An evaluation of a citizen science data collection program for recording wildlife observations along a highway.

Citizen science programs that record wildlife observations on and along roads can help reduce the underreporting of wildlife-vehicle collisions and identify and prioritize road sections where mitigation measures may be required. It is important to evaluate potential biases in opportunistic citizen s...

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Publicado en:Journal of Environmental Management Vol. 139; pp. 180 - 188
Autores principales: Paul, Kylie, Quinn, Michael S., Huijser, Marcel P., Graham, Jonathan, Broberg, Len
Formato: Artículo
Publicado: Academic Press Inc. Jun2014
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Acceso en línea:Ver este registro en EBSCOhost
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      jtl: Journal of Environmental Management
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      dt: Jun2014
      vid: 139
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      pub: Academic Press Inc.
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        95987458
        10.1016/j.jenvman.2014.02.018
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        atl: An evaluation of a citizen science data collection program for recording wildlife observations along a highway.
      aug:
        au:
          Paul, Kylie
          Quinn, Michael S.
          Huijser, Marcel P.
          Graham, Jonathan
          Broberg, Len
        affil:
          Environmental Studies Program, The University of Montana, Jeannette Rankin Hall 106A, Missoula, MT 59812-4320, USA
          Institute for Environmental Sustainability, Mount Royal University, 4825 Mount Royal Gate SW, Calgary Alberta T3E 6K6, Canada
          Western Transportation Institute, Montana State University, PO Box 174250, Bozeman, MT 59717-4250, USA
          Department of Mathematical Sciences, The University of Montana, Missoula, MT 59812-0864, USA
      su:
        Alberta
        Traffic accidents
        Citizen science
        Scientific observation
        Effect of roads on animals
        Acquisition of data
        Research methodology evaluation
        Statistical reliability
      sug:
        subj:
          Traffic accidents
          Alberta
          Citizen science
          Scientific observation
          Effect of roads on animals
          Acquisition of data
          Research methodology evaluation
          Statistical reliability
      keyword:
        Highway
        Hotspot
        Mitigation
        Wildlife
        Highway
        Hotspot
        Mitigation
        Wildlife
      ab: Citizen science programs that record wildlife observations on and along roads can help reduce the underreporting of wildlife-vehicle collisions and identify and prioritize road sections where mitigation measures may be required. It is important to evaluate potential biases in opportunistic citizen science data. We investigated whether the opportunistic observations of live animals by volunteers along a 46-km section of Highway 3 in the Crowsnest Pass area (“Road Watch in the Pass” data collection program) in Alberta, Canada, had a similar spatial pattern as systematically collected data by the researchers along the same road section. A permutation modeling process that compared the number of observations between the two datasets for each 1-km segment, a randomization method that tested for and compared hotspot observation locations, and a bivariate Ripley's L -function analysis along a continuum of spatial scales all showed spatial agreement between the two datasets. There was spatial agreement at a scale between 1 and 4 km, and three clear hotspots of wildlife observation activity were identified for both processes. This suggests that the data collected by the volunteers are reliable and robust enough to be used to help identify road sections that may require mitigation measures. In addition, volunteers proved to be able to collect a sufficient number of observations relatively quickly. Within one year, 24 volunteers collected 640 wildlife observations, and we found that using only 150 or more of these observations always resulted in spatial similarity with the systematic observations collected by the researchers. We conclude with recommendations for other citizen science data collection programs and for further research.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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