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Summary During exploratory surveys in the tributaries (Penganga and Satnala) of Godavari and (Bheema) Krishna basins, specimens of mahseer were collected. The morpho‐meristic characteristics of these specimens conformed to the taxonomic keys for Tor tor. The mitochondrial COI sequences of these specimens clustered with the T. tor specimens from the River Narmada and were distinct from the other mahseer such as T. khudree and T. mussullah, which are known to exist in the rivers of the region. This confirmed the distribution of T. tor in the rivers of peninsular India and indicated an extended distribution of the known range. The major predominating habitat characteristics of collection areas were cobbles mixed with gravel, and a riparian cover of shrubs and trees. The occurrence of fingerling size specimens in the river suggests that the species has adapted and is likely to have established self‐recruiting populations in these rivers.

Length-weight relationship and condition factor in 57 freshwater fish species of 19 families and 38 genera from various rivers, and lakes of different climatic zones in India were studied. The estimates (total 67) for the parameter b of the length-weight relationship ranged between 1.422 and 3.917, with a mean value of 2.994. Correlation coefficient was found to be highly significant (r 2 >0.90) in all except for Crossocheilus latius latius, Acanthocobitis botia and Garra lamta. Growth was found to be isometric (b = 3) in 32 species, positively allometric (b>3) in 21 species and negatively allometric (b<3) in 14 species. It was also found that some species exhibited variations in their growth pattern in different habitats, which showed the influence of preference and availability of suitable habitat conditions.

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Abstract:In India, freshwater environments are experiencing serious threats to biodiversity, and there is an urgent priority for the search of alternative techniques to promote fish biodiversity conservation and management. With this aim, the present study was undertaken to assess the fish biodiversity within and outside a river protected area, and to evaluate whether the protected river area provides some benefits to riverine fish biodiversity. To assess this, the pattern of freshwater fish diversity was studied in river Gerua, along with some physicochemical conditions, from April 2000 to March 2004. For this, a comparison was made between a 15km stretch of a protected area (Katerniaghat Wildlife Sanctuary), and an unprotected one 85km downstream. In each site some physicochemical conditions were obtained, and fish were caught by normal gears and the diversity per site described. Our results showed that water temperature resulted warmest during the pre-monsoon season (25ºC) and low during the winter (14-15ºC); turbidity considerably varied by season. In the protected area, a total of 87 species belonging to eight orders, 22 families and 52 genera were collected; while a maximum of 59 species belonging to six orders, 20 families and 42 genera were recorded from the unprotected areas. Cyprinids were found to be the most dominant genera and Salmostoma bacaila was the most numerous species in the sanctuary area. Other numerous species were Eutropiichthys vacha, Notopterus notopterus, Clupisoma garua and Bagarius bagarius. The results indicated more species, greater abundances, larger individuals, and higher number of endangered fishes within the sanctuary area when compared to the unprotected area. Analysis on the mean abundance of endangered and vulnerable species for the evaluated areas in the sanctuary versus unprotected ones indicated significant differences in fish abundance (p<0.05). These results showed that this riverine protected area could be important for conservation and management of fish diversity in the region, especially for resident and threatened species. Rev. Biol. Trop. 61 (1): 161-172. Epub 2013 March 01.

Ichthyofaunal survey was conducted in the Ib River (21°44’ to 22°50’ N; 83°56’ to 83°54’ E), an important tributary of Mahanadi, to evaluate the diversity and distribution of freshwater fishes along six sites on seasonal basis between February, 2016 and January, 2017. A total of 55 species belonging to 42 genera, 21 families and 9 orders were recorded from the study area. Maximum number of species belonged to the Order Cypriniformes (41.8%) followed by Siluriformes (21.8%) and Perciformes (21.8%). Maximum value of species richness was observed in downstream areas; Sundargarh and Barghat and least in upper stretches of Pamsala. Shannon–Weiner diversity index ranged between 2.47 to 3.68 at the six sites of Ib River indicating healthy state of the river. Bray-Curtis similarity cluster analysis suggests that similarities between sites decrease with increasing distance, with highest similarity found between Sundargarh and Barghat sites. Baseline data on fish biodiversity has been generated in this study which can now be periodically monitored and form basis for future conservation plans.

Image acquisitions are increasing day by day due to progress in social networking and digital technologies. Nowadays, with the evolution of various image capturing devices, an enormous quantity of complex images is being produced.content-primarily based image Retrieval (CBIR) is the answer to access images without difficulty wherein proper indexing and association are required. It makes CBIR a distinguished field in computer vision research. There are several uses of CBIR systems in day to day life for example medical, internet, scientific research and various other communication media. In the CBIR system, the user gives a query to obtain images from large datasets having a large number of images. In information transfer via electronic media using particular formats of data, images play an essential role. The extraction of information from communicated images is necessary with extra processing. In this paper, a comparative survey has been carried out on different content-based image retrieval implementation. These methods are determined by several authors for the feature extraction process of images and classification. This will help to plan the strategy for optimizing the CBIR system.