IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v250y2013icp111-118.html
   My bibliography  Save this article

Estimating stop over duration in the presence of trap-effects

Author

Listed:
  • Choquet, R.
  • Guédon, Y.
  • Besnard, A.
  • Guillemain, M.
  • Pradel, R.

Abstract

Detection probability of individuals is increasingly taken into account during field monitoring schemes and in demographic models. Conversely, it is often taken for granted that trappability of animals will remain fairly constant and broadly similar between individuals present in a given area. However, animals may change their behaviour after being trapped. In this paper, we introduce a new hidden Markovian model to estimate stop over duration in the presence of trap-effects. This model combines nonhomogeneous Markovian states with semi-Markovian states in the non-observable state process, and simple distributions with first-order Markov chains as observation models. This model generalizes previously proposed models and enables the joint modeling of the time of residence and the trap effect. Two cases are considered, depending on whether or not emigration is time-dependent since arrival. We illustrate the latter with teal Anas crecca wintering in Camargue, Southern France and we demonstrate the importance of handling trap-effects.

Suggested Citation

  • Choquet, R. & Guédon, Y. & Besnard, A. & Guillemain, M. & Pradel, R., 2013. "Estimating stop over duration in the presence of trap-effects," Ecological Modelling, Elsevier, vol. 250(C), pages 111-118.
    • Handle: RePEc:eee:ecomod:v:250:y:2013:i:c:p:111-118
    DOI: 10.1016/j.ecolmodel.2012.11.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380012005273
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2012.11.002?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. Bebbington, Mark & Lai, Chin-Diew & Zitikis, RiÄ ardas, 2007. "A flexible Weibull extension," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 719-726.
    2. Roger Pradel, 2005. "Multievent: An Extension of Multistate Capture–Recapture Models to Uncertain States," Biometrics, The International Biometric Society, vol. 61(2), pages 442-447, June.
    3. Fabrizio Sergio & Julio Blas & Manuela G. Forero & José Antonio Donázar & Fernando Hiraldo, 2007. "Sequential settlement and site dependence in a migratory raptor," Behavioral Ecology, International Society for Behavioral Ecology, vol. 18(5), pages 811-821.
    4. Langrock, R. & Zucchini, W., 2011. "Hidden Markov models with arbitrary state dwell-time distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 715-724, January.
    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. Choquet, Rémi & Garnier, Alexandre & Awuve, Edem & Besnard, Aurélien, 2017. "Transient state estimation using continuous-time processes applied to opportunistic capture–recapture data," Ecological Modelling, Elsevier, vol. 361(C), pages 157-163.

    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. Oliver, Lauren J. & Morgan, Byron J.T. & Durant, Sarah M. & Pettorelli, Nathalie, 2011. "Individual heterogeneity in recapture probability and survival estimates in cheetah," Ecological Modelling, Elsevier, vol. 222(3), pages 776-784.
    2. Braglia, Marcello & Carmignani, Gionata & Frosolini, Marco & Zammori, Francesco, 2012. "Data classification and MTBF prediction with a multivariate analysis approach," Reliability Engineering and System Safety, Elsevier, vol. 97(1), pages 27-35.
    3. Hadi Saboori & Ghobad Barmalzan & Seyyed Masih Ayat, 2020. "Generalized Modified Inverse Weibull Distribution: Its Properties and Applications," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 247-269, November.
    4. Gauss M. Cordeiro & Giovana O. Silva & Edwin M. M. Ortega, 2016. "An extended-G geometric family," Journal of Statistical Distributions and Applications, Springer, vol. 3(1), pages 1-16, December.
    5. Bebbington, Mark & Lai, Chin-Diew & Zitikis, RiÄ ardas, 2009. "Balancing burn-in and mission times in environments with catastrophic and repairable failures," Reliability Engineering and System Safety, Elsevier, vol. 94(8), pages 1314-1321.
    6. Nicosia, Aurélien & Duchesne, Thierry & Rivest, Louis-Paul & Fortin, Daniel, 2017. "A general hidden state random walk model for animal movement," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 76-95.
    7. Gupta, Ashutosh & Mukherjee, Bhaswati & Upadhyay, S.K., 2008. "Weibull extension model: A Bayes study using Markov chain Monte Carlo simulation," Reliability Engineering and System Safety, Elsevier, vol. 93(10), pages 1434-1443.
    8. Ting Wang & Jiancang Zhuang & Kazushige Obara & Hiroshi Tsuruoka, 2017. "Hidden Markov modelling of sparse time series from non-volcanic tremor observations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 691-715, August.
    9. Roland Langrock & Thomas Kneib & Alexander Sohn & Stacy L. DeRuiter, 2015. "Nonparametric inference in hidden Markov models using P-splines," Biometrics, The International Biometric Society, vol. 71(2), pages 520-528, June.
    10. Devendra Kumar & Neetu Jain & Mazen Nassar & Osama Eraki Abo-Kasem, 2021. "Parameter Estimation for the Exponentiated Kumaraswamy-Power Function Distribution Based on Order Statistics with Application," Annals of Data Science, Springer, vol. 8(4), pages 785-811, December.
    11. Silva, Rodrigo B. & Bourguignon, Marcelo & Dias, Cícero R.B. & Cordeiro, Gauss M., 2013. "The compound class of extended Weibull power series distributions," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 352-367.
    12. Sofia Ruiz-Suarez & Vianey Leos-Barajas & Juan Manuel Morales, 2022. "Hidden Markov and Semi-Markov Models When and Why are These Models Useful for Classifying States in Time Series Data?," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 339-363, June.
    13. Maruotti, Antonello & Petrella, Lea & Sposito, Luca, 2021. "Hidden semi-Markov-switching quantile regression for time series," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    14. Jennifer Pohle & Roland Langrock & Floris M. Beest & Niels Martin Schmidt, 2017. "Selecting the Number of States in Hidden Markov Models: Pragmatic Solutions Illustrated Using Animal Movement," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 270-293, September.
    15. Zhou, Jie & Song, Xinyuan & Sun, Liuquan, 2020. "Continuous time hidden Markov model for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    16. Przemysław Bodziony & Michał Patyk, 2024. "The Influence of the Mining Operation Environment on the Energy Consumption and Technical Availability of Truck Haulage Operations in Surface Mines," Energies, MDPI, vol. 17(11), pages 1-18, May.
    17. S. Nadarajah & S. Bakar, 2013. "A new R package for actuarial survival models," Computational Statistics, Springer, vol. 28(5), pages 2139-2160, October.
    18. C. Satheesh Kumar & S. Dharmaja, 2014. "On some properties of Kies distribution," METRON, Springer;Sapienza Università di Roma, vol. 72(1), pages 97-122, April.
    19. MARK BEBBINGTON & CHIN-DIEW LAI & RIcARDAS ZITIKIS, 2011. "Modelling Deceleration in Senescent Mortality," Mathematical Population Studies, Taylor & Francis Journals, vol. 18(1), pages 18-37.
    20. Zhang, Tieling & Dwight, Richard, 2013. "Choosing an optimal model for failure data analysis by graphical approach," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 111-123.

    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:250:y:2013:i:c:p:111-118. 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.