IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v25y2020i2d10.1007_s13253-020-00384-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-020-00384-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13253-020-00384-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Little, Roderick J., 2006. "Calibrated Bayes: A Bayes/Frequentist Roadmap," The American Statistician, American Statistical Association, vol. 60, pages 213-223, August.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Steen, Valerie A. & Duarte, Adam & Peterson, James T., 2023. "An evaluation of multistate occupancy models for estimating relative abundance and population trends," Ecological Modelling, Elsevier, vol. 478(C).
    2. Adam Martin-Schwarze & Jarad Niemi & Philip Dixon, 2021. "Joint Modeling of Distances and Times in Point-Count Surveys," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 289-305, June.
    3. Hefley, Trevor J. & Tyre, Andrew J. & Blankenship, Erin E., 2013. "Fitting population growth models in the presence of measurement and detection error," Ecological Modelling, Elsevier, vol. 263(C), pages 244-250.
    4. Whitlock, Steven L. & Womble, Jamie N. & Peterson, James T., 2020. "Modelling pinniped abundance and distribution by combining counts at terrestrial sites and in-water sightings," Ecological Modelling, Elsevier, vol. 420(C).
    5. Xiaoli Fan & Miguel I. Gómez & Shady S. Atallah & Jon M. Conrad, 2020. "A Bayesian State‐Space Approach for Invasive Species Management: The Case of Spotted Wing Drosophila," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1227-1244, August.
    6. Emily B. Dennis & Byron J.T. Morgan & Martin S. Ridout, 2015. "Computational aspects of N-mixture models," Biometrics, The International Biometric Society, vol. 71(1), pages 237-246, March.
    7. Hefley, Trevor J. & Tyre, Andrew J. & Blankenship, Erin E., 2017. "Reprint of: Fitting population growth models in the presence of measurement and detection error," Ecological Modelling, Elsevier, vol. 359(C), pages 461-467.
    8. Duarte, Adam & Adams, Michael J. & Peterson, James T., 2018. "Fitting N-mixture models to count data with unmodeled heterogeneity: Bias, diagnostics, and alternative approaches," Ecological Modelling, Elsevier, vol. 374(C), pages 51-59.
    9. Matthew R. P. Parker & Laura L. E. Cowen & Jiguo Cao & Lloyd T. Elliott, 2023. "Computational Efficiency and Precision for Replicated-Count and Batch-Marked Hidden Population Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 43-58, March.
    10. Rafael A. Moral & John Hinde & Clarice G. B. Demétrio & Carolina Reigada & Wesley A. C. Godoy, 2018. "Models for Jointly Estimating Abundances of Two Unmarked Site-Associated Species Subject to Imperfect Detection," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 20-38, March.
    11. Chun Asaph Young & Schouten Barry & Wagner James, 2017. "JOS Special Issue on Responsive and Adaptive Survey Design: Looking Back to See Forward – Editorial: In Memory of Professor Stephen E. Fienberg, 1942–2016," Journal of Official Statistics, Sciendo, vol. 33(3), pages 571-577, September.
    12. D. A. S. Fraser & N. Reid & E. Marras & G. Y. Yi, 2010. "Default priors for Bayesian and frequentist inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(5), pages 631-654, November.
    13. Mevin B. Hooten & Christopher K. Wikle & Robert M. Dorazio & J. Andrew Royle, 2007. "Hierarchical Spatiotemporal Matrix Models for Characterizing Invasions," Biometrics, The International Biometric Society, vol. 63(2), pages 558-567, June.
    14. David Kaplan & Chansoon Lee, 2018. "Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments," Evaluation Review, , vol. 42(4), pages 423-457, August.
    15. Yinqiu Ji & Christopher C. M. Baker & Viorel D. Popescu & Jiaxin Wang & Chunying Wu & Zhengyang Wang & Yuanheng Li & Lin Wang & Chaolang Hua & Zhongxing Yang & Chunyan Yang & Charles C. Y. Xu & Alex D, 2022. "Measuring protected-area effectiveness using vertebrate distributions from leech iDNA," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    16. Kristensen, Kasper & Nielsen, Anders & Berg, Casper W. & Skaug, Hans & Bell, Bradley M., 2016. "TMB: Automatic Differentiation and Laplace Approximation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i05).
    17. John Geweke, 2007. "Bayesian Model Comparison and Validation," American Economic Review, American Economic Association, vol. 97(2), pages 60-64, May.
    18. Henry T. Reich, 2020. "Optimal sampling design and the accuracy of occupancy models," Biometrics, The International Biometric Society, vol. 76(3), pages 1017-1027, September.
    19. Carles Serrat & Montserrat Ru� & Carmen Armero & Xavier Piulachs & H�ctor Perpi��n & Anabel Forte & �lvaro P�ez & Guadalupe G�mez, 2015. "Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1223-1239, June.
    20. Stavros Nikolakopoulos & Ingeborg van der Tweel & Kit C. B. Roes, 2018. "Dynamic borrowing through empirical power priors that control type I error," Biometrics, The International Biometric Society, vol. 74(3), pages 874-880, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jagbes:v:25:y:2020:i:2:d:10.1007_s13253-020-00384-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.