Age and growth of gilthead sea bream ( Sparus aurata Linnaeus, 1758) from Northern Aegean Sea (Turkey)

The age and growth of gilthead sea bream ( Sparus aurata Linnaeus, 1758) were studied in the present study. A total of 126 specimens were collected from commercial fishmongers during the period between January 2015 and December 2015 from the northern Aegean coasts of Turkey. Fork length and the total weight of aged specimens ranged from 29.5 to 48.0 cm and from 425.00 to 2100.00 g, with a mean of 38.5 cm and 101.23 g, respectively. The length-weight relationship was estimated as W = 0.0053 FL 3.03 (R 2 = 0.95). The von Bertalanffy growth equations were computed as 𝐿 ∞ = 52.8 cm, k = 0.29 year -1 , 𝑡 0 = -1.25 year for all samples. The growth performance index ( 𝛷 ′) was found as 2.91. There is no study on the biology of the species for the northern Aegean Sea. Therefore, this study provides valuable information for the species in this area.


INTRODUCTION
Life history traits like age, growth, reproduction and mortality are principal factors in fisheries research and management (Mgaya and Mahongo, 2017). Fish age is an important biological variable for calculating growth parameter and mortality (Campana, 2001). In this connection, von Bertalanffy (1934) growth parameters (VBGPs), essential for the development of a variety of fisheries models and the management of fisheries resources, could be used for the indirect estimation of other parameters using existing empirical equations (Froese and Binohlan, 2003).
Examples of indirectly estimated parameters include: (a) natural mortality from growth parameters (Pauly, 1980) or from tmax (Hoenig, 1983), (b) length at first maturity from L∞ and/or reason, gilthead sea breams are captured with traditional bottom trawl nets, coastal purseseines, bottom set longline and hand lines, and are regularly present to the markets in Turkey (Akyol and Gamsız, 2011). According to the Turkish Statistical Institute, Sparus aurata yield from fisheries and aquaculture production were 583.7 t and 109.749 t, respectively (TurkStat,

METHODOLOGY
The northern Aegean coasts of Turkey are divided into sub-regions as the Saros Bay, the Gallipoli Peninsula, the Gökçeada and Bozcaada Islands and the Edremit Bay (Cengiz, 2021) (Fig 1). The northern Aegean areas are characterized by an extended continental shelf, smooth muddy/sandy bottoms and higher nutrient concentrations (Maravelias and Papaconstantinou, 2006) and have higher phytoplankton and zooplankton abundance compared with the southern Aegean Sea (Theocharis et al., 1999). W is the total weight (g), L is the fork length (cm), a is the intercept, and b is the slope or allometric coefficient, using the least-squares method. The b value that is higher than 3 shows positive allometric growth, while the b value that is lower than 3 indicates negative allometric growth. It is isometric growth when the b value is equal to 3 (Bagenal and Tesch, 1978 The ages of the specimens were checked using scales. Scales were removed from the base of the pectoral fin and from the flanks below the dorsal fin. They were cleaned in 5% sodium peroxide and then immersed in glycerol in a black Petri dish, and annuli, defined as opaque and hyaline zones were counted by using a binocular microscope (Akyol and Gamsız, 2011). Growth parameters were estimated by using the von Bertalanffy growth equation: where Lt is fish length (cm) at age t, L∞ is the asymptotic fish length (cm), t is the fish age (years), t0 (years) is the hypothetical time at which the fish length is zero, and k is the growth coefficient (year -1 ). FAO-ICLARM Stock Assessment Tools FISAT II) were used to estimate growth parameters, which were calculated with the non-linear least-squares method. The growth parameters obtained in this study were compared with the parameters obtained in other studies from various geographical areas using the growth performance index (Φ') (Pauly and Munro, 1984). It was estimated using the formula, Φ' = log (k) + 2 x log (L∞).

RESULTS
The sample size is 126 individuals, coming from the commercial capture of the northern Aegean Sea coasts of Turkey. Faced with the impossibility of dissecting the fish, because they are intended for sale, then it has been considered all samples, as a whole. The mean ± standard error (and range) of fork length and the total weight of specimens were 38.5 ± 0.34 (29.5 -48.0) cm (Fig 2) and 1090.00 ± 30.86 (425.00 -2100.00) g, respectively. The length-weight relationship was estimated as W = 0.0053FL 3.03 (R 2 = 0.95) (Fig 3).  Results obtained from the scale reading indicated that the ages of the fishes were found to be within the range of II to VI years. Table 1 indicated the fishes belonging to age groups III and IV were the most dominant. The von Bertalanffy growth equations were computed as ∞ = 52.8 cm, k = 0.29 year -1 , 0 = -1.25 year for all samples (Fig 4). The growth performance index ( ′) was found as 2.91.

Figure 3. The length-weight relationships for all samples of Sparus aurata from Northern
Aegean Sea (Turkey).   length-frequency analysis, N = sample size, ∞ = theoretical asymptotic length, K = growth rate coefficient, 0 = theoretical age when fish length is zero, Φ′ =growth performance index, a and b = the parameters of the relationships. Table 2 summarized the results about the length-weight relationships (LWRs), the growth parameters and growth performance indices between the present study to previous ones. The b values in LWRs change between 2.5 and 3.5 (Froese, 2006) or 2 to 4 (Tesch, 1971). In this study, b value of Sparus aurata correspond to these expected ranges. Generally, the b value procured from the same species could change depending on the degree of gonad maturity, sex, diet, sample preservation techniques, stomach fullness (Wootton, 1990;Cengiz et al., 2019), number of specimens analyzed, area/season effects, sampling duration (Moutopoulos and Stergiou, 2002), fishing gear used (Kapiris and Klaoudaos, 2011), and size selectivity of the sampling gear (İşmen et al., 2007).

DISCUSSION
Growth parameters (L∞, K and to) are the basic input data into various models used for managing and assessing the status of the exploited fish stocks and these parameters facilitate the comparison between growth of fishes belonging to different species or to the same species at different times and different localities (Mehanna et al., 2018). The differences among all growth parameters could be attributed to a combination of sample characteristics (sample sizes and range of sizes), geographical differences and aging methodology used (Monterio et al., -Skoko et al,. 2007;Bayhan et al., 2008), size, quantity and quality of food and water temperature (Santic et al., 2002), and differences in length at first maturity (Champagnat, 1983). Besides, the selectivity of the fishing tool used can also affect the estimates of growth parameters (Ricker, 1969;Potts et al., 1998). Therefore, the possible reasons for the differences in the results between the other studies and this study may be related to one or more factors given above.

CONCLUSION
This study provided data on the key life history traits of Sparus aurata, which has been lacking in the studied region, allowing the development of sustainable management strategies. In times to come, appropriate surveys and long-dated studies could be required to confirm this preliminary estimating. More scientific research should be meticulously conducted to collect fundamental biological data. However, the information obtained from investigations such as the present research should be proclaimed to stakeholders (fishermen, middlemen, fisheries scientists, fishing management authorities etc.)

ACKNOWLEDGEMENTS
The author would like to thank the commercial fishermen.