Whether it’s Lake Superior, the mighty Mississippi River, or any of Minnesota’s 10,000 plus lakes, Minnesotans are fortunate to enjoy access to a wide variety of aquatic resources. However, it’s likely that at some point you have heard about one of many aquatic invasive species (AIS) posing a risk to a waterbody that you have visited or care about. AIS spread through various pathways and, when established, have the potential to negatively impact the local ecosystems, recreation, and economy.
The state of Minnesota has become a leader in learning how to manage and efficiently respond to AIS. Each year, Minnesota allocates about $20 million for AIS interventions to help combat the spread of these species and to mitigate the damage where they occur. Yet, knowing how to efficiently allocate resources can still be a daunting task when we have over 10,000 lakes to consider in the state.
Preventing the establishment of new AIS is cheaper than having to control these species after they become a nuisance. Early detection and rapid response can help managers to quickly suppress or eradicate a new invasion before it takes hold. While this idea seems easily accomplishable, in theory, applying a monitoring program across all the lakes in Minnesota could easily exceed our collective capacity and resources. Therefore, it is critical for these monitoring programs to have an informed idea of where to look to prioritize effective prevention measures.
Researchers at the U.S. Geological Survey (USGS) Minnesota Cooperative Fish and Wildlife Research Unit (MNCFWRU) and the Minnesota Aquatic Invasive Species Research Center (MAISRC) are helping water resource managers better allocate limited resources to prevent AIS spread and establishment. Graduate research assistant, Jeremiah Shrovnal, joined a team of researchers from MNCFWRU and MAISRC, along with collaborators at the USGS Upper Midwest Environmental Sciences Center, to leverage the power of machine learning and large international data sets, to generate species distribution models. This work is funded by the USGS Water Resources Research Act Program (project # G23AP00032-00).
A total of 35 AIS of various taxa (i.e., invasive fish, invertebrates, plants) known to threaten Minnesota’s waters, including Silver Carp, Zebra Mussels, Eurasian Watermilfoil, were modeled using all globally known locations paired with various geographic, climatic, and land use data sets to help identify specific waterbody characteristics that are associated with specific infestation potential. Using these models, the research team was able to create suitability and risk scores for waterbodies across Minnesota that can help local managers prioritize where to direct future efforts.
This suitability modeling approach is one of many large-scale modeling efforts being undertaken by project researchers. A boater network model, another machine learning approach that utilizes travel information collected during watercraft inspections, is also helping to inform AIS preventions across the state. These new suitability models complement what has been learned about potential spread through boater movement by expanding our understanding of what makes specific waterbodies prone to AIS introduction and establishment.
As the project researchers move their work through peer-review processes, they are also beginning to set up public data repositories to make their work accessible to any interested party. Additionally, they are setting up workshops for resource managers to be able to use the suite of tools and risk assessment output they have compiled so that resource managers can begin incorporating the new information into their future planning. With the expected completion of this work coming in the summer of 2026, these results will allow resource managers to make more informed decisions as they continue to fine-tune their prevention strategies across Minnesota going forward.