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Discrimination Analysis of Hybrid Pangasianodon Hypophthalmus (Sauvage, 1983) (♀) × Pangasius Nasutus (♂) (Bleeker, 1976) and Its Parental Species

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Discrimination Analysis of Hybrid Pangasianodon Hypophthalmus (Sauvage, 1983) (♀) × Pangasius Nasutus (♂) (Bleeker, 1976) and Its Parental Species Journal of Survey in Fisheries Sciences 5(2) 49-63 2019 Discrimination analysis of hybrid Pangasianodon hypophthalmus (Sauvage, 1983) (♀) × Pangasius nasutus (♂) (Bleeker, 1976) and its parental species Mohamed Yusoff S.F.1; Christianus A.1,2*; Ismail M.F.S.1 ; Esa Y.1 ; Hassan M.D.3 ; Hamid. N.H.3; Siti Nadia A.B.3; Zulkifle M.S. 2 Received: November 2018 Accepted: January 2019 Abstract Comparative analysis was performed to discriminate a hybrid produced from the crossbreed of Pangasianodon hypophthalmus (♀) and Pangasius nasutus (♂) and its parental species based on morphology appearances and morphometric characters. Morphological structures of the vomerin and palatal teeth varied between the hybrid and both parents. Results of the univariate analysis revealed 22 morphometric characters were significantly different between the hybrid and its parental species. Under the stepwise discriminate function analysis, the first Function explained 86.10% of total variations and 13.90% in Function 2. Of the 30 characters, only 10 characters which include prepelvic, caudal peduncle length, dorsal fin length, pectoral fin length, anal fin height, anal fin length, adipose fin length, interorbital length, distant to isthmus, and predorsal length can be used to significantly differentiate these species. The predicted fish groups exhibited characters which 100% differentiate and validate them into their respective group. Examination on vomerin and palatal teeth distinct the hybrid and its parental species. Keywords: Discriminate function analysis, morphometric, Pangasianodon hypophthalmus, Pangasius nasutus, hybrid 1- Department Aquaculture, Faculty of Agriculture, 2- Institute of Bioscience, 3- Aquatic Animal Health Unit, Faculty of Veterinary Medicine, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor Darul Ehsan, Malaysia. *Corresponding author's Email: [email protected] 50 Mohamed Yusoff et al., Discrimiantion analysis of hybrid Pangasianodon hypophthalmus … Introduction principal components and discriminant Fish stock can be differentiating based factorial analyses have contributed to on their morphological variation (Sokal the identification of fish stocks et al., 2009). This stock identification is efficiently (Kuszniers et al., 2008; crucial to facilitate the management of Cronin-Fine et al., 2013). domestication, and sustainability of Morphometric and multivariate aquaculture production (Bailey, 1997; statistical procedures are useful Ibánez et al., 2017). According to combination for testing and graphically Ezeafulukwe et al. (2015), display the differences in fish stock determination of phenotypic variation (Baur and Leuenberger, 2011). using morphometric characters and Selection of characters for meristic counts is the most common morphometric analysis is also important method applied in delineating stocks of to maximize the effectiveness of stock fishes. Morphometric characters are discrimination (Begg et al., 1999). useful in biological studies as it allows Multivariate analysis is applied to quantitative descriptions of organisms. reveal the morphometric variables for This quantitative approach allows stock identification (Kusznierz et al., researchers to differentiate the 2008; Specziár et al., 2009; Yakubu and organisms by comparing the body shape Okunsebor, 2011; Cronin-Fine et al., (Gelsvartas, 2005), and combining 2013). Multivariate morphology has descriptions such as colour and size been used in population studies of (Muchlisin, 2013). Morphometric is various fishes for stock discrimination important to distinguish the phenotypic (Tzeng et al. 2001; Tzeng 2004; Von of fish with variability in term of Cramon-Taubadel et al., 2005; Maynou growth, development, and maturation and Sarda, 1997; Anastasiadou and (Begg and Waldman, 1999). Leonardos, 2008; Ezeafulukwe et al., Identification of fish in different 2015; Banerjee et al., 2017). populations using morphometric Particularly with the existence of a new measurement was applied in previous hybrid, description of the studies (Tzeng, 2004; Torres et al., morphological variation becomes 2014; Chen et al., 2015) because of its important. Furthermore, establishment simplicity and direct application of morphological differentiation for (Bronte et al., 1999; Hockaday et al., identification with the least assumption 2000). is possible when using samples of In recent decades, advanced known hybrid (Neff and Smith, 1979). techniques through morphometric Previous studies (Gustiano et al., 2003; methods have successfully discriminate Baharuddin et al., 2014) were able to fish stocks within fish population discriminate the pangasiids species. (Dwivedi and Dubey, 2013), detecting Lack of adequate information on the differences or similarity among groups discrimination of hybrid in the field has (To and Ci, 2015). Statistical tools like led to the current study for the multivariate techniques such as assessment of phenotypic variations Journal of Survey in Fisheries Sciences 5(2) 2019 51 resulting in important characters being Table 1: Description of the characters used in the study. derived for rapid discrimination and Character Landmarks Description of classification. codes characters SL 1 Standard length HL 2 Head length Materials and methods SNL 3 Snout length Fish Samplings ASW 3A Anterior snout width Samples of hybrid (n=30) were from a PSW 3B Posterior snout width HD 4 Head depth local fish farmer in Temerloh, Pahang HW 5 Head width cultured to adult stage (14 month old). PREDL 6 Predorsal length While samples of P. hypophthalmus CPL 7 Caudal peduncle length CPD 8 Caudal peduncle depth (n=40) and P. nasutus (n=10) were PFL 9 Pectoral fin length taken from our collections. PSL 10 Pectoral spine length DFL 11 Dorsal fin length Data Collections DSL 12 Dorsal spine length PEFL 13 Pelvic fin length Morphometric measurements were AFH 14 Anal fin height taken according to Gustiano (2003). AFL 15 Anal fin length Thirty parts of the fish body were ADIFH 16 Adipose fin height ADIFW 17 Adipose fin width measured (Fig. 1) and character codes ED 18 Eye diameter and landmarks were employed to MW 19 Mouth width represent the description of characters LJL 20 Lower jaw length (Table 1). Prior to measurement, fish IL 21 Interorbital length DSI 22 Distance snout to were anesthetized using MS 222 (35 isthmus mg/L) then measured on a measurement PL 23 Postocular length MAXBL 24 Maxillary barbell length board. Measurements of characters MANBL 25 Mandibulary barbell were taken using digital vernier calliper length BW 26 Body width to the nearest 0.1 mm. PREPL 27 Prepectoral length PREPEL 28 Prepelvic length Data Analyses Data were analysed using univariate and multivariate analyses of variances to test the significance in the morphometric characters. An allometric formula by Elliott et al., (1995) was employed to remove the length effect of the samples due to the morphometric characters (Turan et al., 2005). The formula was calculated as Madj = M b Figure 1: Measurement of the body (A) and (LS/LO) where, Madj: size adjustment head (B) parts of each species. measurement; M: Original (Source: Gustiano, 2003). measurement; LO: standard length; LS: 52 Mohamed Yusoff et al., Discrimiantion analysis of hybrid Pangasianodon hypophthalmus … overall mean of standard length; b: coloration on the head and upper part of estimated for each character from the body may varies from black to grey for observed data as the slope of the P. hypophthalmus, with merely light regression of LogM on Log LO using all gold for P. nasutus and light green for samples in all groups (species). hybrid. Besides body colour, the For univariate analysis, the size- vomerin and palatal teeth of hybrid was adjusted data were submitted to one- distinctively different from its parents. way analysis of variance (ANOVA) The dentition of P. nasutus shows large followed by post hoc multiple tests and nearly square vomerin, meanwhile using Duncan at 5% significant level to P. hypophthalmus with two palatal compare the variation of each character bands. between species. Multivariate analysis under the discriminate factor analysis (DFA) (Ramsay et al., 2009) was carried out to assess the morphological variation. Discriminate analysis was employed to select more important variables as the predictors for the determination of the hybrid and its parental species. The Mahalanobis squared distance was used in a stepwise method. Functions derived are useful in Figure 2: Morphological appearance and explaining the eigenvalue values that closer look of on the maxillary and classified the components. Wilk’s palatal dentition of (A) Pangasianodon hypophthalmus; (B) lambda was projected to test the Pangasius nasutus (C) hybrid. significant of the combination of the variables in different dimension. It was Univariate Analysis defined based on value close to 1. Univariate analysis demonstrated all of Higher discriminator variables indicates the characters were significantly fish within the same group, while value different (p<0.05) among all three close to 0 shows the variability is due to species except for HL, SNL, ASW, species differences. For visual detection PSW, ED and PPEL (Table 2). Means and classification, scatter plots of sizes of every character varied among canonical scores were constructed. All species and hybrid was closer to P. statistical analyses were performed hypophthalmus as most of the using SPSS software version 20.0 characters seem to be insignificant (p>0.05) between these two species. Results and discussion In general, P.
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