Resumen
In the present work, we studied ten new productive traits in meagre (Argyrosomus regius), comprising three related to the carcass (cNiT) and seven related to morphometric (mNiT) characteristics. We harnessed non-invasive technology (NiT) by means of the IMAFISH_ML software. This tool?s potential was leveraged on an industrial scale, encompassing the evaluation of 612 fish from two distinct rearing systems (marine cages and indoor tanks) at the time of harvest. Each fish underwent digital photography for morphometric measurements, manual weighing, and was manually eviscerated and filleted to calculate the carcass and fillet yield. Subsequently, the principal genetic parameters were estimated. The heritabilities for the growth traits were moderate (0.34 and 0.39 for TL and BW), whilst those for the cNiT traits ranged from medium to low (0.32?0.27). For the mNiT, they demonstrated a medium to low range (0.15?0.37), whereas the carcass and fillet yield heritabilities were considered to be medium to high (0.32 and 0.31). Most of the genetic correlations between the growth, NiT, and yield traits were not estimated accurately due to the limited data. As was expected, we observed predominantly high and positive correlations between the growth and mNiT. A genetic correlation to highlight was the fillet yield with the fish maximum height (0.87 ± 0.23) and with the head height (0.87 ± 0.24). This suggests that indirect selection using NiT could improve the growth and yield traits. Employing a multi-trait selection approach enables us to capture a broader spectrum of genetic variability and to potentially identify individuals with superior genetic potential. The use of image analysis software ensures objective and precise measurements, thereby reducing the potential for human error or bias during the selection process. Further studies should be carried out to improve the accuracy of the estimates, especially those of the genetic correlations.