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.
Bibliographical noteFunding Information:
This project was supported by the Minnesota Aquatic Invasive Species Research Center with funding provided by the Minnesota Environmental and Natural Resources Trust Fund, as recommended by the Legislative-Citizen Commission on Minnesota Resources. QH was also supported by the National Key Research and Development Project of China (2017YFC1200603). MT also received support from the Interdisciplinary Disease Ecology Across Scales (IDEAS) Graduate Training Program at the University of Georgia through NSF DGE-1545433.
© 2021, The Author(s).
- Aquatic invasive species
- Boater movements
- Machine learning
- Network analysis
- Network features