The early detection of invasive species such as zebra mussels and Eurasian watermilfoil is crucial to the success of control efforts. However, detecting these species early can be very challenging due to several factors, such as the absence of a surveillance program, relying on public reporting, and limited resource availability, which can result in reporting bias and underreporting.
The goal of this project was to improve the decision-making process and prevent the spread of AIS by implementing risk-based prevention and mitigation management strategies. This project combined clustering detection, network analysis, and probability co-kriging to recognize dispersal patterns and estimate the risk of zebra mussel and Eurasian watermilfoil invasions while attempting to account for the reporting bias and for underreporting.
To evaluate the areas of highest risk for zebra mussel infestations, researchers looked at distance to the nearest zebra mussel infested water body, boater traffic, and road access. The Eurasian watermilfoil model was similar, looking at connectivity to infested water bodies instead of road access. Results confirmed that zebra mussel and Eurasian watermilfoil invasions are potentially confounded by human densities, which is explained by varying human impact on either or both dispersal and reporting of invasions. Considering this impact of human density, this research suggests that a combination of passive and targeted surveillance, where the magnitude of efforts are stratified by human densities, may provide insight into the true invasion status and its progression in the Great Lakes region.