970 resultados para Caspian Sea Region--Maps--Early works to 1800


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[Abdullah Yenişehirli].

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Title supplied by the cataloger.

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On wonders of creation.

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Signatures: A-D⁸ E⁴.

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Some plates by Andreas Bretschneider. Others by or after Henning Grossen, and some after Heinrich Zeising.

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Hand colored.

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Hand colored.

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A total of 361 caudal fin samples were collected from adult A. stellatus specimens caught in the north Caspian Sea, including specimens from Kazakhstan (Ural River), Russia (Volga River), Azerbaijan (Kura River), specimens caught in the south Caspian Sea including specimens from Fishery Zone 1 (from Astara to Anzali), Fishery Zone 2 (from Anzali to Ramsar), Fishery Zone 3 (from Nowshahr to Babolsar), Fishery Zone 4 (from Miyankaleh to Gomishan) as well as from specimens caught in Turkmenistan (all specimens were collected during the sturgeon stock assessment survey). About 2 g of fin tissue was removed from each caudal fin sample, stored in 96% ethyl alcohol and transferred to the genetic laboratory of the International Sturgeon Research Institute. Genomic DNA was extracted using phenol-chloroform method. The quality and quantity of DNA was assessed using 1% Agarose gel electrophoresis and Polymerase Chain Reaction (PCR) was conducted on the target DNA using 15 paired microsatellite primer. PCR products were electrophoresed on polyacrylamide gels (6%) that were stained using silver nitrate. Electrophoretic patterns and DNA bands were analyzed with BioCapt software. Allele count and frequency, genetic diversity, expected heterozygosity and observed heterozygosity allele number, and the effective allele number, genetic similarity and genetic distance, FST and RST were calculated. The Hardy Wienberg Equilibrium based on X2 and Analysis of Molecular Variance (AMOVA) at 10% confidence level was calculated using the Gene Alex software. Dendrogram for genetic distances and identities were calculated using TFPGA program for any level of the hierarchy. It is evident from the results obtained that the 15 paired primers studied, polymorphism was observed in 10 pairs in 12 loci, while one locus did not produce DNA bands. Mean allele number was 13.6. Mean observed and expected heterozygosity was 0.86 and 0.642, respectively. It was also seen that specimens from all regions were not in Hardy Wienberg Equilibrium in most of the loci (P≤0.001). Highest Fst (0.063) was observed when comparing specimens from Fishery Zone 2 and Fishery Zone 4 (Nm=3.7) and lowest FST (0.028) was observed when comparing specimens from the Volga River and those from the Ural River (8.7). Significant differences (P<0.01) were observed between RST recorded in the specimens studied. Highest genetic distance (0.604) and lowest genetic resemblance (0.547) were observed between specimens from Fishery zones 2 and 4. Lowest genetic distance (0.311) and highest genetic resemblance (0.733) was observed between specimens from Turkmenistan and specimens from Fishery zone 1. Based on the genetic dendrogeram tree derived by applying UPGMA algorithm, A. stellatus specimens from Fishery zone 2 or in other words specimens from the Sepidrud River belong to one cluster which divides into two clusters, one of which includes specimens from Fishery zones 1, 3 and 4 and specimens from Turkmenistan while the other cluster includes specimens from Ural, Volga and Kura Rivers. It is thus evident that the main population of this species belongs to the Sepidrud River. Results obtained from the present study show that at least eight different populations of A. stellatus are found in the north and south Caspian Sea, four of which are known populations including the Ural River population, the Volga River population, the Kura River population and the Sepidrud River populations. The four other populations identified belonging to Fishery zones 1, 3, and 4 and to Turkmenistan are most probably late or early spawners of the spring run and autumn run of each of the major rivers mentioned. Specific markers were also identified for each of the populations identified. The Ural River population can be identified using primers Spl-68, 54b and Spl-104, 163 170, 173, the Volga River population can be identified using primers LS-54b and Spl-104, 170, 173 113a and similarly the population from the Kura River can be identified using primers LS-34, 54b and Spl-163, 173 and that from the Sepidrud River can be identified using primers LS-19, 34, 54b and Spl-105, 113b. This study gives evidence of the presence of different populations of this species and calls for serious measures to be taken to protect the genetic stocks of these populations. Considering that the population of A. stellatus in Fishery zone 2 is an independent population of the Sepidrud River in the Gilan Province, the catch of these fishes in the region needs to be controlled and regulated in order to restore the declining stocks of this species.

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Secchi depth is a measure of water transparency. In the Baltic Sea region, Secchi depth maps are used to assess eutrophication and as input for habitat models. Due to their spatial and temporal coverage, satellite data would be the most suitable data source for such maps. But the Baltic Sea's optical properties are so different from the open ocean that globally calibrated standard models suffer from large errors. Regional predictive models that take the Baltic Sea's special optical properties into account are thus needed. This paper tests how accurately generalized linear models (GLMs) and generalized additive models (GAMs) with MODIS/Aqua and auxiliary data as inputs can predict Secchi depth at a regional scale. It uses cross-validation to test the prediction accuracy of hundreds of GAMs and GLMs with up to 5 input variables. A GAM with 3 input variables (chlorophyll a, remote sensing reflectance at 678 nm, and long-term mean salinity) made the most accurate predictions. Tested against field observations not used for model selection and calibration, the best model's mean absolute error (MAE) for daily predictions was 1.07 m (22%), more than 50% lower than for other publicly available Baltic Sea Secchi depth maps. The MAE for predicting monthly averages was 0.86 m (15%). Thus, the proposed model selection process was able to find a regional model with good prediction accuracy. It could be useful to find predictive models for environmental variables other than Secchi depth, using data from other satellite sensors, and for other regions where non-standard remote sensing models are needed for prediction and mapping. Annual and monthly mean Secchi depth maps for 2003-2012 come with this paper as Supplementary materials.

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The coastal districts, as an intersection of two perfectly different ecosystems of dry land and sea, is one of the most complicated and the richest natural system on earth. Considering these areas are constantly exposed to aggregation of water pollutants and also consequence resulting from construction and development activities, they are very vulnerable. Therefore, "sensitive Coastal areas" has become a common word in the related subjects to marine environment recently. The said title relates to the areas of the coastal lines which are vulnerable to the natural condition or human actions because of ecological, social, economic, educational and research importance, also they need particular supports. The southern coasts of Caspian Sea, In Iran prominent samples are of these sensitive areas which their environment are exposed to demolition and destruction intensely, due to increasing and uncontrolled development. The first stage of protecting and managing the coastal areas is identifying sensitive Coastal areas and broadening the Coasts. In this survey, we attempted to examine a definite area in the southern coasts of Caspian Sea. In Iran, by profiting from the world experiences and concluded researches in Iran especially the concluded studies by marine environment office and the Environment protection organization on the subject of determination criteria of the sensitive ecological districts. For this purpose (In Gilan Province) Boujagh national park district which is located in the mouth of sefidroud river and also is possessed of the special ecological and environmental features and distinctions. In this survey, first they said district is divided proportionally on the basis of using a grid system in order to identify the sensitive ecological districts and broaden the coast, and then the desired indices have been determined and scored by numeral valuation method in each unit and then analysis has been done by using of the geography information system (GIS) and final has estimated economic valuation of sensitive ecological areas that is presented in this essay.

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In order to carry out Biometric studies, 75 samples were caught from 3 locations ( Tajan river, Sefidrud and Shirud) using Salic and the length (±1 mm) and weights (± 5 gr) of samples were determined. Using One-way ANOVA by SPPSS software, there wasn’t significant difference between locations in length and fecondity (P ≥0.01(, but there was significant difference between Shirud and tajan samples with sefidrud in weight ) P≤0.01(. In order to carry out genetic variation studies, 210 fish were caught from 3 different regions of the Iranian coastline (Khoshkrud, Tonekabon, Gorganrud) and 1 region in Azerbaijan (Waters of the Caspian Sea close to Kura River mouth) during 2008-2009 . Genomic DNA was extracted of fin using the phenol-chloroform. The quantity and quality of DNA from samples were assessed by spectrophptometer and 1% agarose gel electro-phoresis. PCR was carried out using 15 paired microsatellite primers. PCR products were separated on 8% polyacrylamide gels that were stained using silver nitrate. Molecular weight calculate using UVTech software. The recorded microsatellite genotypes were used as input data for the GENALEX software version 6 package in order to calculate allele and genotype frequencies, observed (Ho) and (He) expected heterozygosities and to test for deviations from Hardy-Weinberg equilibrium. Genetic distance between two populations was estimated from Nei standard genetic distance and genetic similarity index (Nei, 1972). Genetic differentiation between populations was also evaluated by the calculation of pairwise estimates of Fst and Rst values. From 15 SSR markers were used in this investigation, 9 of them were polymorph. Average of expected and observed heterozygosity was 0.54 and 0.49 respectively. Significant deviations from Hardy-Weinberg expectations were observed in all of location except Anzali lagoon- autumn in AF277576 and EF144125, Khoshkrud in EF144125 and Gorganrud and Kura in AF277576. Using Fst and Rst there was significant difference between locations ) P≤0.01(. According to Fst , the highest population differentiation (Fst= 0.217) was between Gorganrud and Khoshkrud that have the lowest Nm and the lowest (Fst= 0.086) was between Gorganrud and Tonekabon that have the highest Nm. Using Rst the highest population differentiation (Rst= 0.271) was between Tonekabon and spring Anzali lagoon and the lowest (Rst= 0.026) was between Tonekabon and Autumn Anzali 159 lagoon. Also the difference between Spring Anzali lagoon and Autumn Anzali lagoon was noticeable (Fst=0.15). AMOVA analysis with consideration of 2 sampling regions (Iran and Azerbaijan) and 7 sampling locations (Iran: Khoshkrud, Tonekabon, Gorganrud, Spring Anzali lagoon and Autumn Anzali lagoon ; Azerbaijan: the Kura mouth) revealed that almost all of the variance in data namely 83% )P≤0.01( was within locations, Genetic variances among locations was 14% )P≤0.01( and among regions was 3% )P≤0.01(. The genetic distance was the highest (0.646) between Gorganrud and Autumn Anzali lagoon populations, whereas the lowest distance (0.237) was between Gorganrud and Tonekabon River. Result obtained from the present study show that at least 2 different population of Rutilus frissi kutum are found in the Caspian sea,which are including the kura river population and the southern Caspian sea samples and it appears that there is more than one population in southern Caspian sea that should be attantioned in artifical reproduction Center and stoke rebilding.