Density estimation in tiger populations: combining information for strong inference

Authors

  • Arjun M. Gopalaswamy,

    Corresponding author
    1. Wildlife Conservation Research Unit (WildCRU), The Recanati-Kaplan Centre, University of Oxford, Department of Zoology, Tubney, Abingdon OX13 5QL United Kingdom
    2. Centre for Wildlife Studies, 26-2, Aga Abbas Ali Road, Bengaluru 560042 India
    3. Wildlife Conservation Society—India Program, 1669, 31st Cross, 16th Main, Banashankari 2nd Stage, Bengaluru 560070 India
    • Address for correspondence: Wildlife Conservation Research Unit (WildCRU), The Recanati-Kaplan Centre, University of Oxford, Department of Zoology, Tubney, Abingdon OX13 5QL United Kingdom. E-mail: arjungswamy@gmail.com

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  • J. Andrew Royle,

    1. U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, Maryland 20708 USA
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  • Mohan Delampady,

    1. Statistics and Mathematics Unit, Indian Statistical Institute, Bangalore Centre, Bengaluru 560059 India
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  • James D. Nichols,

    1. U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, Maryland 20708 USA
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  • K. Ullas Karanth,

    1. Centre for Wildlife Studies, 26-2, Aga Abbas Ali Road, Bengaluru 560042 India
    2. Wildlife Conservation Society—India Program, 1669, 31st Cross, 16th Main, Banashankari 2nd Stage, Bengaluru 560070 India
    3. Wildlife Conservation Society—Global Conservation Program, 2300 Southern Boulevard, Bronx, New York 10460 USA
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  • David W. Macdonald

    1. Wildlife Conservation Research Unit (WildCRU), The Recanati-Kaplan Centre, University of Oxford, Department of Zoology, Tubney, Abingdon OX13 5QL United Kingdom
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  • Corresponding Editor: E. G. Cooch.

Abstract

A productive way forward in studies of animal populations is to efficiently make use of all the information available, either as raw data or as published sources, on critical parameters of interest. In this study, we demonstrate two approaches to the use of multiple sources of information on a parameter of fundamental interest to ecologists: animal density. The first approach produces estimates simultaneously from two different sources of data. The second approach was developed for situations in which initial data collection and analysis are followed up by subsequent data collection and prior knowledge is updated with new data using a stepwise process. Both approaches are used to estimate density of a rare and elusive predator, the tiger, by combining photographic and fecal DNA spatial capture–recapture data. The model, which combined information, provided the most precise estimate of density (8.5 ± 1.95 tigers/100 km2 [posterior mean ± SD]) relative to a model that utilized only one data source (photographic, 12.02 ± 3.02 tigers/100 km2 and fecal DNA, 6.65 ± 2.37 tigers/100 km2). Our study demonstrates that, by accounting for multiple sources of available information, estimates of animal density can be significantly improved.

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