IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v426y2020ics0304380020301095.html
   My bibliography  Save this article

Modelling leatherback biphasic indeterminate growth using a modified Gompertz equation

Author

Listed:
  • Chevallier, Damien
  • Mourrain, Baptiste
  • Girondot, Marc

Abstract

Leatherback turtles (Dermochelys coriacea) are the largest extant marine turtle, with some individuals measuring more than 1.80 m carapace length. Given the exceptional size of this species and that females only return to land every few years to nest, it is difficult to investigate its ontogeny from hatchling to adulthood. Distinct chondro-osseous (cartilage and bone) tissue morphology has led to some speculation that sexual maturity may be reached as early as 3 years, while other studies suggest this could take as long as 25 years. Using a combination of longitudinal measurements obtained from nesting females in French Guiana as well as a reanalysis of the growth trajectories of juveniles maintained in captivity and the age-size relationship of individuals in the wild, we demonstrated that leatherback turtles exhibit a biphasic indeterminate growth pattern and continue to grow as adults. Using the fitted model, we showed that some individuals can reach maturity at 7 years in natural conditions, while others require 28 years or more. This extreme plasticity in age at sexual maturity was already demonstrated in loggerheads in natural conditions and in green turtles in captivity. This could be a general feature of marine turtles.

Suggested Citation

  • Chevallier, Damien & Mourrain, Baptiste & Girondot, Marc, 2020. "Modelling leatherback biphasic indeterminate growth using a modified Gompertz equation," Ecological Modelling, Elsevier, vol. 426(C).
  • Handle: RePEc:eee:ecomod:v:426:y:2020:i:c:s0304380020301095
    DOI: 10.1016/j.ecolmodel.2020.109037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380020301095
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2020.109037?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. Philip Heidelberger & Peter D. Welch, 1983. "Simulation Run Length Control in the Presence of an Initial Transient," Operations Research, INFORMS, vol. 31(6), pages 1109-1144, December.
    2. Soetaert, Karline & Petzoldt, Thomas & Setzer, R. Woodrow, 2010. "Solving Differential Equations in R: Package deSolve," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i09).
    3. Kielbassa, J. & Delignette-Muller, M.L. & Pont, D. & Charles, S., 2010. "Application of a temperature-dependent von Bertalanffy growth model to bullhead (Cottus gobio)," Ecological Modelling, Elsevier, vol. 221(20), pages 2475-2481.
    4. Armstrong, Doug P. & Brooks, Ronald J., 2013. "Application of hierarchical biphasic growth models to long-term data for snapping turtles," Ecological Modelling, Elsevier, vol. 250(C), pages 119-125.
    5. James W. Vaupel & Annette Baudisch & Martin Dölling & Deborah A. Roach & Jutta Gampe, 2004. "The case for negative senescence," MPIDR Working Papers WP-2004-002, Max Planck Institute for Demographic Research, Rostock, Germany.
    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. Keevil, Matthew G. & Armstrong, Doug P. & Brooks, Ronald J. & Litzgus, Jacqueline D., 2021. "A model of seasonal variation in somatic growth rates applied to two temperate turtle species," Ecological Modelling, Elsevier, vol. 443(C).
    2. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    3. Hancock, Joana & Vieira, Sara & Lima, Hipólito & Schmitt, Vanessa & Pereira, Jaconias & Rebelo, Rui & Girondot, Marc, 2019. "Overcoming field monitoring restraints in estimating marine turtle internesting period by modelling individual nesting behaviour using capture-mark-recapture data," Ecological Modelling, Elsevier, vol. 402(C), pages 76-84.
    4. Lada, Emily K. & Wilson, James R., 2006. "A wavelet-based spectral procedure for steady-state simulation analysis," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1769-1801, November.
    5. Enver Yücesan, 1993. "Randomization tests for initialization bias in simulation output," Naval Research Logistics (NRL), John Wiley & Sons, vol. 40(5), pages 643-663, August.
    6. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    7. Burda Martin & Bélisle Louis, 2019. "Copula multivariate GARCH model with constrained Hamiltonian Monte Carlo," Dependence Modeling, De Gruyter, vol. 7(1), pages 133-149, January.
    8. Yu, Jun, 2012. "A semiparametric stochastic volatility model," Journal of Econometrics, Elsevier, vol. 167(2), pages 473-482.
    9. Goldman Elena & Tsurumi Hiroki, 2005. "Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-38, June.
    10. Laplanche, Christophe & Leunda, Pedro M. & Boithias, Laurie & Ardaíz, José & Juanes, Francis, 2019. "Advantages and insights from a hierarchical Bayesian growth and dynamics model based on salmonid electrofishing removal data," Ecological Modelling, Elsevier, vol. 392(C), pages 8-21.
    11. Andr'es Ram'irez-Hassan & Alejandro L'opez-Vera, 2021. "Semi-parametric estimation of the EASI model: Welfare implications of taxes identifying clusters due to unobserved preference heterogeneity," Papers 2109.07646, arXiv.org.
    12. Hostenkamp, Gisela & Stolpe, Michael, 2008. "Optimal health and retirement policies amid population aging," Kiel Working Papers 1428, Kiel Institute for the World Economy (IfW Kiel).
    13. Fatima-Zahra Jaouimaa & Daniel Dempsey & Suzanne Van Osch & Stephen Kinsella & Kevin Burke & Jason Wyse & James Sweeney, 2021. "An age-structured SEIR model for COVID-19 incidence in Dublin, Ireland with framework for evaluating health intervention cost," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-25, December.
    14. Cyrus Chu, C.Y. & Chien, Hung-Ken & Lee, Ronald D., 2010. "The evolutionary theory of time preferences and intergenerational transfers," Journal of Economic Behavior & Organization, Elsevier, vol. 76(3), pages 451-464, December.
    15. Belém Barbosa & José Ramón Saura & Dag Bennett, 2024. "How do entrepreneurs perform digital marketing across the customer journey? A review and discussion of the main uses," The Journal of Technology Transfer, Springer, vol. 49(1), pages 69-103, February.
    16. Amoroso, S., 2013. "Heterogeneity of innovative, collaborative, and productive firm-level processes," Other publications TiSEM f5784a49-7053-401d-855d-1, Tilburg University, School of Economics and Management.
    17. Rougier, Thibaud & Drouineau, Hilaire & Dumoulin, Nicolas & Faure, Thierry & Deffuant, Guillaume & Rochard, Eric & Lambert, Patrick, 2014. "The GR3D model, a tool to explore the Global Repositioning Dynamics of Diadromous fish Distribution," Ecological Modelling, Elsevier, vol. 283(C), pages 31-44.
    18. Michael Edwards, 2010. "A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis," Psychometrika, Springer;The Psychometric Society, vol. 75(3), pages 474-497, September.
    19. Hanneke Geerlings & Cees Glas & Wim Linden, 2011. "Modeling Rule-Based Item Generation," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 337-359, April.
    20. Overstall, Antony M. & Woods, David C. & Martin, Kieran J., 2019. "Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 126-142.

    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:426:y:2020:i:c:s0304380020301095. 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.