IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v34y2023i2ne2761.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.2761
    Download Restriction: no

    File URL: https://libkey.io/10.1002/env.2761?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
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    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. D. L. Borchers & B. C. Stevenson & D. Kidney & L. Thomas & T. A. Marques, 2015. "A Unifying Model for Capture-Recapture and Distance Sampling Surveys of Wildlife Populations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 195-204, March.
    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.
    3. Russell, Robin E. & Walsh, Daniel P. & Samuel, Michael D. & Grunnill, Martin D. & Rocke, Tonie E., 2021. "Space matters: host spatial structure and the dynamics of plague transmission," Ecological Modelling, Elsevier, vol. 443(C).
    4. Bart J Harmsen & Rebecca J Foster & Howard Quigley, 2020. "Spatially explicit capture recapture density estimates: Robustness, accuracy and precision in a long-term study of jaguars (Panthera onca)," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-19, June.
    5. Murray G. Efford & Matthew R. Schofield, 2020. "A spatial open‐population capture‐recapture model," Biometrics, The International Biometric Society, vol. 76(2), pages 392-402, June.
    6. Michael R. Whitehead & Rod Peakall, 2013. "Short-term but not long-term patch avoidance in an orchid-pollinating solitary wasp," Behavioral Ecology, International Society for Behavioral Ecology, vol. 24(1), pages 162-168.
    7. Robert M Dorazio, 2013. "Bayes and Empirical Bayes Estimators of Abundance and Density from Spatial Capture-Recapture Data," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    8. Xinhai Li & Ning Li & Baidu Li & Yuehua Sun & Erhu Gao, 2022. "AbundanceR: A Novel Method for Estimating Wildlife Abundance Based on Distance Sampling and Species Distribution Models," Land, MDPI, vol. 11(5), pages 1-13, April.
    9. Dey, Soumen & Moqanaki, Ehsan & Milleret, Cyril & Dupont, Pierre & Tourani, Mahdieh & Bischof, Richard, 2023. "Modelling spatially autocorrelated detection probabilities in spatial capture-recapture using random effects," Ecological Modelling, Elsevier, vol. 479(C).
    10. Simon J. Bonner & Wei Zhang & Jiaqi Mu, 2024. "On the identifiability of the trinomial model for mark‐recapture‐recovery studies," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    11. Juan Manuel Morales & Agustina Virgilio & María Delgado & Otso Ovaskainen, 2017. "A General Approach to Model Movement in (Highly) Fragmented Patch Networks," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 393-412, September.
    12. Mevin B. Hooten & Michael R. Schwob & Devin S. Johnson & Jacob S. Ivan, 2023. "Multistage hierarchical capture–recapture models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(6), September.
    13. Ben C. Stevenson & Rachel M. Fewster & Koustubh Sharma, 2022. "Spatial correlation structures for detections of individuals in spatial capture–recapture models," Biometrics, The International Biometric Society, vol. 78(3), pages 963-973, September.
    14. Tomáš Jůnek & Pavla Jůnková Vymyslická & Kateřina Hozdecká & Pavla Hejcmanová, 2015. "Application of Spatial and Closed Capture-Recapture Models on Known Population of the Western Derby Eland (Taurotragus derbianus derbianus) in Senegal," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-16, September.
    15. Jennifer B Smith & Bryan S Stevens & Dwayne R Etter & David M Williams, 2020. "Performance of spatial capture-recapture models with repurposed data: Assessing estimator robustness for retrospective applications," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-16, August.
    16. Simone Tenan & Paolo Pedrini & Natalia Bragalanti & Claudio Groff & Chris Sutherland, 2017. "Data integration for inference about spatial processes: A model-based approach to test and account for data inconsistency," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-18, October.
    17. Soumen Dey & Mohan Delampady & Ravishankar Parameshwaran & N. Samba Kumar & Arjun Srivathsa & K. Ullas Karanth, 2017. "Bayesian Methods for Estimating Animal Abundance at Large Spatial Scales Using Data from Multiple Sources," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(2), pages 111-139, June.
    18. David L. Borchers & Tiago A. Marques, 2017. "From distance sampling to spatial capture–recapture," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 475-494, October.
    19. M. G. Efford, 2022. "Efficient Discretization of Movement Kernels for Spatiotemporal Capture–Recapture," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 641-651, December.
    20. Ben C. Stevenson & David L. Borchers & Rachel M. Fewster, 2019. "Cluster capture‐recapture to account for identification uncertainty on aerial surveys of animal populations," Biometrics, The International Biometric Society, vol. 75(1), pages 326-336, March.

    More about this item

    Statistics

    Access and download statistics

    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:wly:envmet:v:34:y:2023:i:2:n:e2761. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1180-4009/ .

    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.