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El pez Cirrhinus mrigala (Cypriniformes: Cyprinidae) en la cuenca del Ganges: estructura del stock en poblaciones silvestres basada en morfometria de hitos.

The Ganges basin fish Cirrhinus mrigala (Cypriniformes: Cyprinidae): detection of wild populations stock structure with landmark morphometry

Over the past few years, science of taxonomy has been suffering from dwindling number of experts (Rodman & Cody, 2003). Moreover, the pace of traditional taxonomy is also very slow. However, in the recent past, the pace of data gathering and analysis in taxonomy has been greatly increased by the development of information technology (Chen, Bart, & Teng, 2005). A family of software tools have been designed for gathering and analyzing data on morphometric variation from images of specimens (Rohlf & Bookstein, 1990). C. mrigala (Hamilton, 1822), (Cypriniformes: Cyprinidae) is a warm-water teleost, inhabitant of Indo-Gangetic riverine system spread across northern and central India, and the rivers of Pakistan, Bangladesh, Nepal and Myanmar (Reddy, 1999; Dahanukar, 2010). It has been effectively transplanted out of its natural range within India and parts of Asia as well as Europe (Chondar, 1999; Talwar & Jhingran, 1991). The major source of seed in India is contributed from natural resources. However, over the last few decades, wild capture fishery appears to be declined (Payne, Sinha, Singh, & Haq, 2004; FAO, 2006-2014) and changes in its distribution, phenotypic traits and biological characteristics have also been reported (Rao, 2001; Sharma, 2003). Therefore, it is essential to examine the stock structure of C. mrigala to overcome this fall in catch of this important Indian major carp.

The long-term isolation of populations and interbreeding can lead to morphometric variations between populations which can provide a basis for discriminating stocks (AnvariFar et al., 2011; Sajina, Chakraborty, Jaiswar, Pazhayamadam, & Sudheesan, 2011). There are many well-documented studies on population differentiation based on traditional morphometric characters (Turan, Yalcin, Turan, Okur, & Akyurt, 2005; Quilang, Basiao, Pagulayan, Roderos, & Barrios, 2007; Saini, Dua, & Mohindra, 2008). Now-a-days Studies of morphometric measurements based on truss network system (Strauss & Bookstein, 1982) constructed with the help of landmark points has been increasingly used for stock identification (Turan, Erg, Grlek, Bapusta, & Turan, 2004; Gopikrishna, 2006; Hossain, Nahiduzzaman, Saha, Khanam, & Alam, 2010; Ujjania & Kohli, 2011; Sajina et al., 2011; AnvariFar et al., 2011; Mir, Sarkar, Dwivedi, Gusain, & Jena, 2013; Sarkar, Mir, Dwivedi, Pal, & Jena, 2014).

Several studies have been conducted on the biology of Indian major carps viz. Labeo rohita, C. mrigala and C. catla, to study differentiation among the populations recently (Mir et al., 2012, 2013, 2014) and genetic variation (Zheng, Zheng, Zhu, Luo, & Xia, 1999; Chauhan et al., 2007; Luhariya et al., 2012, 2014; Das et al., 2012; 2014; Hasant, Mollah, & Alam, 2015), however information available on morphometric variation in natural populations of these major carp is very limited and restricted to a particular region (Mir et al., 2013; Sarkar et al., 2014; Das et al., 2014). In the present communication, we therefore used multivariate statistical techniques to evaluate natural population structure on the size-free landmark based morphological variations of C. mrigala from major rivers of Ganga River basin. It is also necessary to mention here that some populations of the present study were included in the previous study on genetic variation of this economically important species (Chauhan et al., 2007). So, the results obtained from the present study will also be useful in correlation between genetic variation and morphological variation (Poulet, Berrebi, Crivelli, Lek, & Argillier, 2004).

MATERIALS AND METHODS

Study site: The river Ganga has it origin at the confluence of the Bhagirathi and Alaknanda, which descend from the upper Himalayas to Devprayag at an elevation of 4 100 m above mean sea level from the Gaumukh glacier in Uttarakhand, India. It flows south and east for some 2 525 km before reaching Bay of Bengal. The northern tributaries included in the present study are Sharda and Ghaghra and the southern tributaries include Chambal, Sindh, Kalisindh, Son and Tons. The River Banas originates in the Khamnor Hills of the Aravalli Range, about 5 km from Kumbhalgarh in Rajsamand district with approximately 512 km in length, is a tributary of Chambal, which in turn flows into the Yamuna. It flows northeast through the Mewar region of Rajasthan, and meets the Chambal near the village of Rameshwar in Sawai Madhopur District. Hiran River rises in the Bhanrer range in the Jabalpur district of Madhya Pradesh near the Kundam village at an elevation of 600 m and flows in a generally South-westerly direction for a total length of 188 km to join the Narmada from the right near Sankal village.

Sampling: A total of 381 specimens of C. mrigala were collected from ten different drainages of the Ganga River basin including main channel. The sample size and relative information for all the sampling sites selected to document morphometric variation are presented in table 1 and figure 1 (Table 1, Fig. 1).

Digitization of samples

Fish were placed on laminated graph sheets, body posture and fins were teased into a natural position. Each individual was labelled with a specific code for identification. A Cyber shot DSC-W300 digital camera (Sony, Japan) was used to capture the digital images. After image capture, each fish was dissected for sex determination by macroscopic examination of the gonads. The gender was used as the class variable in ANOVA to test for significant differences in morphometric characters, if any, between male and female.

Data collection: Two-dimensional Cartesian coordinates of 12 landmarks were recorded on the left view of each specimen (Fig. 2). Data were generated from digitalized images using a combination of three different softwares: tpsU-til was used for converting graphics images in to 'tps' format; tpsdig was used for fixing landmarks on the images and also setting scale for the image; PAST was used for generating truss data based on the landmarks (Rohlf, 2006). A total of 66 inter-landmark morphometric characters were extracted according to Strauss & Bookstein (1982).

Statistical analysis: The data generated by PAST were log-transformed (Strauss, 1985). Data was transformed in independent form to follow the Elliott, Haskard, & Koslow (1995): [M.sub.adj] = M[([L.sub.s]/[L.sub.0]).sup.b], where M is the original measurement, [M.sub.adj] the size adjusted measurement, [L.sub.0] the standard length of the fish, [L.sub.s] the overall mean of standard length for all fish from all samples in each analysis, and b estimated for each character from the observed data as the slope of the regression of log M on log [L.sub.0] using all fish from each group. Standard length (SL, character code 1-6) was excluded from the final analysis because SL was used as a basis for transformation (Mamuris, Apostolidis, Panagiotaki, Theodorou, & Triantaphyllidis, 1998) and thus 65 morphometric variables were retained.

Univariate analysis of variance (ANOVA) was performed for 65 morphometric characters to evaluate the significant difference among the three locations and those morphometric characters which showed highly significant variations (P < 0.01) were used to achieve the recommended ratio of the number of organisms measured (N) to the parameters included (P), in order to obtain a stable outcome from multivariate analysis (Johnson, 1981; Kocovsky, Adams, & Bronte, 2009).

The significant variables were subjected to linear discriminant function analyses (DFA) and principal component analysis (PCA) to discriminate the three populations. Principal component analysis helps in reduction of morphometric data (Veasey, Schammass, Vencovsky, Martins, & Bandel, 2001), in decreasing redundancy among the variables (Samaee, Mojazi-Amiri, & Hosseini-Mazinani, 2006) and in extracting a number of independent variables for population differentiation (Samaee, Patzner, & Mansour, 2009). In PCA, Jolliffe's rule with eigen values of at least 0.7 was applied to retain principal components (Dunteman, 1989) and factor loading greater than 0.30 is considered significant, 0.40 more important and 0.50 or greater very significant (Nimalathasan, 2009). The Wilks' k was used to compare the differences between and among all groups. The DFA was used to calculate the percentage of correctly classified (PCC). A cross-validation using PCC was done to estimate the expected actual error rates of the classification functions. Box plot was prepared for each discriminant morphometric characters. Statistical analyses for morphometric data were performed using the SPSS version 12 software package and Excel (Microsoft Office 2007).

RESULTS

No significant difference was found in any of the morphometric characters between both sexes and therefore the data for both sexes were pooled for further analyses. No significant correlation was observed between any of the transformed morphometric variables and SL (P > 0.001), indicating that the effect of size was successfully removed. The analysis of variance (ANOVA) showed that fish samples from 10 locations differed significantly (P < 0.05) in all the65 transformed morphometric characters studied. In this study, the N:P ratio was 5.86.

Principal component analysis using varimax rotation of 65 significant variables extracted eight principal components accounting for 94.1 % of the total variation (Table 2). Character code 1-3, 1-4, 1-5, 1-7, 1-8, 1-9, 2-4, 2-5, 2-6, 2-7, 2-8, 2-9, 3-4, 3-5, 3-6, 3-7, 3-8, 3-11, 4-9, 4-10,4-11, 4-12, 5-8, 5-9, 5-10, 5-11, 5-12, 6-8, 6-9, 6-10, 6-11, 6-12, 7-9, 7-10, 7-11, 7-12, 8-9, 8-10, 8-11, 8-12, 9-10, 9-11, and 9-12 showed significant on first principal component (PC1) which explained 68.6% variation of the total variation while character code 4-5, 4-6, 4-7, 5-8, 6-8 and 7-8 showed significant loading on PC2 which explained 6.8 % variation. Visual examination of plots of PC1 and PC2 scores revealed that specimens were grouped into 10 areas, with some degree of overlap among the populations (Fig. 3.).

Forward stepwise discriminant function analysis of the 65 variables produced eight discriminating functions (DFs; Table 3). The first discriminant function (DF) accounted for 37.9 % while second DF accounted for 23.5 % of the among-group variability. The DF1 versus DF2 plot explained 61.4 % of total variance among the specimens and showed low distinction among C. mrigala stocks (Fig. 4.). Forward stepwise discriminant analysis of the all significant variables produced eight discriminating variables (Table 3). The morphometric measurements 10-11 and 2-6 showed highest variation on DF1, while 4-6 and 1-11 on DF2, 4-8 on DF3, 3-10 on DF5, 1-10 on DF6 and 8-9 contributed to DF8 (Table 3). The linear discriminant analysis gave an average PCC of 45.7 % for morphometric characters indicating low rate of correct classification of individuals into their original populations (Table 4).The percentage of correct classification ranged from 24.1 % to 65 %. It was highest for the stock of river Betwa (65 %) followed by river Gomti (63.2 %), Son (57.7 %), Tons and Ganga (50 %), Ken (44.7 %), Ghaghra (42.1 %), Sharda (40 %), Kalisindh (31.7 %) and lowest for river Chambal (24.1 %). The cross-validation test results were comparable to the results obtained from PCC (Table 4).

DISCUSSION

Information on stock structure of any species is very useful in developing conservation strategies for effective management of the natural fish populations. The present study deals with the distribution and pattern of morphometric variation in natural populations of C. mrigala estimated from truss network system. In fish morphology studies, inadequate sample size for multivariate analysis is a subject of question. In past, workers on PCA and DFA suggested that the ratio of the number of organisms (N) relative to the significant morphometric variables (P) in the study be at least 3-3.5 (Johnson, 1981; Kocovsky et al., 2009). Small N values may fail to adequately capture covariance or morphological variation, which may lead to false conclusions regarding differences among groups (McGarigal, Cushman, & Stafford, 2000). AnvariFar et al. (2011) found N:P ratio to be 4.32 in case of C. c. gracilis from Tajan River. However, Mir et al. (2013) reported N:P ratio to be 14.03 in L. rohita.

Discriminant function analysis (DFA) could be a useful method to distinguish different stocks of the same species (Karakousis, Triantaphyllidis, & Economidis, 1991). In the present study, low morphological differences between populations may be exclusively associated to body shape variation and not to size effects which were effectively removed by allometric transformation (AnvariFar et al., 2011). On the other hand, size-related characters play a predominant job in morphometric examination and the results may be flawed if not adjusted prior to statistical analyses of data (Tzeng, 2004). This segregation was partly confirmed by PCA, where the loadings of principal components revealed relatedness between populations. Common ancestry in the prehistoric period and possible exchange of individuals between rivers in different river basins could have been responsible for the observed low level of morphometric differentiation among wild mrigal populations. Das et al. (2014) also studied population structure of C. mrigala from peninsular riverine systems of India using truss landmark based morphometric analysis and indicated low morphometric differences among mrigal populations despite those populations from different geographic locations. Nautiyal & Lal (1988) also observed low morphometric differentiation among T. putitora populations in upstream of Ganga River. Similarly, several studies reported low morphometric differentiation in T. ilisha of Ganga and Hooghly River (Hora & Nair, 1940; Pillay, 1957; Sugunan & Das, 1996).

Primitive rivers like Indus, Ganga and Brahmaputra are formed during the late Pleistocene (Daniel, 2001) while Satluj, Beas, Yamuna, Ghagara and other Himalayan rivers were formed as lateral rivers to the Ganges more recently. Assuming that the fish stocks are distributed in space as gradients (Murta, Abaunza, & Cardador Sanchez, 2008), it is likely that fish from one tributary could belong to another, within the basin. The classification results of discriminant function clearly support this theory. The similarity between the stocks within a basin may be due to a common environment, similar genetic origin at earlier period, and the similarity may also be due to the genetic introgression of the fishes especially those in the transition zones. Study conducted by Hasant et al. (2015) on microsatellite DNA marker analysis revealed low genetic variation in the wild (three rivers namely the Halda, the Padma and the Jamuna) and captive (three hatcheries such as Brahmaputra, Raipur, and Sonali) populations of Cirrhinus cirrhosus. Luhariya et al. (2014; 2012) also reported low to moderate genetic divergence estimated from mtDNA cyto b and ATPase6/8 gene sequence in the natural populations of L. rohita among the major river systems of Indus and Ganges basins may result from gene flow across common flood plains. Das et al. (2014) also observed low genetic structure using mitochondrial DNA gene, cytochrome b among mrigal populations from peninsular riverine systems of India despite those populations from different geographic locations. Das et al. (2012) also reported low to moderate genetic diversity in wild Catla catla populations assessed through mtDNA cytochrome b sequences. Chauhan et al. (2007) also reported low genetic divergence estimated from allozyme and microsatellite markers in the natural populations of C. mrigala among the major river systems of Indus and Ganges basins may result from gene flow across common flood plains.

However, in the previous study on landmark based morphometry of Labeo rohita from six Indian rivers viz. Ganga, Ghaghra, Ken, Sharda Betwa, and Gomti of Ganga basin, Mir et al. (2013) reported significant variations among the populations. Sarkar et al. (2014) also reported high morphometric variation among the populations in C. catla collected from three rivres viz. Ken, Betwa and Ganga. Ujjania & Kohli (2011) also reported morphological difference in C. catla inhabiting MBS (large reservoir), SD (medium reservoir) and AP (small pond) in southern Rajasthan. In other major carps, Hossain et al. (2010) applied DFA and PCA on three populations of L. calbasu from Jamuna, Halda and Hatchery and reported high morphological discrimination among them due to the environmental factors and local migration of the fish.

The separation of the stocks within the basin may be due to different biotic and abiotic factors such as food availability, salinity, temperature, which are affecting the morphometry of a fish (Rohfritsch & Borsa, 2005). Morphometric variation of a fish can be subjective to genetic, environment and the interaction between both (Cadrin, 2000). Correlation between genetic variation and morphological variation has been confirmed in natural populations (Poulet et al., 2004) and both have been widely used in population differentiation (Buth & Crabtree, 1982; Agnese, Teugels, Galbusera, Guyomard, & Volckaert, 1997; Ibanez-Aguirre, Cabral-Solis, Gallardo-Cabello, & Espino-Barr, 2006; Das et al., 2014). The individual's phenotype is more acquiescent to environmental influence is of particular importance during the early development stages (Pinheiro, Teixeira, Rego, Marques, & Cabral, 2005). The phenotypic variability may not necessarily reflect population differentiation at the molecular level (Ihssen et al., 1981). Apparently, the fragmentation of river impoundments can lead to an enhancement of pre-existing genetic differences, providing a high inter-population structuring (Esguicero & Arcifa, 2010).

In this study, the landmark protocol revealed low distinctness among the populations observed from different drainages of Ganga basin in India. Application of other stock identification tools such as life history properties, otolith chemistry, tagging experiments and genetic studies in coherent manner would be effective for planning proper management decisions and restoration of the natural populations of mrigal in the river ecosystem. The outcomes of this study would offer essential information to resource enhancement and help in delineating populations for fishery management of this commercially important fish species.

Ethical statement: authors declare that they all agree with this publication and made significant contributions; that there is no conflict of interest of any kind; and that we followed all pertinent ethical and legal procedures and requirements. All financial sources are fully and clearly stated in the acknowledgements section. A signed document has been filed in the journal archives.

Received 14-IX-2018. Corrected 09-III-2019. Accepted 02-V-2019.

ACKNOWLEDGMENTS

The authors would like to express their sincere thanks to National Bureau of Fish Genetic Resources, Lucknow, for providing necessary facilities to carry out this study. The study is a part of the National Bureau of Fish Genetic Resources (ICAR) project entitled, "Network Programme on Fish Genetic Stocks: As an Outreach Activity."

REFERENCES

Agnese, J. F., Teugels, G. G., Galbusera, P., Guyomard, R., & Volckaert, F. (1997). Morphometric and genetic characterization of sympatric populations of Clarias gariepinus and C. anguillaris from Senegal. Journal of Fish Biology, 50, 1143-1157.

AnvariFar, H., Khyabani, A., Farahmand, H., Vatandoust, S., AnvariFar, H., & Jahageerdar, S. (2011). Detection of morphometric differentiation between isolated up- and downstream populations of Siah Mahi (Capoeta capoeta gracilis) (Pisces: Cyprinidae) in the Tajan River (Iran). Hydrobiologia, 673, 41-52.

Buth, D. G., & Crabtree, C. B. (1982). Genetic variability and population structure of Catostomus santaanae in the Santa Clara drainage. Copeia, 2, 439-44.

Cadrin, S. X. (2000). Advances in morphometric identification of fishery stocks. Reviews in Fish Biology and Fisheries, 10, 91-112.

Chondar, S. L. (1999). Biology of Finfish and Shellfish (1st Ed.). India: SCSC Publishers.

Chauhan, T., Lal, K. K., Mohindra, V., Singh, R. K., Punia, P., Gopalakrishnan, A., Sharma, P. C., & Lakra, W. S. (2007). Evaluating genetic differentiation in wild populations of the Indian major carp, Cirrhinus mrigala (Hamilton-Buchanan, 1882): Evidence from allozyme and microsatellite markers. Aquaculture, 269, 135-149.

Chen, Y., Bart Jr, H. L., & Teng, F. (2005, November). A content-based image retrieval system for fish taxonomy. In Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval (pp. 237-244). ACM.

Daniel, R. J. R. (2001). Endemic fishes of the Western Ghats and the Satpura hypothesis. Current Science, 81, 240-244.

Das, R., Mohindra, V., Singh, R. K., Lal, K. K., Punia, P., Masih, P., Mishra, R. M., & Lakra, W. S. (2012). Intraspecific genetic diversity in wild Catla catla (Hamilton, 1822) populations assessed through mtDNA cytochrome b sequences. Journal of Applied Ichthyology, 28(2), 280-283.

Das, S. P., Bej, D., Swain, S., Mishra, C. K., Sahoo, L., Jena, J. K., Jayasankar, P., & Das, P. (2014). Genetic divergence and structure of Cirrhinus mrigala populations from peninsular rivers of India, revealed by mitochondrial cytochrome b gene and truss morphometric analysis. Mitochondrial DNA, 25(2), 157-64.

Dahanukar, N. (2010). Cirrhinus mrigala. The IUCN Red List of Threatened Species 2010: e.T166146A6183216. Retrieved from http://dx.doi.org/10.2305/IUCN. UK.2010-4.RLTS.T166146A6183216.en

Dunteman, G. H. (1989). Principal components analysis. Sage University Paper series on Quantitative Applications in the Social Sciences. Beverly Hills, CA: Sage Publications.

Elliott, N. G., Haskard, K., Koslow, J. A. (1995). Morphometric analysis of orange roughly (Hoplostethus atianticus) off the continental slope of Southern Australia. Journal of Fish Biology, 46, 202-220.

Esguicero, A. L. H., & Arcifa, S. A. (2010). Fragmentation of a Neotropical migratory fish population by a century old dam. Hydrobiologia, 638, 41-53.

FAO (2006-2014) FishStat Plus: Universal software for fishery statistical time series. FAO Fisheries and Aquaculture Department. Rome. Retrieved from http://www.fao.org/fishery/statistics/software/ fishstat/en

Frimodt, C. (1995). Multilingual illustrated guide to the world's commercial warm water fishes. Oxford, Osney Mead: Fishing News Books.

Gopikrishna, G., Sarada, C., & Sathianandan, T. V. (2006). Truss morphometry in the Asian Seabass- Lates calcarifer. Journal of the Marine Biological Association of India, 48(2), 220-223.

Hanumanthrao, L. (1973). Studies on the biology of Cirrhinus mrigala (Ham.) of the river Godavari. Indian Journal of Fisheries, 21(2), 303-322.

Hasanat, A. M., Mollah, F. A. Md., & Alam, S. Md. (2015). Microsatellite DNA Marker Analysis Revealed Low Levels of Genetic Variability in the Wild and Captive Populations of Cirrhinus cirrhosus (Hamilton) (Cyprinidae: Cypriniformes). British Biotechnology Journal, 5(4), 206-215.

Hora, S. L., & Nair, K. K. (1940). Further observations on the bionomics and fishery of the Indian shad, Hilsa ilisha (Ham.) in Bengal waters. Records of the Indian Museum, 42(1), 35-50.

Hossain, M. A. R., Nahiduzzaman, M., Saha, D., Khanam, M. U. H., & Alam, M. S. (2010). Landmark-Based Morphometric and Meristic Variations of the Endangered Carp, Kalibaus Labeo calbasu, from Stocks of Two Isolated Rivers, the Jamuna and Halda, and a Hatchery. Zoological Studies, 49(4), 556-563.

Ibanez-Aguirre, A. L., Cabral-Solis, E., Gallardo-Cabello, M., & Espino-Barr, E. (2006). Comparative morphometrics of two populations of Mugil curema (Pisces: Mugilidae) on the Atlantic and Mexican Pacific coasts. ScientiaMarina, 70(1), 139-45.

Ihssen, P. E., Evans, D. O., Christie, W. J., Rechahn, J. A., & DesJardine, D. L. (1981). Life history, morphology, and electrophoretic characteristics of five allopatric stocks of Lake Whitefish (Coregonus clupeaformis) in the Great Lakes region. Canadian Journal of Fisheries and Aquatic Sciences, 38, 1790-1807.

Johnson, D. H. (1981). How to measure habitat: a statistical perspective. U.S. Forest Service General Technical Report RM-GTR. USA: US Forest Service.

Karakousis, Y., Triantaphyllidis, C., & Economidis, P. S. (1991). Morphological variability among seven populations of brown trout, Salmon trutta L., in Greece. Journal of Fish Biology, 38, 807-817.

Kocovsky, P. M., Adams, J. V., & Bronte, C. R. (2009). The effect of sample size on the stability of principal component analysis of truss-based fish morphometrics. Transactions of the American Fisheries Society, 138, 487-496.

Luhariya, R. K., Lal, K. K., Singh, R. K., Mohindra, V, Gupta, A., Masih, P., Dwivedi, A. K., Das, R., Chauhan, U. K., & Jena, J. K. (2014). Genealogy and phylogeography of Cyprinid fish Labeo rohita (Hamilton, 1822) inferred from ATPase 6 and 8 mitochondrial DNA gene analysis. Current Zoology, 60(4), 460-471.

Luhariya, R. K., Lal, K. K., Singh, R. K., Mohindra, V, Punia, P., Chauhan, U. K., Gupta, A., & Lakra, W. S. (2011). Genetic divergence in wild population of Labeo rohita (Hamilton, 1822) from nine Indian rivers, analyzed through MtDNA cytochrome b region. Molecular Biology Reports, 39(4), 3659-3665.

Mamuris, Z., Apostolidis, A. P., Panagiotaki, P., Theodorou, A. J., & Triantaphyllidis, C. (1998). Morphological variation between red mullet populations in Greece. Journal of Fish Biology, 52, 107-117.

McGarigal, K., Cushman, S., & Stafford, S. (2000). Multivariate Statistics for Wildlife and Ecology Research. New York, USA: Springer Verlag.

Mir, J. I., Sarkar, U. K., Dwivedi, A. K., Gusain, O. P., & Jena, J. K. (2014). Comparative Pattern of Reproductive Traits in Labeo rohita (Hamilton 1822) from Six Tropical Rivers of Ganges Basin: A New Insight. Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, 84(1), 91-103.

Mir, J. I., Sarkar, U. K., Dwivedi, A. K., Gusain, O. P., & Jena, J. K. (2013). Stock structure analysis of Labeo rohita (Hamilton, 1822) across the Ganga basin (India) using a truss network system. Journal of Applied Ichthyology, 29(5), 1097-1103.

Mir, J. I., Sarkar, U. K., Dwivedi, A. K, Gusain, O. P., Pal, A., & Jena, J. K. (2012). Pattern of Intrabasin Variation in Condition Factor, Relative Condition Factor and Form Factor of an Indian Major Carp, Labeo rohita (Hamilton-Buchanan, 1822) in the Ganges Basin, India. European Journal of Biological Sciences, 4(4), 126-135.

Mir, J. I., Sarkar, U. K., Gusain, O. P., Dwivedi, A. K, Pal, A., & Jena, J. K. (2013). Age and growth in the Indian major carp Labeo rohita (Cypriniformes: Cyprinidae) from tropical rivers of Ganga basin, India. Revista de Biologia Tropical, 61(4), 1955-1966.

Murta, A., Abaunza, P., Cardador, F., & Sanchez, F. (2008). Ontogenic migrations of horse mackerel along the Iberian coast. Fisheries Research, 89, 186-195.

Nautiyal, P., & Lal, M. S. (1988). Natural History of the Garhwal Himalayan Mahseer: Racial Composition. Indian Journal of Animal Sciences, 58(2), 283-294.

Nimalathasan, B. (2009). Determinants of key performance indicators (KPIs) of private sector banks in Srilanka: an application of exploratory factor analysis. The annals of the Stefan cel Mare University of Suceava. Fascicle of the Faculty of Economics and Public Administration, 9, 9-17.

Payne, A. I., Sinha, R., Singh, H. R., & Huq, S. (2004). A review of Ganges basin: its fish and fisheries. In R. Welcomme & T. Petr (Eds.), Proceedings of the second International Symposium on the Management of Large Rivers for Fisheries. Sustaining Livelihoods and Biodiversity in the New Millennium (vol. I) Bangkok, Thailand: FAO Regional Office for Asia and the Pacific / RAP Publication.

Pillay, T. V. R. (1957). A morphometric study of the populations of hilsa, Hilsa ilisha of the river Hoogly and of the Chilka lake. Indian Journal of Fisheries, 4(2), 344-386.

Pinheiro, A., Teixeira, C. M., Rego, A. L., Marques, J. F., & Cabral, H. N. (2005). Genetic and morphological variation of Solea lascaris (Risso, 1810) along the Portuguese coast. Fisheries Research, 73, 67-78.

Poulet, N., Berrebi, P., Crivelli, A. J., Lek, S., & Argillier, C. (2004). Genetic and morphometric variations in the pikeperch (Sander lucioperca L.) of a fragmented delta. Archiv fuer Hydrobiologie, 159, 531-554.

Quilang, J. P., Basiao, Z. U., Pagulayan, R. C., Roderos, R. R., & Barrios, E. B. (2007). Meristic and morphometric variation in the silver perch, Leiopotherapon plumbeus (Kner, 1864), from three lakes in the Philippines. Journal of Applied Ichthyology, 23, 561-567.

Rao, R. J. (2001). Biological resources of the Ganga River. Hydrobiologia, 458, 159-68.

Reddy, P V. G. K. (1999). Genetic Resources of Indian Major Carps (FAO Fisheries Technical Paper. No. 387). Rome: FAO.

Rodman, J. E., & Cody, J. H. (2003). The taxonomic impediment overcome: NSF's partnerships for enhancing expertise in taxonomy (PEET) as a model. Systematic Biology, 52, 428-435.

Rohfritsch, A., & Borsa, P. (2005). Genetic structure of Indian scad mackerel Decapterus russelli: Pleistocene variance and secondary contact in the Central Indo-West Pacific seas. Heredity, 95, 315-322.

Rohlf, F. J. (2006). tpsDig2 (Version 2.1). Stony Brook, NY: State University of New York. Retrieved from http://life.bio.sunysb.edu/morph

Rohlf, F. J., & Bookstein, F. L. (1990). Proceedings of the Michigan Morphometrics Workshop, No. 2. Michigan, USA: The University of Michigan Museum of Zoology.

Sneath, P. H. (6). Sokal, RR (1973). Numerical taxonomy. Theory and Application of Genetics, 93, 613-617.

Saini, A., Dua, A., & Mohindra, V. (2008). Comparative morphometrics of two populations of giant river catfish (Mystus seenghala) from the Indus river system. Integrative Zoology, 3, 219-226.

Sajina, A. M., Chakraborty, S. K., Jaiswar, A. K., Pazhayamadam, D. G., & Sudheesan, D. (2011). Stock structure analysis of Megalaspis cordyla (Linnaeus, 1758) along the Indian coast based on truss network analysis. Fisheries Research, 108, 100-105.

Samaee, M., Patzner, R. A., & Mansour, N. (2009). Morphological differentiation within the population of Siah mahi, Capoeta capoeta gracilis, (Cyprinidae, Teleostei) in a river of the south Caspian Sea basin: a pilot study. Journal of Applied Ichthyology, 25, 583-590.

Samaee, S. M., Mojazi-Amiri, B., & Hosseini-Mazinani, S. M. (2006). Comparison of Capoeta capoeta gracilis (Cyprinidae, Teleostei) populations in the south Caspian Sea River basin, using morphometric ratios and genetic markers. Folia Zoologica, 55, 323-335.

Sarkar, U. K., Mir, J. I., Dwivedi, A. K., Pal, A., & Jena, J. K. (2014). Pattern of Phenotypic Variation Among Three Populations of Indian Major Carp, Catla catla (Hamilton, 1822) Using Truss Network System in the Ganga Basin, India, Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, 84(4), 1005-1012.

Sharma, R. (2003). Protection of an endangered fish Tor tor and Tor putitora population impacted by transportation network in the area of Tehri dam project, Garhwal Himalaya, India. ICOET Proceedings, 83-90.

Strauss, R. E. (1985). Evolutionary allometry and variation in the body form in the South American catfish genus Corydoras (Callichthydae). Systematic Zoology, 34, 381-396.

Strauss, R. E., & Bookstein, F. L. (1982). The truss: body form reconstruction in morphometrics. Systematic Zoology, 31, 113-135.

Sugunan, V. V., & Das, M. K. (1996). Hilsa can negotiate Farakka barrage. The Inland Fisheries News, 1, 2-2.

Talwar, P. K., & Jhingran, A. G. (1991). Inland fishes of India and adjacent countries. 2. Rotterdam: AA Balkema Publishers.

Turan, C., Erguden, D., Gurlek, M., Bacusta, N., & Turan, F. (2004). Morphometric Structuring of the Anchovy (Engraulis encrasicolus L.) in the Black, Aegean and Northeastern Mediterranean Seas. Turkish Journal of Veterinary and Animal Sciences, 28, 865-871.

Turan, C., Yalcin, S., Turan, F., Okur, E., & Akyurt, I. (2005). Morphometric comparisons of African catfish, Clarias gariepinus, populations in Turkey. Folia Zoologica, 54, 165-172.

Tzeng, T. D. (2004). Morphological variation between populations of spotted mackerel (Scomber australasicus) off Taiwan. Fisheries Research, 68, 45-55.

Ujjania, N. C., & Kohli, M. P. S. (2011). Landmark-Based Morphometric Analysis for Selected Species of Indian Major Carp (Catla catla, Ham. 1822). International Journal of Food, Agriculture and Veterinary Sciences, 1(1), 64-74.

Veasey, E. A., Schammass, E. A., Vencovsky, R., Martins, P. S., & Bandel, G. (2001). Germplasm characterization of Sesbania accessions based on multivariate analyses. Genetic Resources and Crop Evolution, 48, 79-90.

Zheng, G., Zheng, Y., Zhu, X., Luo, J., & Xia, S. (1999). The genetic markers of Cirrhina molitorella, C. mrigala and Labeo rohita from RAPD. Journal of Shanghai Fisheries University, 8, 215-220.

Arvind Kumar Dwivedi (1) *, Uttam Kumar Sarkar (2), Javaid Iqbal Mir (3), Praveen Tomat (4), & Vipin Vyas (5)

(1.) ICAR-National Bureau of Fish Genetic Resources, Lucknow-226002 (U.P.). Present Address: Wildlife Institute of India, Dehradun-248001 (Uttarakhand), India; arvindbio@rediffmail.com

(2.) ICAR-Central Inland Fisheries Research Institute, Barrackpore, Kolkata-700120 (W. B.), India; usarkar1@rediffmail.com

(3.) ICAR-Directorate of Coldwater Fisheries Research, Bhimtal, Nainital-263136, (Uttarakhand), India; r.javaid@rediffmail.com

(4.) Department of Zoology, Govt. Motilal Vigyan Mahavidyalaya (MVM), Bhopal-462008 (M.P), India; tamot03@yahoo.co.in

(5.) Department of Environmental Science and Limnology, Barkatullah University, Bhopal-462026 (M.P), India; vyasvipin992@gmail.com

Caption: Fig. 1. Collection localities of C. mrigala from ten drainages of Ganga basin.

Caption: Fig. 2. Locations of 12 landmarks and truss network used for shape analysis. Land marks refer to 1. anterior tip of snout at upper jaw 2. most posterior aspect of neurocranium (beginning of scaled nape) 3. origin of dorsal fin 4. end of dorsal fin 5. anterior attachment of dorsal membrane from caudal fin 6. posterior end of vertebrae column 7. anterior attachment of ventral membrane from caudal fin 8. origin of anal fin 9. insertion of pelvic fin 10. insertion of pectoral fin 11. posterior end of eye 12. anterior end of eye.

Caption: Fig. 3. Plot of the factor scores for PC1 and PC2 of morphometric measurements for C. mrigala from ten drainages of Ganga River basin.

Caption: Fig. 4. Discriminant analysis plot of C. mrigala from ten drainages of Ganga basin.
TABLE 1
Collection localities, sample size and size statistics
(based on the Standard Length) of C. mrigala

Rivers           Sampling sites            Coordinates

Tons        Chakghat, Madhya Pradesh   24[degrees]78'15" N
                                       81[degrees]78'20" E
Son         Beohari, Madhya Pradesh    24[degrees]1'20" N
                                       81[degrees]23'35" E
Chambal     Kota, Rajasthan            26[degrees]14'38" N
                                       78[degrees]10'12" E
Kalisindh   Shivpuri, Madhya Pradesh   25[degrees]26'60" N
                                       77[degrees]39'0" E
Ken         Patan, Madhya Pradesh      24[degrees]41'40" N
                                       79[degrees]54'95" E
Betwa       Bhojpur, Madhya Pradesh    23[degrees]45'39" N
                                       78[degrees]14'93" E
Ganga       Narora, Uttar Pradesh      28[degrees]4'14" N
                                       78[degrees]44'54" E
Sharda      Palia, Uttar Pradesh       22[degrees]49'60" N
                                       75[degrees]47'60" E
Ghaghra     Faizabad, Uttar Pradesh    26[degrees]75'25" N
                                       81[degrees]99'40" E
Gomti       Lucknow, Uttar Pradesh     26[degrres]52'22" N
                                       80[degrees]54'58" E

Rivers      Sample size   Size range (cm)   Mean SL (cm) [+ or -] SD

Tons            62          27.00-93.00       51.30 [+ or -] 15.85
Son             78          16.00-92.00       51.31 [+ or -] 17.00
Chambal         58          28.00-92.00       53.54 [+ or -] 14.14
Kalisindh       41          16.00-85.00       46.22 [+ or -] 15.60
Ken             38          16.00-92.00       51.38 [+ or -] 17.60
Betwa           20          30.50-80.00      47.08 [+ or -] 125.36
Ganga           26          31.00-71.00       47.69 [+ or -] 10.76
Sharda          20          30.80-92.00       50.30 [+ or -] 16.59
Ghaghra         19          26.50-88.00       45.89 [+ or -] 14.86
Gomti           19          16.00-85.00       45.25 [+ or -] 18.47

TABLE 2
Eigen values, percentage of variance and percentage of
cumulative variance for the 8 PCs in case of morphometric
measurements for C. mrigala

                           Initial Eigenvalues
Component   Eigen-values      % of Variance      Cumulative %

1              44.590            68.599             68.599
2              4.414              6.791             75.391
3              4.061              6.247             81.638
4              2.778              4.274             85.911
5              1.651              2.539             88.451
6              1.398              2.150             90.601
7              1.210              1.861             92.462
8              1.047              1.611             94.073

TABLE 3
Contribution to discriminant functions (DFs) of morphometric
variables of C. mrigala collected from 10 locations
(* indicates largest absolute correlation between each
variable and any discriminant function)

Character     DF1       DF2       DF3      DF4
code        (37.9%)   (23.5%)   (19.4%)   (6.9%)

10-11       -.842 *    .260      .329     -.066
2-6         .566 *     -.169     -.520     .213
4-6          .176     -.642 *    -.011    -.035
1-11         -.267    .565 *     .337     -.224
4-8          -.335     -.048    .665 *    -.424
3-10         .437      -.105     -.079     .203
1-10         -.380     .450      -.243    -.157
8-9          .305      -.306     .204      .143

Character    DF5      DF6      DF7       DF8
code        (5.6%)   (3.8%)   (2,4%)   (0.5%)

10-11        .228     .037    -.194     .143
2-6          .006     .254     .509     .107
4-6         -.353     .163     .074     .632
1-11        -.550     .371    -.071     .020
4-8          .269    -.202    -.144     .360
3-10        .751 *    .391    -.128     .133
1-10        -.179    .630 *    .327     .182
8-9          .418     .302     .427    -.550 *

TABLE 4
Percentage of specimens classified in each group and
after cross validation for morphometric measurements
for C. mrigala from ten drainages of Ganga River basin
45.7% of original grouped cases correctly classified,
39.1% of cross-validated grouped cases correctly
classified

Rivers      Tons   Son    Chambal   Kalisindh   Ken

                       Original (%)

Tons        50.0   16.1     3.2        3.2       .0
Son         1.3    57.7     2.6        3.8      6.4
Chambal     6.9    5.2     24.1        6.9      17.2
Kalisindh   9.8    2.4      9.8       31.7      12.2
Ken         10.5   2.6      7.9       10.5      44.7
Betwa        .0     .0     15.0        .0        .0
Ganga        .0    11.5     3.8       19.2       .0
Sharda      10.0   5.0      5.0        5.0      10.0
Ghaghra      .0    10.5     .0         .0       21.1
Gomti        .0     .0     10.5       10.5      10.5

                     Cross-validated (%)

Tons        46.8   16.1     3.2        4.8       .0
Son         2.6    52.6     2.6        5.1      6.4
Chambal     6.9    5.2     19.0        6.9      17.2
Kalisindh   9.8    2.4      9.8       26.8      14.6
Ken         10.5   2.6      5.3       13.2      42.1
Betwa        .0     .0     15.0        .0        .0
Ganga        .0    15.4     3.8       19.2       .0
Sharda      10.0   5.0      5.0        5.0      10.0
Ghaghra      .0    10.5     .0         .0       21.1
Gomti        .0     .0     10.5       10.5      10.5

Rivers      Betwa   Ganga   Sharda   Ghaghra   Gomti   Total

                             Original (%)

Tons        12.9     1.6     9.7       .0       3.2    100.0
Son          2.6     5.1     7.7       6.4      6.4    100.0
Chambal      1.7     8.6     10.3      8.6     10.3    100.0
Kalisindh    .0     19.5     2.4       .0      12.2    100.0
Ken          2.6    15.8      .0       2.6      2.6    100.0
Betwa       65.0    10.0     5.0       .0       5.0    100.0
Ganga        7.7    50.0      .0       7.7      .0     100.0
Sharda       .0      5.0     40.0     20.0      .0     100.0
Ghaghra      .0     10.5     15.8     42.1      .0     100.0
Gomti        5.3     .0       .0       .0      63.2    100.0

                           Cross-validated (%)

Tons        12.9     1.6     11.3      .0       3.2    100.0
Son          2.6     5.1     7.7       7.7      7.7    100.0
Chambal      5.2     8.6     10.3      8.6     12.1    100.0
Kalisindh    .0     22.0     2.4       .0      12.2    100.0
Ken          2.6    15.8      .0       5.3      2.6    100.0
Betwa       60.0    10.0     5.0       .0      10.0    100.0
Ganga        7.7    46.2      .0       7.7      .0     100.0
Sharda       .0      5.0     15.0     45.0      .0     100.0
Ghaghra      .0     10.5     42.1     15.8      .0     100.0
Gomti       10.5     .0       .0       .0      57.9    100.0
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Author:Kumar Dwivedi, Arvind; Kumar Sarkar, Uttam; Iqbal Mir, Javaid; Tomat, Praveen; Vyas, Vipin
Publication:Revista de Biologia Tropical
Date:Jun 1, 2019
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