Md. Yeamin Hossain
Department of Fisheries Faculty of Agriculture, University of Rajshahi
Rajshahi 6205, Bangladesh
Ferdous Ahamed
Department of Fisheries Faculty of Agriculture University of Rajshahi
Rajshahi 6205, Bangladesh
Jun Ohtomi
Faculty of Fisheries Kagoshima University
4-20-50 Shimoarata Kagoshima 890-0056
Japan
Md. Mosaddequr Rahman
Department of Fisheries Faculty of Agriculture University of Rajshahi
Rajshahi 6205, Bangladesh
Md. Abu Sayed Jewel
Department of Fisheries Faculty of Agriculture University of Rajshahi
Rajshahi 6205, Bangladesh
Md. Akhtar Hossain
Department of Fisheries Faculty of Agriculture University of Rajshahi
Rajshahi 6205, Bangladesh
Anannya Sen Tumpa
Department of Fisheries Faculty of Agriculture University of Rajshahi
Rajshahi 6205, Bangladesh
E lgorban M. Abdallah
Center of Excellence of Biotechnology Research King Saud University
Riyadh 11451 Saudi Arabia
Bangladesh, Condition factor, Eutropiichthys vacha, Length-weight relationship
Bogra district, Bangladesh.
Resource Development and Management
Aquatic animal
This study was conducted in the Jamuna River (Sariakandi, Bogra region: Latitude 24°88’ N; Longitude 89°57’ E) of Bangladesh. The Jamuna is the main distributary of the Brahmaputra River, Bangladesh and is one of the world’s largest rivers, ranked among the top three rivers in terms of sediment and water discharge volumes. The high water and sediment discharge are attributed to the monsoon flooding and tectonic setting which supplies profuse sediment from the Himalayan uplift into the subsiding Bay of Bengal. A large number commercially important species in this river are targeted by both small and large scale fishermen throughout the year. The river is also believed to be an important spawning and feeding ground for many riverine fish species of Bangladesh. Sampling and laboratory analysis: Samples were collected on a seasonal basis from the commercial catches landed at the Sariakandi fish landing center of Bogra region during March 2010 to February 2011. The main gears used by the commercial fishers include traditional fishing gears; jhaki jal (cast net), tar jal (square lift net) and dughair (conical trap). The fresh samples were immediately chilled in ice on site and fixed with 10% buffered formalin upon arrival in the laboratory. All morphometric measurements were conducted. The fixed specimens were individually measured, and weighed. Total length (TL), fork length (FL) and standard length (SL) were measured to the nearest 0.01 cm using digital slide calipers (Mitutoyo, CD-15PS) and total body weight (BW) was measured using an electronic balance (Shimadzu, EB-430DW) with 0.01 g accuracy. length -weight and length -length relationships: The relationship between length and weight was calculated using the expression: W= a Lb, where W is the total body weight (BW, g), L the total length (TL, cm), fork length (FL, cm) or standard length (SL, cm), a is the intercept of the regression and b is the slope or regression coefficient. Parameters a and b of the weight-length relationship were estimated by linear regression analysis based on natural logarithms: ln(W) = ln(a) + b ln(L). Additionally, 95% confidence limits of the parameters a and b and the statistical significance level of r2 (coefficient of determination) were estimated. The latter as an indicator of the quality of the linear regressions. The coefficient of determination (r2) is the square of the correlation coefficient (r). The r2 value of the coefficient lies between 0 and 1 and it describes the proportion of the variation of one of the correlated variables which can be explained by the variation of the other variable. Although the r2 may indicate a relationship between the variables, the correlation may not be significant because of small sample sizes or correlation in comparison to the other value. In this case, a one-tailed t-test, t = r √ (n-2)/ √ (1-r2) for independent means might be applied to express correlation between two variables. In addition, to confirm whether b values were significantly different (p≤0.05) from the isometric value (b≈3), we applied the equation ts= (b-3) / sb, where ts is the sample t-test value, b is the slope and sb is the standard error of the slope (b). The comparison between ts and tabled, critical values for b allowed determination statistical significance and their classification as isometric (b≈3) or allometric (negative allometry for b<3 or positive allometry for b>3). If there are several LWRs > 3 and the a and b parameters are available for the species, then a plot of log a over b which form a straight line can be used to detect outliers. In this study, prior to the regression analysis of ln BW on ln TL, ln-ln plots of length and weight values were performed for visual inspection of outliers, with extremes being excluded from the regression analyses. Furthermore, SL vs. TL; SL vs. FL; and TL vs. FL relationships were estimated by linear regression. Condition factors: Fulton’s condition factor (KF) was calculated using the equation: KF =100× (W/L3), where W is the total body weight (BW, g) and L is the total length (TL, cm). The scaling factor of 100 was used to bring the KF close to unit. The relative condition factor (KR) for each individual was calculated via the equation: KR = W/a×Lb, where W is the BW, L is the TL and a and b are the LWLWR parameters. In addition, the allometric condition factor (KA) was calculated: W/Lb, where W is the BW, L is the TL and b is the LWLWRs parameter. Furthermore, relative weight (WR) was calculated as WR = (W / WS) × 100, where W is the weight of a particular individual and WS is the predicted standard weight for the same individual as calculated by WS = a Lb where the a and b values are obtained from the relationships between TL and BW. Form Factor: The form factor (a3.0) for each species was calculated as: a3.0 = 10 log a – s (b-3), where a and b are regression parameters of LWRs and S is the regression slope of log a vs b. During this study, a mean slope S = -1.358 was used for estimating the form factor because the information on LWRs is not available for these species for estimation of the regression (S) of ln a vs b. Statistical Analysis: Statistical analyses were performed using Microsoft® Excel-add-in-DDXL, GraphPad Prism 5 and VassarStats online software (http://faculty.vassar.edu/lowry/VassarStats.html). Tests for normality of each group were conducted by visual assessment of histograms and box plots and confirmed using the Kolmogorov-Smirnov test.
Sains Malaysiana 42(3)(2013): 265–277
Journal