|Estimation of age at maturity of loggerhead sea turtles Caretta caretta in the Mediterranean using length-frequency data|Casale, P.; Mazaris, A.D.; Freggi, D. (2011). Estimation of age at maturity of loggerhead sea turtles Caretta caretta in the Mediterranean using length-frequency data. Endang. Species Res. 13(2): 123-129. hdl.handle.net/10.3354/esr00319
In: Endangered Species Research. Inter-Research: Oldendorf/Luhe. ISSN 1613-4796, more
Loggerhead sea turtle · Growth rate · Age at maturity · Length-frequency analysis · Mediterranean Sea
|Authors|| || Top |
- Casale, P.
- Mazaris, A.D.
- Freggi, D.
It is widely accepted that the age at sexual maturity of sea turtles is a critical parameter for studying population dynamics and persistence. Estimates of the age at maturity for such long-lived species are derived using somatic growth models, which are still lacking for several regions of the world. In the present study, the growth rate of the loggerhead sea turtle Caretta caretta in the Mediterranean was investigated using a length-frequency analysis of a dataset collected over a 19 yr period (1990 to 2008). A total of 2255 individuals were measured in the central Mediterranean, with turtle size ranging from 16.8 to 97.5 cm curved carapace length (CCL). Monthly length-frequency histograms were constructed, and strong size modes were identified, assumed to represent individual cohorts. Growth rates were calculated by tracking the progression of the modes, by means of a modal progression analysis. Annual growth rates ranged from 0.37 to 6.5 cm yr–1. A von Bertalanffy growth function was used to estimate the time required by turtles to grow within the observed size range. The results indicate that turtles would take from 23.5 to 29.3 yr to reach 80 cm CCL, considered an approximation of the size at maturity. This estimation integrates and confirms a previous estimate obtained using a different method. It provides information vital to understanding the population dynamics of loggerhead turtles in the Mediterranean, and highlights the value of datasets of long-term series when investigating critical demographic parameters.