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Modelling leatherback biphasic indeterminate growth using a modified Gompertz equation

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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.
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