A spatial mark–resight model augmented with telemetry data

Authors

  • Rahel Sollmann,

    Corresponding author
    1. North Carolina State University, Department of Forestry and Environmental Resources, Fisheries and Wildlife Program, 110 Brooks Avenue, Raleigh, North Carolina 27607 USA
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  • Beth Gardner,

    1. North Carolina State University, Department of Forestry and Environmental Resources, Fisheries and Wildlife Program, 110 Brooks Avenue, Raleigh, North Carolina 27607 USA
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  • Arielle W. Parsons,

    1. USGS, NC Cooperative Fish and Wildlife Research Unit, Department of Biology, North Carolina State University, Raleigh, North Carolina 27695 USA
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  • Jessica J. Stocking,

    1. USGS, NC Cooperative Fish and Wildlife Research Unit, Department of Biology, North Carolina State University, Raleigh, North Carolina 27695 USA
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  • Brett T. McClintock,

    1. National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, 7600 Sand Point Way NE, Seattle, Washington 98115 USA
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  • Theodore R. Simons,

    1. USGS, NC Cooperative Fish and Wildlife Research Unit, Department of Biology, North Carolina State University, Raleigh, North Carolina 27695 USA
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  • Kenneth H. Pollock,

    1. North Carolina State University, Department of Biology, 234 David Clark Labs, Raleigh, North Carolina 27695-7617 USA
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  • Allan F. O'Connell

    1. USGS Patuxent Wildlife Research Center, 10300 Baltimore Avenue, Beltsville, Maryland 20708 USA
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  • Corresponding Editor: N. J. Gotelli.

Abstract

Abundance and population density are fundamental pieces of information for population ecology and species conservation, but they are difficult to estimate for rare and elusive species. Mark–resight models are popular for estimating population abundance because they are less invasive and expensive than traditional mark–recapture. However, density estimation using mark–resight is difficult because the area sampled must be explicitly defined, historically using ad hoc approaches. We developed a spatial mark–resight model for estimating population density that combines spatial resighting data and telemetry data. Incorporating telemetry data allows us to inform model parameters related to movement and individual location. Our model also allows <100% individual identification of marked individuals. We implemented the model in a Bayesian framework, using a custom-made Metropolis-within-Gibbs Markov chain Monte Carlo algorithm. As an example, we applied this model to a mark–resight study of raccoons (Procyon lotor) on South Core Banks, a barrier island in Cape Lookout National Seashore, North Carolina, USA. We estimated a population of 186.71 ± 14.81 individuals, which translated to a density of 8.29 ± 0.66 individuals/km2 (mean ± SD). The model presented here will have widespread utility in future applications, especially for species that are not naturally marked.

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