Encounter data in resource management and ecology: pitfalls and possibilities

Authors

  • Aidan Keane,

    Corresponding author
    1. Centre for Environmental Policy and Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, Berkshire, UK
      Correspondence author. E-mail: aidan.keane@ucl.ac.uk
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    • Present address: Department of Anthropology, University College London & Institute of Zoology, Zoological Society of London, London, UK.

  • Julia P. G. Jones,

    1. School of the Environment, Natural Resources and Geography, Bangor University, Bangor, Gwynedd, UK
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  • E. J. Milner-Gulland

    1. Centre for Environmental Policy and Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, Berkshire, UK
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Correspondence author. E-mail: aidan.keane@ucl.ac.uk

Summary

1. Simple indices based on the number of encounters with a study object are used throughout ecology, conservation and natural resource management (e.g. indices of abundance used in animal surveys or catch per unit effort (CPUE) data in fisheries management). All forms of encounter data arise through the interaction of two sets of behaviours: those of the data generators and those of the data collectors. Analyses of encounter data are prone to bias when these behaviours do not conform to the assumptions used to model them.

2. We review the use of CPUE indices derived from patrol data, which have been promoted for the study of rule-breaking in conservation, highlighting potential sources of bias and noting how similar problems have been tackled for other forms of encounter data.

3. We identify several issues that must be addressed for analyses of patrol data to provide useful information, including the definition of suitable measures of catch and effort, the choice of appropriate temporal and spatial scales, the provision of suitable incentives for ranger patrols and the recording of sufficient information to describe the spatial pattern of sampling. The same issues are also relevant to encounter data more generally.

4.Synthesis and applications. This review describes a common conceptual framework for understanding encounter data, based on the interactions that produce them. We anticipate that an appreciation of these commonalities will lead to improvements in the analysis of encounter data in several fields, by highlighting the existence of methodological approaches that could be more widely applied, and important characteristics of these data that have so far been neglected.

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