Browsing by Author "Minnesota Aquatic Invasive Species Research Center, University of Minnesota"

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    Data for Open water dreissenid mussel control projects: lessons learned from a retrospective analysis
    (2022-08-15) Dahlberg, Angelique, D.; Waller, Diane, L.; Hammond, David; Lund, Keegan; Phelps, Nicholas, B. D.; edge0023@umn.edu; Dahlberg, Angelique, D.; Minnesota Aquatic Invasive Species Research Center, University of Minnesota
    Dreissenid mussels are one of the most problematic aquatic invasive species (AIS) in North America, causing significant ecological and economic impacts in waterbodies where established. To date, dreissenid mussel control efforts in open water have included physical, biological, and chemical methods. The feasibility of successfully managing or even eradicating dreissenid mussels in lakes is relatively undocumented and unstudied in freshwater management literature. Additionally, control efforts are sometimes stymied by perceptions that the impacts to nontarget species will be unacceptable. The published literature evaluating both these two aspects is limited. Here, we present information on 33 open water dreissenid mussel control projects in 23 lakes across North America. Projects were categorized as rapid response eradication (n=16), established population eradication (n=8), suppression (n=3), or research (n=6).
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    Network connectivity patterns of Minnesota waterbodies and implications for aquatic invasive species prevention
    (2020-10-28) Kao, Szu-Yu; Enns, Eva A; Tomamichel, Megan; Doll, Adam; Escobar, Luis E; Qiao, Huijie; Craft, Meggan E; Phelps, Nicholas B D; phelp083@umn.edu; Phelps, Nicholas B D; Minnesota Aquatic Invasive Species Research Center, University of Minnesota; Division of Health Policy and Management, School of Public Health, University of Minnesota; Odum School of Ecology, University of Georgia; Department of Fish and Wildlife Conservation, Virginia Polytechnic Institute and State University; Key Laboratory of Animal Ecology and Conservation Biology, Chinese Academy of Sciences; Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota
    The data contains simulated boater movements across lakes in the state of Minnesota (MN). The data were simulated based on the boater inspection program conducted by the Minnesota Department of Natural Resources in 2014-2017. Using the inspection survey, we employed machine learning technique, XGBoost, to construct three predictive boater movement models. First, we predicted the number of boater traffic on a lake for a year. Second, we predicted the boater connection between any pair of lakes in MN. Third, we predicted the number of boaters between two lakes that were predicted to have connection.