Modern aquaculture recirculation systems (RASs) are a necessary tool to provide sustainable and continuous aquaculture production with low environmental impact. But, productivity and efficiency of such RAS still have to be optimized to ensure economic viability, putting growth performance into the focus. Growth is often reported as absolute (gain per day), relative (percentage increase in size) or specific growth rate (percentage increase in size per day), based on stocking and harvesting data. These functions describe growth very simplified and are inaccurate because intermediate growth data are not considered. In contrast, nonlinear growth models attempt to provide information of growth across different life stages. On the basis of an empirical RAS data set of 150 all‐female turbot reared in an RAS during a period of 340 days of outgrowth, this paper reviews the most commonly used growth rates (relative, absolute, specific), the thermal‐unit growth coefficient and five nonlinear growth functions (logistic, Gompertz, von Bertalanffy, Kanis and Schnute). Goodness of fit is expressed by R2 and as mean percentage deviation. Nonlinear growth models are also compared by their residual standard error (RSE) and the Akaike information criterion. All processed functions are modelled to illustrate the shape of the generated curve and the possibility of the function to realistically predict growth. Further, the biological meaning of their regression parameters is discussed. This way we can point out differences in nonlinear growth models in contrast to purely descriptive growth rates and the specific advantages, disadvantages and possible applications of each function we review.

Growth data of two different commercial turbot (Scophthalmus maximus) strains reared in recirculating aquaculture systems were analysed with the aim to determine the most suitable model for turbot. To assess the model performance three different criteria were used: (1) The mean percentage deviation between the estimated length and actual length; (2) the residual standard error with corresponding degrees of freedom and (3) the Akaike information criterion. The analyses were carried out for each strain separately, for sexes within strains and for a pooled data set containing both strains and sexes. We tested a pre-selection of six models, containing three to four parameters. Models were of monomolecular shape or sigmoid shape with a flexible point of inflection including the special case of monomolecular shape in defined cases of their parameters. The 4-parametric Schnute model achieved best fit in 62% of all cases and criteria tested, followed by the also 4-parametric generalized Michaelis-Menten equation in 48% and the 4-parametric Janoschek model (38%). The von Bertalanffy growth function achieved only 29%, Brody 24% and a new flexible function 19% best fit. In a 1-1000 day growth-simulation sigmoid shaped curves were produced by the Schnute model in 71% of cases. The Janoschek and the Michaelis-Menten model each produced sigmoid curves in 57% of all cases. This indicates that a flexible 4-parametric function reflects the growth curve of turbot the best and that this curve is rather sigmoid than monomolecular shaped.

Seeking the most suitable model to describe the growth of turbot, we analysed growth data of two different turbot (Scophthalmus maximus) strains reared communally in a recirculating aquaculture system. We fitted 10 different nonlinear growth models to individual weight gain data (n = 2,010) during the grow‐out phase. Analyses were carried out for each strain, for sexes within strains and for a pooled data set containing both strains and sexes. To assess the model performance, three different criteria are used. Further, a growth‐simulation was performed to evaluate the shape of the generated curve. This way we could assess the capability of the models to predict future growth. The 3‐parametric Gompertz model achieved the best fit in 42.9% of all cases tested and the lowest Bayesian information criterion in 100% of cases. The model produced realistically shaped curves and asymptotic values matching the biological attributes of the species. In contrast, 5‐parametric functions projected unrealistically shaped curves and predicted improbable mature sizes. Our results show that increasing number of parameters do not lead to increasing goodness of fit, but tend to result in overfitting, and demonstrate the advantages of the 3‐parametric Gompertz model for describing the growth of turbot.

Information on phenotypic and genetic (co)variance for production traits in turbot is required to improve breeding programs. So far, information on morphometric growth traits is sparse and completely lacking on quality carcass traits like fillet weight or fillet yield for turbot. As part of a long-term study we explored the phenotypic and genetic (co)variance of 16 biometrical and carcass traits of three different European turbot strains. Fish were reared under commercial grow-out conditions, including size grading. We used molecular relatedness (MR) methods based on genotyping with 96 microsatellite markers and animal models. We included an adapted condition factor for Pleuronectiformes (FCIPLN) and average daily weight gain (ADG) between the ages of 300 and 500 d post-hatch (dph) for their potential correlation with body weight at harvest. Heritability estimates for all traits were low to medium (0.04–0.29) when strains were jointly analyzed. Separate analysis of strains yielded higher heritability estimates (0.12–0.43). Genetic correlations between weight-related traits were highly positive (0.70–0.99), while runs with yield and ratio traits often resulted in unreliable estimates of genetic correlation due to high standard errors. Body weight (hnormal2=0.19), fillet yield (hnormal2=0.15), and dressing percentage (hnormal2=0.17) are particularly promising selection traits for turbot breeding.