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  • 1
    Publication Date: 2018-10-19
    Description: This study aimed to assess ecological quality status of hard substratum macroinvertebrates communities of the Caspian Sea with three ecological indices and their relationship with environmental factors. For this purpose, benthic communities of the Caspian Sea basin were studied seasonally during 2014 in 8 sampling sites. Temperature, salinity, dissolved oxygen; pH, nitrate, nitrite, silicate and phosphate were measured as environmental factors. The benthic classification indices AMBI (AZTI Marine Biotic Index), M-AMBI (Multivariate AMBI) and BENTIX (BENthic IndeX) were applied to assess the ecological status of the studied area. Results showed low dissimilarity based on species composition and abundance among seasons, while all seasons discriminated clearly based on environmental factors. In addition, AMBI index was more successful to assess ecological health of hard substratum in the Caspian Sea basin than M-AMBI and BENTIX. Furthermore, AMBI showed high sensitivity to environmental variation. Results indicated that temperature, nitrate, silicate, phosphate and nitrite were the most important factors in the composition and abundance fluctuation of hard substratum macroinvertebrates communities, respectively.
    Keywords: Ecology
    Repository Name: Aquatic Commons
    Type: Article , PeerReviewed
    Format: application/pdf
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  • 2
    Publication Date: 2018-05-31
    Description: Computer Vision (CV) is a relatively young discipline which has been widely used to automate quality evaluation. CV inspection of fish and fish products can provide efficient, consistent and cost effective alternative, so efforts focused on speed and accuracy of machine vision as a substitute for human inspection of foods. Machine vision is explained as the construction of explicit informative and meaningful descriptions of a physical object via image analysis. Actually it encloses the capturing, processing and analysis of two-dimensional images, and by modeling human vision electronically perceives and understands images. ... This study tries to evaluate the relationship between weight of fish and visual features derived from image processing and present best fit relationship between weight and visual features.
    Keywords: Engineering ; Fisheries
    Repository Name: Aquatic Commons
    Type: Article , PeerReviewed
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  • 3
    Publication Date: 2018-06-19
    Description: In the current paper reproductive strategy and growth rates of Luciobarbus esocinus were investigated from 2010 to 2012, in Gamasiab River, Kermanshah, Iran. Samples were collected seasonally by gillnet and electro-fisher device. Sex ratio was evaluated from 162 individuals and the percentage of male and female individuals were 54.76% and 45.24% respectively. Age structure was distributed between I and V and most frequency numbers for males and females belonged to II and IV age group, respectively. Length-weight relationship was computed as W=8E-05TL^2.6546 for all individuals. Mean total length and weight was 333.82±13.10 mm and 297.67±4.85 g for males, and 371.5±21.17 mm and 307.85±7.64 g for females. The von-Bertalanffy growth models of L. esocinus were described as Lt =357.14×[1-e^(-0.77×(t-0.090))] for males, Lt =367.02×[1-e^(-0.78×(t–0.094))] for females and Lt =363.57×[1-e^(-0.76×(t –0.087))] for both sexes. Results indicated that growth factors were highly similar in spring, summer and winter. CDA analysis revealed that the total and gonad weight are the main factors in discriminant analysis. The highest GSI values were found in spring for females and winter for males, however, it decreased rapidly after spring in females. Maximum growth rates were observed in early ages (I-II) and depleted with ages. K value and L∞ were determined as 0.77±0.09 Y^-1 and 357.14±10.38 mm for males and 0.78±0.24 Y^-1 and 367.02±24.15 mm for females, respectively. In conclusion this study provided basic information for stock assessment and management of L. esocinus in Gamasiab Reservoir.
    Keywords: Biology ; Fisheries ; Management
    Repository Name: Aquatic Commons
    Type: Article , PeerReviewed
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