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. 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.
    4. 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".
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Serrouya, R. & Dickie, M. & DeMars, C. & Wittmann, M.J. & Boutin, S., 2020. "Predicting the effects of restoring linear features on woodland caribou populations," Ecological Modelling, Elsevier, vol. 416(C).
    11. Annette Baudisch & James W. Vaupel, 2009. "Senescence vs. sustenance: evolutionary-demographic models of aging," MPIDR Working Papers WP-2009-040, Max Planck Institute for Demographic Research, Rostock, Germany.
    12. Aburto, José Manuel & Basellini, Ugofilippo & Baudisch, Annette & Villavicencio, Francisco, 2022. "Drewnowski’s index to measure lifespan variation: Revisiting the Gini coefficient of the life table," Theoretical Population Biology, Elsevier, vol. 148(C), pages 1-10.
    13. Ralf van der Lans & Bram Van den Bergh & Evelien Dieleman, 2014. "Partner Selection in Brand Alliances: An Empirical Investigation of the Drivers of Brand Fit," Marketing Science, INFORMS, vol. 33(4), pages 551-566, July.
    14. Wei Chen & Yixin Lu & Liangfei Qiu & Subodha Kumar, 2021. "Designing Personalized Treatment Plans for Breast Cancer," Information Systems Research, INFORMS, vol. 32(3), pages 932-949, September.
    15. Jobst, Rainer & Kellner, Ralf & Rösch, Daniel, 2020. "Bayesian loss given default estimation for European sovereign bonds," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1073-1091.
    16. Zadoki Tabo & Chester Kalinda & Lutz Breuer & Christian Albrecht, 2023. "Adapting Strategies for Effective Schistosomiasis Prevention: A Mathematical Modeling Approach," Mathematics, MDPI, vol. 11(12), pages 1-18, June.
    17. Terence D.Agbeyegbe & Elena Goldman, 2005. "Estimation of threshold time series models using efficient jump MCMC," Economics Working Paper Archive at Hunter College 406, Hunter College Department of Economics, revised 2005.
    18. Ockerman, Daniel H. & Goldsman, David, 1999. "Student t-tests and compound tests to detect transients in simulated time series," European Journal of Operational Research, Elsevier, vol. 116(3), pages 681-691, August.
    19. Hong, Yi & Jin, Xing, 2022. "Pricing of variance swap rates and investment decisions of variance swaps: Evidence from a three-factor model," European Journal of Operational Research, Elsevier, vol. 303(2), pages 975-985.
    20. Shofiqul Islam & Sonia Anand & Jemila Hamid & Lehana Thabane & Joseph Beyene, 2020. "A copula-based method of classifying individuals into binary disease categories using dependent biomarkers," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 871-897, December.

    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.