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Trends in morphological relationships among Gangetic Cyprinids

Citation

Dwivedi, Arvind; Verma, Hariom; Dewan, Saurabh; Verma, Sushil (2022), Trends in morphological relationships among Gangetic Cyprinids, Dryad, Dataset, https://doi.org/10.5061/dryad.hdr7sqvdx

Abstract

Cyprinidae is the predominant and most diverse taxonomic group of freshwater fishes, exhibiting enormous diversity in shape, size and biology. The morphological diversity of Indian Cyprinids especially the Gangetic Cyprinids is largely unquantified. Geometric morphometrics (GM) approach is an efficient tool to quantify overall body shape variations and has wide application in taxonomic, evolutionary and ecological studies. In this study, digital photographs of 47 Cyprinid fish species from Ganga River were used to measure geometric body shape so as to evaluate interspecific morphometric relationships. Combined results of principal component analysis (PCA), canonical variate analysis (CVA) and Cluster analysis (CA) revealed interspecific differences in Cyprinid fishes. Phenogram showed that all the species of different genus like Barilius, Garra, Labeo, Pethia, Schizothorax and Tor are clustered together in their respective group. This indicates that species of respective genus share common phenotypic traits and there is evolutionary relatedness among them. The present study showcases the efficient use of digital images and strongly supports the application of costeeffective GM method in discriminating distinct groups and representing the morphological relationships between species of Cyprinid fishes. Overall, we suggest that the body shape disparity and proximity among groups can therefore be used as an effective approach in rapid diagnosis of a species and understanding evolutionary relationships between species.

Methods

Study Area

The river Bhagirathi which descend from the Gaumukh glacier in Uttarakhand of upper Himalayas at an elevation of 4,100 m above mean sea level, confluence with river Alaknan­da at Devprayag and acquire new name called “Ganga”, India’s longest river which flows for nearly 2,525 km before joining Bay of Bengal. The river basin has a catchment area of 861,452 km2. The distinctive geography, hydrology and climatic conditions in the upper, middle and lower stretch of Ganga River support rich fish diversity (Sarkar et al., 2012; Dwivedi et al., 2019). The upper stretch of river habitat is of mountainous nature with torrential water flow, deep gorges and steeped gradients. River flow in the upper stretch is altered by human interventions in form of dams. The middle stretch of river is wide and flows in flood plains on bed of fine sand, and altered through diversion/abstraction by barrages and subjected to high degree of pollution loads from household, industrial and agricultural activities. This lower stretch is the estuarine zone with massive loads of sediment deposited between Kolkata city and Gangasagar and tidal variation dominates river hydrology combining with house hold and industrial effluents affecting water quality and aquatic life. The Ganga River system harbours 65 indigenous fish species belonging to family Cyprinidae including the endangered golden mahseer, Tor putitora, economically important aquaculture preferable Indian major carps (Labeo rohita, Cirrhinus mrigala and Gibelion catla) and ornamental barbs (Sarkar et al., 2012).

Sampling

Sampling was conducted once at all the 13 sites in the Ganga River and its head tributary, Bhagirathi River using gill nets (30 × 3.0 m) with stretched mesh size of 19–152 mm during pre monsoon and post monsoon season between September, 2018 and August, 2019 (Fig. 1). Fish were identified using keys given in taxonomic literature (Hamilton, 1822; Talwar, 1991; Talwar and Jhingran, 1991; Das et al., 2010; Jayaram, 2010). Valid nomenclature for names of species was adopted as per the Catalogue of Fishes of the California Academy of Sciences (Fricke et al., 2019). Preserved specimens can affect geometric morphometric analyses (Fruciano et al., 2020), therefore fresh sampled specimens were uniformly placed on laminated sheets of graph paper, and the body posture and fins were teased into a natural position. Each individual was labelled with a specific code for identification. A digital camera (Cyber Shot DSC-W300; Sony, Tokyo, Japan) was used to capture the digital lateral image of the left side of each individual.

Landmark based Geometric Morphometric data

tpsUtil (ver. 1.52, F. J. Rohlf, see http://life.bio.sunysb.edu/ee/rohlf/software.html, accessed 8 September 2019) was used for converting graphics images in to tps format. On each specimen, 14 homologous landmarks were digitised using the software tpsDig (ver. 2.16, F. J. Rohlf, see http://life.bio.sunysb.edu/ee/rohlf/software.html, accessed 8 September 2019; Fig. 2). The landmarks were selected on the ability to best describe the geometry of the form under study (Dwivedi, 2019). Scale factor using a set scale option for reference length was performed on each specimen. Two–dimensional x,y coordinate data for 14 landmarks of all the specimens were extracted using software ImageJ (ver. 1.50i, see http://imagej.nih.gov/ij/, accessed 10 September 2019) and saved in TPS format.

Statistical Analysis

Landmarks were converted to shape coordinates by Procrustes superimposition (Rohlf and Slice, 1990) standardising each specimen to unit centroid size, or an estimate of overall body size (Bookstein, 1991). Shape coordinates data extracted from 14 homologous landmarks of fish images covering whole body form were superimposed to remove size effect, which was evident from close values of Procrustes (1.920) and tangent sums of squares (1.888). Procrustes ANOVA on the shape and size was performed to determine if the centroid size has an effect on the shape. The pattern of covariation between shape and size was analysed using partial least squares (PLS) analysis (Bookstein et al., 2003; Zelditch et al., 2004). Principal component analysis (PCA), canonical variate analysis (CVA) and cluster analysis (CA) were performed to assess the shape variation among species. PCA was used to outline groups of samples and to identify influential variables (Johnson and Wichern, 1998). To extract loading of influential variables on principal components, 2d–landmark coordinates were converted to landmark distances using PAST (Hammer, 2001). The data generated by PAST were log-transformed to conserve allometries and to standardize variances (Strauss 1985). The effect of size eliminated using the following (Elliott et al., 1995): Madj = M(Ls/L0)b, where M was the original measurement, Madj was the size adjusted measurement, L0 was the standard length of the fish, Ls was the overall mean of standard length for all fish from all samples in each analysis, and b was estimated for each character from the observed data as the slope of the regression of log(M) on log(L0) using all fish from each group. Standard length (SL, character code 1-6) was excluded because SL was used as a basis for transformation (Mamuris et al., 1998), and thus, 90 morphometric variables were retained. CVA is a method of finding the set of axes that allows for the greatest possible ability to discriminate between two or more groups (Mardia et al., 1979). To interpret similarity–dissimilarity in shape of species, CA was applied to extract phenogram from the squared Mahalanobis distance between the group centroids and CVA scores. All statistical analyses (PLS, PCA, CVA and CA) were performed using the MorphoJ (ver. 1.06d, see http://www.flywings.org.uk/MorphoJ_page.htm, accessed 10 September 2019; Klingenberg, 2011).