spatial distribution

Dust storm is one of the destructive weather phenomena that strongly affects regions with arid and semiarid climates like Yazd province. This phenomenon leads to many losses, including environmental, socio-economic, human health, climate and microclimate problems. Accurate spatial and temporal monitoring of dust can help identify the trajectories and sources of this atmospheric hazard and play a vital role in management and reduction of possible damages of storm. In the present study, three examples of dust storms that occurred in 2022 in Yazd province were analyzed. When the visibility is less than 3.5 km and one of the codes 06 to 09 or 30 to 35 is reported, it is considered as an effective dust storm. The lagrangian HYSPLIT model was used to identify the trajectories of dust transfer to Yazd province. The results of this model indicate that dust masses travel three main pathways to reach Yazd province: southwest, west-northwest, and northeast. In order to investigate the spatial distribution of dust and also to identify dust sources more accurately, the aerosol optical depth data based on remote sensing, the MOD04/MYD04_L2 product and also the MOD08_D3 product, were used. These images showed that the external sources of dust are the large deserts of Iraq, Syria, the Arabian Peninsula, and the desert of Turkmenistan, and the internal source of dust is the desert areas located in the provinces of Semnan, Isfahan in the northeast of Yazd province. In addition, the Gavkhoni wetland in the northwest of the study area acts as an intensifier of the dust transferred from the western borders of the country. Also, the results obtained from the satellite data and the HYSPLIT model are consistent with each other.

The semi-parasitic European yellow mistletoe (Loranthus europaeus Jacq.) is one of the harmful and damaging factors to the Zagros forests. This study to analyze the causes of infected trees by European yellow mistletoe was conducted in the Ghalajeh forests in Kermanshah province. To carry out this study, completely infected trees were examined by using the tree-to-tree sampling method. Required data including habitat characteristics and host tree characteristics such as tree species, diameter at breast height (DBH), length of the tree, crown area, and severity of European yellow mistletoe infection recorded in three parts on crown through the DMR method. The results showed that Persian oak includes 80.4% of the region's trees. The state of tree infection to European yellow mistletoe showed that the highest infection rate consisted of the Persian oak species (91.9%). Typically, 60 and 65 cm diameter classes, 15-meter-high tree height, and 9 square-meter crown class had the highest infection. Among the physiographic factors in the study site, the highest impact on the abundance of European yellow mistletoe-infected trees is related to the altitude factor. The variance test results demonstrated a significant difference between the severity of infected trees in the DBH classes, height, and crown area of trees (p=0.5). By increasing the number of characteristics of trees (i.e. diameter, length, and crown area), the severity of infection increases.  Therefore, it suggested that to control the spread of mistletoe in different habitat conditions such as slope, altitude, geographical direction, two-story, and old-growth trees with outsized dimensions should be given priority.

The present study aimed to model potential habitat distribution of A. sieberi, and its ecological requirements using generalized additive model (GAM) and classification and regression tree (CART) in in the Poshtkouh rangelands of Yazd province. For this purpose, pure habitats of the species was delineated and the species presence data was recorded by the systematic-randomize sampling method. Using DEM and geostatistical method, digital layers of environmental variables (soil and physiographic variables) were prepared with the same spatial resolution (pixel size 30×30 meter). Plant distribution modeling was conducted using CART and GAM models in the R.3.3.1 software environment. The prediction performance of the models was evaluated by the AUC (Area Under the Curve) In addition, The TSS (True Skill Statistic) was used to determine the optimal threshold limitof species presence. The classification accuracy of the presence/ absence map was investigated using the Kappa index. Based on the results, The CART model had a better predictive performance than the GAM models (AUC=0.97 and 0.89, respectively). Furthermore, the Kappa coefficient of the CART model was higher than GAM, based on the obtained Kappa coefficient values (0.97 and 0.89, respectively). This study concludes that the CART model were more accurate in estimating the distribution range of A. sieberi in comparison with the GAM model. The analysis of the importance of variables showed that the electrical conductivity (EC) and acidity (pH) of the first soil depth had the highest effect on the distribution of A. sieberi. In general, it can be concluded that application of data-driven methods, such as CART model, can be useful for accurate estimation of the potential habitat distribution of plant species on a local scale. Therefore, the application of these models to introduce the suitable species in vegetation reclamation plans of Iran's desert rangelands is recommended.

Ordinary Kriging (OK) has been widely used for prediction and spatial distribution study of surface water quality parameters. This method uses a constant mean to capture the trend in the model. In this method, the prediction can be poor if there are strong trends. To overcome this problem, an alternative model called Blind Kriging (BK) was developed. The mean function in BK is polynomial and the unknown mean function parameters are obtained using the Bayesian variable selection technique which requires a lot of calculations. To solve these problems, in the present study, OK was modified based on BK. In the modified method, the polynomial mean function parameters were determined using linear Regression instead of Bayesian inference method. Then, the performance of OK and modified Kriging in the estimation of trophic indices and water quality parameters in Anzali Wetland was evaluated using different statistical criteria (the coefficient of determination, the root mean square error and the mean absolute error). For this purpose, the concentrations of 14 parameters in six sampling points of Anzali Wetland water were analyzed during 2018. Subsequently, the modified and Ordinary Kriging were applied to water parameters and trophic indices. The results showed that the modified Kriging had the best results. In addition, the improvement of the modified Kriging compared to OK is achieved up to 88%. The results of the present study can be useful in the planning of the future monitoring program in Anzali Wetland.