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Articles by Neeshma Jaiswal
Total Records ( 1 ) for Neeshma Jaiswal
  Neeshma Jaiswal , Sushil K. Upadhyay , Anshu Malhotra and Sandeep K. Malhotra
  The investigations on ecological dynamics of larvae of Eustrongylides tubifex in silver whiting, Sillago sihama were conducted at the Central West Coast of India at Goa during 2008-2010.The fish were borrowed from commercial vessels and trawlers as well as purchased from market for the parasitological investigations. All the hosts were brought to laboratory, separated sexwise, freshly weighed, their total length and standard length measured. Viscera of the hosts were teased and carefully examined for helminthes parasites. The collected worms were processed for the morphometric analysis to establish indentify of worms. The dynamics of E. tubifex larvae was monitored and impact of environmental parameters established by the application of Principal Component Analysis using SYSTAT 11 software. The dominant 1st component (PCIp) of Principal Component Analysis elucidated critical negative impact (-0.560) of alkalinity of water on mean intensity of nemic populations in female fish, while the temperature optimum was 28°C, besides influence of seasonality depicting highest peak of infection prevalence in winter and the other lower peak during summer period. The appropriateness of Principal Component Analysis could be substantiated by the cumulative percentage of variance explained by Sillago sihama- E. tubifex model that could be used as post-hoc measure. Further, the stability of cumulative percentage in this study, which progressed from 28.190 to 30.167%, suggested that the model became more relevant each year. The robustness of the applications within Principal Component Analysis are strengthened in this study by the availability of two year’s samples under natural conditions in an aquatic ecosystem. The higher expression variability of certain select physico-chemical characteristics like, alkalinity, hardness, DO and water temperature were marked out by application of Principal Component Analysis. It also highlighted the reliability of correlation matrix as compared to covariance matrix because of ordered variability structure.
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