Growth and mortality parameters of the three spot crab, Portunus sanguinolentus (Herbst, 1783) from Gulf of Mannar, South East Coast of India

1534-1539The stock characteristics of growth and mortality parameters of portunus sanguinolentus were studied from Gulf of Mannar. The carapace width of male and female P. sanguinolentus was ranged from 3.9 cm to 19.10 cm, carapace length 1.9 cm to 10.3 cm and the weight ranged from 15 to 328 g. The growth parameters of P. sanguinolentus (Male- L∞ = 19.31 cm, K = 1.08 yr-1, t0 = -0.165: Female - L∞ = 20.49 cm , K = 1.43yr-1, t0 = -0.121). The mortality parameters like natural mortality (M), fishing mortality, total instantaneous mortality (Z) and exploitation ratio (E) of P. sanguinolentus (Male- M = 2.00; F = 1.97; Z = 3.97 & E=0.4962: Female -M =2.3; F = 2.39; Z= 4.69 and E = 0.5095) were observed. The results showed that P. sanguinolentus population is marginally over exploited at Gulf of Mannar

Stock assessment and exploitation status of Lethrinus nebulosus (Lacepede, 1802) exploited off Thoothukudi coast, Tamil Nadu, India

825-828Lethrinus nebulosus (Lacepede, 1802), although a commercially important fish species, has not been studied (population studies) in Thoothukudi coast of Tamil Nadu, India. Research on recruitment pattern, virtual population analysis and exploitation status of L. nebulosus off Thoothukudi coast was conducted from July 2011 to June 2012. During the study period, 4590 specimens of L. nebulosus were collected for studying the above said parameters by using FiSAT software. Recruitment pattern of the species expressed continuously with one peak each in April and August. The fishing pressure occurred more in the length group of 43 cm onwards. The results revealed that the total annual catch of 7566.28 tonnes was obtained from 52210 boat days. The maximum catch per unit was during December and January. The maximum sustainable yield (MSY) was estimated at 12203.68 tonnes. The discrepancy between MSY and annual catch was 4664.399. The present fishing effort may be increased to 61.29% to achieve MSY with 19310 boat days. It was revealed that L. nebulosus is underexploited in this region

Sea urchin diversity and its resources from the Gulf of Mannar

Gulf of Mannar is the richest marine biodiversity hotspot along the Southeast coast of India, encompassing the territorial waters from Dhanushkodi in the north to Kanyakumari in the south. It has a chain of 21 islands, located 2 to 10 km from the mainland along the 140 km stretch between Thoothukudi and Rameswaram. The area of Gulf of Mannar under the Indian EEZ is about 15,000 km2 where commercial fishing takes place only in about 5,500 km2 and that too only up to a depth of 50m. This marine ecosystem holds nearly 117 species of corals, 441 species of fin-fishes, 12 species of sea grasses, 147 species of seaweeds, 641 species of crustaceans, 731 molluscan species (Kumaraguru, 2006). There are around 950 species of sea urchin in class Echinoidea which comes under two subclasses found around the world’s oceans

EVALUATION OF MAXIMUM ENTROPY METHOD OF SPECTRUM ESTIMATION

The parametric models autoregressive (AR)/AR-moving average (MA)/MA are sometimes not capable of finding out the power spectral densities of random sequences. Under such circumstances, the non-parametric methods outperform the parametric ones because of the sensitivity of the latter to model specifications. The maximum entropy method (MEM) is regarded as the non-parametric method of spectrum estimation; it suggests one possible way of extrapolating the autocorrelation sequence so that a more accurate estimate of the spectrum can be obtained with better resolution. This paper investigates the work of realizing MEM method and evaluating its performance with minimum variance method

A NOVEL APPROACH TO STATE SPACE TIME DOMAIN AUTOREGRESSIVE SIGNAL PROCESSING USING OPTIMAL RECURSIVE ESTIMATOR

This work describes the concept of filtering of signals using discrete Kalman filter. The true state of constant, random constant having process noise and autoregressive (p) process when corrupted by measurement noise are estimated using discrete Kalman filter and results are presented using MATLAB