The Arctic region is experiencing rapid climate change, leading to significant consequences including sea ice loss and increased shipping activity. This heightened shipping activity dramatically increases the risk of introducing harmful non-native marine species into the Arctic ecosystem. The economic consequences of invasive species are substantial, with the World Wildlife Fund estimating tens of billions of dollars in damage between 2004 and 2009. Prevention is crucial due to the difficulty and expense of eradication. International agreements, such as the Convention on Biological Diversity, highlight the need for improved invasive species management, particularly prevention strategies. A detailed understanding of species introduction risk via transportation networks is a prerequisite for effective prevention. Aquatic invasive species pose a significant threat to the Arctic's unique and fragile ecosystem. The interplay of climate change, increased shipping, and environmental conditions complicates risk assessment. The number of ships traveling through the Arctic has increased substantially in recent years, escalating the introduction risk. Climate change also influences the range of invasive species by altering environmental conditions. The complexity of ship-mediated species introduction, the economic importance of global shipping, and the uncertain effects of climate change make addressing this issue urgent. The Arctic Council's Fairbanks Declaration emphasizes the importance of reducing the impact of invasive species, highlighting shipping as a primary pathway. This research utilizes network analysis and data mining to assess, visualize, and project aquatic species introduction into and within the Arctic via shipping. This network approach offers a unique perspective on how species introduction affects multiple ports and how a ship's history influences subsequent introduction risks. Such analysis is crucial for prioritizing surveillance, prevention, and management efforts, aligning with the Arctic Council's goals.

The introduction of non-native species into new ecosystems through global trade and transportation networks is a well-documented phenomenon. Thousands of cases of land, sea, and air-mediated introductions have been reported. As trade volumes and connectivity increase, so does the potential for human-assisted species introduction. The global shipping network is recognized as the dominant vector for the unintentional introduction of aquatic species, primarily through ballast water discharges and biofouling. Successful ship-borne invasions require species survival during transport, establishment in the new environment, and subsequent spread. Numerous factors influence these processes, including ship type, voyage duration, ballast water handling, discharge location, environmental differences between source and destination, and organism tolerance. Assessing species introduction and dispersal risks is a complex task demanding integration of diverse data sources. Previous modeling frameworks have assessed the relative risk of introduction posed by ships, but this study expands upon these by leveraging a network approach to evaluate the current and future risk of species introduction into and dispersal within the Arctic, focusing on ballast water discharges, with potential extension to biofouling risks.

This study utilizes a risk assessment network model tailored to Arctic shipping. Data sources include global ship movement data from Lloyd's List Intelligence (LLI), ballast water discharge data from the U.S. National Ballast Water Information Clearinghouse (NBIC), biogeographical data, and environmental data (temperature, salinity). The analysis comprises several components: First, a first-order network is constructed using voyages originating outside the Arctic and ending within it to estimate the relative risk of species introduction. This is based on factors such as shipping frequency, ship size, type, trip duration, and ballast water exchange patterns. Predictive analysis projects network evolution and illustrates the emergence of Arctic shipping hubs. Second, the influence of species' sensitivity to environmental differences between origin and destination ports is investigated by visualizing how the network adapts to environmental constraints. Species are categorized into groups reflecting different environmental tolerances (temperature and salinity). Third, the focus shifts to within-Arctic ballast-mediated species dispersal. A first-order network is compared to a higher-order network that incorporates the dependency of a ship's next destination on its previous voyages. Finally, a case study demonstrates how the higher-order network identifies high-risk dispersal pathways and informs targeted management policies. The relative risk of species introduction (*P_ij*) is calculated for each voyage and aggregated for pathways and ports. The higher-order network (SF-HON) accounts for the path-dependency of ship movements, refining estimates of indirect species dispersal. Species dispersal is modeled as a random walk process on the networks. The PageRank algorithm approximates species dispersal dynamics, considering both direct and indirect dispersal pathways. The analysis uses the Arctic Council's definition of the Arctic boundary for defining Arctic ports. Pathways within the same or neighboring ecoregions are excluded to focus on human-mediated dispersal. A linear model is used for predictive analysis of shipping trends due to data sparsity. Environmental tolerance of species is incorporated by categorizing species into groups based on their temperature and salinity tolerances. A clustering algorithm is used to group ports based on their connectivity in the higher-order network. The study uses ArcMap Desktop for visualization and NetworkX for higher-order network analysis.

The analysis of species introduction pathways to the Arctic reveals 2,874 active pathways between 1997 and 2012 after excluding pathways between ports in the same or neighboring ecoregions. A predictive analysis, using a linear model due to limited data, projects an increase in shipping activity in the Arctic, with an increase in the number of voyages, total dead weight tonnage, and average ship capacity. Interestingly, the number of distinct introduction pathways increased only slightly. This suggests an increased shipping intensity per pathway rather than an expansion of pathways. Murmansk emerges as a significant shipping hub with a substantial increase in introduction pathways over the period. The relative risk of species introduction (*P_ij*) shows a significant increase over time. High-risk pathways primarily originate from Northwestern Europe, targeting Arctic ports like Narvik and Murmansk. Aggregated introduction risk varies among Arctic ports, with some ports showing high aggregated risk even with low-risk individual pathways due to the number of connections. The analysis further incorporates environmental constraints on species establishment. Filtering pathways based on species' temperature and salinity tolerance reveals that certain pathways pose higher establishment risk for species with specific tolerances. Churchill (Canada) emerges as the most vulnerable port for species with narrow tolerances, while Afognak and Dutch Harbor (USA) become more vulnerable when considering species with broader tolerances. The study shifts focus to within-Arctic ballast-mediated species dispersal, analyzing both first-order and higher-order networks. A higher-order network, which incorporates ship movement patterns, provides a more accurate reflection of indirect species dispersal. The analysis shows that ports with weak direct connections can be at high risk of indirect dispersal. Reykjavik (Iceland), for instance, ranks highly in indirect dispersal risk despite having weak direct connections. The case study of Murmansk highlights the advantages of the higher-order network approach. It reveals distinct propagation patterns based on a species' origin and reveals different vulnerable ports at each propagation step, emphasizing the necessity of this approach for developing targeted management strategies.

This research provides a novel assessment and projection of ballast-mediated non-native species introduction and establishment in the Arctic, integrating higher-order network approaches to analyze within-Arctic dispersal risk for the first time. The findings can inform Arctic policy and management. The observed trends, while projected linearly for simplicity, likely underestimate future risks given climate change projections and positive feedback on sea ice loss. The increased shipping intensity and rerouting through hubs like Murmansk underscore the importance of targeted management efforts. The high-risk pathways from Northwestern Europe and distant locations like Australia and South America emphasize the broad scope of the threat. The environmental sensitivity analysis helps tailor management strategies to specific species groups with different tolerances. The higher-order network analysis demonstrates the importance of considering indirect dispersal pathways for long-term prevention strategies. The case study further highlights the benefits of this approach for efficient resource allocation in management efforts.

This study presents a valuable framework for risk-based prioritization of surveillance and prevention efforts in the Arctic. The findings emphasize the importance of considering both direct and indirect dispersal pathways, and the need to tailor management strategies to the environmental tolerances of specific species. Future improvements could involve incorporating more comprehensive data sets, real-time data integration, more detailed biological models, and extending the framework to include biofouling. This framework, paired with biodiversity monitoring, can enhance invasion prediction and mitigation, leading to more effective policies and enforcement.

The study's linear projections of shipping trends are simplistic given the complex dynamics of climate change and its impact on shipping. The data sets used, while the most comprehensive available, have limitations in terms of geographic coverage and completeness, particularly in some areas of the Arctic. The model's reliance on relative rather than absolute risk estimates limits the ability to quantify the precise magnitude of the threat. The study assumes that temperature-salinity interactions, environmental plasticity, and adaptive evolution are negligible, which might not always hold true. Furthermore, a lack of comprehensive biodiversity surveys across multiple Arctic ports limits the ability to rigorously test the model's predictions.