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Estimating Reproduction and Survival of Unmarked Juveniles Using Aerial Images and Marked Adults

Author

Listed:
  • Perry J. Williams

    (University of Nevada)

  • Cody Schroeder

    (Nevada Department of Wildlife)

  • Pat Jackson

    (Nevada Department of Wildlife)

Abstract

Methods for estimating juvenile survival of wildlife populations often rely on intensive data collection efforts to capture and uniquely mark individual juveniles and observe them through time. Capturing juveniles in a time frame sufficient to estimate survival can be challenging due to narrow and stochastic windows of opportunity. For many animals, juvenile survival depends on postnatal parental care (e.g., lactating mammals). When a marked adult gives birth to, and provides care for, juvenile animals, investigators can use the adult mark to locate and count unmarked juveniles. Our objective was to leverage the dependency between juveniles and adults and develop a framework for estimating reproductive rates, juvenile survival, and detection probability using repeated observations of marked adult animals with known fates, but imperfect detection probability, and unmarked juveniles with unknown fates. Our methods assume population closure for adults and that no juvenile births or adoptions take place after monitoring has begun. We conducted simulations to evaluate methods and then developed a field study to examine our methods using real data consisting of a population of mule deer in a remote area in central Nevada. Using simulations, we found that our methods were able to recover the true values used to generate the data well. Estimates of juvenile survival rates from our field study were 0.96, (95% CRI 0.83–0.99) for approximately 32-day periods between late June and late August. The methods we describe show promise for many applications and study systems with similar data types, and our methods can be easily extended to unmanned aerial platforms and cameras that are already commercially available for the types of images we used. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Perry J. Williams & Cody Schroeder & Pat Jackson, 2020. "Estimating Reproduction and Survival of Unmarked Juveniles Using Aerial Images and Marked Adults," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(2), pages 133-147, June.
  • Handle: RePEc:spr:jagbes:v:25:y:2020:i:2:d:10.1007_s13253-020-00384-5
    DOI: 10.1007/s13253-020-00384-5
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    References listed on IDEAS

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    1. Little, Roderick J., 2006. "Calibrated Bayes: A Bayes/Frequentist Roadmap," The American Statistician, American Statistical Association, vol. 60, pages 213-223, August.
    2. Richard J. Barker & Matthew R. Schofield & William A. Link & John R. Sauer, 2018. "On the reliability of N†mixture models for count data," Biometrics, The International Biometric Society, vol. 74(1), pages 369-377, March.
    3. J. Andrew Royle, 2004. "N-Mixture Models for Estimating Population Size from Spatially Replicated Counts," Biometrics, The International Biometric Society, vol. 60(1), pages 108-115, March.
    4. D. Dail & L. Madsen, 2011. "Models for Estimating Abundance from Repeated Counts of an Open Metapopulation," Biometrics, The International Biometric Society, vol. 67(2), pages 577-587, June.
    5. Joseph M Northrup & Brian D Gerber, 2018. "A comment on priors for Bayesian occupancy models," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-13, February.
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    Cited by:

    1. Xinyi Lu & Mevin B. Hooten & Andee Kaplan & Jamie N. Womble & Michael R. Bower, 2022. "Improving Wildlife Population Inference Using Aerial Imagery and Entity Resolution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 364-381, June.

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