2020
DOI: 10.3989/scimar.04999.19a
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Explorative analysis on red mullet (<em>Mullus barbatus</em>) ageing data variability in the Mediterranean

Abstract: The uncertainty in age estimation by otolith reading may be at the root of the large variability in red mullet (Mullus barbatus) growth models in the Mediterranean. In the MEDITS survey, red mullet age data are produced following the same sampling protocol and otolith reading methodology. However, ageing is assigned using different interpretation schemes, including variations in theoretical birthdate and number of false rings considered, in addition to differences in the experience level of readers. The presen… Show more

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Cited by 9 publications
(8 citation statements)
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“…Hydrological parameters and productivity are different in these two regions (Azouz, 1971; Ben Othman, 1973) and the fishing pressure is known to be higher in the Gulf of Gabes. Geographic variations in the estimated growth parameters of fishes may be attributed to several factors, such as gear selectivity, methodologies of study (reader experience), fishing exploitation and geographic location of samples (Carbonara et al ., 2019); however, they generally represent epigenetic responses to environmental conditions (Ibrahim et al ., 2008).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Hydrological parameters and productivity are different in these two regions (Azouz, 1971; Ben Othman, 1973) and the fishing pressure is known to be higher in the Gulf of Gabes. Geographic variations in the estimated growth parameters of fishes may be attributed to several factors, such as gear selectivity, methodologies of study (reader experience), fishing exploitation and geographic location of samples (Carbonara et al ., 2019); however, they generally represent epigenetic responses to environmental conditions (Ibrahim et al ., 2008).…”
Section: Discussionmentioning
confidence: 99%
“…The variable age ranges may be attributed to geographic and environmental differences, as well as differences in sampling. According to Carbonara et al (2019) geographic location was the most important factor significantly correlated to age variability in Mullus barbatus in the Mediterranean, with the longitudinal (west-east) sample geographic component influencing more than the latitudinal (north-south) one.…”
Section: Age and Growthmentioning
confidence: 97%
“…The estimated growth parameters are obviously model-dependent; therefore, ignoring the model selection uncertainty may introduce substantial bias (Katsanevakis and Maravelias, 2008). This has serious implications for stock status assessments (Carbonara et al, 2019b). The AIC allows the selection of the "best model" (or models) from a set of candidate models (Katsanevakis, 2006), minimizing the risks associated with an a priori selected growth model (Katsanevakis and Maravelias, 2008).…”
Section: Discussionmentioning
confidence: 99%
“…This is reasonable, considering the wide geographic area of origin. However, several other factors may be responsible for higher variability of age estimation, apart from the geographic area (Carbonara et al, 2018) and genetic origin (Matić-Skoko et al, 2018): differences in sampling methodologies (Coggins et al, 2013); age schemes/criteria (International Council for the Exploration of the Sea [ICES], 2008;Hüssy et al, 2016;Carbonara et al, 2018); preparation methods (Smith et al, 2016); and reader experience (Kimura and Lyons, 1991;Carbonara et al, 2019b).These combined factors can thus lead to considerably greater differences than those observed in R. clavata. Then, it could be needful, taking in account the results obtained by the present work, considering the use of thin sectioned vertebra as suggested preparation method.…”
Section: Discussionmentioning
confidence: 99%
“…Age was assigned to each back‐calculated TL, following the criteria reported in Carbonara, Zupa et al (2019) for the congeneric Mullus barbatus . Considering that the transparent growth increments are deposited on the otolith during the winter months and considering the spring spawning period (Kousteni, Anastasopoulou & Mytilineou, 2019), for each back‐calculated length an age to these growth increments was assigned as follows: 1° growth increment 0.5 years, 2° growth increment 1.5 years, 3° growth increment 2.5 years and so on (Carbonara et al, 2018).…”
Section: Methodsmentioning
confidence: 99%