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This study aimed to estimate the genetic parameters of growth-related traits (weight and length), morphological traits (cephalothorax, abdomen, length, height, segment width and volume), and the correlated response of weight traits via the selection for morphological traits to evaluate the relative efficiency of indirect selection in P. vannamei from a selective breeding growth programme, PMG-BIOGEMAR®, under industrial production conditions in Ecuador. A total of 595 shrimps from 89 full-sibling families were reared under an extensive culturing system (estuaries) (PRODUMAR, Duran, Ecuador), and were evaluated for genetic parameters at harvest size. The heritability of growth traits was moderate-medium (0.25–0.34), whereas it was quite variable for morphological traits (0.01–0.77). Among the morphological traits, Sixth Segment Width (SW6) (0.77) and Sixth Segment Volume (SV6) (0.35) had medium-high heritability, and genetic correlations between almost all morphological and growth traits were high and positive, except those corresponding to the fourth segment. According to these genetic parameters, SW6 and SV6 as the correlated response to weight are 9.7% and 5.8%, respectively, being more efficient than direct selection for weight. Thus, the sixth segment should be considered an indirect selection criterion for growth in future breeding programmes as it is a precise, non-invasive, and low-cost morphological trait for growth improvement in the industrial sector.

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