Growth, Mortality, Recruitment and Yield of Rainbow Trout, Oncorhynchus mykiss Walbaum, 1792 in Karacaören-I Dam Lake, Turkey

Cılbız, Mehmet; Yalım, F. Banu

doi:10.17582/journal.pjz/2017.49.3.825.832

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…This is in agreement with the assumption that isometric-growing fish can grow more allometrically in terms of weight-length relationship under aquaculture conditions (Jobling, 2003). Furthermore, cultured rainbow trout are known to have a largely varying parameter b, ranging from as low as 1.3 to as high as 6.6 (Dumas et al, 2007); in contrast to values of 2.88 to 3.39 for wild rainbow trout (Cilbiz and Yalim, 2017).…”

Section: Discussionsupporting

confidence: 85%

“…This means that the larger the numeric value, the quicker the fish will reach the asymptotic or infinite weight (Malhado et al, 2009). Estimated B values in this study (between 0.008 to 4.9661) have the tendency to be greater than those values (between 0.061 to 1.76) obtained from wild rainbow trout (Blair et al, 2013;Sloat and Reeves, 2014;Cilbiz and Yalim, 2017). Similarly, Lugert et al (2016) found similar differences in parameter B between cultured and wild Scophthalmus maximus, relating these differences to the positive effect of controlled environmental conditions in recirculation production systems.…”

contrasting

confidence: 49%

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Modeling the weight gain of freshwater-reared rainbow trout ( Oncorhynchus mykiss ) during the grow-out phase

Janampa-Sarmiento

Takata

Freitas

et al. 2020

Revista Brasileira De Zootecnia

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Aquaculture Full-length research article Modeling the weight gain of freshwater-reared rainbow trout (Oncorhynchus mykiss) during the grow-out phase ABSTRACT-We used five nonlinear models to calculate the weight gain of rainbow trout (122.11±15.6 g) during the final grow-out phase of 98 days under three different feed types (two commercials diets, A and B, and one experimental diet, C) in triplicate groups. We fitted the von Bertalanffy growth function with allometric and isometric scaling coefficient, Gompertz, Logistic, and Brody functions to weight (g) at age data of 900 fish, distributed in nine tanks. The equations were fitted to the data based on the least squares method using the Marquardt iterative algorithm. The accuracy of the fitted models was evaluated using a model performance metrics, combining mean squared residuals (MSR), mean absolute error (MAE), and Akaike's Information Criterion corrected for small sample sizes (AICc). All models converged in all cases tested. The evaluation criteria for the Logistic model indicated the best overall fit (0.704) under all different feed types, followed by the Gompertz model (0.148), and the von Bertalanffy-I and von Bertalanffy-A with 0.074 each. The obtained asymptotic values are in agreement with the biological attributes of the species, except for the Brody model, whose values were massively exceeding the biologic traits of rainbow trout in 0.556 of tested cases. Additionally, ∆AICc results identify the Brody model as the only model not substantially supported by the data in any case. All other models are capable of reflecting the effects of various feed types; these results are directly applicable in farm management decisions.

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Section: Discussionsupporting

confidence: 85%

contrasting

confidence: 49%

Modeling the weight gain of freshwater-reared rainbow trout ( Oncorhynchus mykiss ) during the grow-out phase

Janampa-Sarmiento

Takata

Freitas

et al. 2020

Revista Brasileira De Zootecnia

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“…Estimated B values for Logistic and Gompertz models (0.0001 and 0.0144) in this study have the tendency to be greater than those being obtained from wild rainbow trout (0.002 to 0.049) in 7 and 6 of 9 cases, respectively (Blair et al, 2013;Sloat and Reeves, 2014;Cilbiz and Yalim, 2017). Similarly, Lugert et al (2016) found similar differences in parameter B between cultured and wild Scophthalmus maximus, referring these differences to the positive effect of controlled environmental conditions in recirculating aquaculture systems (RAS).…”

Section: Nonlinear Regression…supporting

confidence: 56%

Nonlinear regression analysis of length growth in cultured rainbow trout

Janampa-Sarmiento

Takata

Freitas

et al. 2020

Arq. Bras. Med. Vet. Zootec.

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Length growth as a function of time has a non-linear relationship, so nonlinear equations are recommended to represent this kind of curve. We used six nonlinear models to calculate the length gain of rainbow trout (Oncorhynchus mykiss) during the final grow-out phase of 98 days under three different feed types in triplicate groups. We fitted the von Bertalanffy, Gompertz, Logistic, Brody, Power Function, and Exponential equations to individual length-at-age data of 900 fish. Equations were fitted to the data based on the least square method using the Marquardt iterative algorithm. Accuracy of the fitted models was evaluated using a model performance metrics combining mean squared residuals (MSR), mean absolute error (MAE) and Akaike's Information Criterion corrected for small sample sizes (AICc). All models converged in all cases tested. Evaluation criteria for the Logistic model indicated the best overall fit (0.67 of combined metric MSR, MAE and AICc) under all different feeding types, followed by the Exponential model (0.185), and the von Bertalanffy and Brody model (0.074, respectively). Additionally, ∆AICc results identify the Logistic and Gompertz models as being substantially supported by the data in 100% of cases. The logistic model can be suggested for length growth prediction in aquaculture of rainbow trout.

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“…Several studies have emphasised the importance of determining length-weight relationships in fish, providing valuable insights into various aspects of fish biology, not only about the general health of fish but also provide information about the morphological characteristics of the fish, growth pattern, habitat conditions, life history, and fish fatness and condition (Schneider et al, 2000;Froese, 2006;Gerritsen & McGrath, 2007;Cilbiz & Yabim, 2017). This concept derives from the empirical observation that fish inhabiting more favourable ecological conditions tend to exhibit higher body weights relative to their length measurements (Froese, 2006;Nguyen et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

An empirical comparison of sex ratio, condition factor and length–weight relationships in Mediterranean horse mackerel from the Sea of Marmara, the Middle and Eastern Black Sea

DÜRRANİ

2023

JAES

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Fish length–weight relationships are essential for estimating biomass and assessing fish population health, typically showing low variation within and between neighbouring stocks. This study investigates the length–weight relationships of Mediterranean horse mackerel (Trachurus mediterraneus) collected from the Sea of Marmara, the Middle Black Sea, and the Eastern Black Sea in Türkiye during 2022. In addition, the study assessed the sex ratio and condition factor of Mediterranean horse mackerel at each station, alongside an examination of length-frequency samples, providing a comprehensive analysis. The one-way ANCOVA analysis showed no significant differences in length–weight regression between the sexes. However, it identified a considerable area effect, with samples from the Sea of Marmara significantly differing from those in the middle Black Sea and Eastern Black Sea. Fish samples from the latter areas displayed moderate length–weight variation, which was close to the significance threshold with a p-value of 0.069. The growth pattern of Mediterranean horse mackerel consistently exhibited negative allometry across all sampled areas for both males and females, except for female Mediterranean horse mackerel from the middle Black Sea, which tended to follow an isometric growth pattern. This study revealed consistent results within neighbouring areas (Middle Black Sea and Eastern Black Sea) and significant variations when comparing the Black Sea and Sea of Marmara Mediterranean horse mackerel, emphasising notable large-scale spatial trends.

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Growth, Mortality, Recruitment and Yield of Rainbow Trout, Oncorhynchus mykiss Walbaum, 1792 in Karacaören-I Dam Lake, Turkey

Cited by 4 publications

References 3 publications

Modeling the weight gain of freshwater-reared rainbow trout ( Oncorhynchus mykiss ) during the grow-out phase

Modeling the weight gain of freshwater-reared rainbow trout ( Oncorhynchus mykiss ) during the grow-out phase

Nonlinear regression analysis of length growth in cultured rainbow trout

An empirical comparison of sex ratio, condition factor and length–weight relationships in Mediterranean horse mackerel from the Sea of Marmara, the Middle and Eastern Black Sea

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