Long time series of reliable individual growth estimates are crucial for understanding the status of a fish stock and deciding upon appropriate management. Tagging data provide valuable information about fish growth, and are especially useful when age‐based growth estimates and stock assessments are compromised by age‐determination uncertainties. However, in the literature there is a lack of studies assessing possible changes in growth over time using tagging data. Here, data from tagging experiments performed in the Baltic Sea between 1971 and 2019 were added to those previously analysed for 1955–1970 to build the most extensive tagging dataset available for Eastern Baltic cod (Gadus morhua, Gadidae), a threatened stock with severe age‐determination problems. Two length‐based methods, the GROTAG model (based on the von Bertalanffy growth function) and a Generalized Additive Model, were used to assess for the first time the potential long‐term changes in cod growth using age‐independent data. Both methods showed strong changes in growth with an increase until the end of the 1980s (8.6–10.6 cm/year for a 40 cm cod depending on the model) followed by a sharp decline. This study also revealed that the current growth of cod is the lowest observed in the past 7 decades (4.3–5.1 cm/year for a 40 cm cod depending on the model), indicating very low productivity. This study provides the first example of the use of tagging data to estimate multidecadal changes in growth rates in wild fish. This methodology can also be applied to other species, especially in those cases where severe age‐determination problems exist.
Accurate age data is essential for reliable fish stock assessment. Yet many stocks suffer from inconsistencies in age interpretation. A new approach to obtain age makes use of the chemical composition of otoliths. This study validates the periodicity of recurrent patterns in 25Mg, 31P, 34K, 55Mn, 63Cu, 64Zn, 66Zn, 85Rb, 88Sr, 138Ba, and 208Pb in Baltic cod (Gadus morhua) otoliths from tag-recapture and known-age samples. Otolith P concentrations showed the highest consistency in seasonality over the years, with minima co-occurring with otolith winter zones in the known-age otoliths and in late winter/early spring when water temperatures are coldest in tagged cod . The timing of minima differs between stocks, occurring around February in western Baltic cod and one month later in eastern Baltic cod; seasonal maxima are also stock-specific, occurring in August and October, respectively. The amplitude in P is larger in faster-growing western compared to eastern Baltic cod. Seasonal patterns with minima in winter/late spring were also evident in Mg and Mn, but less consistent over time and fish size than P. Chronological patterns in P, and to a lesser extent Mg and Mn, may have the potential to supplement traditional age estimation or to guide the visual identification of translucent and opaque otolith patterns used in traditional age estimation
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When size-selective fishing removes faster-growing individuals at higher rates than slower-growing fish, the surviving populations will become dominated by slower-growing individuals. When this “Rosa Lee phenomenon” is ignored, bias may occur in catch and stock projections. In a length-and-age-based model we quantified the effects through simulations of a simplified fishery on a stock that resembles Western Baltic cod. We compared outcomes of runs with and without taking account of the Rosa Lee phenomenon in scenarios of changes in fishing mortality. We found that, when only fishing rate was changed, the biases in predictions of spawning-stock biomass (SSB), yield and catches of undersized fish were relatively small (<10% in absolute values). When the selectivity parameters of the gear were increased, the bias in the prediction of the catches of undersized fish was very substantial (+120 to 160%). When the selectivity parameters were decreased, the biases in the predictions of SSB, yield and catches of undersized fish, were substantial (25–50% in absolute values). With slower mean growth the biases became more pronounced. We conclude that in short-term forecasts, medium-term projections, and MSE simulations featuring selectivity changes, the Rosa Lee phenomenon should be accounted for, ideally by using length-based models.
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