In aquaculture, numerous species exhibit significant physiological and behavioural variations. Characterizing species similarity is crucial for generalizing and transferring knowledge effectively. A rigorous comparison between species should consider different aspects, such as physiological characteristics, morphology, metabolism, ecology, and behaviour, giving special importance to criteria that can be assessed objectively. In this study, the relationship of similarity between commercial species (Atlantic salmon (Salmo salar), gilthead seabream (Sparus aurata), European seabass (Dicentrarchus labrax), Nile tilapia (Oreochromis niloticus), rainbow trout (Oncorhynchus mykiss), turbot (Scophthalmus maximus) and Senegalese sole (Solea senegalensis) in terms of parameters and predictions of body composition and growth models was evaluated. Different models (e.g., isometric and allometric, in the case of body composition models) and calibration methods (e.g., least squares, Huber loss regression and quantile regression) were used. Species were compared based on the distance between model parametrizations and between predictions. Results suggest that the similarities within salmonid species are strong. Despite having some differences in growth, European seabass and gilthead seabream also show similarities, especially in the early life stages. However, flatfish species do not group clearly. The variability observed between species in terms of model parameters and predictions may be related to taxonomy, physiological stages, ecological features, fish activity, body mass, as stated in previous studies. Results also suggest the metabolic body weight exponents for energy and protein are likely to be species-specific, ranging from 0.60 to 0.90, which agrees with previous studies that challenge the theory of universal metabolic allometry. This study provides important insights about body composition and growth patterns of different species. Finally, this research methodology can have a wide range of practical application for both aquaculture and fisheries industries by supporting more accurate data quality control, model calibration, synthetic data generation, and assessment of species similarity for effective resource management.
