Network connectivity patterns of Minnesota waterbodies and implications for aquatic invasive species prevention
2020-10-28
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2014-04-25
2017-12-01
2017-12-01
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Title
Network connectivity patterns of Minnesota waterbodies and implications for aquatic invasive species prevention
Published Date
2020-10-28
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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
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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.
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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).
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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 Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/DJW8-2V86.
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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)
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