Effects of Stocking Density on Growth Performance and Stress Responses of Bester and Bester ♀ × Beluga ♂ Juveniles in Recirculating Aquaculture Systems.

The study aimed to compare the growth performance and physiological responses of bester (B) and backcrossed bester ♀ × beluga ♂ (BB) in response to crowding stress under different stocking densities, as well as to establish a threshold stocking density for rearing BB in a recirculating aquaculture system (RAS) without welfare impairment. For this purpose, in the first trial (T1), B (181.15 ± 21.21 g) and BB fingerlings (181.98 ± 28.65 g) were reared in two stocking densities of 2 kg/m2 and 4 kg/m2 in fiberglass tanks (1 m3) for 6 weeks. In a parallel trial (T2), the BB hybrids (335.24 ± 39.30 g) were kept in four initial stocking densities, ranging from 5 kg/m2 to 12 kg/m2. The results of T1 revealed better growth indices (i.e., final mean weight, weight gain, specific growth rate) at lower stocking densities for both hybrids; however, in terms of growth performance, the BB hybrid showed better results when compared with the B hybrid. BB hybrids registered significantly (p < 0.05) lower serum cortisol and MDA and higher lysozyme than B hybrids, showing higher tolerance to crowding stress. Nevertheless, at higher densities, selected serum parameters (i.e., hematological indices, cortisol, glucose, protein, malondialdehyde, lysozyme) and growth performance indices used to evaluate the hybrids indicate that high stocking density could affect the growth and welfare of BB hybrids, and that the selected serum parameters could be used as good indicators for chronic stress caused by overcrowding conditions.

Preliminary investigation of lower Danube pollution caused by potentially toxic metals.

The current study aims to assess the pollution status of the European river-sea system lower Danube River-Danube Delta-North West Black Sea, through an integrated analysis of metal concentrations in water, sediments and fish community. The Danube flows through numerous industrial cities and receives a significant amount of pollutants due to the reception of urban and industrial emissaries, as well as agricultural land runoff. Samples of water, sediments and fish (10 species) were collected from 7 representative sites along Danube River, Danube Delta and Black Sea shore. For the analysed fish species, potentially toxic and essential elements (Pb, Cd, As, Cu, Fe, Zn, Mg, Ca, Na, K) from muscle and liver samples were measured and discussed. Measurement of elements and other environmental quality parameters were determined for water and sediments. The Black Sea area, represented by S6 and S7, received sediments from Danube with the lowest concentrations of Cd (0.05 ± 0.01 µg g-1, respectively 0.01 ± 0.001 µg g-1), Pb (3 ± 0.03 µg g-1, respectively 2 ± 0.03 µg g-1), As (2 ± 0.02 µg g-1, respectively 1.4 ± 0.3 µg g-1), Ni (8.9 ± 0.1 µg g-1, respectively 5.2 ± 0.2 µg g-1), Cr (8 ± 0.7 µg g-1, respectively 5 ± 0.2 µg g-1), Cu (3 ± 0.1 µg g-1, respectively 2 ± 0.04 µg g-1), Fe(6 ± 0.3 µg g-1, respectively 3 ± 0.1 µg g-1) and Zn (0.03 ± 0.003 µg g-1, respectively 0.017 ± 0.001 µg g-1). These results suggest that the Danube Delta system plays an important role in filtering the pollutants. Based on the biota and water analysis, there was no correlation observed between Cd, respectively Pb concentration in the environment and fish body (Person Coef. = -0.02 in muscle tissue and -0.01 in liver tissue, respectively Pearson Coeff. = -0.06 in muscle tissue and 0.1 in liver tissue). Cadmium remained an active element in the pollution of the Danube area (S1 and S2), with high concentration in the water matrix (0.14 ± 0.02 µg L-1, respectively 0.05 ± 0.01 µg L-1) and fish muscle (0.15 ± 0.03 µg g-1f.w. in C. carpio - S2). This fact was confirmed by several other studies.

A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues.

Metals are considered to be one of the most hazardous substances due to their potential for accumulation, magnification, persistence, and wide distribution in water, sediments, and aquatic organisms. Demersal fish species, such as turbot (Psetta maxima maeotica), are accepted by the scientific communities as suitable bioindicators of heavy metal pollution in the aquatic environment. The present study uses a machine learning approach, which is based on multiple linear and non-linear models, in order to effectively estimate the concentrations of heavy metals in both turbot muscle and liver tissues. For multiple linear regression (MLR) models, the stepwise method was used, while non-linear models were developed by applying random forest (RF) algorithm. The models were based on data that were provided from scientific literature, attributed to 11 heavy metals (As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Ni, Zn) from both muscle and liver tissues of turbot exemplars. Significant MLR models were recorded for Ca, Fe, Mg, and Na in muscle tissue and K, Cu, Zn, and Na in turbot liver tissue. The non-linear tree-based RF prediction models (over 70% prediction accuracy) were identified for As, Cd, Cu, K, Mg, and Zn in muscle tissue and As, Ca, Cd, Mg, and Fe in turbot liver tissue. Both machine learning MLR and non-linear tree-based RF prediction models were identified to be suitable for predicting the heavy metal concentration from both turbot muscle and liver tissues. The models can be used for improving the knowledge and economic efficiency of linked heavy metals food safety and environment pollution studies.