farmed). The Spearman correlation tests were performed using
the R {corrplot} package, whose Spearman correlation test
values are represented as correlation coefficients (color intensity and circle size are proportional to the correlation coefficients). In the figure, correlations with a p-value > 0.05 are
considered insignificant, and hence, the correlation coefficient
values are crossed out. All analysis was performed using R
software (3.5.2). The significance level of p < 0.05 was taken.
Results and discussion
Biometric parameters
A total of 39 individuals of sea bream from different habitats
were analyzed for our study. The collected specimens had an
average length and weight of [mean = 26.97 ± 1.48 cm] and
[mean = 313.24 ± 39.52 g], respectively. Estimating biometric
data is important for stock assessment and management
(Balazadeh and Litvak 2018), and it is considered an indication of food availability in the environment (Gauthier et al.
2008). Fish biometry varies relative to the origin of the species
(wild, raceway, and cage; Friedman’s test, p < 0.05). This
variation is likely due to the high density which could affect
adversely the body weight, total length, and metabolism (de
las Heras et al. 2015; Sánchez-Muros et al. 2017). The better
growth performance of gilthead sea bream was noted at low
density (Carbonara et al. 2019), which could explain that the
maximum values of biometric data were observed in a wild
S. aurata. However, no significant difference was observed
between rearing systems (raceway or floating cage) (MannWhitney test, p-value > 0.05) in accordance with the result of
Vasconi et al. (2019) about the different farms.
The length-weight relationship was evaluated for all specimens on the basis of their origin (Fig. 2). The coefficients of
determination R
2 values were 0.76, 0.81, and 0.86 for the
raceway, wild, and floating cages, respectively.
The results indicate that all species showed a positive and
significant correlation (p < 0.05) between length and weight, in
line with the results of Hadjou et al. (2017) for sea bream from
the Algerian coasts. Hence, it appears that the origin does not
alter the length-weight relationship. Therefore, other parameters
control fish sizes such as the season (Jisr et al. 2018), the gonadal
development (Arfuso et al. 2017), climate change, and ocean
acidification (Réveillac et al. 2015), as well as aquaculture
practices (Palmeri et al. 2008), feeding rate (Hossain et al.
2016), and their behavior (Castanheira et al. 2016).
Metal concentrations in wild and farmed sea bream
The mean and the range concentrations of As, Cd, Cu, Pb, and
Zn (mg/kg wet weight) in the muscle of wild and farmed
gilthead sea bream are presented in Table 2. The most abundant elements were zinc and arsenic. On the opposite, the
lowest concentrations were to lead and cadmium. However,
the highest concentrations of Cd, Cu, and Zn were recorded in
farmed sea bream, whereas the highest As and Pb concentrations were registered in a wild one. The farmed fish samples
show concentration levels below the wild fish ones, in accordance with Iamiceli et al. (2015). A significant correlation
between fish diet and trace elements was identified in sea
bream (Psoma et al. 2014). Feed pallets were enriched with
Zn and Cu for the good performance of the fish metabolism
(Wang et al. 2020). It is assumed that cadmium has mainly
accumulated through feed since aquaculture compound feeds
are recognized as the main source of contaminants (Amlund
et al. 2012). Furthermore, the fish feed was produced by small
fish that come from the polluted area. The origin of the fish is
determined for the concentration of metals; moreover, distinctions in the metal content were found between the different
rearing systems (farms) (Friedman test, p < 0.05). However,
this may be due to production cycle time, in this study (36
months for caged rearing against 18 months in raceway) as
well as the origin of juvenile fish (genetic factor) and composition of fish feed.
In wild fish, a negative correlation was observed between
arsenic and copper. While zinc provides a positive correlation
with fish size in all habitats, in agreement with Tapia et al. (2012)
Raceway
Wild
Cage
Fig. 3 Correlation matrix between heavy metals and fish size. (The intensity of the color and the size of the circles are proportional to the correlation
coefficients, p-value > 0.05 are barred by a cross)
Environ Sci Pollut Res
the R {corrplot} package, whose Spearman correlation test
values are represented as correlation coefficients (color intensity and circle size are proportional to the correlation coefficients). In the figure, correlations with a p-value > 0.05 are
considered insignificant, and hence, the correlation coefficient
values are crossed out. All analysis was performed using R
software (3.5.2). The significance level of p < 0.05 was taken.
Results and discussion
Biometric parameters
A total of 39 individuals of sea bream from different habitats
were analyzed for our study. The collected specimens had an
average length and weight of [mean = 26.97 ± 1.48 cm] and
[mean = 313.24 ± 39.52 g], respectively. Estimating biometric
data is important for stock assessment and management
(Balazadeh and Litvak 2018), and it is considered an indication of food availability in the environment (Gauthier et al.
2008). Fish biometry varies relative to the origin of the species
(wild, raceway, and cage; Friedman’s test, p < 0.05). This
variation is likely due to the high density which could affect
adversely the body weight, total length, and metabolism (de
las Heras et al. 2015; Sánchez-Muros et al. 2017). The better
growth performance of gilthead sea bream was noted at low
density (Carbonara et al. 2019), which could explain that the
maximum values of biometric data were observed in a wild
S. aurata. However, no significant difference was observed
between rearing systems (raceway or floating cage) (MannWhitney test, p-value > 0.05) in accordance with the result of
Vasconi et al. (2019) about the different farms.
The length-weight relationship was evaluated for all specimens on the basis of their origin (Fig. 2). The coefficients of
determination R
2 values were 0.76, 0.81, and 0.86 for the
raceway, wild, and floating cages, respectively.
The results indicate that all species showed a positive and
significant correlation (p < 0.05) between length and weight, in
line with the results of Hadjou et al. (2017) for sea bream from
the Algerian coasts. Hence, it appears that the origin does not
alter the length-weight relationship. Therefore, other parameters
control fish sizes such as the season (Jisr et al. 2018), the gonadal
development (Arfuso et al. 2017), climate change, and ocean
acidification (Réveillac et al. 2015), as well as aquaculture
practices (Palmeri et al. 2008), feeding rate (Hossain et al.
2016), and their behavior (Castanheira et al. 2016).
Metal concentrations in wild and farmed sea bream
The mean and the range concentrations of As, Cd, Cu, Pb, and
Zn (mg/kg wet weight) in the muscle of wild and farmed
gilthead sea bream are presented in Table 2. The most abundant elements were zinc and arsenic. On the opposite, the
lowest concentrations were to lead and cadmium. However,
the highest concentrations of Cd, Cu, and Zn were recorded in
farmed sea bream, whereas the highest As and Pb concentrations were registered in a wild one. The farmed fish samples
show concentration levels below the wild fish ones, in accordance with Iamiceli et al. (2015). A significant correlation
between fish diet and trace elements was identified in sea
bream (Psoma et al. 2014). Feed pallets were enriched with
Zn and Cu for the good performance of the fish metabolism
(Wang et al. 2020). It is assumed that cadmium has mainly
accumulated through feed since aquaculture compound feeds
are recognized as the main source of contaminants (Amlund
et al. 2012). Furthermore, the fish feed was produced by small
fish that come from the polluted area. The origin of the fish is
determined for the concentration of metals; moreover, distinctions in the metal content were found between the different
rearing systems (farms) (Friedman test, p < 0.05). However,
this may be due to production cycle time, in this study (36
months for caged rearing against 18 months in raceway) as
well as the origin of juvenile fish (genetic factor) and composition of fish feed.
In wild fish, a negative correlation was observed between
arsenic and copper. While zinc provides a positive correlation
with fish size in all habitats, in agreement with Tapia et al. (2012)
Raceway
Wild
Cage
Fig. 3 Correlation matrix between heavy metals and fish size. (The intensity of the color and the size of the circles are proportional to the correlation
coefficients, p-value > 0.05 are barred by a cross)
Environ Sci Pollut Res