The significantly negative impact of marine invasive species underscores the need to understand the dynamics of invasion success. MicroRNAs (miRNAs) play a crucial role in regulating gene expression in response to stresses during invasions. Using the invasive tunicate Ciona robusta as a model, here we aim to study intragenic miRNA–host gene co-expression and functional regulation in response to recurrent salinity challenges. Despite genomic nestedness, only 9% of miRNA–host gene pairs showed significant co-expression (p < 0.05, correlation coefficient > 0). Recurring stresses dynamically altered the co-expression, revealing distinct miRNA–host gene expression at different stress times and stages. These differentially expressed miRNAs (p_adj < 0.05, |log₂foldchange| > 1) regulated biological processes, including free amino acid metabolism, water channel function, and ion transport to maintain osmotic homeostasis. These functional regulations were specific to time and stage, targeting the same type of osmolytes through varied pathways. Our findings highlight the diverse regulatory roles of miRNAs in enabling rapid responses to environmental stresses during invasions, providing new insights into miRNA-driven phenotypic plasticity under changing conditions.

Global climate change is expected to accelerate biological invasions, necessitating accurate risk forecasting and management strategies. However, current invasion risk assessments often overlook adaptive genomic variation, which plays a significant role in the persistence and expansion of invasive populations. Here we used Molgula manhattensis, a highly invasive ascidian, as a model to assess its invasion risks along Chinese coasts under climate change. Through population genomics analyses, we identified two genetic clusters, the north and south clusters, based on geographic distributions. To predict invasion risks, we employed the gradient forest and species distribution models to calculate genomic offset and species habitat suitability, respectively. These approaches yielded distinct predictions: the gradient forest model suggested a greater genomic offset to future climatic conditions for the north cluster (i.e., lower invasion risks), while the species distribution model indicated higher future habitat suitability for the same cluster (i.e, higher invasion risks). By integrating these models, we found that the south cluster exhibited minor genome-niche disruptions in the future, indicating higher invasion risks. Our study highlights the complementary roles of genomic offset and habitat suitability in assessing invasion risks under climate change. Moreover, incorporating adaptive genomic variation into predictive models can significantly enhance future invasion risk predictions and enable effective management strategies for biological invasions in the future.