IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0227468.html
   My bibliography  Save this article

Spatially explicit capture recapture density estimates: Robustness, accuracy and precision in a long-term study of jaguars (Panthera onca)

Author

Listed:
  • Bart J Harmsen
  • Rebecca J Foster
  • Howard Quigley

Abstract

Camera trapping is the standard field method of monitoring cryptic, low-density mammal populations. Typically, researchers run camera surveys for 60 to 90 days and estimate density using closed population spatially explicit capture-recapture (SCR) models. The SCR models estimate density, capture probability (g0), and a scale parameter (σ) that reflects ranging behaviour. We used a year of camera data from 20 camera stations to estimate the density of male jaguars (Panthera onca) in the Cockscomb Basin Wildlife Sanctuary in Belize, using closed population SCR models. We subsampled the dataset into 276 90-day sessions and 186 180-day sessions. Estimated density fluctuated from 0.51 to 5.30 male jaguars / 100 km2 between the 90-day sessions, with comparatively robust and precise estimates for the 180-day sessions (0.73 to 3.75 male jaguars / 100 km2). We explain the variation in density estimates from the 90-day sessions in terms of temporal variation in social behaviour, specifically male competition and mating events during the three-month wet season. Density estimates from the 90-day sessions varied with σ, but not with the number of individuals detected, suggesting that variation in density was almost fully attributable to changes in estimated ranging behaviour. We found that the models overestimated σ when compared to the mean ranging distance derived from GPS tracking data from two collared individuals in the camera grid. Overestimation of σ when compared to GPS collar data was more pronounced for the 180-day sessions than the 90-day sessions. We conclude that one-off (‘snap-shot’) short-term, small-scale camera trap surveys do not sufficiently sample wide-ranging large carnivores. When using SCR models to estimate the density from these data, we caution against the use of poor sampling designs and/or misinterpretation of scope of inference. Although the density estimates from one-off, short-term, small-scale camera trap surveys may be statistically accurate within each short-term sampling period, the variation between density estimates from multiple sessions throughout the year illustrate that the estimates obtained should be carefully interpreted and extrapolated, because different factors, such as temporal stochasticity in behaviour of a few individuals, may have strong repercussions on density estimates. Because of temporal variation in behaviour, reliable density estimates will require larger samples of individuals and spatial recaptures than those presented in this study (mean +/- SD = 14.2 +/- 1.2 individuals, 37.7 +/- 4.7 spatial recaptures, N = 276 sessions), which are representative of, or higher than published sample sizes. To satisfy the need for larger samples, camera surveys will need to be more expansive with a higher density of stations. In the absence of this, we advocate longer sampling periods and subsampling through time as a means of understanding and describing stability or variation between density estimates.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0227468
    DOI: 10.1371/journal.pone.0227468
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227468
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0227468&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0227468?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.
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Felix T. Petersma & Len Thomas & Aaron M. Thode & Danielle Harris & Tiago A. Marques & Gisela V. Cheoo & Katherine H. Kim, 2024. "Accommodating False Positives Within Acoustic Spatial Capture–Recapture, with Variable Source Levels, Noisy Bearings and an Inhomogeneous Spatial Density," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 29(3), pages 471-490, September.
    8. 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.
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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).
    19. 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.
    20. 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.

    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:plo:pone00:0227468. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.