Wetlands play a crucial role in maintaining biodiversity and ecological balance. Preserving the ecological security of wetlands is critically important for regional environmental protection and sustainable development. However, in the core area of the Wuhan metropolitan circle, which is rapidly urbanizing, its wetlands are more susceptible to external natural environmental risks, such as changes in temperature and rainfall, as well as risks to human activity, such as social and economic activities, urban expansion, land use changes, and population growth. Meanwhile, the internal vulnerability of wetlands in terms of their spatial extent, structure, and functions also exacerbates ecological risks. These factors collectively influence the formation and development of wetland ecological risks. This study aims to comprehensively assess wetland ecological risks in the core area of the Wuhan metropolitan circle by combining external hazards and internal vulnerabilities and to construct and optimize the wetlands’ ecological security pattern. We used the MSPA method to identify potential ecological sources. Additionally, the MCR model was employed to integrate ecological risk assessment results into the resistance surface, identify potential ecological corridors and nodes, construct the wetland ecological security pattern for the urban circle, and propose specific optimization strategies. In total, 31 primary and 106 secondary ecological sources were selected, along with 20 primary and 42 secondary ecological nodes. Furthermore, 10 major ecological corridors were constructed. Considering the landscape characteristics of the wetlands in the core area of the Wuhan metropolitan circle, the southern Yangtze River region will center around the Liangzi Lake group to establish a crucial corridor network, promoting overall wetland restoration and connectivity. Meanwhile, the northern Yangtze River region will form a chain-like distribution along the river, creating diverse ecosystems. This study provides a theoretical foundation for constructing and optimizing the ecological security pattern of wetlands, laying a solid groundwork for promoting regional wetland conservation and sustainable development.

The ecological network serves as a vital spatial strategy for addressing climate change, biodiversity loss, and habitat fragmentation. Addressing limitations in existing ecological network studies—such as strong subjectivity and insufficient accuracy in structural element identification, cross-regional integration, and resistance surface weighting—this research uses the Zhengzhou–Kaifeng–Luoyang region (ZKLR) as a case study. It introduces the self-organizing map (SOM) model to identify ecological sources and employs the XGBoost-SHAP model to optimize resistance surface weights, thereby reducing subjective weighting biases. Subsequently, the Linkage Mapper tool is utilized to construct the regional ecological network. The superiority of the SOM model for identifying ecological sources was confirmed by comparison with a traditional network based on morphological spatial pattern analysis (MSPA). Further integrating complex network topology theory, nodes attack the simulations-assessed network resilience and proposed optimization strategies. The results indicate the following: (1) The area of ecological sources identified by the SOM model is three times that of the MSPA model; (2) SHAP feature importance analysis revealed that elevation (DEM) exerted the greatest influence on the composite resistance surface, contributing over 40%, followed by land use and slope, with each contributing approximately 15%. High-resistance areas were primarily distributed in western and central mountainous regions and built-up urban areas, while low-resistance areas were concentrated in the central and eastern plains; (3) topological analysis indicates that the integrated ecological network (IEN) exhibits superior robustness compared to the structural ecological network (SEN). The edge-adding strategy generated 22 additional ecological corridors, significantly enhancing the overall resilience of the integrated ecological network; and (4) based on ecological network construction and optimization results, a territorial spatial protection strategy of “one belt, two cores, two zones, and three corridors” is proposed. This study provides a novel methodological framework for ecological network construction, with findings offering reference for ecological conservation and spatial planning in the ZKLR and similar areas.

Rapid urbanization has significantly impacted plant growth rhythms through increased surface temperatures and enhanced artificial light at night (ALAN). However, the effects of thermal environments and ALAN on urban vegetation phenology (UVP) remain unclear, limiting our knowledge of how plant growth responds to urbanization and climate change. This study employed high-resolution multi-source data to assess the changing characteristics of the start of the growing season (SOS) and the end of the growing season (EOS) along the urban–rural gradient in Shenyang City. The structural equation model (SEM) and the accumulated local effect model were employed to determine the combined effects and thresholds of thermal environments and ALAN on UVP. We found that the nighttime light duration is associated with delayed autumn vegetation phenology in urban areas. In addition to land surface temperature (standardized path effect, β = 0.41, p < 0.05), the nighttime light duration (β = 0.27, p < 0.05) and intensity (β = 0.05) were key factors influencing EOS. The impact of nighttime light duration on phenology exhibited distinct seasonal variations, with a significantly greater impact on EOS (β = 0.27, p < 0.05) than on SOS (β = 0.14, p < 0.05). This might be related to vegetation’s carbon assimilation capacity and its reaction to climate change. These drivers exhibited complex nonlinear relationships with UVP, and the same driver also displayed distinct variation patterns with SOS and EOS. Our findings indicate that nighttime light duration is an important associated factor of plant growth, with great significance for predicting vegetation dynamics under rapid urbanization.