Identifying the distribution dynamics of invasive alien species can help in the early detection of and rapid response to these invasive species in newly invaded sites. Ageratina adenophora, a worldwide invasive plant, has spread rapidly since its invasion in China in the 1940s, causing serious damage to the local socioeconomic and ecological environment. To better control the spread of this invasive plant, we used the MaxEnt model and ArcGIS based on field survey data and online databases to simulate and predict the spatial and temporal distribution patterns and risk areas for the spread of this species in China, and thus examined the key factors responsible for this weed’s spread. The results showed that the risk areas for the invasion of A. adenophora in the current period were 18.394° N–33.653° N and 91.099° E–121.756° E, mainly in the tropical and subtropical regions of China, and densely distributed along rivers and well-developed roads. The high-risk areas are mainly located in the basins of the Lancang, Jinsha, Yalong, and Anning Rivers. With global climate change, the trend of continued invasion of A. adenophora is more evident, with further expansion of the dispersal zone towards the northeast and coastal areas in all climatic scenarios, and a slight contraction in the Yunnan–Guizhou plateau. Temperature, precipitation, altitude, and human activity are key factors in shaping the distribution pattern of A. adenophora. This weed prefers to grow in warm and precipitation-rich environments such as plains, hills, and mountains; in addition, increasing human activities provide more opportunities for its invasion, and well-developed water systems and roads can facilitate its spread. Measures should be taken to prevent its spread into these risk areas.

Understanding why some, but not other, plant communities are vulnerable to alien invasive species is essential for predicting and managing biological invasions. Darwin proposed two seemingly contradictory hypotheses on how native‐invader relatedness influences invasion success, emphasizing, respectively, the importance of environmental filtering and competition between natives and invaders. Despite much recent empirical research on this topic, reconciling these two hypotheses, known as Darwin's naturalization conundrum, remains a challenge. Using plot‐level data from natural forests along elevational transects covering strong environmental gradients, we examined whether the invasion of the globally invasive species crofton weed (Ageratina adenophora) can be explained by environmental filtering and/or competition from closely related species linked to environmental gradients. Abundant precipitation, warm temperatures, open canopies and postfire environments facilitatedA. adenophorainvasion, whereas resident taxonomic richness suppressed its invasion. Importantly, we found that invader‐resident relatedness had a strong negative effect on invader cover under resource scarcity conditions (e.g. low water availability), but not under non‐resource environmental stress gradients (e.g. low temperature). Synthesis and applications.Our results suggest that the impact of species phylogenetic relatedness on invasion success varies distinctly along resource versus non‐resource environmental gradients. These results help to reconcile Darwin's naturalization conundrum, thereby improving the ability to predict the success of alien plant invasions in a changing world. Our study stresses the need to consider adjusting forest species composition to strengthen their resistance to invasion, while taking into account resource and non‐resource environmental gradients, particularly after wildfires.

Located at an average elevation of approximately 2000 m, the Minjiang River Basin (MJB), a key tributary of the Upper Yangtze River, straddles the Western Sichuan Plateau and the Sichuan Basin. Vegetation here is crucial for human life, providing oxygen and energy. However, the influence of climatic variables, human activities, and rugged terrain on vegetation vitality is still debated. This study mainly leverages data from the Normalized Difference Vegetation Index (NDVI), meteorological stations data, and land use data. Analytical techniques include trend analysis, partial correlation coefficient analysis (PCC), principal component analysis (PCA), and partial least squares structural equation modeling (PLS-SEM). Results indicate a stable upward trend in vegetation growth with minimal fluctuations, with a growth rate of 0.95 × 10⁻³/a (p < 0.01). PCC analysis shows a positive correlation between NDVI and key climatic elements in over 60% of the area. The areas with significant vegetation growth had the highest average PCC. PCA and PLS-SEM identify temperature and precipitation as primary growth drivers, while elevation and land use intensity hinder growth. The MJB landscape reveals thresholds and tipping points, with specific temperature and precipitation benchmarks varying by elevation, delineating the boundary between flourishing vegetation and growth inhibition.

Species distribution models (SDMs) are critical in conservation decision-making and ecological or biogeographical inference. Accurately predicting species distribution can facilitate resource monitoring and management for sustainable regional development. Currently, species distribution models usually use a single source of information as input for the model. To determine a solution to the lack of accuracy of the species distribution model with a single information source, we propose a multimodal species distribution model that can input multiple information sources simultaneously. We used ResNet50 and Transformer network structures as the backbone for multimodal data modeling. The model’s accuracy was tested using the GEOLIFE2020 dataset, and our model’s accuracy is state-of-the-art (SOTA). We found that the prediction accuracy of the multimodal species distribution model with multiple data sources of remote sensing images, environmental variables, and latitude and longitude information as inputs (29.56%) was higher than that of the model with only remote sensing images or environmental variables as inputs (25.72% and 21.68%, respectively). We also found that using a Transformer network structure to fuse data from multiple sources can significantly improve the accuracy of multimodal models. We present a novel multimodal model that fuses multiple sources of information as input for species distribution prediction to advance the research progress of multimodal models in the field of ecology.