M. Vasilyeva, L., Elnakeeb, M. (2019). Comparative assessment of the seasonal breeding patterns of the paddlefish Polyodon spathula (Walbaum, 1792) and the impact of growth tendency during winter and summer feeding. Egyptian Journal of Aquatic Biology and Fisheries, 23(4), 335-346. doi: 10.21608/ejabf.2019.56783
Lydia M. Vasilyeva; Mahmoud Elnakeeb. "Comparative assessment of the seasonal breeding patterns of the paddlefish Polyodon spathula (Walbaum, 1792) and the impact of growth tendency during winter and summer feeding". Egyptian Journal of Aquatic Biology and Fisheries, 23, 4, 2019, 335-346. doi: 10.21608/ejabf.2019.56783
M. Vasilyeva, L., Elnakeeb, M. (2019). 'Comparative assessment of the seasonal breeding patterns of the paddlefish Polyodon spathula (Walbaum, 1792) and the impact of growth tendency during winter and summer feeding', Egyptian Journal of Aquatic Biology and Fisheries, 23(4), pp. 335-346. doi: 10.21608/ejabf.2019.56783
M. Vasilyeva, L., Elnakeeb, M. Comparative assessment of the seasonal breeding patterns of the paddlefish Polyodon spathula (Walbaum, 1792) and the impact of growth tendency during winter and summer feeding. Egyptian Journal of Aquatic Biology and Fisheries, 2019; 23(4): 335-346. doi: 10.21608/ejabf.2019.56783
Comparative assessment of the seasonal breeding patterns of the paddlefish Polyodon spathula (Walbaum, 1792) and the impact of growth tendency during winter and summer feeding
Seasonal rhythms affect paddlefish farming practices due to the interactions among internal and external environmental factors in the aquatic ecosystem, including different types of biological, chemical, or physical stressors. In the present study, seasonal variations and the succession of the seasons during the farming periods represent a striking force that reflects the reality of sustainable paddlefish development under Russian aquaculture conditions. Five age groups (28, 29, 30; 26, 27, 28; 24, 25, 26; 19, 20, 21; and 18, 19, 20) were studied based on years of commissioning (1993, 1995, 1997, 2002 and 2003, respectively) and showed different growth rates throughout the seasons during 2017, 2018, and 2019. Consequently, rapid growth rate changes; increasing and decreasing, were observed, as demonstrated in weight gain and specific growth rate parameters. All the growth parameter variables, knowledge base and inference in the growth monitoring analysis model were carried out through computation of weight gain, growth rate, and specific growth rate related to age groups. The results revealed that the descriptive analysis of seasonal variability agreed with the statistical growth performance analysis. Moreover, the findings show that 2017 and 2018 winter and 2017 summer seasons recorded the lowest values of significance (30: 55%, SGR) among the five age groups. In contrast, high values were obtained for 2018 and 2019 winter and 2018 summer seasons (55: 80%, SGR), despite sustainable production. However, it is insufficient to determine the value of the seasonal response in growth rates over four seasons. Therefore, we need to create simulation programmes to predict the impact of seasonal patterns instead of spending more effort and cost on other methods.
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