Connectivity between waterbodies influences the risk of aquatic invasive species (AIS) invasion. Understanding and characterizing the connectivity between waterbodies through high-risk pathways, such as recreational boats, is essential to develop economical and effective prevention intervention to control the spread of AIS. Fortunately, state and local watercraft inspection programs are collecting significant data that can be used to quantify boater connectivity. We created a series of predictive models to capture the patterns of boater movements across all lakes in Minnesota, USA. Informed by more than 1.3 million watercraft inspection surveys from 2014–2017, we simulated boater movements connecting 9182 lakes with a high degree of accuracy. Our predictive model accurately predicted 97.36% of the lake pairs known to be connected and predicted 91.01% of the lake pairs known not to be connected. Lakes with high degree and betweenness centrality were more likely to be infested with an AIS than lakes with low degree (p < 0.001) and centrality (p < 0.001). On average, infested lakes were connected to 1200 more lakes than uninfested lakes. In addition, boaters that visited infested lakes were more likely to visit other lakes, increasing the risk of AIS spread to uninfested lakes. The use of the simulated boater networks can be helpful for determining the risk of AIS invasion for each lake and for developing management tools to assist decision makers to develop intervention strategies.

水体之间的连通性会影响水生入侵物种（AIS）入侵的风险。通过娱乐船等高风险途径了解水体之间的连通性并对其进行表征，对于开发经济有效的预防干预措施以控制AIS的传播至关重要。幸运的是，州和地方船只检查计划正在收集可用于量化划船者连通性的重要数据。我们创建了一系列预测模型，以捕获美国明尼苏达州所有湖泊中划船者运动的模式。在2014年至2017年间进行了130万次以上的船只检查调查后，我们模拟了连接9182个湖泊的划船运动，其准确性很高。我们的预测模型准确地预测了已知对接的湖泊对的97.36％，并预测了91个。已知有01％的湖泊对没有连接。具有高度和中间度中心性的湖泊比具有低度度的湖泊更容易受到AIS的侵扰（p  <0.001）和中心度（p  <0.001）。平均而言，受感染的湖泊比未受感染的湖泊多连接了1200个湖泊。此外，拜访出没湖泊的船夫更有可能拜访其他湖泊，从而增加了AIS扩散到未出没湖泊的风险。模拟划船网络的使用有助于确定每个湖泊的AIS入侵风险，并有助于开发管理工具以帮助决策者制定干预策略。