Afshin Alizadeh Shabani

Modeling relationships between catchment attributes and river water quality in southern catchments of the Caspian Sea

Increasing land utilization through diverse forms of human activities, such as agriculture, forestry, urban growth, and industrial development, has led to negative impacts on the water quality of rivers. To find out how catchment attributes, such as land use, hydrologic soil groups, and lithology, can affect water quality variables (Ca2+, Mg2+, Na+, Cl−, HCO3−, pH, TDS, EC, SAR), a spatio-statistical approach was applied to 23 catchments in southern basins of the Caspian Sea. All input data layers (digital maps of land use, soil, and lithology) were prepared using geographic information system (GIS) and spatial analysis. Relationships between water quality variables and catchment attributes were then examined by Spearman rank correlation tests and multiple linear regression. Stepwise approach-based multiple linear regressions were developed to examine the relationship between catchment attributes and water quality variables. The areas (%) of marl, tuff, or diorite, as well as those of good-quality rangeland and bare land had negative effects on all water quality variables, while those of basalt, forest land cover were found to contribute to improved river water quality. Moreover, lithological variables showed the greatest most potential for predicting the mean concentration values of water quality variables, and noting that measure of EC and TDS have inversely associated with area (%) of urban land use.

Application of the modified universal soil loss equation (MUSLE) in prediction of sediment yield (Case study: Kengir Watershed, Iran)

Modified Universal Soil Loss Equation (MUSLE) application study is undertaken in order to estimate the sediment yield of the Kengir watershed in Iyvan City, Ilam Province, Iran. The runoff factor of MUSLE is computed using the measured values of runoff and peak rate of runoff at outlet of the watershed. Topographic factor (LS) and crop management factor(C) are determined using geographic information system (GIS) and field-based survey of land use/land cover. The conservation practice factor (P) is obtained from the literature. Sediment yield at the outlet of the study watershed is simulated for six storm events spread over the year 2000 and validated with the measured values. The high coefficient of determination value (0.99) indicates that MUSLE model sediment yield predictions are satisfactory for practical purposes.

Efficiency of current reserve network in Golestan Province (Iran) for the protection of hoofed ungulates

Because of the current high extinction rate of species, it is necessary to select areas more efficiently for the conservation of threatened wildlife species. We postulate that part of the failure in wildlife conservation efforts in Iran is related to shortage of funds, incomplete data, lack of experts, and perhaps inefficient selection of reserve networks. Most natural reserves in Iran are selected on an ad-hoc basis or at best through consideration of the general IUCN criteria for threatened species. In cases where these areas are remote and inaccessible, the reserve network selection problems are further complicated and the fate of the protected areas and their species becomes more uncertain. On the other hand, as yet there has been no systematic and official evaluation of the efficiency of the current reserves in Iran. This study, the first effort in Iran to evaluate the efficiency of the current nature reserve system in Golestan Province, tries to offer a way of circumventing problems, at least to some extent. In our case study, we chose those wildlife species that belong to the Artiodactyla order because of their high extinction rates through habitat destruction and hunting. We use multi-criteria evaluation to provide a habitat suitability map of the species. Then, using Marxan, we optimize site selection for protected areas and show that the present reserves are insufficient to protect even 20% of the suitable habitats of the selected species. We run Marxan again to complement these reserves with the aim of conserving at least 20% of species in Golestan Province. Our results show that to achieve the new conservation target, another 272 km2 must be added to the current reserve system of the province. These areas are mainly located in the north and south west of Jahannama protected area and Golestan National Park, respectively.

Accumulation of heavy metals and As in liver, hair, femur, and lung of Persian jird (Meriones persicus) in Darreh Zereshk copper mine, Iran.

Rodents frequently serve as bioindicator to monitor the quality of the environment. Concentrations of 11 elements (Cd, Co, Ti, Fe, Mn, Cu, Sb, As, Sr, Ni, and Cr) were investigated and compared in liver, hair, femur, and lung of the Persian jird (Meriones persicus) from Darreh Zereshk copper mine, Iran. Metals were determined in different tissues of 39 individuals of Persian jird, collected by snap trap in 2014 from five areas of Darreh Zereshk copper mine. Samples were prepared by wet digestion method, and the contents of elements were analyzed with ICP-OES (VARIAN, 725-ES) instrument. Cadmium, Sb, and Co were below the limit of detection, and Mn and As were found only in hair and liver tissues. We detected the highest concentration of Cu, As, Ti, Fe, Mn, Cr, and Ni in hair in comparison with other tissues. Significant higher levels of Ti in femur and hair; Fe in liver and hair; Mn in liver; As in hair; Sr in lung; Cr in lung, hair, femur, and liver; Cu in femur; and Ni in liver and lung tissues were observed in females. Nearly all element concentrations in the tissues of Persian jird from flotation site, Darreh Zereshk and Hasan Abad villages and leaching site (mining areas) were higher than those from tailing dump site (reference site). We found the highest concentrations of As in liver and hair; Ni and Cr in liver, hair, and lung; and Sr in lung and hair tissues of Persian jird in leaching site. We tried to specify the status of elements before fully exploitation of Darreh Zereshk copper mine by using bioindicator species. Based on our achievements, initial activities did not strongly pollute the surrounded environment of the mine. The high abundance of Persian jird as well as their several proper features makes them a suitable species for biomonitoring programs especially for further studies will be performed after full exploitation of Darreh Zereshk copper mine.

Genetic structure and differentiation of four populations of Afghan Pika (Ochotona rufescens) in Iran based on mitochondrial cytochrome b gene

The population genetics of the Afghan Pika (Ochotona rufescens) was studied in Northern Khorasan Province, Iran. For prediction of the genetic differentiation of four populations, the DNA of mitochondrial cytochrome b of 32 individuals from four areas was sequenced and a Bayesian analysis based on the HKY model was constructed. In total, 15 polymorphic sites, 1125 conserved sites (98.7%) and 14 different haplotypes were found. The phylogenetic tree resulting from the Bayesian analysis and network analysis showed that all samples were clustered in two major groups and the haplotypes of the four populations did not separate geographically. An analysis of molecular variance (AMOVA) indicated that a large majority of the genetic variance was due to the variance within populations. The results of fixation indices showed significant genetic structure among populations in both methods. The pairwise Fst revealed that two northern populations have a significant genetic differentiation from two southern populations, but no significance pairwise Fst value was demonstrated between the closed populations. Neis genetic distances between closed populations were not significant, while significant values occurred between distant populations. It seems that there is not a major discontinuity between populations of Afghan Pika based on cyt-b mitochondrial gene. However, phylogenetic analysis did not separate populations and a large majority of the genetic variance was found within populations. However, AMOVA analysis showed a significant level of genetic structure among populations (p < 0.001) and between groups (p < 0.5). It seems that these results suggest shallow genetic differentiation between populations of different geographic groups.

Examining Relationships Between Socioeconomic Factors and Landscape Metrics in the Southern Basin of the Caspian Sea

Socioeconomic forces are not only among the main drivers of landscape dynamics; they are also influenced by landscape patterns. Landscape structure and functions are closely related to natural and social factors. The objective of this study was to investigate the relationships among some human-related factors and landscape ecological metrics as landscape pattern indicators and to identify suitable metrics for modeling these relationships. To this goal, landscape ecological metrics were calculated for each of the 32 counties of Mazandaran and Guilan provinces located in the southern basin of the Caspian Sea using land use/cover maps in class level. Stream network metrics were calculated using a digital elevation model, road density metrics were calculated using map of main roads separately, and significant metrics were selected according to results of correlation tests and factor analysis. The correlations between these metrics and socioeconomic factors were tested, and their relationships were modeled with multiple linear regressions. Significant relationships were found among socioeconomic factors and landscape ecological metrics, and land use/cover data are applicable for modeling socioeconomic factors, especially demographic and employment structure factors. Among the landscape metrics applied in this study, road density, mean patch size, mean nearest neighbor distance, and percentage of a land use/cover class in landscape were important metrics for predicting socioeconomic factors. Our findings indicated that road density metric and percentages of urban class are useful for predicting urban socioeconomic factors and percentage of agriculture and forest classes in the landscape are suitable metrics for predicting rural socioeconomic factors.