Diversity of wetland fish and its impact on the income of fishermen community of Assam.

A survey was conducted in 3000 fishermen households surrounding 54 wetlands (Beels) of Assam. The fish diversity of the wetlands has been decreasing during the last few years due to some extrinsic and intrinsic factors. The total number of fish species recorded so far during the present study is 67 belonging to 21 families. Cyprinidae is the most dominant family represented by major group species (8), intermediate group species (3) and minor group species (12) of high commercial value. Among these three groups, the diversity of fish species is higher in the minor group fish. The present paper deals with the economic condition of the fishermen who mainly fish in the wetlands. The economic condition of the fishermen community is found very poor. The income of fishermen varies from Rs. 4.478 to Rs.7,484 per annum. A regression analysis shows that the income of fishermen is not dependent alone on the fish production but it is exclusively dependent on the value of the fish catch. All the three groups (in terms of value) have significant influence at 10.00% confidence level. But analysis of β shows that the intermediate fish group exhibits the highest influence on the variation of the fishermen income followed by minor and major group respectively.

The genetic diversity of Acipenser stellatus populations in the Caspian Sea was studied using microsatellite technique

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.

The genetic structure and phylogenetics of pikeperch (Sander lucioperca) Anzali and Amirkolaye wetlands and perch (Perca fluviatilis) populations in Aras Dam and south-west of the Caspian Sea

The genetic structure of pikeperch (Sander lucioperca) and perch (Perca fluviatilis) populations was studied using microsatellite technique. A total of 207 specimens of adult pikeperch were collected from Aras dam (57 specimens), Anzali wetland (50 specimens), Talesh (50 specimens) and Chaboksar (50 specimens) coasts. Also a total of 158 specimens of adult perch were collected from Anzali (Abkenar (50 specimens)and Hendekhale(48 specimens)) and Amirkolaye(60 specimens) wetlands. About 2 g of each specimen's dorsal fin was removed, stored in 96% ethyl alcohol and transferred to the genetic laboratory of the International Sturgeon Research Institute. Genomic DNA was extracted using ammonium-acetate method. The quality and quantity of DNA was assessed using 1% agarose gel electrophoresis. Polymerase Chain Reaction (PCR) was conducted on the target DNA using 15 pairs of microsatellite primers. PCR products were electrophoresed on poly acryl amide gels (6%) that were stained that were stained using silver nitrate. DNA bands were analyzed with BioCapt software. Allele count and frequency, genetic diversity, expected and observed heterozygosity , allele number and the effective allele number, genetic similarity and genetic distance, Fst, Rst, Hardy Weinberg Equilibrium based on X2 and Analysis of Molecular Variance (AMOVA) at 10% confidence level was calculated using the Gene Alex software. Dendogram for genetic distances and identities were calculated using TFPGA program for any level of hierarchy. The results for P. fluviatilis showed that from 15 pair of primers that were examined 6 polymorphic and 7 monomorphic loci were produced, while 2 loci didn't produce any DNA bands. Mean allele number was 4.1±1.1 and mean observed and expected heterozygosity was 0.56±0.12 and 0.58±0.14 respectively. It was also seen that specimens from all regions were not in Hardy Weinberg Equilibrium in some of loci (P<0.001). Highest Fst (0.095) with Nm=2.37 was observed between Hendekhale and Amirkolaye and the lowest Fst (0.004) with Nm=59.31 was observed between Abkenar and Hendekhale. According to AMOVA Significant difference (P<0.05) was observed between recorded Rst in the studied regions in Anzali and Amirkolaye lagoons. In another words there are two distinct populations of this species in Anzali and Amirkolaye lagoons. The highest genetic distance (0.181) and lowest genetic resemblance (0.834) were observed between specimens from Hendekhale and Amirkolaye and the lowest genetic distance (0.099) and highest genetic 176 resemblance (0.981) were observed between specimens from Abkenar and Hendekhale. Based on the genetic dendogram tree derived by applying UPGMA algorithm, specimens from Anzali and Amirkolaye wetlands have the same ancestor. On the other hand there is no noticeable genetic distance between the specimens of these two regions. Also the results for S. lucioperca showed that from 15 pair of primers that were examined 6 polymorphic and 7 monomorphic loci were produced, while 2 loci didn't produce any DNA bands. Mean allele number was 3.0±0.6 and mean observed and expected heterozygosity was 0.52±0.21 and 0.50±0.14 respectively. It was also seen that specimens from all regions were not in Hardy Weinberg Equilibrium in some of loci (P<0.001). Highest Fst (0.093) with Nm=2.43 was observed between Aras dam and Anzali wetland and the lowest Fst (0.022) with Nm=11.27 was observed between Talesh and Chaboksar coasts. Significant differences (P<0.05) were observed between recorded Rst in the studied regions exept for Talesh and Chaboksar Coasts. In another words there are three distinct populations of this species in Caspian sea, Anzali wetland and Aras dam. Highest genetic distance (0.110) and lowest genetic resemblance (0.896) were observed between specimens from Aras dam and Anzali wetland and the lowest genetic distance (0.034) and highest genetic resemblance (0.966) were observed between specimens from Talesh and Chaboksar coasts. Based on the genetic dendogram tree derived by applying UPGMA algorithm, specimens from Talesh and Chaboksar coasts have the lowest genetic distance. On the other hand the main population of this species belongs to Anzali wetland. Phylogenetic relationship of these two species was inferred using mitochondrial cytochrome b gene sequencing. For this purpose 2 specimens of P. fluviatilis from Anzali wetland, 2 specimens of S. lucioperca from Aras dam and 2 specimens of S. lucioperca from Anzali wetland were sequenced and submitted in Gene Bank. These sequences were aligned with Clustal W. The phylogenic relationships were assessed with Mega 4. The results of evolutionary history studies of these species using Neighbor-Joining and Maximum Parsimony methods showed that the evolutionary origin of pikeperch in Aras Dam and Anzali wetland is common. On the other hand these two species had common ancestor in about 4 million years ago. Also different sequences of any region specimens are supposed as different haplotypes. 177 As a conclusion the results of this study showed that microsatellite and mtDNA sequencing methods respectively are effective in genetic structure and phylogenic studies of P. fluviatilis and S. lucioperca.

Some considerations on coastal processes relevant to sea level rise

The effects of potential sea level rise on the shoreline and shore environment have been briefly examined by considering the interactions between sea level rise and relevant coastal processes. These interactions have been reviewed beginning with a discussion of the need to reanalyze previous estimates of eustatic sea level rise and compaction effects in water level measurement. This is followed by considerations on sea level effects on coastal and estuarine tidal ranges, storm surge and water level response, and interaction with natural and constructed shoreline features. The desirability to reevaluate the well known Bruun Rule for estimating shoreline recession has been noted. The mechanics of ground and surface water intrusion with reference to sea level rise are then reviewed. This is followed by sedimentary processes in the estuaries including wetland response. Finally comments are included on some probable effects of sea level rise on coastal ecosystems. These interactions are complex and lead to shoreline evolution (under a sea level rise) which is highly site-specific. Models which determine shoreline change on the basis of inundation of terrestrial topography without considering relevant coastal processes are likely to lead to erroneous shoreline scenarios, particularly where the shoreline is composed of erodible sedimentary material. With some exceptions, present day knowledge of shoreline response to hydrodynamic forcing is inadequate for long-term quantitative predictions. A series of interrelated basic and applied research issues must be addressed in the coming decades to determine shoreline response to sea level change with an acceptable degree of confidence. (PDF contains 189 pages.)

Improving reporting of uncertainties in sea level rise assessments

Sea level rise (SLR) assessments are commonly used to identify the extent that coastal populations are at risk to flooding. However, the data and assumptions used to develop these assessments contain numerous sources and types of uncertainty, which limit confidence in the accuracy of modeled results. This study illustrates how the intersection of uncertainty in digital elevation models (DEMs) and SLR lead to a wide range of modeled outcomes. SLR assessments are then reviewed to identify the extent that uncertainty is documented in peer-reviewed articles. The paper concludes by discussing priorities needed to further understand SLR impacts. (PDF contains 4 pages)