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The Hezar-Masjed Mountain range is one of the main highlands of the northeasten Iran. The vegetation of its upper elevations has been poorly studied. This study aimed to compare the plant biodiversity of different aspects of the Hezar-Masjed Summit. A total number of 139 random 1m² plots were recorded from different aspects of the study area. In each plot, the abundance and canopy cover percent of vascular plant species were recorded. Hill’s numbers (i.e., N₁, N₂) along with the Camargo evenness index and total species richness with bootstrapping approach were calculated. Rarefaction method was used to compare species richness among the aspects. Also, Hill’s diversity profile for each aspect was drawn. For each aspect, rank-abundance method was used to evaluate the dominant species. Biodiversity calculations and statistical analyses were performed using R software. The east- and south- facingslopes were found to have the highest and lowest indices in richness, evenness, and diversity, respectively. Despite adequate water availability in north-facing slopes, they show lower diversity than that of the east-facing slope, probably due to the of dominance of a poisonous species (Seseli transcaucasicum). Our results shown that in the Hezar-Masjed Summit, aspects have different biodiversity which is a result of their various environmental conditions.
 

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Overgrazing affects plant communities, and is a significant disturbance factor in arid and semi-arid regions. The immediate changes of plant communities after overgrazing in the disturbed arid ecosystems of Iran have been poorly studied. We recorded data from 100 random samples before and after overgrazing in the Golbahar plain located in the northeastern Iran to determine the changes in the plant physiognomic, species composition, and diversity after overgrazing. We compared life-forms spectra, change in the RIVI of the recorded plant species, species composition, and species diversity before and after the grazing. Our results showed that therophytes were the dominant life-form in the area, and decreased after overgrazing. The community composition of the area remained unchanged after overgrazing. Species diversity at the level of rare and frequent species reduced after overgrazing. Our findings implied that overgrazing could not immediately affect the community structure of degraded arid areas. However, it causes changes that might reduce ecosystem services in them. It is not possible to completely exclude grazers in such areas, fencing or reducing the number of the livestock entries should be applied to restore the vegetation in the area.
 
 

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In recent years, the use of machine learning methods in various fields of agriculture is increasing, and these methods provide us with very good information for predicting and checking different levels of performance in plants. In the current research, according to the results of the preliminary experiment carried out previously with specific levels of salinity stress and fertilization (salinity stress levels of zero, 75 and 150 mM sodium chloride and fertilization levels of zero and 3 grams per liter of silica) which were previously carried out and using the nonlinear regression model (NLR) and Python programming language, the morphological and physiological traits of the fenugreek medicinal plant at the newly defined levels of salinity stress and silica fertilization (salinity of up to 300 mM level and silica fertilization in two levels of 1 and 2 grams per liter) were predicted without conducting practical tests and based on the levels of salinity and initial fertilization. The non-linear regression model is a widely used algorithm in data analysis where the relationship between variables is non-linear and can create meaningful relationships between variables using non-linear functions. The results showed that the positive effect of silica on the amount of chlorophyll fluorescence (Fv/Fm) can be seen from zero to 180 mM salinity level and the amount of greenness index (SPAD) from zero to 100 mM salinity level. It seems that according to the results of the present research, it is possible to use machine learning to investigate and analyze the morphological and physiological characteristics of the fenugreek medicinal plant at other defined levels of salinity stress and other defined silica fertilization with no need conduct a practical experiment.