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    Application of environmental DNA-based occurrence data in modeling wood frog (Rana sylvatica) distribution in Interior Alaska

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    Thumbnail
    Name:
    ModelData2015.xlsx
    Size:
    71.46Kb
    Format:
    Microsoft Excel 2007
    Description:
    Raw eDNA dataset
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    Name:
    SiteAveragePredictionLayer.zip
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    29.93Mb
    Format:
    Unknown
    Description:
    Interpolated raster for prediction ...
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    Name:
    PresAbs1PredictionLayer.zip
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    31.42Mb
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    Unknown
    Description:
    Interpolated raster for prediction ...
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    Name:
    PresAbs2PredictionLayer.zip
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    32.05Mb
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    Interpolated raster for prediction ...
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    Name:
    PresAbs3PredictionLayer.zip
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    31.12Mb
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    Unknown
    Description:
    Interpolated raster for prediction ...
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    Name:
    SiteAverage_TN.grv
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    703.5Kb
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    Unknown
    Description:
    Grove for model based on site ...
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    Name:
    PresAbs1TN.grv
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    507.3Kb
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    Description:
    Grove for model based on qPCR ...
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    PresAbs2TN.grv
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    571.0Kb
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    Grove for model based on qPCR ...
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    PresAbs3TN.grv
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    525.3Kb
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    Grove for model based on qPCR ...
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    1Klatticejoined.zip
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    Description:
    1-km point lattice covering study ...
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    Thumbnail
    Name:
    random5k.zip
    Size:
    450.7Kb
    Format:
    Unknown
    Description:
    5,000 random psuedo-absence points
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    Author
    Spangler, Mark A.
    López, J. Andrés
    Huettmann, Falk
    Keyword
    eDNA
    wood frog
    species distribution modeling
    TreeNet
    Alaska
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/11122/8006
    Abstract
    Knowledge of wood frog distribution in Alaska is incomplete due to insufficient baseline occurrence data. A short season of activity and difficult access to remote areas restrict implementation of consistent monitoring efforts. Detecting the presence of species in aquatic landscapes using environmental DNA (eDNA) assays is increasingly applied as a monitoring method in wildlife surveys. However, uncertainties regarding the technique’s sensitivity to environmental variables and human error have thus far prevented its widespread adoption in studies of species distribution. Predictive models built on machine learning algorithms can help provide precise descriptions of species distribution using eDNA occurrence data, but they will require ground-truthing efforts to confirm accuracy in under-sampled landscapes. Here we assess the ability of wood frog eDNA occurrence data to inform species distribution models under five criteria for data use. We sampled 60 wetlands for eDNA in the Fairbanks North Star Borough during summer 2015. Samples were processed using a species-specific quantitative PCR assay. Wood frog presence at each site was inferred from the PCR results. This data was used to construct four different wood frog distribution models. From each model we produced a predictive distribution map encompassing the Fairbanks North Star Borough. We assess the performance of each model using available wood frog presence data. Our highest performing model achieves moderate predictive accuracy (Area Under the Curve = 0.74). Weak signals in eDNA occurrence data are important in revealing species presence at low abundance, but strict lab hygiene, quality control practices, and detailed metadata are needed to retain confidence in the results. We show a powerful new way to study wood frog distribution by combining eDNA occurrence data with machine learning techniques. Wider implementation of eDNA surveys and increased availability of high resolution GIS data will help to refine these models.
    Description
    Raw data, GIS layers, and model reports for the original research conducted in this study
    Date
    2017
    Type
    Dataset
    Collections
    Spangler, Mark

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