Masters Defense

Predicting recreational boater traffic and networks in Minnesota throughout the COVID-19 pandemic for aquatic invasive species management

Many aquatic invasive species (AIS) are primarily spread through recreational boating pathways. Understanding these dynamic pathways is essential for developing effective prevention and resource allocation strategies. In Minnesota, USA, boat movement networks have been used to develop decision support tools for AIS management. Existing tools are based upon static boater-movement data, which may not reflect temporal shifts. Here, we advance our understanding of boater traffic and of changes in connectivity networks over time, building upon past work to enhance tools and evaluate the potential impact of the COVID-19 pandemic. To predict boater activity, we used data from the Minnesota Department of Natural Resources’ watercraft inspection program from 2018 to 2023 and three XGBoost machine learning algorithms. We found that integrating site-variability impacts overall lake traffic estimates, and that boating activity increased during the peak COVID-19 pandemic restrictions and decreased in the two years following.