Factors Affecting Predator-Prey Distribution in a Protected Area,Tehran, Iran (a Case with Wolves and Wild Sheep)

We tried to model habitat suitability of two prey and predator species including wild sheep (Ovis orientalis) and wolf (Canis lupus) in Varjin protected area located in northern east of Tehran using a presence only method, maximum entropy (MaxEnt). Totally 11 environmental variables were measured in the species presence points which can be classified in three groups including topographical, vegetation and distal variables. Resulted maps indicated that habitat variables such as slope (ranging from 35 to 40 percent) and elevation (lower than 1700 meters above sea level) are both institute those factors which mostly affect studied prey and predator habitat use. Our results regarding prey and predator geographical range of used habitat indicated that wolves cover most area than wild sheep which show more dispersed habitat resources for the prey species. ENMTools test revealed that wolf’s niche breadth is more than twice as much as wild sheep’s. Wild sheep in Varjin protected area has a relatively narrow geographical extent and shows a tendency to marginal habitats while wolves cover obviously more areas which denotes its high mobility and low dependency to specific habitats.     

Mitigating Impact of Devils Lake Flooding on the Sheyenne River Sulfate Concentration

Devils Lake is a terminal lake located in northeast North Dakota. Because of its glacial origin and accumulated salts from evaporation, the lake has a high concentration of sulfate compared to the surrounding water bodies. From 1993 to 2011, Devils Lake water levels rose by ~10 m, which flooded surrounding communities and increased the chance of an overspill to the Sheyenne River. To control the flooding, the State of North Dakota constructed two outlets to pump the lake water to the river. However, the pumped water has raised concerns about of water quality degradation and potential flooding risk of the Sheyenne River. To investigate these perceived impacts, a Soil and Water Assessment Tool (SWAT) model was developed for the Sheyenne River and it was linked to a coupled SWAT and CE‐QUAL‐W2 model that was developed for Devils Lake in a previous study. While the current outlet schedule has attempted to maintain the total river discharge within the confines of a two‐year flood (36 m³/s), our simulation from 2012 to 2018 revealed that the diversion increased the Sheyenne River sulfate concentration from an average of 125 to >750 mg/L. Furthermore, a conceptual optimization model was developed with a goal of better preserving the water quality of the Sheyenne River while effectively mitigating the flooding of Devils Lake. The optimal solution provides a “win–win” outlet management that maintains the efficiency of the outlets while reducing the Sheyenne River sulfate concentration to ≤600 mg/L.     

Evaluating the impact of uncertainty and variability on the value chain optimization of a forest biomass power plant using Monte Carlo Simulation

Mathematical programming models have been used to optimize the design and management of forest bioenergy supply chains. A deterministic mathematical model is beneficial for making optimum decisions; however, its applicability to real-world problems may be limited because it does not capture all the complexities, including uncertainties in the parameters, in the supply chain. In this paper, a combination of Monte Carlo Simulation and optimization model is used to evaluate the impact of uncertainty in biomass quality, availability and cost, and electricity prices on the supply chain of a forest biomass power plant. The optimization model is a deterministic mixed integer non-linear model with monthly time steps over a 1-year planning horizon. Variability in biomass quality, i.e. moisture content (MC) and higher heating value (HHV), based on the historical data of a real case study is studied in detail and fitted probability distributions are used in the model, while for electricity prices different scenarios are considered. The results show that the impact of variability in the MC on profit is higher than that of uncertainty in HHV. It is observed that the annual profit ranges between $13.3 million and $17.9 million in the presence of all possible uncertainties while its average is $15.5 million. Uncertainty in biomass availability and cost and electricity price results in the risks of having annual profit of less than $14 million and low monthly storage levels.     

The spatial distribution dynamic and convergence of CO2 emissions in Iran’s provinces

Environmental Science and Pollution Research - It is essential to study CO2 emissions intensity as the most critical factor affecting temperature increase and climate change in a country like Iran,...     

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.     

Optimized processing power and trainability of neural network in numerical modeling of Al Matrix nano composites

In this research, an experimental study of reinforcing alumina nano-particles into the aluminum alloy matrix was implemented to verify the accuracy of modeling results obtained by feed forward neural networks. Artificial neural network combined with numerical technique were used to predict the various parameters of mechanical properties such as hardness, tensile and compressive yield stress, UTS and elongation percentage. Much experimentation were taken to discover a suitable number of hidden neurons, avoid detraction from the trainability and enable feed forward neural networks to solve more complex problems. The predictions were found to be consistent with experimental measurements.     

Modeling Water Quantity and Sulfate Concentrations in the Devils Lake Watershed Using Coupled SWAT and CE‐QUAL‐W2

Devils Lake is an endorheic lake in the Red River of the North basin in northeastern North Dakota. During the last two decades, the lake water level has risen by nearly 10 m, causing floods that have cost more than 1 billion USD in mitigation measures. Another increase of approximately 1.5 m in the lake water level would cause spillage into the Sheyenne River. To alleviate this potentially catastrophic spillage, two artificial outlets were constructed. However, the artificial drainage of water into the Sheyenne River raises water quality concerns because the Devils Lake water contains significantly higher concentrations of dissolved solids, particularly sulfate. In this study, the Soil and Water Assessment Tool (SWAT) was coupled with the CE‐QUAL‐W2 model to simulate both water balance and sulfate concentrations in the lake. The SWAT model performed well in simulating daily flow in tributaries with E_NS > 0.5 and |PBIAS| < 25%, and reproduced the lake water level with a root mean square error of 0.35 m for the study period from 1995 to 2014. The water temperature and sulfate concentrations simulated by CE‐QUAL‐W2 for the lake are in general agreement with the field observations. The model results show that the operation of the two outlets since August 2005 has lowered the lake level by 0.70 m. Furthermore, the models show pumping water from the two outlets raises sulfate concentrations in the Sheyenne River from ~100 to >500 mg/L. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Comparing different environmental variables in predictive models of bird distribution

The response variable (often the presence of a species) in predictive habitat models relies on a set of environmental predictors. Among all known environmental predictors, vegetation has the most effect on species abundance and on their habitat preferences, due to the wide range of necessary resources that it provides for the survival of bird species. However, other predictors, in turn, affect bird distribution, and some-times they play a more important role in habitat selection, depending on the natural history and ecological needs of the bird species. In this regard, different analyses have been conducted to predict the distribution, and define habitat suitability (such as discriminant function analysis, General Linear Models, and ANOVA). In this study, all three analytical designs were used to investigate the relationship of seven bird species to the major environmental gradients in the study area, to find out the significance of each of these factors on habitat selection. GIS has been used to prepare spatial distributional data, and to overlay and calculate different aspects of the environmental factors. The results suggest that potential individual habitat patches play a small role compared to the landscape (entire corresponding habitat patches), when considering vegetation. The influence of built-up areas is significant for all the species, and the proximity to the sea shore is very significant for at least one of the species, however, it is not neutral for all other species.     

A mechanistic view on the neurotoxic effects of air pollution on central nervous system: risk for autism and neurodegenerative diseases

Environmental Science and Pollution Research - Many reports have shown a strong association between exposure to neurotoxic air pollutants like heavy metal and particulate matter (PM) as an active...