IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v472y2022ics0304380022002034.html
   My bibliography  Save this article

Density estimation of non-independent unmarked animals from camera traps

Author

Listed:
  • Hayashi, Kohta
  • Iijima, Hayato

Abstract

Population density estimation is essential for wildlife management and conservation. Camera trap (CT) surveys have become popular as non-invasive monitoring, and several methods have been developed to estimate population densities of individually unrecognizable animals exclusively from CT surveys, including the random encounter model (REM), the random encounter and staying time (REST) model, the instantaneous sampling (IS) model, and the space-to-event (STE) model. However, these models assume that individual detections are independent events. This assumption is violated when animals live in groups and behave correlatedly. Thus, this study aims to compare the performances of the REM, the REST, the IS, and the STE when animals move correlatedly in groups. Their extensions to account for overdispersion of detection count data and an application of group encounters in the REM were also examined. Seven group sizes and two cohesive patterns were simulated using correlated random walk moving biased to the group centers mimicking red deer, and the detection data were fitted to the models. A goodness-of-fit procedure was used to evaluate the fit of the models. It was found that the original model of the IS and the REST, and the REM of the individual encounters to were robust to the violation of independence in detections while the STE underestimated the density; they were, however, poor at data fitting. Although their extensions to the overdispersion improved the model fitting, the negative binomial (NB) models biased the estimates upward in the IS and the REST due to the limitation of sample size, the zero-inflated Poisson in the IS did not bias the estimates but was still poor at fitting in large groups, and the Lomax in the STE did not resolve the underestimation. Applying the group encounters in the REM also biased the density upward. The overestimation in the NB model occurs when the Bayesian p-value exceeded 0.5. This relationship would be informative in evaluating the possibility of overestimations in the NB model.

Suggested Citation

  • Hayashi, Kohta & Iijima, Hayato, 2022. "Density estimation of non-independent unmarked animals from camera traps," Ecological Modelling, Elsevier, vol. 472(C).
    • Handle: RePEc:eee:ecomod:v:472:y:2022:i:c:s0304380022002034
    DOI: 10.1016/j.ecolmodel.2022.110100
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380022002034
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2022.110100?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. James O Lloyd-Smith, 2007. "Maximum Likelihood Estimation of the Negative Binomial Dispersion Parameter for Highly Overdispersed Data, with Applications to Infectious Diseases," PLOS ONE, Public Library of Science, vol. 2(2), pages 1-8, February.
    2. Ying Yuan & Valen E. Johnson, 2012. "Goodness-of-Fit Diagnostics for Bayesian Hierarchical Models," Biometrics, The International Biometric Society, vol. 68(1), pages 156-164, March.
    3. Denwood, Matthew J., 2016. "runjags: An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i09).
    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. Merkle, Edgar C. & Steyvers, Mark & Mellers, Barbara & Tetlock, Philip E., 2017. "A neglected dimension of good forecasting judgment: The questions we choose also matter," International Journal of Forecasting, Elsevier, vol. 33(4), pages 817-832.
    2. Johnson, Fred A. & Zimmerman, Guthrie S. & Jensen, Gitte H. & Clausen, Kevin K. & Frederiksen, Morten & Madsen, Jesper, 2020. "Using integrated population models for insights into monitoring programs: An application using pink-footed geese," Ecological Modelling, Elsevier, vol. 415(C).
    3. Dexen DZ. Xi & C.B. Dean & Stephen W. Taylor, 2020. "Modeling the duration and size of extended attack wildfires as dependent outcomes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    4. Suyu Liu & Ying Yuan & Richard Castillo & Thomas Guerrero & Valen E. Johnson, 2014. "Evaluation of image registration spatial accuracy using a Bayesian hierarchical model," Biometrics, The International Biometric Society, vol. 70(2), pages 366-377, June.
    5. Ng'ombe, John, 2019. "Economics of the Greenseeder Hand Planter, Discrete Choice Modeling, and On-Farm Field Experimentation," Thesis Commons jckt7, Center for Open Science.
    6. S. Towers & B. Amdouni & R. Cordova & K. Funderburk & C. Montalvo & M. Thakur & J. Velazquez-Molina & C. Castillo-Chavez, 2021. "The rising prevalence of weapons in unsafe arming configurations discovered in American airports," Journal of Transportation Security, Springer, vol. 14(1), pages 1-18, June.
    7. Laura Melissa Guzman & Elizabeth Elle & Lora A. Morandin & Neil S. Cobb & Paige R. Chesshire & Lindsie M. McCabe & Alice Hughes & Michael Orr & Leithen K. M’Gonigle, 2024. "Impact of pesticide use on wild bee distributions across the United States," Nature Sustainability, Nature, vol. 7(10), pages 1324-1334, October.
    8. Aghabazaz, Zeynab & Kazemi, Iraj, 2023. "Under-reported time-varying MINAR(1) process for modeling multivariate count series," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
    9. Guangbao Guo & Guoqi Qian & Lu Lin & Wei Shao, 2021. "Parallel inference for big data with the group Bayesian method," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(2), pages 225-243, February.
    10. Sileshi, Gudeta & Hailu, Girma & Nyadzi, Gerson I., 2009. "Traditional occupancy–abundance models are inadequate for zero-inflated ecological count data," Ecological Modelling, Elsevier, vol. 220(15), pages 1764-1775.
    11. Mahdiyeh, Zahra & Kazemi, Iraj, 2019. "An innovative strategy on the construction of multivariate multimodal linear mixed-effects models," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    12. Fangpo Wang & Anirban Bhattacharya & Alan E. Gelfand, 2018. "Process modeling for slope and aspect with application to elevation data maps," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 749-772, December.
    13. Krishna K. Saha & Debaraj Sen & Chun Jin, 2012. "Profile likelihood-based confidence interval for the dispersion parameter in count data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(4), pages 765-783, August.
    14. Larissa S. Melo & Veber A. F. Costa & Wilson S. Fernandes, 2023. "Assessing the Anthropogenic and Climatic Components in Runoff Changes of the São Francisco River Catchment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3615-3629, July.
    15. Calvin Pozderac & Brian Skinner, 2021. "Superspreading of SARS-CoV-2 in the USA," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-10, March.
    16. Gaudeul, A. & Crosetto, P., 2019. "Fast then slow: A choice process explanation for the attraction effect," Working Papers 2019-06, Grenoble Applied Economics Laboratory (GAEL).
    17. Afnizanfaizal Abdullah & Safaai Deris & Mohd Saberi Mohamad & Sohail Anwar, 2013. "An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-16, April.
    18. Jingjing Yang & Dennis D. Cox & Jong Soo Lee & Peng Ren & Taeryon Choi, 2017. "Efficient Bayesian hierarchical functional data analysis with basis function approximations using Gaussian–Wishart processes," Biometrics, The International Biometric Society, vol. 73(4), pages 1082-1091, December.
    19. Mehmet Kutluay & Roy Brouwer & Richard S. J. Tol, 2017. "Preference updating in public health risk valuation," Working Paper Series 1517, Department of Economics, University of Sussex Business School.
    20. Ernest Lo & Dan Vatnik & Andrea Benedetti & Robert Bourbeau, 2016. "Variance models of the last age interval and their impact on life expectancy at subnational scales," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 35(15), pages 399-454.

    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:eee:ecomod:v:472:y:2022:i:c:s0304380022002034. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    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.