Permafrost, a major component of the cryosphere, is undergoing rapid degradation due to climate change, human activities, and other external disturbances, profoundly impacting ecosystems, hydroclimate, engineering geological stability, and infrastructure. In Northeast China, the thermal dynamics of Xing'an permafrost (XAP) are particularly complex, complicating the accurate assessment of its spatial extent. Many earlier mapping efforts, despite significant progress, fall short in accounting for some key local geo-environmental factors. Thus, this study introduces a new approach that incorporates four key driving factors-biotic, climatic, physiographic, and anthropogenic-by integrating multisource datasets and in situ observations. Four machine learning (ML) models [random forest (RF), support vector machine (SVM), logistic regression (LR), and extreme gradient boosting (XGB)] are applied to simulate permafrost distribution and probability, as well as to evaluate their performance. The results indicate that models' accuracy, ranked from highest to lowest, is as follows: RF (area under the curve (AUC) =0.88 and accuracy =0.81), XGB (0.86 and 0.77), LR (0.81 and 0.73), and SVM (0.76 and 0.66), with RF emerging as the most effective model for permafrost mapping in Northeast China. Analysis of the relationships between predictors and permafrost occurrence probability (POP) indicates that vegetation and snow cover exert nonlinear effects on permafrost, while human activities significantly reduce POP. Additionally, finer soil textures and higher soil organic matter content are positively correlated with increased POP. The modeling results, combined with field survey data, also show that permafrost is more prevalent in lowlands than in uplands, confirming the symbiotic relationship between permafrost and wetlands in Northeast China. This spatial variation is influenced by local microclimates, runoff patterns, and soil thermal properties. The primary sources of model error are uncertainties in the accuracy of multisource datasets at different scales and the reliability of observational data. Overall, ML models demonstrate great potential for mapping permafrost in Northeast China.

Hydrangea macrophylla is a shrub endemic to Japan, and inhabits the limited coastal zones in the Izu, Miura and Boso peninsulas, and the Izu and Bonin islands. Even though H. macrophylla is demanded as an important genetic resource for ecological conservation and utilities of landscaping and gardening in salinity-stressed lands, there is still a lack of information regarding its ecology and adaptative response to salinity-stressed coastal environments. This study aimed to understand the ecology and adaptation to salinity stress of H. macrophylla. Thus, we examined distribution, geography, vegetation, morphology, soil characteristics, and cation concentrations of the leaves in the native habitats. Most of the populations were mainly distributed in coastal zones that have sea-salt aerosols triggered by high wind speed and high soil salinity, causing severe damage to other plant species. The cation analysis suggested H. macrophylla adapted to coastal zones by regulating Na+ allocation of leaves and tolerating to high Na+ concentration. Otherwise, we found many populations inhabited inland or semi-coastal areas with mountainous vegetation in Izu islands. They had thinner leaves with weaker glossy, and some individuals developed trichomes which are not originally present in H. macrophylla, suggesting in initial stage of the process of adaptative radiation to mountainous environment. The other individuals grew in oligotrophic environments such as rocky surfaces and volcanic ash scoria, and epiphyte-like individuals inhabited stem surfaces of Cyathea spinulosa, implying these individuals had adaptability to oligotrophic environments. The comprehensive information should help facilitate further studies on ecological conservation and horticultural utilities.