Urbanization presents significant challenges to biodiversity but also offers opportunities for its protection and development. While uncontrolled urban expansion has a destructive impact on biodiversity, effective urban planning can play a positive role in protecting and maintaining urban biodiversity. The positive role of human factors, such as urban planning, can protect and maintain the healthy development of urban biodiversity. This study conducted an in-depth analysis of the evolution of various wildlife migration corridors throughout the full-cycle construction of Xiong’an New Area (Xiong’an) in China, revealing the impact of urbanization on these networks. Habitats for species like Sus scrofa, Bufo gargarizans, and Parus minor have notably increased. Between 2016 and 2023, Sus scrofa habitats grew from 35 to 44, large-toed frog habitats from 24 to 35, and Chinese tit habitats remained stable. By the planning phase, Sus scrofa habitats expanded to 87, large-toed frog habitats to 97, and Chinese tit habitats to 58. Habitat areas also grew significantly, especially for Sus scrofa, which increased from 2873.84 hectares in 2016 to 7527.97 hectares in the planning phase. Large-toed frog habitats grew from 2136.86 hectares to 6982.78 hectares, while Chinese tit habitats expanded from 1894.25 hectares to 3679.71 hectares. These changes suggest that urban parks and green spaces have provided more extensive habitats for these species. In terms of migration networks, the number of dispersal routes increased considerably. In 2016, Sus scrofa had 77 routes, large-toed frogs had 16, and Chinese tits had 77. By 2023, Sus scrofa and large-toed frog routes increased to 91 and 49, respectively, while Chinese tit routes remained stable. In the planning phase, Sus scrofa routes surged to 232, large-toed frogs to 249, and Chinese tits to 152, indicating a denser migration network. The distribution of ecological pinchpoints also changed significantly. By 2023 and in the planning phase, pinchpoints were concentrated in densely built areas, reflecting urbanization’s impact on the ecological network. The ecological resilience, assessed through network performance, showed a gradual recovery. The ecological connectivity index decreased from 8.25 in 2016 to 7.29 in 2023, then rebounded to 11.37 in the planning phase, indicating that the ecosystem had adapted after initial urbanization pressures.

Increasing climate uncertainty poses a significant threat to the yield stability of high-value crops such as tobacco (Nicotiana tabacum L.). Existing methods have inherent limitations in quantifying these uncertainties at a fine-grained (sub-plot) level and in guiding dynamic, adaptive management, thus struggling to address complex and variable field conditions. To counter this challenge, this study proposes an innovative grid-based, multimodal AI framework designed to precisely quantify the impacts of climate change on tobacco yield and to enable dynamic risk stratification and adaptive management. The framework is centered on a Multi-modal Large Model (MLM) as its cognitive core, which models the complex interactions of the crop-environment system by deeply fusing multi-source, heterogeneous data from UAV remote sensing, meteorological time-series, and soil sensors. Results from large-scale field trials in the core production region of Guangxi demonstrate that the framework can achieve hourly dynamic risk assessment. Compared to the traditional mechanistic model (DSSAT), its disaster response efficiency is improved by nearly five-fold, and it significantly reduces the average yield reduction rate from 18.0% to 7.1% (a 60.6% decrease). Ablation studies prove that the MLM, as the core engine, increases the accuracy of risk assessment and decision-making from 78.2% to 94.5%. Furthermore, by introducing a human-in-the-loop mechanism, the success rate of critical interventions reached as high as 99.2%. Cross-regional back-testing on an independent dataset (91.7% accuracy) also validated the framework’s strong generalization capability. This study provides a powerful, interpretable, and quantitative decision-making tool for implementing precision agriculture management under uncertainty, showcasing the immense potential of advanced AI technology in ensuring the resilience and sustainability of key cash crop supply chains.