فهرست مطالب elham ghehsareh ardestani

Most rangleands and grasslands around the world are changing and transitioning to new emerging ecosystems under the influence of many factors. The abundance or relative dominance of herbaceous plants and woody vegetation in these ecosystems has been very dynamic in different time scales. Over the past several decades, there has been a shift toward increasing abundance of woody vegetation worldwide.The aim of the research: Woody plant encroachment (WPE) in many rangeland and grasslands of the world, causes changes in ecosystem functions, goods and services in the affected areas compared to the services that were previously available in the region and creates new services that are unknown to some extent.

This study, reviews related research and identifies the functions, goods and services resulting from this phenomenon in these ecosystems.The geographical scope of the research: Living factors, including woody plants and non-living factors such as climate and soil in the territory of the distribution of woody plants and the geography of the presence of nomads and herders, have different effects on regulating exchanges between ecosystem responses to this phenomenon.

Considering the extent and effects caused by the occurrence of the phenomenon of WPE , identifying the functions, goods and services resulting from this phenomenon are of special importance that should be given special attention.

Following the WPE phenomenon, through identifying these goods, services, functions and benefits with regard to the replaced species, it is possible to take advantage of the potentials resulting from new functions, goods and services in order to improve the management and livelihood of local communities and rangeland users with paying attention to the size of the land and the affected population.

The phenomenon of woody plant encroachment (WPE) has been recognized as a global example of the transition and change in vegetation composition, which occurs in grasslands and rangeland ecosystems around the world. Considering the scope of this phenomenon and its impacts on the ecological services and the livelihood of millions of local people, it is necessary to investigate the factors and contexts of the phenomenon emergence. By reviewing related studies of the phenomenon in natural ecosystems, this paper examines and recognizes the factors and effective areas and contexts of its emergence. In general, the summary of the research showed that long-term factors such as climate, soil and human management and short-term factors including climate fluctuations and human activities affect WPE, the dynamics of woody plants and the species composition of each region. In addition, at the local level, changes in the amount and type of precipitation, herbivory, types of fires, and soil characteristics affect the probability of the abundance of woody plants and the WPE phenomenon. This research suggests that future research should examine and identify a combination of traits of woody plants needed to cross all obstacles and stages of WPE, this will help to explain the WPE phenomenon in the past and its prediction in the future.

This study, by reviewing the findings related to this phenomenon in natural ecosystems, investigates and recognizes the effective factors and contexts of emergence, has dealt with this phenomenon.

The causes and factors of the WPE are still something like a puzzle, and several stimuli may interact and intensify the occurrence of this phenomenon. Therefore, understanding the phenomenon of WPE requires considering multiple interactions between stimulating and limiting factors and their changes over time in a specific location. There is uncertainty in quantifying the importance of these stimuli and understanding their interaction with each other. In general, the WPE is affected by long-term (such as climate and carbon dioxide concentration) and short-term (such as fire and herbivory) driving factors that affect the dynamics of woody plant cover and the

Ecosystem functions has a vital role in environmental sustainability, improving people's livelihood and sustainable development. The most recent studies have focused on aspects of economic valuation of ecosystem, while little attention has been paid to their social aspects This study investigates the attitude of users without and with rangeland management plan of Beldaji rangelands in Borujen County, Chaharmahal and Bakhtiari provinces in relation to the functions and services of rangeland ecosystems. A structured questionnaire based on the Likert scale was designed, its validity was confirmed by a panel of experts and its reliability was confirmed by Cronbach's alpha test. The results of the Kruskal- Wallis test showed that there was a significant difference between all four regulatory, production, habitat and cultural parameters of the group without a rangeland management plan. In addition, a significant difference at the level of five percent was observed between the three criteria related to the regulatory function in the group of users without a rangeland management plan and between the three criteria related to the cultural function of the group of users with a rangeland management plan. The Mann-Whitney test showed a significant difference in relation to production functions and three related criteria and the heritage value at the five percent level. In general, convergence of views to the contexts and common interests of users and the divergence of views can be related to conflicts, the level of enjoyment and the difference in the attitude of the rangeland users.

The plant growth-promoting rhizobacteria (PGPR) are one of the famous sources when applying drought stress management in rangeland plants. This research was conducted to investigate the effect of PGPRs on the germination and growth of Astragalus cyclophyllon Beck under drought stress. A factorial experiment in a completely randomized design with three replications, was conducted in the seed laboratory. The effects of the main factors of PGPR inoculation as well as control and drought stress at four levels and their interactions on weight, length, and germination indices were investigated. Bacillus cereus had the highest effect on increasing shoot, seedling dry weight, and vigour indices. Pseudomonas aeruginosa had the highest effect on promoting radicle length. Additionally, results revealed that -0.8 MPa drought stress had a significant effect on radicle and shoot fresh weight, shoot and seedling length, germination percentage, germination rate and mean daily germination as compared to 0.0, -0.2 and -0.4 MPa indicating A. cyclophyllon is a drought stress resistance species. B. cereus at the control level of drought stress significantly increased shoot and seedling dry weight and vigour indices, whilst, P. aeruginosa at the drought stress level of -0.8 MPa significantly increased shoot lengths compared to control. In general, B. cereus significantly increased biomass by increasing shoot and seedling dry weight but P. aeruginosa affected elongation growth of the plant by increasing radicle and shoot lengths of A. cyclophyllon. Suitable PGPR as biotic elicitors can enhance the growth of the rangeland plants.

Quantitative methods to the evaluation of vegetation are the basis for describing and analyzing the plant communities and density as one of the main characteristics plays an important role in evaluating the performance (short and long effects) of rangeland ecosystems and management decisions. Therefore, the efficiency of plotless plant density methods in different density intensities and distribution patterns have been examined using a simulated population based on the real density of Astragalus albispinus (control method) in a semi-arid region of Marjan, Boroujen. Simulation schemes in the three density intensities and three distribution patterns within eight 40×100 m sampling units were designed. The predictive precision and accuracy of seven distance-based methods in various density intensities and distribution patterns were investigated comparing with the control method and the ideal point error index. Overall, angel order method in regular distribution pattern and point centered quarter method in random distribution pattern were the best plotless density methods of plant populations in low density. Third closest individual method was the most accurate and precise method of density among the studied methods in other different density intensities and distribution patterns. Therefore, in selecting the most appropriate distance method for estimating the density, the type of distribution pattern and in some cases the intensity of plant species density should be considered to achieve an unbiased estimation of plant populationʼs density, accordingly, the different habitats of this plant species can be monitored and evaluated in terms of management and utilization.

Trees can cover a wide range of environmental, aesthetic, historical, tourism benefits and social, physiological, as well as economic benefits, however, the trees can cause environmental risks. The aim of current study was to reduce the insignificant variables by methods of index criteria of tree and analysis of the main components and then using the remaining variables in the process of modeling the risk possibility falling of elm tree by Neural Network Method. For this purpose, the quantitative and qualitative variables of elm trees were collected in Seyed Alikhan Street of Isfahan by counting 100% of these trees in 2018. The results of elimination of tree diameter and contact with power transmission lines in predictive models of risk of falling dangerous trees indicated that the variables that were eliminated during the study process did not have a significant effect on predicting the risk of falling dangerous trees. This creates a significant change in the accuracy of the model training data and decrease the number of repetitions of network training to reach the optimal network and reduces the time to reach the optimal neural network. Therefore, we recommended the use of tree fall modeling for management of urban green spaces.

As one of the most valuable parts of a municipal system, the green space has long been praised. For that matter, all the data related to street-side trees must be assessed and recorded. A large part of old plane trees (Platanus orientalis) planted in industrial, dense populated cities are subjected to all kinds of air and water pollutions, frequent droughts and various physical stresses, which make them less likely to sustain. Identification of the most vulnerable tree individuals can be prioritized by a variety of statistical methods. A less applied statistical model in this field is the Artificial Neural Network (ANN). In this study, we present the results of applying ANN model in risk assessment of plane trees planted along Kuala Lumpur Avenue in Isfahan in 2018.

In the current study, the risk level of keeping plane trees in Kuala Lumpur Ave. Isfahan was studied using data acquired by a full survey method, and using quantitative tree body proportions and few risk factors (qualitative or imperfect properties). Following coining the share of each of seven hazard criteria and their trio importance indices, a Kruskal-Wallis test compared the number of trees in different risk levels. Then, all the trees were scored via the biased levels of their risk level. Accordingly, based on the weighted scores, they were divided into five hazardous categories. To develop an understanding of the quantitative variables, risk factors, weight parameters, and hazard classes, we carried out a principle component analysis (PCA) and a multi-layer perceptron (MLP) network procedure.

The results of the proportion of each hazard index revealed the importance of the trunk and root wounds (83%), the structural tree weakness (61%), root problems (54%), and branch and twig dieback (50%). Also, results of Kruskal-Wallis test showed that the risk levels of the planted trees can be significantly classified into four classes of: with no risk or healthy, low, moderate, and high-risk classes, at one percent error level. The results of Duncan's mean test showed that the number of trees in no risk and low-risk classes were significantly higher than the other classes at one percent error level. The results of the PCA indicated that the first and second components explained 44.69 percent of the total variation. The risk and weighting parameters of the branch and twig dieback, the tree diameter, advanced decay, and wound on the trunk and root were highly and positively correlated. In general, the two variables of the trunk and root wounding, as well as branch and twig dieback, were among the most important variables in terms of risk assessment of the plane trees. The high coefficient of determination values of training, validation, verification, and finally, all neural network data (0.999, 0.949, 0.996, and 0.991) and the least mean square error values (training data = 0.052, verification 0.114, and validation = 0.044) indicated the accuracy and desirability of the ANN in the prediction of the risk classes for street side trees.

According to the results of risk assessment diagnostic criteria and main components analysis, the two variables of trunk wound and root and branch mortality of plane trees should be regularly reviewed. Moreover, decision-makers may use the neural network method to identify and detect the risk possibility of planted trees. Therefore, this approach can be proposed as a suitable and useful solution in urban green space management and preventive measures.

Introduction Urban green space plays an important role in city functioning and its lacking can lead to city life disruption. Urban green space trees include a wide range of environmental, aesthetic, social, physiological and economic benefits. Tree falling due to various factors, especially storms and high-speed winds, can cause severe economic damage, even endangers public health and safety, which is very important due to their widespread presence, especially in the urban areas. It is attracted by municipal green space and urban crisis management to identify and quantify the severity of the hazardous trees and only can provide risk management and suitable preventive measures. Hazardous trees referres to absolutely dried or drying trees, dead components or highly unstable living trees that may be come from structural damage or other factors. These trees pose to a high risk of threatening citizen lives or their property in the urban environment. Normally, healthy trees are more resistant to applied forces and damaged while defective trees are more prone to fracture and eradication in the crown, trunk and root. Hazardous trees management inspects tree risks in the natural and synthetic green space by modeling the behavior and measuring tree conditions in the urban green space. There are several ways to correlate the quantitative and qualitative variables (defects) of trees with their probability of tree hazardous, including artificial neural networks and multivariate regression that have less constraints and assumptions than statistical methods for modeling process. The purpose of logistic regression is describing the relationships between quantitative and qualitative variables as a set of independent variables and risk intensity as a dependent variable. This model maximizes the probability of an event will occur and does not necessarily have a linear relationship between the independent and dependent variables. The neural network model is able to detect the relationship between a set of inputs and output data to predict the output corresponding to arbitrary inputs regardless of the parameters. Recently, studies have been performed on the risk possibility of the trees. Elm tree (Ulmuscarpinifolia var. Umbelifera) belongs to the family Ulmaceae. This genus has various species that are distributed in most parts of the world, especially in Asia, Europe and North America.These trees have relatively dense distribution in the natural forests of Iran, especially the northern and southern slopes of Alborz.They are also used in the most urban green spaces due to their beautiful appearance and wide shade as an ornamental and shaded tree. According to available information, in our country, despite the great extent of the urban green space and its importance and the need to be aware of street tree dangers that pose to a high risk to the life-threatening safety of citizens or their property, on the other hand, there has been limited applied research to predict the risk possibility of the fallen trees. Therefore, this study aimed to determine the most important independent quantitative and qualitative variables affecting the probability of falling trees and developing multilayer perceptron neural network models and logistic regression to predict the probability of falling of elm hazardous trees and also to compare these two models in predicting the probability of falling in a tree in Shariati has done Shahrekord. Material and methods The study area is located in Chaharmahal anf Bakhtiari province, Shahrekord, between 49 degrees and 22 minutes to 50 degrees and 49 minutes and latitude 32 degrees and 20 minutes to 33 degrees and 31 minutes, respectively. The study area was a part of Shariati Boulevard between Basij Square and the intersection of North Ferdowsi Street with a length of 584.13 meters. In order to compare the ability of neural network model with logistic regression to predict the risk possibility of elm trees, the study was performed on 129 elm trees in Shariati St. Boulevard. For this purpose, the variables including tree diameter, tree height, dry branches and woods, cracks, structural and physical weakness (vertical deviation), root and wound and root problems as independent. Quantitative and qualitative variables were classified and the risk classification of elm trees were measured as dependent variables. A complete survey method used to measure and record the quantitative and qualitative variables of elm trees on both sides of the street. Firstly, Normalization of the data was performed and then Principal Component Analysis was used to select the main variables. If KMO (Kaiser-Meyer-Olkin) index is less than 0.5, the data are not suitable for principal factor analysis and if the value is between 0.5 and 0.69, it should be used with higher caution. Also this method used for binary logistic regression models that is a step-by-step method were included in the model. By choosing this method it is possible to isolate significant independent variables related to the probability of elm falling trees (dependent variable). In this study, 70%, 15% and 15% of the total data were allocated to multi-layer perceptron network training, validation and testing, respectively. Qualitative and quantitative variables are considered as input variables and dependent variable of risk intensity class in each tree as output. In order to predict neural network model for prediction of the risk of hazardous elm tree two hazard severity classes were used based on weighting method. According to general approximation theorem, a neural network with a hidden layer and a sufficient number of neurons per layer can approximate any arbitrary continuous function. This study aimed to train neural network from Multilayer Perceptron network of artificial neural networks. Neural network training was used with learning rate reduction 10 (BDLRF). The activity functions in the hidden layers for all networks were considered as sigmoid function and in the output layer linear transfer function. Accordingly, multilayer perceptron network with 5 neurons in input layer, one hidden layer with 20 neurons and one neuron in output layer were used to modeling the risk possibility of elm trees. Surface area under ROC curve and Kappa curve were used to evaluate model prediction accuracy. Results and discussion Results showed that three criteria for branches, root and trunk problems were 87%, 79% and 77%, respectively, and criteria for contact with power lines had the least rate (29%) in the risk of elm trees. Regarding the analysis of principal components tree height, tree diameter, dried branches, structural status or physical weakness (vertical deviation), root problems and advanced decay are the most important variables affecting logistic regression and neural network models. Logistic regression results indicated that with 65.9% confidence iccluding 5 independent variables of tree height, tree diameter, branch and dried branches, structural weakness or physical weakness, advanced decay are predictor variables in probability of tree falling in the present study.The results of the NIMBUS tests to exam the model's explanatory power and efficiency show that the fit of the model is acceptable at the error level of less than 0.01. Independent variables used have moderate explanatory power regarding variance and variations dependent on the probability of falling trees. In fact, these variables were able to explain between 0.33 to 0.456% of tree fall variations. Conclusion Diagnostic criteria for hazardous trees showed that three criteria of dried branches, root problems and trunk cracks had the highest rate in the risk possibility of elm trees. Using the principal component analysis reduced the number of input variables to the models. It also eliminated the correlation between the input variables to the model and easier interpretation of the models. According to this analysis, the variables of tree height, tree diameter, twigs and branches, structural or physical weakness (vertical deviation), root problems and advanced rot were identified as the most important variables. Therefore, two criteria for woods and shoots and root problems in both analyzes are selected the most important variables.

Drought stress is one of the most important factors limiting plant growth all around the world. Plant Growth Promoting Rhizobacteria (PGPR) can significantly reduce drought stress effects on plants growth. This study was aimed to investigate the influence of PGPR (inoculation by Azospirillum lipoferum, Azotobacter chroococcum, Pseudomonas aeruginosa, and Bacillus cereus) on seed germination, seedling growth and photosynthetic pigments of Astragalus caragana under four levels of drought stress (0, -0.2, -0.4, -0.8 MPa) in laboratory conditions in Shahrekord University, Iran, 2017. Among the four PGPR treatments, A. chroococcum was eliminated because seeds failed to germinate and were identified as agent pathogenies. The results showed that inoculation by PGPR had a significant effect on all germination traits except shoot length. The effect of drought stress was significant on all traits expect mean germination time. The PGPR by drought stress interaction effect was significant on shoot length, embryonic leaf and seedling fresh weight, embryonic leaf and seedling dry weight, and root length. The highest and lowest seed germination indices and seedling length were related to first (control and -0.2 MPa) and second (-0.4, -0.8 MPa) levels of drought stress, respectively. It was concluded that P. aeruginosa was more effective in embryonic leaf fresh weight (0 and -0.2 MPa), embryonic leaf and seedling dry weight (total levels of drought stress); B. cereus (0 MPa) and A. lipoferum (0, -0.2, and -0.4 MPa) showed relatively better performance on root length and B. cereus (0 MPa), A. lipoferum (total levels of drought stress) and P. aeruginosa(-0.2 and -0.4 MPa; total levels of drought stress, respectively) improved growth of shoot and seedling fresh weight of the Astragalus caragana.

Vegetation is the most important factor influencing the sustainability of rangeland ecosystems. Therefore, it is important to understand the environmental factors and their effect on vegetation and restoration of rangeland ecosystems. The aim of this study was to identify factors influencing on Gundelia tournefortii L. distribution. Accordingly, the information of Gundelia tournefortii L. was determined in six habitats in Isfahan province. Random- systematic sampling was conducted to calculate canopy and density of the species. In each rangeland place, climate, physiography and soil factors were identified. RDA ordination analysis using CANOCO software was used to investigate the vegetation relationship with environmental factors. In the cluster analysis and RDA ordination analysis, six main habitats of the species were classified into three groups. The first axis of ordination showed a very strong correlation with canopy cover and density and the second axis showed a very strong correlation with soil depth. According to study of the habitats of this species, annual precipitation about 250 to 500 mm, altitude 2300 to 2500 meter, slope about 10 to 40 percent and average of annual temperature about 10 ̊C to 12 ̊C are appropriate for presence of this species.