Monitoring carnivore populations at the landscape scale: occupancy modelling of tigers from sign surveys

Authors

  • Kota Ullas Karanth,

    Corresponding author
    1. Wildlife Conservation Society – Global Conservation Program, 2300 Southern Boulevard, Bronx, NY 10460, USA
    2. Centre for Wildlife Studies, 26-2, Aga Abbas Ali Road (Apt: 403), Bengaluru 560 042, India
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  • Arjun M. Gopalaswamy,

    1. Centre for Wildlife Studies, 26-2, Aga Abbas Ali Road (Apt: 403), Bengaluru 560 042, India
    2. Wildlife Conservation Society – India Program, 1669, 31st Cross, 16th Main, Banashankari 2nd Stage, Bengaluru 560 070, India
    3. Wildlife Conservation Research Unit (WildCRU), The Recanati-Kaplan Centre, Department of Zoology, Tubney House, University of Oxford, Abingdon Road, Tubney, Abingdon OX13 5QL, UK
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  • Narayanarao Samba Kumar,

    1. Centre for Wildlife Studies, 26-2, Aga Abbas Ali Road (Apt: 403), Bengaluru 560 042, India
    2. Wildlife Conservation Society – India Program, 1669, 31st Cross, 16th Main, Banashankari 2nd Stage, Bengaluru 560 070, India
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  • Srinivas Vaidyanathan,

    1. Wildlife Conservation Society – India Program, 1669, 31st Cross, 16th Main, Banashankari 2nd Stage, Bengaluru 560 070, India
    2. Foundation for Ecological Research, Advocacy and Learning, No 27, 2nd Cross Appavounagar, Vazhakulam, Pondicherry 605 012, India
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  • James D. Nichols,

    1. United States Geological Survey, Patuxent Wildlife Research Center, Laurel, MD 20708, USA
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  • Darryl I. MacKenzie

    1. Proteus Wildlife Research Consultants, PO Box 5193, Dunedin, New Zealand
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Correspondence author. E-mail ukaranth@gmail.com

Summary

1. Assessing spatial distributions of threatened large carnivores at landscape scales poses formidable challenges because of their rarity and elusiveness. As a consequence of logistical constraints, investigators typically rely on sign surveys. Most survey methods, however, do not explicitly address the central problem of imperfect detections of animal signs in the field, leading to underestimates of true habitat occupancy and distribution.

2. We assessed habitat occupancy for a tiger Panthera tigris metapopulation across a c. 38 000-km2 landscape in India, employing a spatially replicated survey to explicitly address imperfect detections. Ecological predictions about tiger presence were confronted with sign detection data generated from occupancy sampling of 205 sites, each of 188 km2.

3. A recent occupancy model that considers Markovian dependency among sign detections on spatial replicates performed better than the standard occupancy model (ΔAIC = 184·9). A formulation of this model that fitted the data best showed that density of ungulate prey and levels of human disturbance were key determinants of local tiger presence. Model averaging resulted in a replicate-level detection probability inline image = 0·17 (0·17) for signs and a tiger habitat occupancy estimate of inline image = 0·665 (0·0857) or 14 076 (1814) km2 of potential habitat of 21 167 km2. In contrast, a traditional presence-versus-absence approach underestimated occupancy by 47%. Maps of probabilities of local site occupancy clearly identified tiger source populations at higher densities and matched observed tiger density variations, suggesting their potential utility for population assessments at landscape scales.

4.Synthesis and applications. Landscape-scale sign surveys can efficiently assess large carnivore spatial distributions and elucidate the factors governing their local presence, provided ecological and observation processes are both explicitly modelled. Occupancy sampling using spatial replicates can be used to reliably and efficiently identify tiger population sources and help monitor metapopulations. Our results reinforce earlier findings that prey depletion and human disturbance are key drivers of local tiger extinctions and tigers can persist even in human-dominated landscapes through effective protection of source populations. Our approach facilitates efficient targeting of tiger conservation interventions and, more generally, provides a basis for the reliable integration of large carnivore monitoring data between local and landscape scales.

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