IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v221y2010i3p423-427.html
   My bibliography  Save this article

Modelling environmental stochasticity in adult survival for a long-lived species

Author

Listed:
  • Samaranayaka, Ari
  • Fletcher, David

Abstract

Stochastic matrix population models are often used to help guide the management of animal populations. For a long-lived species, environmental stochasticity in adult survival will play an important role in determining outcomes from the model. One of the most common methods for modelling such stochasticity is to randomly select the value of adult survival for each year from a distribution with a specified mean and standard deviation. We consider four distributions that can provide realistic models for stochasticity in adult survival. For values of the mean and standard deviation that cover the range we would expect for long-lived species, all four distributions have similar shapes, with small differences in their skewness and kurtosis. This suggests that many of the outcomes from a population model will be insensitive to the choice of distribution, assuming that distribution provides a realistic model for environmental stochasticity in adult survival. For a generic age-structured model, the estimate of the long-run stochastic growth rate is almost identical for the four distributions, across this range of values for the mean and standard deviation. Model outcomes based on short-term projections, such as the probability of a decline over a 20-year period, are more sensitive to the choice of distribution.

Suggested Citation

  • Samaranayaka, Ari & Fletcher, David, 2010. "Modelling environmental stochasticity in adult survival for a long-lived species," Ecological Modelling, Elsevier, vol. 221(3), pages 423-427.
    • Handle: RePEc:eee:ecomod:v:221:y:2010:i:3:p:423-427
    DOI: 10.1016/j.ecolmodel.2009.10.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380009006693
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2009.10.016?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. Kenneth Burnham & Gary White, 2002. "Evaluation of some random effects methodology applicable to bird ringing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 245-264.
    2. Dias, Carlos Tadeu dos Santos & Samaranayaka, Ari & Manly, Bryan, 2008. "On the use of correlated beta random variables with animal population modelling," Ecological Modelling, Elsevier, vol. 215(4), pages 293-300.
    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. Jorge A. Sefair & Oscar Guaje & Andrés L. Medaglia, 2021. "A column-oriented optimization approach for the generation of correlated random vectors," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 777-808, September.
    2. Löhndorf, Nils, 2016. "An empirical analysis of scenario generation methods for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 255(1), pages 121-132.
    3. Alan Franklin & David Anderson & Kenneth Burnham, 2002. "Estimation of long-term trends and variation in avian survival probabilities using random effects models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 267-287.
    4. George Seber & Carl Schwarz, 2002. "Capture-recapture: Before and after EURING 2000," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 5-18.
    5. Devin S. Johnson & Jennifer A. Hoeting, 2003. "Autoregressive Models for Capture-Recapture Data: A Bayesian Approach," Biometrics, The International Biometric Society, vol. 59(2), pages 341-350, June.
    6. Mark Huber & Nevena Marić, 2019. "Admissible Bernoulli correlations," Journal of Statistical Distributions and Applications, Springer, vol. 6(1), pages 1-8, December.
    7. S. C. Barry & S. P. Brooks & E. A. Catchpole & B. J. T. Morgan, 2003. "The Analysis of Ring-Recovery Data Using Random Effects," Biometrics, The International Biometric Society, vol. 59(1), pages 54-65, March.
    8. Anne Loison & Bernt-Erik Sæther & Kurt Jerstad & Ole Wiggo Røstad, 2002. "Disentangling the sources of variation in the survival of the European dipper," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 289-304.
    9. O. Gimenez & C. Crainiceanu & C. Barbraud & S. Jenouvrier & B. J. T. Morgan, 2006. "Semiparametric Regression in Capture–Recapture Modeling," Biometrics, The International Biometric Society, vol. 62(3), pages 691-698, September.
    10. James Nichols, 2002. "Discussion comments on: 'Occam's shadow: Levels of analysis in evolutionary ecology-- where to next?' by Cooch, Cam and Link," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 49-52.
    11. J. D. Lebreton & R. Choquet & O. Gimenez, 2012. "Simple Estimation and Test Procedures in Capture–Mark–Recapture Mixed Models," Biometrics, The International Biometric Society, vol. 68(2), pages 494-503, June.
    12. Laura Di Giorgio & Abraham D Flaxman & Mark W Moses & Nancy Fullman & Michael Hanlon & Ruben O Conner & Alexandra Wollum & Christopher J L Murray, 2016. "Efficiency of Health Care Production in Low-Resource Settings: A Monte-Carlo Simulation to Compare the Performance of Data Envelopment Analysis, Stochastic Distance Functions, and an Ensemble Model," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    13. Barbora Šútorová & Petr Teplý, 2014. "The Level of Capital and the Value of EU Banks under Basel III," Prague Economic Papers, Prague University of Economics and Business, vol. 2014(2), pages 143-161.

    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:221:y:2010:i:3:p:423-427. 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.