Making messy data work for conservation

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  • Andrew D.M. Dobson
    University of Edinburgh
  • EJ Milner-Gulland
    University of Oxford
  • Nicholas J Aebischer
    Game & Wildlife Conservation Trust
  • Colin Beale
    University of York
  • Robert Brozovic
    Frankfurt Zoological Society
  • Peter Coals
    WildCru,Oxford University
  • Rob Critchlow
    University of York
  • Anthony Dancer
    ZSL Institute of Zoology, London.
  • Michelle Greve
    University of Pretoria
  • Amy Hinsley
    University of Oxford
  • Harriet Ibbett
  • Alison Johnston
    Cornell University
  • Tomothy Kuiper
    University of Oxford
  • Steven Le Comber
    Queen Mary University, London
  • Simon P Mahood
    Wildlife Conservation Society
  • Jennifer F. Moore
    University of Florida
  • Erlend B Nilsen
    Norweigian Institute for Nature Research
  • Michael J.O. Pocock
    Centre for Ecology and Hydrology, Wallingford
  • Anthony Quinn
    University of Southampton
  • Henry Travers
    University of Oxford
  • Paulo Wilfred
    The Open University of Tanzania
  • Joss Wright
    University of Oxford
  • Aidan Keane
    University of Edinburgh
Conservationists increasingly use unstructured observational data, such as citizen science records or ranger patrol observations, to guide decision making. These datasets are often large and relatively cheap to collect, and they have enormous potential. However, the resulting data are generally ‘‘messy,’’ and their use can incur considerable costs, some of which are hidden. We present an overview of the opportunities and limitations associated with messy data by explaining how the preferences, skills, and incentives of data collectors affect the quality of the information they contain and the investment required to unlock their potential. Drawing widely from across the sciences, we break down elements of the observation process in order to highlight likely sources of bias and error while emphasizing the importance of cross-disciplinary collaboration. We pro- pose a framework for appraising messy data to guide those engaging with these types of dataset and make them work for conservation and broader sustainability applications.
Original languageEnglish
Pages (from-to)455-465
JournalOne Earth
Volume2
Issue number5
DOIs
Publication statusPublished - 22 May 2020

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