of ancient trees in the region, revealed the driving factors for the evolution of the spatial pattern of ancient trees in highly urbanized areas, and provided guidelines for policies and measures for enhancing biodiversity and conserving germplasm resources in the region.

The balance between the production and ecological supply–demand of cultivated land is crucial for regional food and ecological security. However, most studies focus on either the production or ecological aspect, neglecting the trade-offs/synergies of multifunctional cultivated land supply–demand. This study utilized the Pearl River Delta (PRD) mega urban agglomeration in China as a case study. It employed the Enhanced Two-step Floating Catchment Area (E2SFCA) and Ecological Footprint (EF) methods to quantify the production and ecological supply–demand at municipal and grid levels. Spatial correlation analysis was applied to identify trade-offs/synergies, which provided a basis for delineating cultivated land protection areas. The findings indicated that: (1) Cultivated land in the PRD faced severe supply–demand imbalances in the production and ecological functions, with urbanized areas exhibiting high demands but insufficient supply. (2) Production and ecological supply–demand ratios were spatially distributed as “lower in central plains, higher in surrounding hills,” inversely correlated with urbanization and population density. (3) There were significant trade-offs/synergies relationship between production and ecological supply–demand, with synergies being dominant. (4) Four zoning categories—priority protection, strict regulation, production enhancement, and remediation/restoration—were proposed. Overall, the goal is to maintain a balanced supply and demand in the priority protection zones, address the imbalance in the strict regulate zones, enhance production functions in the production enhancement zones and ecological functions in the remediation and restoration zones. This study offers insights into spatially zonal protection strategies and sustainable cultivated land management for mega urban agglomerations.

Under the dual pressures of rapid urbanization and climate change, urban expansion in high-density estuarine urban agglomerations has intensified economic–ecological trade-offs in the aquatic–terrestrial ecotone, necessitating land-use planning that reconciles economic growth with ecological protection. Here, we integrated linear programming with the CLUE-S model and incorporated marine–terrestrial integration objectives and typical natural disturbance factors. With this approach, a landscape pattern simulation framework capable of jointly optimizing ecological and economic benefits was developed. The framework was applied to the estuarine aquatic–terrestrial ecotone of the Pearl River Delta. This study drew on a land-use dataset, landscape dynamics, socioeconomic and biophysical drivers, and regional planning constraints to conduct simulation experiments under alternative development scenarios. The model achieved a Kappa coefficient of 0.904. From 2010 to 2020, built-up land expanded rapidly and encroached on ecological space. Simulations indicated that the natural evolution scenario increased fragmentation and ecological conflicts despite economic gains, whereas the sustainable development scenario balanced expansion and protection, reduced forestland fragmentation, safeguarded key ecological spaces, and improved ecological benefits while maintaining economic growth. Ecological benefits in the coastal aquatic–terrestrial ecotone from −0.2 to 0 km increased by 283.3%. The framework embeds land-use dynamics and spatial constraints, providing decision support for territorial spatial planning and ecological security pattern optimization.