Rapid temperature changes in mountain ecosystems pose a great threat to alpine plant species and communities. Rhododendron species, as the major component of alpine and sub-alpine vegetation, have been demonstrated to be sensitive to climate changes. Therefore, understanding how alpine Rhododendron species spread to new habitats and how their geographical distribution range shifts is crucial for predicting their response to global climate change and for facilitating species conservation and reintroduction. In this study, we applied MaxEnt modeling and integrated climate, topography, and soil variables in three periods under three climate change scenarios to predict the suitable habitat for four Rhododendron species in China. We measured the potential distribution change in each species using the change ratio and the direction of centroid shifts. The predicted results showed that (1) the threatened species R. protistum would have a maximum decrease of 85.84% in its distribution range in the 2070s under RCP 8.5, and R. rex subsp. rex as a threatened species would experience a distribution range expansion (6.62–43.10%) under all of the three climate change scenarios in the 2070s. (2) R. praestans would experience a reduction in its distribution range (7.82–28.34%) under all of the three climate change scenarios in the 2070s. (3) The four Rhododendron species would be moved to high latitudes in the north-westward direction as a whole in the future, especially the two threatened species R. protistum and R. rex subsp. rex. (4) Aside from climate variables, soil factors also exert an important influence on the distribution of Rhododendron species. This study revealed the species-specific response of Rhododendron species to climate change. The results can not only provide novel insights into conservation strategies of Rhododendron species, but also propose a valuable method for the habitat selection during the reintroduction of endangered species.

Our knowledge of how niche dynamic patterns respond to invasion trajectories and influence invasion risk prediction is elusive for the majority of notorious invaders, hindering scientific understanding, biosecurity planning and practice, and management implementation of biological invasions. We used Mikania micrantha, one of the world's worst invasive alien species (IAS), to test the hypothesis that multiple invasion trajectories could induce niche dynamics inconsistency and increase risk uncertainty of IAS. We compiled a robust database of M. micrantha occurrence across its native range in Central and South America and invaded range in China. This database was used to clarify different invaded ranges and invasion trajectories of M. micrantha in China. Principal components analysis of climatic variables associated with the database was used to explore the niche dynamic patterns associated with multiple invasion trajectories of M. micrantha. Maximum entropy algorithm was used to predict the high‐risk area of M. micrantha invasion using occurrence datasets for invaded ranges where niches remained conservative, and to detect area changes with the inclusion of occurrence datasets for invaded ranges where niche shifts occurred. M. micrantha invasion occurred in three geographically distinct regions, with conservative climate niches in southern and southeastern China and climatic niche shifts in southwestern China. A high‐risk area for M. micrantha invasion spanned multiple provinces and cities and expanded considerably with the inclusion of the occurrence dataset for southwestern China. Our findings contribute to the theoretical understanding of invasion mechanisms and the practical optimization of biosecurity planning and implementation.

Public understanding is critical for invasive alien species (IAS) management, yet national-scale cognition patterns remain understudied. We analyzed TikTok and Sina Microblog data to assess public cognition in China regarding three IAS: Solidago canadensis , Solenopsis invicta , and Trachemys scripta . Three dimensions of cognition, including focus (high-frequency terms), associations (term-pair linkages), and tendencies (sentiment polarity), were quantified using word frequency, semantic co-occurrence networks, and quantile-based sentiment analyses, respectively. Results revealed cross-species disparities: On TikTok, S. canadensis was concerned with its invasiveness (e.g., "Found everywhere", "Fertility → Overlord flower"), while Sina Microblog emphasized its agricultural utility (e.g., "Hu Sheep", "Digestion → Sheep dung"). S. invicta discussions centered on recognition/coping measures on TikTok (e.g., "Red fire ant", "Wound → Soapy water") versus ecological impacts on Sina Microblog (e.g., "Invasive species", "Consequence → Environment"), and T. scripta focused on pet raising issues (TikTok) (e.g., "My home", "Eyes → Can't open eyes") and regulatory policies (Sina Microblog) (e.g., "Release", "Heavy penalty → Country"). These disparities likely stem from species-specific traits and their interactions with human. Cross-platform differences were pronounced: TikTok’s experiential narratives showed polarized sentiment ( S. canadensis median = 0.63; lower/upper quartiles = 0.06/0.94), while Sina Microblog’s policy-driven discourse was predominantly positive (medians: 0.60–0.89). Algorithmic biases amplified these differences: prioritizing emotional content (TikTok) versus institutional narratives (Sina Microblog). A key gap was the scarcity of management strategies, limiting public engagement. Our findings advocate for platform-specific interventions (e.g., TikTok demos of removal techniques) and algorithmic transparency to improve IAS management.

Our knowledge of how niche dynamic patterns respond to invasion trajectories and influence invasion risk prediction is elusive for the majority of notorious invaders, hindering scientific understanding, biosecurity planning and practice, and management implementation. We used Mikania micrantha, one of the most notorious invasive alien species in the world, to test the hypothesis that multiple invasion trajectories could induce niche dynamics inconsistency and increase risk uncertainty of invasive alien species. We compiled a robust database of M. micrantha occurrence across its native range in Central and South America and invaded ranges in China. This database was used to clarify different invaded ranges and invasion trajectories of M. micrantha in China. Principle Component Analysis of climatic variables associated with the database was used to detect its niche dynamic patterns associated with multiple invasion trajectories. Maximum Entropy algorithm was used to predict the high-risk area of M. micrantha invasion using occurrence datasets for invaded ranges where niches remained conservative, and to identify area changes with the inclusion of occurrences datasets for invaded ranges where niche shifts occurred. M. micrantha invasion occurred in three geographically distinct regions, with conservative climate niches in southern and southeastern China and climatic niche shifts in southwestern China. A high-risk area for M. micrantha invasion spanned multiple provinces and cities, and expanded considerably with the inclusion of the occurrence dataset for southwestern China. Our findings contribute to the theoretical understanding of invasion mechanisms and the practical optimization of biosecurity planning and implementation.