Estimation of the growth curve parameters in Macrobrachium rosenbergii

Growth is one of the most important characteristics of cultured species. The objective of this study was to determine the fitness of linear, log linear, polynomial, exponential and Logistic functions to the growth curves of Macrobrachium rosenbergii obtained by using weekly records of live weight, total length, head length, claw length, and last segment length from 20 to 192 days of age. The models were evaluated according to the coefficient of determination (R2), and error sum off square (ESS) and helps in formulating breeders in selective breeding programs. Twenty full-sib families consisting 400 PLs each were stocked in 20 different hapas and reared till 8 weeks after which a total of 1200 animals were transferred to earthen ponds and reared up to 192 days. The R2 values of the models ranged from 56 – 96 in case of overall body weight with logistic model being the highest. The R2 value for total length ranged from 62 to 90 with logistic model being the highest. In case of head length, the R2 value ranged between 55 and 95 with logistic model being the highest. The R2 value for claw length ranged from 44 to 94 with logistic model being the highest. For last segment length, R2 value ranged from 55 – 80 with polynomial model being the highest. However, the log linear model registered low ESS value followed by linear model for overall body weight while exponential model showed low ESS value followed by log linear model in case of head length. For total length the low ESS value was given by log linear model followed by logistic model and for claw length exponential model showed low ESS value followed by log linear model. In case of last segment length, linear model showed lowest ESS value followed by log linear model. Since, the model that shows highest R2 value with low ESS value is generally considered as the best fit model. Among the five models tested, logistic model, log linear model and linear models were found to be the best models for overall body weight, total length and head length respectively. For claw length and last segment length, log linear model was found to be the best model. These models can be used to predict growth rates in M. rosenbergii. However, further studies need to be conducted with more growth traits taken into consideration

Application of the Leslie Model to Commercial Catch and Effort of the Slipper Lobster, Scyllarides squammosus, Fishery in the Northwestern Hawaiian Islands

Commercial catch and effort data were fit to the Leslie model to estimate preexploitation abundance and the catchability coefficient of slipper lobster, Scyllarides squammosus, in the Northwestern Hawaiian Islands (NWHI). A single vessel fished for 34 consecutive days in the vicinity of Laysan Island and caught 126,127 total slipper lobster in 36,170 trap hauls. Adjusted catch of legal slipper lobster dropped from a high of 3.70 to 1.16 lobster per trap haul. Preexploitation abundance at Laysan Island was an estimated 204,000 legal slipper lobster, which was extrapolated to yield an estimate of 1.2 X 106 to 3.8 X 106 lobster for the entire NWHI slipper lobster fishery.

A habitat-use model to determine essential fish habitat for juvenile brown shrimp (Farfantepenaeus aztecus) in Galveston Bay, Texas

A density prediction model for juvenile brown shrimp (Farfantepenaeus aztecus) was developed by using three bottom types, five salinity zones, and four seasons to quantify patterns of habitat use in Galveston Bay, Texas. Sixteen years of quantitative density data were used. Bottom types were vegetated marsh edge, submerged aquatic vegetation, and shallow nonvegetated bottom. Multiple regression was used to develop density estimates, and the resultant formula was then coupled with a geographical information system (GIS) to provide a spatial mosaic (map) of predicted habitat use. Results indicated that juvenile brown shrimp (<100 mm) selected vegetated habitats in salinities of 15−25 ppt and that seagrasses were selected over marsh edge where they co-occurred. Our results provide a spatially resolved estimate of high-density areas that will help designate essential fish habitat (EFH) in Galveston Bay. In addition, using this modeling technique, we were able to provide an estimate of the overall population of juvenile brown shrimp (<100 mm) in shallow water habitats within the bay of approximately 1.3 billion. Furthermore, the geographic range of the model was assessed by plotting observed (actual) versus expected (model) brown shrimp densities in three other Texas bays. Similar habitat-use patterns were observed in all three bays—each having a coefficient of determination >0.50. These results indicate that this model may have a broader geographic application and is a plausible approach in refining current EFH designations for all Gulf of Mexico estuaries with similar geomorphological and hydrological characteristics.