Genetic parameter estimations of new traits of morphological quality on gilthead seabream (Sparus aurata) by using IMAFISH_ML software

In this study, a total of 18 novel productive traits, three related to carcass [cNiT] and fifteen morphometric [mNiT]), were measured in gilthead seabream (Sparus aurata) using Non-invasive Technologies (NiT) as implemented IMAFISH_ML (MatLab script). Their potential be used industrial breeding programs evaluated 2348 offspring reared under different production systems (estuarine ponds, oceanic cage, inland tank) at harvest. All animals photographed, digitally main genetic parameters estimated. Heritability for growth traits was medium (0.25–0.37) whereas NiT medium-high (0.24–0.61). general, correlations between mNiT, cNiT high positive. Image analysis artifacts such fin unfold or shades, that may interfere the precision some digital measurements, discarded major bias factor since heritability after correcting them no significantly from original ones. Indirect selection through produced better predicted response than directly measuring Body Weight (13–23%), demonstrating methodological approach is highly cost-effective terms accuracy data processing time.