Morphometric and meristic characters
The traditional morphometric and meristic methods, along with otolith shape analysis, were used to discriminate the stocks structure of U. vittatus. Both univariate and multivariate statistical analyses were used to check the otolith shape variation among the populations. This study revealed significant variations among the populations based on the otolith shape analysis, traditional morphometric and meristic characters. Studies based on morphometry and meristic analysis have been carried out for stock identification in several species, such as Megalaspis cordyla (Sajina et al. 2011), Nemipterus japonicus (Sreekanth et al. 2015), Nemipterus randalli (Sri Hari et al. 2020).
Factor analysis of the morphometric traits showed that factor 1 shows variation in the anterior portion and factor 2 is in the posterior portion of the fish body. The mean caudal peduncle depth of specimens collected from India's east and west coast was 1.54 cm and 1.25 cm respectively, indicating well-developed caudal peduncle in the east coast populations than the west coast. Variation in the caudal peduncle region could be due to unique hydrographical conditions on both the coast. Several studies reported significant differences in the caudal peduncle region of the fish body along the Indian coast (Sajina et al. 2013; Sreekanth et al. 2015; Sri Hari et al. 2020). The turbidity of the Bay of Bengal is generally much higher than the Arabian sea (Kumar et al. 2001) as the Bay of Bengal receives a heavy riverine discharge. Because of high turbidity, the fishes might have to swim more to find suitable food items, leading to a strong caudal peduncle region. Imre et al. (2002) studied the population of brook charr (Salvelinus fontinalis) from different microhabitats with the difference in the velocity of the water. They found that those highly turbulent species have a deeper and well-developed caudal peduncle.
The mean total length of fishes studied from Mumbai, Kakinada, and Odisha was 15.56 cm, 14.03 cm and 13.96 cm, respectively. The variation in the total length may be due to the abundance of food and higher feeding intensity. Kumar et al. (2009) stated that continuous upwelling of cold nutrient-rich water, wind-driven mixing, and lateral advection had enhanced the productivity of the Arabian sea. In contrast, the biological productivity of the Bay of Bengal is considered low (Kumar et al. 2001). The supportive environmental condition along with continuous availability of sufficient quantity of food items lead to the higher growth performance of Arabian sea populations. The mean head length was higher for the Mumbai population (3.61 cm) and the lowest for the Odisha population (3.01 cm). The difference in the morphometric characters between the samples may be due to habitat changes, whereas the variation in relative head length could be related to the size of prey (Gatz 1979). The dorsal fin plays a crucial role in the swimming patterns of coral reef fishes (Korsmeyer 2002). U. vitatus is a reef-dwelling fish. The variation in dorsal fin length may be due to different current patterns and velocities existing in India's coast.
Otolith Shape Analysis
Multivariate analysis of the discrete Wavelet transforms successfully characterized individuals from India's East and West coast. The Wavelet transform is proven to be an effective tool in otolith shape analysis because it can detect otolith contour outlines which contribute significantly to overall shape variation (Postrostrum and Pararostrum in the present finding). The result of the present study shows the usefulness of otolith shape analysis in the possible identification of two different stocks of U. vittatus along the Indian coast. However, the vast geographical distance between India's East and West coast may be a barrier for larval dispersion between these areas and form the different genetic stock. In this study, the specimens with a total length of above 10 cm were used to ensure all 197 specimens were adult to avoid the confounding effect of allometric growth on otolith shape (Cardinale et al. 2004). The use of otolith shape analysis might be of imperceptible value to discriminate the stock if applied to sexually immature specimens (Campana & Casselman 1993). Elliptic Fourier or Wavelet transform functions use harmonics to describe the shape of the otolith (Parisi-Baradad et al. 2005). Each harmonic amplitude represents a shape characteristic such as elongation and triangularity (Bird 1986).
The environmental and genetic factors are responsible for otolith shape differences (Lombarte & Lleonart 1993; Cardinale et al. 2004; Teacher et al. 2013), leading to the distinctive features of each stock (Friedland and Reddin 1994; Begg and Waldman 1999). Several studies revealed that local environmental conditions play a crucial role in the growth of fishes (Silva et al. 2008; Veron et al. 2020) and otolith shape variation (Cardinale et al. 2004; Vignon 2012). Vignon & Morat (2010) stated that the species-specific otoliths are genetically regulated, while environmental effects on otolith shape are mainly expressed at an intraspecific level. This indicates that the otolith's shape can differ in the absence of any growth-related differences if genetic differences exist between populations (Galley et al. 2006).
So, the individuals who breed and grow in different environmental conditions are expected to have unique morphology and growth rates and form distinct demographic stocks. East and West coast of India experienced different environmental conditions throughout the year. So, the observed variation in the otolith shape of U. vittatus may be due to environmental differences among the sampling areas or may have a genetic basis. Truss morphometric analysis of U. vittatus along the Indian coast indicates that the unique demographic features in India's East and West coast may attribute to separate stock (Nama et al. 2022).
Furthermore, the LDA showed that Mumbai samples significantly differed from Kakinada and Puri populations, forming a separate cluster suggesting that the individuals captured were exposed to different environmental conditions. The classification matrix of shape contours derived from the Wavelet transform analysis of otolith showed a separation of populations collected from three different locations along the Indian coast. One possible explanation for those specific differences is related to the dominant oceanographic processes along the west coast of India. Habitat temperature is a vital environmental factor that affects the growth of fish and otolith morphometry. Neat et al. (2008) investigated that 2°C changes in water temperature can lead to variation in opacity and daily increments of otoliths width in Atlantic cod (Gadus morhua). Variation in otolith shape may occur due to changes in water column temperature along the latitudinal gradient (Castro et al. 2015; Cuevas et al 2019). It is supported by the theory given by Lombarte & Lieonart (1993) otolith shape can be varied among the populations that might have grown up at the same temperature and growing conditions like other morphometric traits, which reflects the combined effect of genetic variation and local environmental condition, such as water depth, salinity, pressure, and water temperature.
East and West coast of India exhibited wide seasonal temperature fluctuation of its surface water. Moreover, salinity is higher on the west coast of India than on the east coast, most probably due to the heavy discharge of freshwater from the major rivers into the east coast. This might be the probable reason for the otolith shape variation and sampling locations. Different multivariate analyses of the Wavelet shape descriptors, i.e., permutation tests, showed significantly different dorsal and ventral parts than in other locations. Food availability in that area may be the possible attribute for the fastest deposition of otolith increments. Food availability in the Arabian Sea is much higher than in the Bay of Bengal. Because seasonal upwelling of cold, nutrient-rich waters along the western coast of India contribute significantly to primary production (Panikkar and Jayaraman 1996). Thus, different growth rates of fishes contribute to the otolith shape variations among the observed populations. The protein content of food items consumed by fish directly affects otolith development. The fish's growth rate is directly related to the otolith shape (Gauldie and Nelson 1990) because faster-growing fishes usually have daily ring deposition, whereas fewer rings are observed in slow-growing fishes (Geffen 1982; Fox et al. 2003). Thus, differences in the consumption of type and quality of food items result in differences in otolith shape (Mille et al. 2016). Fluctuations of the diet in terms of quantity and frequency also determine otolith's shape significantly over a short period (Gagliano and McCormick 2004). A similar study conducted by Hussy (2008) stated that the size and number of lobes formed in the otolith are greatly influenced by feeding. However, otolith shape development depends on fed consumption and fishes' growth rate. Therefore, differences in food items consumed by Yellow striped" goatfish in the different environmental conditions may affect the shape of the otolith.