Amir Mirzaie Mousivand; Ardavan Ghorbani; Mohammad ali Zare Chahooki; Farshad Keyvan Behjoo; Kiomars Sefidi
Volume 24, Issue 4 , January 2018, , Pages 791-804
Abstract
The aim of this study was to investigate the effects of environmental factors on distribution of Prangos uloptera in rangelands of Ardabil province. The habitats of Prangos were identified and the habitats, in which the study species was present, were selected. Sampling was also carried out in the vicinity ...
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The aim of this study was to investigate the effects of environmental factors on distribution of Prangos uloptera in rangelands of Ardabil province. The habitats of Prangos were identified and the habitats, in which the study species was present, were selected. Sampling was also carried out in the vicinity of each habitat where the study species was not present. Three transects of 100-m length were established, on which canopy cover percentage and density of species were measured within 10 plots of 4m2. Soil samples were taken from the beginning, middle and end of each transect. In sampling places, altitude, slope, aspect, and soil characteristics were measured. Independent t test and cluster analysis were applied to comparison and classification of presence and non-presence areas and determination function was applied to determine the importance of factors affecting the presence of this species. The results of t test showed that there were significant differences between all variables except for temperature and precipitation. According to the results of cluster analysis, the studied species had more distribution in places with high altitude and steep slopes, high organic matter, and high nitrogen and sand. The results clearly showed that climatic parameters including precipitation and temperature as awell as altitude and sand percentage in the first grade and then aspect and soil characteristics including nitrogen and phosphorus in the second grade were the most important factors affecting the distribution of study species. According to the results, better decisions could be taken to use this species for range management, improvement and reclamation.
Javad Esfanjani; Mohammadali Zare Chahouki; Hamed Rouhani; Majid Mohammad Esmaeli; Bahare Behmanesh
Volume 23, Issue 3 , January 2017, , Pages 516-526
Abstract
This research was aimed to modeling the habitat suitability for plant species in southern rangelands of Golestan province by ENFA method with Biomapper software. For this purpose, the spatial data of species presence and environmental conditions were used to identify the suitable sites for the study ...
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This research was aimed to modeling the habitat suitability for plant species in southern rangelands of Golestan province by ENFA method with Biomapper software. For this purpose, the spatial data of species presence and environmental conditions were used to identify the suitable sites for the study species and their habitat requirements. For vegetation sampling in each vegetation type, three 50-meter transects were established, along which measurements were performed within 10 plots with five-meter intervals. The plot size was calculated to be 1m2. In each vegetation type, soil sampling was done at 0-30 cm depth. Then data were entered to the Biomapper software and the middle algorithm and harmonic algorithm were used to produce the habitat suitability map. The kappa coefficient was used to check the conformity of the predicted map with actual vegetation map. The kappa coefficient was calculated to be 0.57, 0.70, 0.58, and 0.50 for Artemisia aucheri, Festuca ovina-Astragalus gossypinus, Bromus tomentellus, and Bromus tomentellus- Festuca ovina, respectively. According to the obtained results, the most important environmental factors affecting plant species in the study vegetation types were as follows: clay content and slope (A. aucheri); sand content and altitude (F.ovina-A.gossypinus); sand content and northwest aspect (B.tomentellus); sand content, altitude and organic carbon (F.ovina-A.gossypinus). The models developed in this study can be used to identify suitable areas for range improvement practices.
Leila Khalasi Ahvazi; Mohammad ali zare chahouki
Volume 23, Issue 2 , September 2016, , Pages 287-275
Abstract
Artificial Neural Network (ANN) is new information processing structures that uses special methods for biological neural networks. The main purpose of this study was to modeling of Seidlitzia rosmarinus distribution in northeast rangelands of Semnan by ANN model. For this purpose, vegetation sampling ...
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Artificial Neural Network (ANN) is new information processing structures that uses special methods for biological neural networks. The main purpose of this study was to modeling of Seidlitzia rosmarinus distribution in northeast rangelands of Semnan by ANN model. For this purpose, vegetation sampling was carried out in each vegetation type along three transects of 750 m, on which 15 plots were established with an interval of 50 m. Soil samples were taken from two depths of 0-20 cm and 20-80 cm in starting and ending points of each transect. To provide the prediction map of plant species distribution, different layers of environmental factors used in the model are required. The geostatistics method was applied for mapping soil properties based on the prediction model obtained from ANN method for S. rosmarinus. The back-propagation neural network with three-layer- perceptron network was designed to generate the ANN model and seven neurons in the input layer, ten neurons in the hidden layer, and one neuron in the output layer were used. The accuracy of the prediction map was tested with actual vegetation maps and the Kappa coefficient was calculated to be 72%, indicating a very good agreement. Results showed that this species is distributed in rangelands with a pH of 8.1-8.3, an EC 0.22-0.26 dS/m, in a silty-sandy textured soil, and an altitude of 1600-1750 meters. it is highly correlated with lime and pH in two depths.
Narges Naseri Hesar; Mohammad ali Zare chahouki; Mohammad Jafari
Volume 23, Issue 2 , September 2016, , Pages 310-299
Abstract
Spatial correlation is the first step in the interpolation of field data and mapping of soil properties.The aim of this research was to study the efficiency of two spatial statistics methods i.e., Kriging and inverse distance weighting for mapping of soil properties. Five sampling units were selected ...
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Spatial correlation is the first step in the interpolation of field data and mapping of soil properties.The aim of this research was to study the efficiency of two spatial statistics methods i.e., Kriging and inverse distance weighting for mapping of soil properties. Five sampling units were selected in the region, and the location of soil profiles was so determined to cover the whole area. In each unit, six profiles and totally 30 soil profiles were dug in the whole area. Soil samples were taken from two depths of 0-20 cm and 20-80cm. Soil variables including gravel, clay, silt, lime, organic matter, pH and EC were measured in both soil depths. In the GS+ software, the accuracy of two spatial statistics methods was tested using cross validation with the help of two statistical parameters: MAE and MBE. According to the results, MAE and MBE, related to the Kriging method, for the majority of soil parameters, are less than that of inverse distance weighting method; therefore, Kriging is a more accurate method to interpolate soil properties.