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1. Invasive species are a major threat to global biodiversity and their introduction can have significant economic consequences. The invasive tunicate Didemnum vexillum is a notorious invader with significant negative impacts on cultured shellfish and natural benthic communities, including commercially important ones. 2. We conducted an expert survey, identifying the five most important transport vectors for D. vexillum along the west coast of North America. We determined the spatially explicit vector density for all vectors in order to identify introduction hotspots. Additionally, we developed an environmental niche model based on 46 occurrence points and nine environmental variables to identify areas suitable for D. vexillum. 3. Spatial distribution of the most important transport vectors (slow-moving vessels, aquaculture, fishing vessels, small vessels, and large commercial vessels) identified several hotspots with high vector densities. These proved to be a very good predictor of current D. vexillum occurrence in British Columbia (BC). Ecological niche modelling (Genetic Algorithm for Rule-set Prediction) predicted suitable environments in southern BC, parts of central BC and along the east coast of the Queen Charlotte Islands. Independent validation of the model based on the current distribution in BC indicated good predictive accuracy. Additional analytical steps confirmed that no environmental variable dominated the predictions and we identified ranges of environmental conditions predicted suitable by the model. 4. We identified areas of high establishment probability for D. vexillum by combining the vector model and environmental niche model. Parts of central BC, the west coast of Vancouver Island and the Strait of Georgia are areas where D. vexillum is most likely to establish. 5. Synthesis and applications. Spatially explicit predictions of the potential distribution of biological invaders are crucial for informing risk assessments, development of management strategies, and resource allocation. While most studies only focus on one step in the invasion process, we successfully combined the likelihood of introduction and establishment. Results from this study are informing the canadian risk assessment of invasive tunicates, guiding current monitoring efforts, and providing a basis for potential intervention/mediation measures.

Background Classical malaria models often focus solely on vector-borne transmission and employ integer-order dynamics that neglect memory effects. Yet malaria spread can also occur through non-vector exposure routes, and its progression is influenced by historical infection and immunity patterns. To capture these effects, a fractional-order modeling approach is required. Methods We develop a Caputo fractional-order malaria model of order that integrates both vector and non-vector transmission pathways while embedding memory effects in human and mosquito dynamics. Analytical properties—including positivity, boundedness, disease-free equilibrium, and fractional local stability—are derived. The Adams–Bashforth–Moulton (ABM) predictor–corrector scheme is implemented for numerical simulation and validated against the classical case ( q  = 1) to ensure accuracy and convergence. Results Numerical experiments reveal that decreasing the fractional order q substantially modifies malaria dynamics: epidemic peaks are delayed, oscillatory persistence is prolonged, and long-term infection memory is amplified. Incorporating non-vector exposure pathways increases infection persistence and improves correspondence with field data. Parameter estimation and data fitting using weekly malaria incidence from the Nigeria Centre for Disease Control (NCDC) confirm the model’s reliability in reproducing outbreak patterns. Conclusion The proposed fractional-order malaria model provides a unified analytical and computational framework that captures both memory-dependent and multi-route transmission effects. The ABM scheme proves efficient and accurate for fractional epidemic systems, and the accompanying MATLAB implementation supports reproducibility and application to malaria forecasting and control strategies.