Cumulative inbreeding rate in hatchery-reared indian major carps of Karnataka and Maharashtra states

The state fisheries department hatcheries are the major suppliers of seed to the farmers in Karnataka and Maharashtra. The brood stocks of these hatcheries are genetically closed units. In the present study, effective population size and cumulative inbreeding rates were estimated. The cumulative inbreeding rates ranged from 2.69 to 13.75, 8.63 to 15.21 and 3.02 to 5.88 per cent for catla, mrigal and rohu, respectively, in Karnataka state hatcheries. In Maharashtra, the cumulative inbreeding rates for catla ranged from 7.81 to 39.34 per cent and it was 5.84 to 14.09 and 2.46 to 10.20 per cent for mrigal and rohu, respectively. To estimate the inbreeding rates in future generations, predictive models were developed using linear regression, and polynomial and power equations separately for each hatchery. Their multiple correlation and standard errors suggested that simple linear regression can predict the future inbreeding rate efficiently

Forecasting quarterly landings of total fish and major pelagic fishes and modelling the impacts of climate change on Bombay duck along India’s north-western Gujarat coast

557-565Quarterly landings or catches of total fishes and the major pelagic fish species, were forecasted using the methods and models viz. autoregressive integrated moving average (ARIMA), non-linear autoregressive (NAR) artificial neural network (ANN), autoregressive integrated moving average with exogenous inputs (ARIMAX), non-linear autoregressive with external (exogenous) inputs (NARX) artificial neural network. The models were also developed by considering only two important variables (differ for total fish and selected fish species) obtained from the ANN model. These simplified models proved nearly as good in their predictions. Simulated sea surface temperature (SST) for the A2 climate change scenario was used as an input for the NARX model to estimate the catches of Bombay duck over a short term (2020 – 2025) and a long term (2030 – 2050) with the last two years’ (2012 – 2013) average catch of training data as a benchmark. The catches increased on average by 41 % in the short term but decreased by 17.72 % in the long term

Morphometric and meristic variation of congeneric sciaenid fishes Otolithes cuvieri Trewavas, 1974 and Otolithes ruber (Schneider, 1801) from Maharashtra, west coast of India

80-86Two closely related species Otolithes cuvieri, Trewavas, 1974 and Otolithes ruber, (Schneider, 1801) have been differentiated based on morphometric and meristic traits. A simple yet useful criterion based on a pair of canine teeth present on the upper and lower jaw as well as position of the mouth is currently used to differentiate two congeneric sciaenid fish species the O. cuvieri and O. ruber. Findings of the present study indicated that simply two morphometric and meristic characters are sufficient to differentiate these two species. MANOVA (Multivariate analysis of variance) and stepwise discriminant function were used to decide the morphometric traits, significant for differentiation of the species of family Sciaenidae. Discriminant function analysis revealed that 98 % of the species were correctly classified based on five morphometric characters namely Pre-pectoral fin length (PPFL), Pre-anal fin length (PAL), Post orbital head length (POHL), Post anal fin length (POAL) and Body depth (BD). The m-transformed morphometric traits were found to be useful tools in generating canonical variables in differentiating the species. The first canonical variables showed altogether 98 % variance. The scatter plots by first three canonical variables have well differentiated the species. Two meristic characters such as the number of gillrakers present on lower limb of first gill arch and figure of arborescent appendages on the swim bladder are important in differentiation of these species

Comparison between different modeling techniques for assessing the role of environmental variables in predicting the catches of major pelagic fishes off India’s north-west coast

The contribution of four variables, namely Chlorophyll-a (Chl-a), Sea Surface Temperature (SST), diffuse attenuation coefficient (Kd_490 or Kd), and Photosynthetically Active Radiation (PAR), in predicting the catches of major pelagic fish species (Indian mackerel, horse mackerel, Bombay duck, oil sardine, and other sardines) was evaluated using Canonical Correlation Analysis (CCA). The outcome of the analysis was compared with those obtained by using the following models and methods: the Generalized Linear Model (GLM), the Generalized Additive Model (GAM), connection weight methods, and the explanatory methods of Artificial Neural Networks (ANNs). Both the sets of results were in agreement. Neither the GAM nor the ANNs method showed any clear advantage over each other, although the GAM performed better than the GLM

Forecasting quarterly landings of total fish and major pelagic fishes and modelling the impacts of climate change on Bombay duck along India’s north-western Gujarat coast

Quarterly landings or catches of total fishes and the major pelagic fish species, were forecasted using the methods and models viz. autoregressive integrated moving average (ARIMA), non-linear autoregressive (NAR) artificial neural network (ANN), autoregressive integrated moving average with exogenous inputs (ARIMAX), non-linear autoregressive with external (exogenous) inputs (NARX) artificial neural network. The models were also developed by considering only two important variables (differ for total fish and selected fish species) obtained from the ANN model. These simplified models proved nearly as good in their predictions. Simulated sea surface temperature (SST) for the A2 climate change scenario was used as an input for the NARX model to estimate the catches of Bombay duck over a short term (2020 – 2025) and a long term (2030 – 2050) with the last two years’ (2012 – 2013) average catch of training data as a benchmark. The catches increased on average by 41 % in the short term but decreased by 17.72 % in the long term

Use of different modeling approach for sensitivity analysis in predicting the Catch per Unit Effort (CPUE) of fish

1729-1741The contribution (Sensitivity analysis) of four variables, namely chlorophyll-a (Chl-a), sea surface temperature (SST), photosynthetically active radiation (PAR) and diffuse attenuation coefficient (Kd_490 or Kd) in predicting the Catch per Unit Effort (CPUE) of fish was evaluated using simple General Linear Model, Generalized Linear Model (GLM), Generalized Additive Model (GAM) and different explanatory methods of Artificial Neural Networks (ANN) technique. The models were assessed for their accuracy in determining the relative importance of the four variables in predicting the CPUE. GAM was an improvement over the General Linear Model, while ANN was found better than GAM. The six explanatory methods which can give the relative contribution or importance of variables were compared using ANN modeling techniques: (i) Connection weights algorithm, (ii) Garson’s algorithm (iii) Partial derivatives (PaD) (iv) Profile method (v) Perturb method, and (vi) Classical stepwise (forward and backward) method. Our results showed that the PaD method, Profile method, Input perturbation (50 % noise), and Connection weight approaches were only consistent in identifying the two most important variables (Chlorophyll-a and Kd) in the network. The distribution of profile plot & partial derivative helped indirectly in finding the other three variables in decreasing order of importance (PAR > fishing hour > SST). It was observed that the significance (sensitivity) of independent variables under GAM and explanatory methods of ANN were similar

Stock structure analysis of Johnius borneensis (Bleeker, 1851) from Indian waters

1215-1221Johnius borneensis (Bleeker 1851) contributes substantially to the marine fishery of India. The stock structure analysis of the species is essential for its sustainable management and utilization. The study is based on 411 specimens of the species randomly collected from the commercial landings at four marine fish landing centres in India. A truss network with 28 distance variables, based on 10 landmarks, was developed utilizing the digital images of the specimens, by means of tps Dig2 and PAST software platforms. Multivariate test statistics – Mahalanobis distance, Wilks’ lambda and Pillais’ test indicated significant difference between the East coast stocks and some extent of mixing among West coast stocks. Truss measurements transformed for allometric variations were subjected to Canonical Discriminant analysis and bivariate plot between the canonical variables showed existence of different morphometric stocks of the species. Major truss distances that contributed to the delineation were that on the head and posterior region of the fish body. The truss morphometric traits, that best discriminated the stocks, were subjected to the discriminant function analysis which appropriately classified 80 % of the specimens to the particular location. The present study is the first account on the stock structure analysis of J. borneensis from India and will help in developing policies for the management of the fishery and the sustainable utilization of the resource

Morphometric and meristic variation of congeneric sciaenid fishes Otolithes cuvieri Trewavas, 1974 and Otolithes ruber (Schneider, 1801) from Maharashtra, west coast of India

Two closely related species Otolithes cuvieri, Trewavas, 1974 and Otolithes ruber, (Schneider, 1801) have been differentiated based on morphometric and meristic traits. A simple yet useful criterion based on a pair of canine teeth present on the upper and lower jaw as well as position of the mouth is currently used to differentiate two congeneric sciaenid fish species the O. cuvieri and O. ruber. Findings of the present study indicated that simply two morphometric and meristic characters are sufficient to differentiate these two species. MANOVA (Multivariate analysis of variance) and stepwise discriminant function were used to decide the morphometric traits, significant for differentiation of the species of family Sciaenidae. Discriminant function analysis revealed that 98 % of the species were correctly classified based on five morphometric characters namely Pre-pectoral fin length (PPFL), Pre-anal fin length (PAL), Post orbital head length (POHL), Post anal fin length (POAL) and Body depth (BD). The m-transformed morphometric traits were found to be useful tools in generating canonical variables in differentiating the species. The first canonical variables showed altogether 98 % variance. The scatter plots by first three canonical variables have well differentiated the species. Two meristic characters such as the number of gillrakers present on lower limb of first gill arch and figure of arborescent appendages on the swim bladder are important in differentiation of these species

Not Available

Not AvailableThe contribution (Sensitivity analysis) of four variables, namely chlorophyll-a (Chl-a), sea surface temperature (SST), photosynthetically active radiation (PAR) and diffuse attenuation coefficient (Kd_490 or Kd) in predicting the Catch per Unit Effort (CPUE) of fish was evaluated using simple General Linear Model, Generalized Linear Model (GLM), Generalized Additive Model (GAM) and different explanatory methods of Artificial Neural Networks (ANN) technique. The models were assessed for their accuracy in determining the relative importance of the four variables in predicting the CPUE. GAM was an improvement over the General Linear Model, while ANN was found better than GAM. The six explanatory methods which can give the relative contribution or importance of variables were compared using ANN modeling techniques: (i) Connection weights algorithm, (ii) Garson‘s algorithm (iii) Partial derivatives (PaD) (iv) Profile method (v) Perturb method, and (vi) Classical stepwise (forward and backward) method. Our results showed that the PaD method, Profile method, Input perturbation (50 % noise), and Connection weight approaches were only consistent in identifying the two most important variables (Chlorophyll-a and Kd) in the network. The distribution of profile plot & partial derivative helped indirectly in finding the other three variables in decreasing order of importance (PAR > fishing hour > SST). It was observed that the significance (sensitivity) of independent variables under GAM and explanatory methods of ANN were similar.Not Availabl

Not Available

Not AvailableIn-situ data of chlorophyll-a concentrations (Chl-a) and sea surface temperature (SST) of the Gujarat region for the period, 2002-2009 were obtained from Indian National Centre for Ocean Information Services (INCOIS), Hyderabad. Out of nearly 100 sampling points, 22 and 67 points qualified for comparison with the satellite measurements of Chl-a and SST, respectively. Chl-a concentrations were estimated from the MODIS satellite data (4 km resolution) with the existing global ocean color algorithms, namely, OC2V4, OC4V4, and OC3M. The SST was calculated with the help of bands 31 and 32 using MODIS-Aqua sensor long wave SST algorithm and European Centre for Medium-Range Weather Forecasts (ECMWF) assimilation SST retrieval model (split window method). The satellite images were processed using global Sea WiFS Data Analysis System (SeaDAS) software v.7.3.1. Chl-a retrieved from OC3M algorithm had high coefficient of determination (R2=0.74) and less root mean square error (RMSE=1.24) as compared to OC2V4 and OC4V4 (R2=0.541 & 0.542 and RMSE=1.94 and 1.84, respectively) with in-situ data. The SST retrieved from MODIS-Aqua sensor long wave SST algorithm had a high coefficient of correlation as compared to ECMWF assimilation model (0.798 & 0.32 respectively) with in-situ data and RMSE were 0.80 and 2.65, respectively. SST and Chl-a showed an inverse correlation, with a coefficient of correlation (R) =0.530. Daily retrieval of Chl-a and SST value had very high degree of correlation with remote sensed eight days composite and monthly composite value (0.958 & 0.876, respectively). Retrieval of the value of diffuse attenuation coefficient at 490 nm wavelength (Kd or Kd_490), photosynthetically active radiation (PAR) and vertical attenuation coefficient of PAR (Kd(PAR)) were done and found that Kd and Kd(PAR) had very high degree of positive correlation (R=0.994). In addition, it was found that PAR had a positive correlation with SST(R=0.512) and negative correlation with Chl-a (R=-0.446). The range of this parameter values supports the case-I water and fish assemblage area.Not Availabl