No abstract

Iran lies at the southernmost range limit of brown bears globally. Therefore, understanding the habitat associations and patterns of population connectivity for brown bears in Iran is relevant for the species’ conservation. We applied species distribution modeling to predict habitat suitability and connectivity modeling to identify population core areas and corridors. Our results showed that forest density, topographical roughness, NDVI and human footprint were the most influential variables in predicting brown bear distribution. The most crucial core areas and corridor networks for brown bear are concentrated in the Alborz and Zagros Mountains. These two core areas were predicted to be fragmented into a total of fifteen isolated patches if dispersal of brown bear across the landscape is limited to 50,000 cost units, and aggregates into two isolated habitat patches if the species is capable of dispersing 400,000 cost units. We found low overlap between corridors, and core habitats with protected areas, suggesting that the existing protected area network may not be adequate for the conservation of brown bear in Iran. Our results suggest that effective conservation of brown bears in Iran requires protection of both core habitats and the corridors between them, especially outside Iran’s network of protected areas.

Snakebite envenoming is an important public health problem in Iran, despite its risk not being quantified. This study aims to use venomous snakes’ habitat suitability as an indicator of snakebite risk, to identify high-priority areas for snakebite management across the country. Thus, an ensemble approach using five distribution modelling methods: Generalized Boosted Models, Generalized Additive Models, Maximum Entropy Modelling, Generalized Linear Models, and Random Forest was applied to produce a spatial snakebite risk model for Iran. To achieve this, four venomous snakes’ habitat suitability (Macrovipera lebetinus, Echis carinatus, Pseudocerastes persicus and Naja oxiana) were modelled and then multiplied. These medically important snakes are responsible for the most snakebite incidents in Iran. Multiplying habitat suitability models of the four snakes showed that the northeast of Iran (west of Khorasan-e-Razavi province) has the highest snakebite risk in the country. In addition, villages that were at risk of envenoming from the four snakes were identified. Results revealed that 51,112 villages are at risk of envenoming from M. lebetinus, 30,339 from E. carinatus, 51,657 from P. persicus and 12,124 from N. oxiana. Precipitation seasonality was identified as the most important variable influencing distribution of the P. persicus, E. carinatus and M. lebetinus in Iran. Precipitation of the driest quarter was the most important predictor of suitable habitats of the N. oxiana. Since climatic variables play an important role in shaping the distribution of the four venomous snakes in Iran, thus their distribution may alter with changing climate. This paper demonstrates application of species distribution modelling in public health research and identified potential snakebite risk areas in Iran by using venomous snakes’ habitat suitability models as an indicating factor. Results of this study can be used in snakebite and human–snake conflict management in Iran. We recommend increasing public awareness of snakebite envenoming and education of local people in areas which identified with the highest snakebite risk.

SummaryLittle Bustard Tetrax tetrax surveys were conducted in Iran in autumn and winter from 2005/2006 to 2008/2009. Across northern Iran, Little Bustard presence was confirmed at 15 of the 84 sites visited during the surveys. Three main wintering regions were identified: the Moghan Plain in the north-west of the country, the Turkmen Sahra Plain at the south-east corner of the Caspian Sea and the Sarakhs Plain in the north-east, close to the Afghan border. Up to 10,050 individuals were counted in winter 2009 in the Moghan Plain, which was recognised as the most important area for wintering Little Bustards in Iran. The species was also found in good numbers in Miankaleh, Gomishan, Soufikam, Shour Lake and Sarakhs. Flocks comprising 51 to 500 individuals were the most commonly observed and represented 32% of occurrences. A marked increase in wintering population size was noted in recent years. The main wintering period in Iran extends from November to February. At a national scale, we estimated the Iranian wintering population of Little Bustards at 5,000–10,000 individuals. This preliminary assessment suggests the need for a nationwide survey, emphasising in particular the western, south-western and central parts of the country that were overlooked in our study. It suggests further that Little Bustards in Iran would greatly benefit from national and regional conservation programmes.

Habitat fragmentation threatens biodiversity, causes population isolation and reduces the availability of resources. When species ranges span geopolitical borders, management of transboundary populations and securing their connectivity can be compromised by different juridical efforts and priorities. Using a combination of species distribution modelling and circuit theory, we modelled suitable habitats for four conservation-dependent mammalian megafauna in northeastern Iran, bordering Turkmenistan and Afghanistan which is part of the larger Kopet Dag Ecoregion in central Asia. Our multispecies approach was aimed to identify key habitats and potential national and international corridors for Persian leopard (Panthera pardus), bezoar goat (Capra aegagrus), urial sheep (Ovis orientalis) and goitered gazelle (Gazella subgutturosa). Between 18 to 34% of the region was identified as suitable habitat for each species with a moderate variability in coverage by conservation network (14 to 43%). Importantly, we identified three key landscapes which can enhance the connectivity between main populations of the species in northeastern Iran as well as neighboring countries. Most of the suitable landscapes along the Iran-Turkmenistan borderlands are protected on the Iranian side, providing a source for species movement across the border. In contrast, the main suitable landscapes for megafauna in northeastern Iran are located far from the Afghan border. Our multispecies approach provided an empirical framework for spatial conservation planning for the mammalian megafauna across the Kopet Dag Ecoregion and can direct future survey efforts to identify critical wildlife areas in Turkmenistan and Afghanistan, two countries with scarce data on biodiversity.

No abstract

A novel, simple and simultaneous synthesis-immobilization of nano ZnO on perlite (nZnO-P) as a photocatalyst for photocatalytic degradation of Acid orange 7 (AO7) in aqueous solution was investigated. The effect of operational parameters such as initial dye concentration, initial pH, fl ow rate, photocatalyst granule size, temperature and the kinetic of the removal of AO7 in terms of the Langmuir-Hinshelwood model in a designed semi batch packed bed photoreactor connected to an on-line sampling UV-Vis spectrophotometer was studied. The results showed that AO7 removal effi ciency increased with nZnO-P using the designed setup and the proposed photocatalyst was more effi cient than TiO 2 as a standard catalyst. Our results confi rmed the pseudo-fi rst-order kinetics model. The values of the adsorption equilibrium constant, K AO7 , the kinetic rate constant of surface reaction, k c , and the activation energy (E a ) were found to be 0.57 (mg.l −1 ) −1 , 0.41 mg.l −1.min −1 and 11.44 kJ/mol, respectively.

High concentrations of N-containing compounds in drinking water cause health problems such as cyanosis among children and cancer of the alimentary canal. Therefore, removal of nitrate from water samples is of significant importance from the health and environmental point of view. In this work, the effective parameters on removal of nitrate by adsorption process, which included the amount of granular activated carbon (m), initial concentration (C0), contact time, pH and temperature (T), were investigated. The removal process was monitored using an on-line spectrophotometric analysis system. It was found that the content of adsorption followed decreasing order: m= 10>5>2>1g, C0= 20>15>25>10 ppm, pH=4>7>10>1 and T=25>35>45>55 0C. The three-layered feed forward back propagation neural network was used for modeling of nitrate adsorption on granular activated carbon. The comparison between the predicted results of the designed ANN model and the experimental data proved that modeling of nitrate adsorption process using artificial neuron network was a good and precise method to predict the extent of adsorption of nitrate on GAC under different conditions.