Stock structure analysis of Decapterus russelli (Ruppell, 1830) from east and west coast of India using truss network analysis

Abstract

a b s t r a c tDecapterus russelli (Indian Scad) is an important pelagic carangid distributed on both east and west coast of India. Despite its wide distribution, the stock structure of the species is not well known. The present study was conducted to investigate stock structure of D. russelli, based on body shape morphometrics using truss network system. A total number of 360 samples of the species were collected from two centres, Digha and Visakhapatnam in Bay of Bengal from east coast and on the west coast from Mumbai and Cochin in Arabian Sea. A truss network was constructed by interconnecting 11 landmarks to form a total of 23 distance variables extracted from digital images of samples using tps Dig2 and PAST software platforms. The transformed truss measurements were subjected to factor analysis and classification by cross-validation of discriminant analysis. Factor analysis showed meaningful loading of the middle portion, the portion below the second dorsal fin, above anal fin, and the caudal portion on first and second factor, respectively. The factor analysis revealed the existence of two morphologically different stocks of D. russelli between east and west coast of India. The discriminant analysis was conducted by the combination of the truss distances that loaded on Factor-1 and Factor-2. The measurements that belonged to the middle portion and caudal portion of the body produced minimum misclassification rate of 5% between the coasts; whereas, the misclassification was 28% for all the four stocks. The misclassification was higher between the stocks within the coast. The high rate of misclassification observed within the coast is probably the result of no demarcation in fishing area. The occurrence of a separate stock on each coast may be the result of different physical and ecological condition of Bay of Bengal and Arabian Sea.

Stock structure analysis of ‘Bombay duck’ (Harpadon nehereusHamilton, 1822) along the Indian coast using truss network morphometrics

“…Results from anova on the first factor were also found significant for the coast effect and hence this may indicate their swimming adaptations to the respective environment (Table ). Similar observations have been reported previously for stocks of Megalaspis cordyla and Decapterus russelli on the Indian coast (Sajina et al., ; Sen et al., ).…”

Multivariate morphometric variability in sardine, Amblygaster clupeoides (Bleeker, 1849), from the Bay of Bengal coast, Bangladesh

“…It is also imperative to collect actual biological information of fish such as ecology, evolution, behavior, and stock assessment (Hanif, Siddik, Chaklader, Pham, & Kleindienst, 2017;Islam et al, 2017;Silva, 2003;Yakubu & Okunsebor, 2011) linked with diverse species, subspecies, and races (Chaklader et al, 2016). The truss network system is considered superior to traditional morphometrics that use morphometric traits to represent the complete shape of fish, which has been commonly used in the field of fish taxonomy and fisheries management (Francoy, Franco, & Roubik, 2012;Sen et al, 2011;Turan, 2004). A number of studies has been conducted based on truss network system worldwide including Caspian lamprey (Caspiomyzon wagneri), black stripe minnow (Alosa pseudoharengus), Alewife (Alosa pseudoharengus), roho labeo (Labeo rohita), and so on (Cronin-Fine et al, 2015;Galeotti, Castalanelli, Groth, McCullough, & Lund, 2015;Mir, Sarkar, Dwivedi, Gusain, & Jena, 2012;Solomon, Okomoda, & Ogbenyikwu, 2015;Vatandoust, Mousavi-Sabet, Razeghi-Mansour, AnvariFar, & Heidari, 2015), but its application in the coast of Bangladesh is still scarce.…”

Untitled

“…In the present study also, the classification success of otolith shape analysis was lower less compared to the rate of classification based on morphometric data. The sources of misclassification in the analysis of otoliths may include methodological inaccuracies, individual variability and migration (Campana and Casselman, 1993;Tracey, Lyle & Duhamel, 2006) Morphotype differences, along with spatial differences, could be caused by different environmental conditions, such as temperature, salinity, depth, water masses, current and food availability for fish (Tsujita & Kondo, 1957;Sen, Jahageerdar, Jaiswar, Chakraborty, Sajina & Dash, 2011) or differences in genetic makeup (Khan, Miyan & Khan, 2013). Geographical separation and isolation of year classes at early life stages causes the morphometric variability of different stocks (Swain, Hutchings & Foote, 2005).…”