Study of feeding competition between invasion ctenophore (M. leidyi) and anchovy (C. engrauliformis) in the Southern Caspian Sea (Mazandran Coast)

Mnemiopsis leidyi one species of phylum Ctenophora, is a native species in America. It has most likely moved across the Atlantic in the ballast water of cargo ships to the Black sea in 1982, and then to the Caspian Sea thought the Volga-Don Channel, in Nov 1999. The population of M. leidyi rows rapidly and by end of 2000, the entire sea was teeming with them. This survey was arranged In order to study the relationship between the invasion of M. leidyi and sharp decline in main stocks pelagic fish such as Kilka. Dietary analysis was conducted on Anchovy Killka (Clupeonella engrauliformis) and M. leidyi from August 2001 to October 2002 in two stations, located at the costal water near Babolsar (52.38° E ,36.42° N ) and Noshahar (51.33° E,36.39° N) in the Caspian Sea province of Mazandaran, Iran. M. leidyi was caught by plankton net, at three vertical strata of both station at surface 5 in, 10m, and 15 m the Kilka was caught by fisheries boat at Babolasar fishery harbour. Samples of M. leidyi were not fixed in its common fixative, we used 96% Ethanol In order to study of M. leidyi digestion system some alive samples, directly, were studied by the fluorescence microscope which was connected to a computer prepared specially for this process. In many cases, the light was directly reflected on the sample and microscopic image was prepared in dark background. We found that there were some common organisms in diet of both species. The Schoener index analysis reflected these similarities, as values more than critical level of overlap (>89 in Babolsar samples and >84 in Noshahar samples) were found. Results from this study suggests that M. leidyi and Anchovy have a similar feeding niche and computation between them is one of the reasons to decline in anchovy stocks. Economical effects of M. leidyi s invasion in research area were studied by data on kilka caught before and after introducing M. leidyi.

An investigation on benthic macro-invertebrates species diversity as bio-indicators of environmental health in Bahrekan Bay

The purpose of this study was to evaluate benthic macro-invertebrates species diversity as bio-indicators of environmental health in Bahrekan bay (in the Northwest of Persian gulf). Seasonal sediments sampling along 5 transects, 15 stations at 4 replicates (3 replicates for macrobenthos and 1 replicate for sediment analysis) was done from November 2008 to August 2009 by 0.025 m2 Van Veen grab sampler. Physical and chemical parameters of water, grain size analysis, %TOM and Ni and Va concentrations of sediments were assessed through four seasons. Macrobenthic communities after staining and sorting, using stereomicroscope have been identified. Their density in every station and every season calculated. For using of AMBI index, identified macrobenthos according to their sensitivity to stressors and pollutants, categorized into 5 ecological groups and for using of Bentix index categorized into 3 ecological groups. The diversity indices and indicators that showing ecological status were calculated. Also, the differences between physiochemical parameters of sea water, sediments TOM% and grain size, diversity indices in stations and seasons were recorded (P=0.05). The correlation coefficient determined for all parameters. According to the results of grain size analysis, bottom grain size categorized as clay. Highest percent of TOM was belong to autumn (36.39±.075) and lowest was belong to summer (19.01±0.51). Also there was positive correlation (p=0.01) between %TOM and %Clay that showing sediments with lowest size containing highest amounts of organic matters. Ni concentrations in sediments (87.80±21.25)mg/kg showed the amounts over than standards levels but Va concentrations in sediments (53.54±17.60)mg/kg showed the amounts lower than standards level. The highest density of macrobenthos was recorded for summer (8254±485) N/m2 and the lowest density was recorded for spring (3775±172)N/m2. The highest annual density was belong to mollusca (81%) and then polycheates (13%), Others (4%) and crustaceae (2%). The highest diversity was recorded for winter (Simpson index: 0.13±0.01, H':3.47±0.06) and the lowest diversity recorded for autumn (Simpson index: 0.16±0.01, H':3.17±0.06). in all stations, the highest amount of Shanon index was belong to T2S3 station in summer (4.11± 0.32) and the lowest amount was belong to T1S1 station in autumn (2.42± 0.41). The annual mean of Simpson diversity index: (0.15 ±0.04) and Shanon diversity index (3.36±0.03), illustrated that macrobenthos in Bahrekan bay have a good variation. The results of Brilluin and N1 (Number of equally common species) indices confirm the results of Simpson index. For study on the regions that diversity has a little difference between stations, with use of Ni index, the degree of differences could be better ono recognizable. According to the results of AMBI index in all seasons (autumn: 0.46±0.03; summer: 0.22±0.01; annual mean:0.31±0.01) and standards (0.0

Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network

Sea- level variations have a significant impact on coastal areas. Prediction of sea level variations expected from the pre most critical information needs associated with the sea environment. For this, various methods exist. In this study, on the northern coast of the Persian Gulf have been studied relation to the effectiveness of parameters such as pressure, temperature and wind speed on sea leve and associated with global parameters such as the North Atlantic Oscillation index and NAO index and present statistic models for prediction of sea level. In the next step by using artificial neural network predict sea level for first in this region. Then compared results of the models. Prediction using statistical models estimated in terms correlation coefficient R = 0.84 and root mean square error (RMS) 21.9 cm for the Bushehr station, and R = 0.85 and root mean square error (RMS) 48.4 cm for Rajai station, While neural network used to have 4 layers and each middle layer six neurons is best for prediction and produces the results reliably in terms of correlation coefficient with R = 0.90126 and the root mean square error (RMS) 13.7 cm for the Bushehr station, and R = 0.93916 and the root mean square error (RMS) 22.6 cm for Rajai station. Therefore, the proposed methodology could be successfully used in the study area.