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Shooting for abundance: Comparing integrated multi‐sampling models for camera trap and hair trap data

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  • Mehnaz Jahid
  • Holly N. Steeves
  • Jason T. Fisher
  • Simon J. Bonner
  • Saman Muthukumarana
  • Laura L. E. Cowen

Abstract

Abundance estimation is a vital goal in wildlife monitoring. Camera‐traps are a tool to survey wildlife populations noninvasively and can be used for abundance estimation if individuals are identifiable. However, for species without individual identification characteristics, camera‐trap surveys have often been combined with some other survey method such as capture‐recapture (CR, using traditional tags or DNA through hair snags or scat) to inform an integrated model. We discuss and apply two integrated models involving presence‐absence data from camera traps and CR data from hair traps to compare bias and precision to estimate the population density of grizzly bears of the central Rocky Mountains of Alberta, Canada. Unlike many other studies, we found that integrating presence‐absence data with CR data does not improve the precision of the density estimates. The possible reasons for such results are discussed in detail.

Suggested Citation

  • Mehnaz Jahid & Holly N. Steeves & Jason T. Fisher & Simon J. Bonner & Saman Muthukumarana & Laura L. E. Cowen, 2023. "Shooting for abundance: Comparing integrated multi‐sampling models for camera trap and hair trap data," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
    • Handle: RePEc:wly:envmet:v:34:y:2023:i:2:n:e2761
    DOI: 10.1002/env.2761
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    References listed on IDEAS

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    1. D. L. Borchers & M. G. Efford, 2008. "Spatially Explicit Maximum Likelihood Methods for Capture–Recapture Studies," Biometrics, The International Biometric Society, vol. 64(2), pages 377-385, June.
    2. Murray G. Efford & Christine M. Hunter, 2018. "Spatial capture–mark–resight estimation of animal population density," Biometrics, The International Biometric Society, vol. 74(2), pages 411-420, June.
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