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Morphometric variations and Length-weight relationships of the Caspian marine shad (Alosa braschnikowi Borodin, 1904) populations across the southern basin of the Caspian Sea
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Omid Jafari   1, Maryam Nasrolahpourmoghadam , Mohammad Hassanzadeh Saber |
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Abstract: (225 Views) |
| Fish population segregation through diagnostic morphometric traits has a great importance in fish conservation and fisheries management programs. In the current study, morphometric variations of 31 morphometric characteristics of Alosa braschnikowi was investigated in the Southern Caspian Sea in three provinces including Guilan, Mazandaran and Golestan, during winter 2021. The average standard length (±SD) was 231.73±4.38, 231.52±4.14 and 200.93±32 in Guilan, Mazandaran and Golestan, respectively. The results obtained from the one-way ANOVA showed a significant difference (p<0.05) in 17 out of 31 morphometric traits. The PCA showed that the investigated areas can be distinguished through nine components, so that the main changes in the first component (PC1) are in the head area while variations in the pectoral and dorsal fins were identified as important through the second component (PC2). Investigating the growth pattern of A. braschnikowi illustrated a negative allometric growth for Anzali (b=2.78) and Sari (b=2.61) regions while Miankaleh (b=3.12) had a positive allometric growth pattern. Furthermore, condition factors estimation revealed a better nutritional condition in Anzali (CF=1.10) and Miankaleh (CF=1.09) compared to Sari region (CF=0.71). Morphometric-based clustering showed that there are at least two separate populations of A. braschnikowi in the studied areas of the Southern Caspian Sea. Due to the evenness in sampling season and way of sampling, it seems that the environmental features in each region have caused the ensuing high morphological diversity in A. braschnikowi, however, investigations through molecular methods are needed for this claim. Considering the high rate of morphometric variations in Alosa genus in the Caspian Sea, the usage of machine-learning based analytical methods is highly recommended for future studies. |
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| Keywords: Condition factor, Population, Biometry, Fisheries management, Stock identification |
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Full-Text [PDF 777 kb]
(78 Downloads)
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Type of Study: Research |
Subject:
بيولوژي آبزيان
Received: 2024/05/27 | Accepted: 2024/08/31 | Published: 2024/10/2
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