Network connectivity patterns of Minnesota waterbodies and implications for aquatic invasive species prevention
2020-10-28
Loading...
Persistent link to this item
Statistics
View StatisticsCollection period
2014-04-25
2017-12-01
2017-12-01
Date completed
Date updated
Time period coverage
Geographic coverage
Source information
Journal Title
Journal ISSN
Volume Title
Title
Network connectivity patterns of Minnesota waterbodies and implications for aquatic invasive species prevention
Published Date
2020-10-28
Group
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
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
Author Contact
Phelps, Nicholas B D
phelp083@umn.edu
phelp083@umn.edu
Type
Dataset
Simulation Data
Simulation Data
Abstract
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.
Description
The data sets contain annual and weekly simulated boater movements. The annual movement data are provided in .csv format. The weekly movement data are in .txt format. We provided an example python script to demonstrate how to load the weekly boater movement data.
Referenced by
Related to
Replaces
Publisher
Funding information
Funding for this project was provided by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Minnesota Aquatic Invasive Species Research Center (MAISRC) and the Legislative-Citizen Commission on Minnesota Resources (LCCMR).
item.page.sponsorshipfunderid
item.page.sponsorshipfundingagency
item.page.sponsorshipgrant
Previously Published Citation
Suggested citation
Kao, Szu-Yu; Enns, Eva A; Tomamichel, Megan; Doll, Adam; Escobar, Luis E; Qiao, Huijie; Craft, Meggan E; Phelps, Nicholas B D. (2020). Network connectivity patterns of Minnesota waterbodies and implications for aquatic invasive species prevention. Retrieved from the University Digital Conservancy, https://doi.org/10.13020/DJW8-2V86.
View/Download file
| File View/Open | Description | Size |
|---|---|---|
| ss_dow.csv | lakes infested with starry stonewort | 186 B |
| zm_dow.csv | lakes infested with zebra mussel | 2.47 KB |
| load_data.py | Python script to load weekly boater movement datasets | 2.11 KB |
| lake_attribute.csv | Lake attribute data | 539.14 KB |
| Readme.txt | Description of the Dataset | 6.43 KB |
| boat_dict_all.zip | Weekly boater movement (20 .txt files) | 87.96 MB |
| boats_all.zip | Simulated annual boater movement (20 .csv files) | 125.42 MB |
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.