Network connectivity of Minnesota waterbodies and implications for aquatic invasive species prevention

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dc.contributor.authorKao, Szu-Yu Zoeen
dc.contributor.authorEnns, Eva A.en
dc.contributor.authorTomamichel, Meganen
dc.contributor.authorDoll, Adamen
dc.contributor.authorEscobar, Luis E.en
dc.contributor.authorQiao, Huijieen
dc.contributor.authorCraft, Meggan E.en
dc.contributor.authorPhelps, Nicholas B. D.en
dc.contributor.departmentFish and Wildlife Conservationen
dc.coverage.stateMinnesotaen
dc.date.accessioned2021-07-27T19:10:50Zen
dc.date.available2021-07-27T19:10:50Zen
dc.date.issued2021-05-23en
dc.description.abstractConnectivity between waterbodies influences the risk of aquatic invasive species (AIS) invasion. Understanding and characterizing the connectivity between waterbodies through high-risk pathways, such as recreational boats, is essential to develop economical and effective prevention intervention to control the spread of AIS. Fortunately, state and local watercraft inspection programs are collecting significant data that can be used to quantify boater connectivity. We created a series of predictive models to capture the patterns of boater movements across all lakes in Minnesota, USA. Informed by more than 1.3 million watercraft inspection surveys from 2014-2017, we simulated boater movements connecting 9182 lakes with a high degree of accuracy. Our predictive model accurately predicted 97.36% of the lake pairs known to be connected and predicted 91.01% of the lake pairs known not to be connected. Lakes with high degree and betweenness centrality were more likely to be infested with an AIS than lakes with low degree (p < 0.001) and centrality (p < 0.001). On average, infested lakes were connected to 1200 more lakes than uninfested lakes. In addition, boaters that visited infested lakes were more likely to visit other lakes, increasing the risk of AIS spread to uninfested lakes. The use of the simulated boater networks can be helpful for determining the risk of AIS invasion for each lake and for developing management tools to assist decision makers to develop intervention strategies.en
dc.description.notesThis project was supported by the Minnesota Aquatic Invasive Species Research Center with funding provided by the Minnesota Environmental and Natural Resources Trust Fund, as recommended by the LegislativeCitizen Commission on Minnesota Resources. QH was also supported by the National Key Research and Development Project of China (2017YFC1200603). MT also received support from the Interdisciplinary Disease Ecology Across Scales (IDEAS) Graduate Training Program at the University of Georgia through NSF DGE-1545433.en
dc.description.sponsorshipMinnesota Aquatic Invasive Species Research Center; Minnesota Environmental and Natural Resources Trust Fund; National Key Research and Development Project of China [2017YFC1200603]; Interdisciplinary Disease Ecology Across Scales (IDEAS) Graduate Training Program at the University of Georgia through NSF [DGE-1545433]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1007/s10530-021-02563-yen
dc.identifier.eissn1573-1464en
dc.identifier.issn1387-3547en
dc.identifier.urihttp://hdl.handle.net/10919/104415en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectAquatic invasive speciesen
dc.subjectBoater movementsen
dc.subjectNetwork analysisen
dc.subjectNetwork featuresen
dc.subjectMachine learningen
dc.titleNetwork connectivity of Minnesota waterbodies and implications for aquatic invasive species preventionen
dc.title.serialBiological Invasionsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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