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Highlights • comparing Iranian coronavirus data with other countries. • predicting the trends of deaths from COVID-19 using regression. • spatial modelling, risk mapping, and change detection of COVID-19 using the random forest (RF) machine learning technique. • validation of the modelled risk maps., Objectives Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries and 2 international conveyance with more than 432,902 recorded deaths and 7,898,442 confirmed global worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. Methods This is the first comprehensive study of COVID-19 in Iran and it undertakes spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends; prediction of mortality trends using regression modelling; spatial modelling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT); and validation of the modelled risk map. Results The results show that from February 19 to June 14, 2020 the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on World Health Organisation (WHO) data, Iran’s fatality (deaths/0.1 M pop) is 10.53. Other countries’ fatality rates were, for comparison, Belgium – 83.32, UK – 61.39, Spain – 58.04, Italy – 56.73, Sweden – 48.28, France – 45.04, USA – 35.52, Canada – 21.49, Brazil – 20.10, Peru – 19.70, Chile – 16.20, Mexico– 12.80, and Germany – 10.58. This fatality rate for China is 0.32 (deaths/0.1 M pop). The heatmap of the infected areas over time identified two critical time intervals for the COVID-19 outbreak in Iran. The provinces were classified in terms of disease and death rates into a large primary group and three provinces that had critical outbreaks that were separate from others. The heatmap of countries of the world show that China and Italy were distinguished from other countries in terms of nine viral infection-related parameters. The regression models for death cases showed an increasing trend but with some evidences of turning. A polynomial relationship was identified between coronavirus infection rate and province population density. In addition, a third-degree polynomial regression model for deaths showed an increasing trend recently, indicating that subsequent measures taken to cope with the outbreak have been insufficient and ineffective. The general trend of deaths in Iran is similar to the worlds, but it shows lower volatility. Change detection of COVID-19 risk maps with a random forest model for the period from March 11th to March 18th showed an increasing trend in COVID-19 in Iran’s provinces. It is worth noting that using the LASSO MLT to evaluate variables’ importance indicated that the most important variables were distance from bus stations, bakeries, hospitals, mosques, ATMs (automated teller machines), banks, and the minimum temperature of the coldest month. Conclusions We believe that the risk maps provided by this study is the primary, fundamental step for managing and controlling COVID-19 in Iran and its provinces.

The Maharloo watershed has witnessed many gullies in the recent due to the specific topo-climatic conditions and man-made activities in that area. The present study is set out to address this issue by producing gully erosion prediction maps via three machine learning algorithms including RF, SVM and BRT in Maharloo watershed, Fars province, Iran. Also, this research attempted to consider the importance of effective factors in the occurrence of gully erosion using Boruta algorithm. To this end, gully erosion locations were identified by extensive field surveys as well as the use of already prepared gully raster map of Maharloo watershed. Then, sixteen causative factors of gully erosion such as elevation, slope degree, slope aspect, plan curvature, TWI, distance from rivers, distance from roads, drainage density, lithology, annual mean rainfall, NDVI, land use and some soil characteristics (pH, clay percent, electrical conductivity-EC, and silt percent) were identified and their maps were produced and classified in the GIS. In this study, the relationships among each agent and gully erosion were defined employing the evidential belief function (EBF) algorithm and the weight of each factor's classes was determined. On the other hand, the results of the collinearity test among the factors showed that sand percentage agent had a VIF > 5; therefore, this covariate was removed from the model. Also, the results of the importance of effective factors using Boruta algorithm indicated that three factors including land use, distance from river, and clay percent had the most noticeable importance in the occurrence of gully erosion in the study area. Finally, the gully erosion susceptibility maps were produced using the RF, BRT, and SVM models in the R statistical software. The results of machine learning techniques were evaluated employing 30% of unused locations in the modeling process as well as the receiver operating characteristic (ROC) curve. Also, in the current research, try to assess the fitting performance of models and their robustness using sensitivity rate, specificity rate, Cohen's Kappa, and 4-fold cross-validation measures. Results showed that the final gully erosion susceptibility maps had an excellent accuracy with AUC values of validation data sets by scenarios 7 and 9 independent factors on gully erosion, respectively (SVM = 0.957, 0.975, RF = 0.991, 0.986, BRT = 0.913, 0.913). The fitting performance measures and robustness technique (4-fold cross-validation) also confirmed the achieved validation results. In order to control and prevent this type of erosion in the Maharloo watershed, there should be protective actions and watershed management measures in place at the primary stages, especially at the beginning of the gully erosion, to control the development of the gully erosion.

Ziziphora clinopodioides Lam. is medicinal and aromatic plant, belonging to the Lamiaceae family. The chemical composition of the essential oils from aerial parts of Z. clinopodioides were extracted using hydrodistillation method and analysed using GC and GC-MS. Forty nine compounds were identified in the essential oils (EOs) of aerial parts of Z. clinopodioides. The main chemical compositions were pulegone (27.38%), iso-menthone (10.12%), iso-menthol (9.92%), menthyl acetate (9.61%), p-menth-3-en-8-ol (7.60%), neo iso- menthol (5.51%), limonene (4.45%), piperitenone (4.17%), 1,8-cineole (3.50%), piperitone (2.43%), spathulenol (1.60%), β-pinene (1.33%), borneol (1.15%), germacrene D (1.13%) and β-bourbonene (1.06%). This medicine plant has high potential for use in medicine, food and perfumery industries with high contents of pulegone, iso- menthone, neo iso-menthol, and p-menth-3-en-8-ol and menthyl acetate.

Habitat suitability models can be a powerful tool in the prediction of spatial distribution of plant species. In the current study, the generalized linear model (GLM) was applied to model spatial distribution and suitable habitats of Artemisia aucheri Boiss, an endemic important species in mountainous parts of the northern Fars Province, Iran. The spatial locations of A. aucheri were recorded and mapped using geographic information system (GIS) tools. All identified locations of species presence were randomly split into 70% for model training and 30% for model validation. The conditioning variables, including mean annual rainfall, mean annual temperature, elevation, slope degree, aspect, geology, sand percentage, soil acidity (pH), and soil organic carbon (OC), were used to model the spatial distribution of A. aucheri using the GLM model. After preparing thematic maps, the multicollinearity test was performed to select the favorite variables. In the next step, habitat suitability maps (HSMs) were produced and categorized into four classes of low, moderate, high, and very high. The model performance was determined using the area under the curve (AUC) of the receiver operating characteristic (ROC). The results revealed that the elevation, mean annual temperature, mean annual rainfall, geology, pH, and sand percentage had significant impact on the distribution of A. aucheri in the study area. The validation of HSM produced using the GLM model showed an AUC value of 0.855 (85.5%). Based on HSM, 54.43% of the total study area had very high suitability habitats. Meanwhile, the area with low potential was 14.38%. In general, the GLM model shows a reasonably accurate approach for habitat modeling of A. aucheri in the study area. The HSMs could be a useful tool for local rangeland managers, conservational agencies, and environmental planners.

The main purpose of the present study was to model gully erosion susceptibility by Evidential Belief Function (EBF) data-driven technique via different scenarios in Maharloo Watershed, Fars Province, Iran. So, according to extensive field surveys, the locations of the head cut, end cut, and also the boundary of the gully locations were identified and the gully inventory map was prepared. Then, different spatial layers such as: elevation, slope degree, plan curvature, TWI, distance from rivers, distance from roads, drainage density, slope aspect, lithology, annual mean rainfall, NDVI, land use, and soil characteristics (pH, clay percent, electrical conductivity (EC), and silt percentage), were identified as effective factors on the occurrence of gullies and their maps were prepared and classified in the GIS software. In the next stage, the correlation among each agent and gully erosion positions was considered using the EBF algorithm. Finally, gully erosion spatial maps were prepared and evaluated using ROC curve. The accuracy of the maps prepared using EBF algorithm by three scenarios was 0.833, 0.756, and 0.809 for the head cut, end cut, and polygon of gully locations, respectively.

Due to the necessity of prevention of desertification, in range renovation programs, the identification of drought resistant plants and also using multiple purposes of such a plant species is extremely important. The expanse and the protection of medicinal plants can be an income source for local people in desert region and also provide part of pharmacy factory requirements. According to phytogeography classification, Platychaete aucheri belongs to the Khalij-Omani region. This endemic shrub of Iran grows at Fars province (near Jahrom and near Firoozabad), Kerman and Hormozgan. In the present research Platychaete aucheri has been considered in regard to phytochemical characteristic. The aerial parts of Pl. aucheri were collected in the flowering stage. The collected dried samples were extracted by hydrodistillation method. The amount and percentage of essential oil components was 1.76%. 48 components representing 91.3% of the total oil were characterized. The results of chemical components analysi...

The Morina genus has 13 species in the world, out of which only M. persica L. is found to be growing wild in Iran. The aim of this research is to predict the spatial distribution and model the habitat suitability for M. persica species using four data mining models: maximum entropy (MaxEnt), support vector machine (SVM), generalized linear model (GLM), and boosted regression trees (BRT). A total of 404 M. persica locations were identified during extensive field surveys, and their geographical locations were recorded using a global positioning system (GPS) device. Furthermore, seventeen environmental predictors including topographical, geological, climatic, and edaphic factors were selected, and their thematic layers were mapped in ArcGIS. Lastly, habitat suitability was modeled using data mining techniques. The validity of the results was assessed using the area under the receiver operating characteristic curve (AUROC). Moreover, three cutoff-dependent metrics, Cohen’s kappa, sensitivity, and specificity, were used for more scrutinized performance assessment. The results revealed that the highest effects on M. persica distribution were mostly associated with edaphic factors, followed by climatic, lithological, and topographical factors. The results showed that MaxEnt with an AUROC value of 95% showed an outstanding performance in terms of prediction power and generalization capacity, followed by SVM (94.1%), GLM (87.4%), and BRT (84.7%). Comparing the AUROC values, MaxEnt was selected as the premier model with the best performance for M. persica distribution modelling across the study area. Cutoff-dependent metrics were also in line with AUROC values; however, the latter made a more discernible distinction between the performance of SVM and MaxEnt. The GLM, BRT, SVM, and MaxEnt models classified 37.37%, 27.28%, 23.31%, and 6.51% of the study area as high and very high suitable habitats for M. persica, respectively. The inferences of this research would be of interest to authorities in the natural resources sector, the research community, local stakeholders, and biodiversity conservation agencies for use in conserving and reclaiming M. persica habitats in the study area.

Polyphenolic compounds, total phenolic content (TPC) and antioxidant activity from aerial parts of three Iranian species of Cuscuta (C. campestris, C. monogyna and C. approximata) were examined by using HPLC analysis, microplate reader and DPPH microplate method. The HPLC analysis revealed the presence of various compounds in Cuscuta species. HPLC/DAD chromatograms recorded at 280 and 320 nm confirmed the presence of eleven contents as the main compounds in plants extract. The highest concentration of phenolic compounds was found in C. monogyna and C. approximata, respectively. TPC of plant methanolic extracts were determined. The dilute methanolic extracts of C. approximata and C. monogyna had the highest total phenolic (56.67 mg/g and 49.59 mg/g), respectively. The highest antioxidant potential plants were C. monogyna and C. approximata, respectively. This study confirms the potential of antioxidant activity of Cuscuta extract in which is correlated to its phenolic content.

The chemical composition of the essential oils from aerial parts of Salvia mirzayanii Rech. f. & Esfand. growing endemic in southern Iran were extracted using hydrodistillation method and analysed using GC and GC-MS. Sixty-two and seventy-seven compounds were identified in the essential oils of leaves and flower of S. mirzayanii, respectively. The main chemical constituents of leaves were 5-neo-cedranol (13.46 %), α-cadinol (6.40 %), linalool (6.10 %), α-terpinyl acetate (6.04 %), caryophyllene oxide (5.88 %), linalyl acetate (5.56 %), epi-α-cadinol (5.31 %), bicyclogermacrene (5.13 %) and δ-cadinene (4.90 %) while the major compounds extracted from flower oil were pulegone (7.40 %), δ-cadinene (6.15 %), lanilool (6.11 %), 5-neo-cedranol (5.69 %), linalyl acetate (5.25 %), α-cadinol (5.09 %), neoiso-menthol (4.27 %) and α-terpinyl acetate (3.64 %). This endemic medicine plant has high potential for use in food and perfumery industries with high contents of 5-neo-cedranol, linalool, δ-cadinene, pul...

The expansion of arid and semi-arid areas and consequently water scarcity are affected by climate change. This can influence on availability and quality of water while demands on food and water are increasing. As pressure on freshwater is increasing, utilization of saline water in a sustainable approach is inevitable. Therefore, bioremediation using salt tolerant plants that is consistent with sustainable development objectives might be an alternative and effective approach. In this study, saline wastewater from a local desalination treatment plant was utilized to irrigate four halophyte plants, including Aloevera , Tamarix aphylla , Rosmarinus officinalis and Matricaria chamomilla. A field experiment was designed and conducted in Zarrindasht, south of Iran in years 2012-2013 accordingly. Two irrigation treatments consisting of freshwater with salinity of 2.04 dS.m -1 and desalination wastewater with salinity of 5.77dSm -1 were applied. The experiment was designed as a split plot in the form of randomized complete block design (RCB) with three replications. The results of variance analysis, ANOVA, on salt concentration in Aloevera showed that there was no significant difference between the effects of two irrigation water qualities except for Na. In Rosmarinus officinalis, only the ratio of K/Na showed a significant difference. None of the examined salt elements showed a significant difference in Tamarix aphylla irrigated with both water qualities. In Matricaria chamomilla, only Mg and K/Na ratio showed a significant difference (Duncan 5%) . As a result , no significant difference was observed in salt absorption by the examined plants in treatments which were irrigated by desalination wastewater and freshwater. This could be a good result that encourages the use of similar wastewater to save freshwater in a sustainable system.

Micromeria persica Boiss. is medicinal and aromatic plant, belonging to the Lamiaceae family. The chemical composition of the essential oils (EOs) from aerial parts of M. persica were extracted using hydro-distillation method and analysed using GC and GC–MS. Fifty-two compounds were identified in the EOs of aerial parts of M. persica. The main chemical compositions were n-hexadecanoic acid (14.9%), thymol (9.5%), linoleic acid (8.0%), carvacrol (5.6%), (E)-nerolidol (5.5%), linolenic acid (5.5%), α-cadinol (2.7%), linalool (2.7%), borneol (2.6%), caryophyllene oxide (2.3%) and pulegone (2.0%). Presence of borneol, thymol, carvacrol and pulegone suggests the potential of this plant as a flavouring source in the food industry, being used in perfumery and cosmetics industry, vitamin E synthesis and exhibit strong fungicidal, antibacterial and antimicrobial activities.

The essential oils of leaves and flowers of Tanacetum dumosum Boiss., an endemic medicinal shrub, were extracted by using hydrodistillation method and analysed using GC and GC–MS. A total of 43 and 44 compounds were identified in the essential oils from the leaves and flowers of T. dumosum, respectively. The major chemical constituents of leaves oil were borneol (27.9%), bornyl acetate (18.4%), 1,8-cineol (17.5%), α-terpineol (5.3%), cis-chrysanthenyl acetate (3.3%), camphene (2.7%) and terpinene-4-ol (1.9%), while the main components of the flower oil were isobornyl-2-methyl butanoate (41.1%), trans-linalyl oxide acetate (11.9%), 1,8-cineole (7.7%), thymol (4.2%), linalool (3.9%), camphor (2.9%), isobornyl propanoate (2.9%), α-terpineol (2.1%) and caryophyllene oxide (2.0%). Major qualitative and quantitative variations for some main chemical compounds among different aerial parts of T. dumosum were identified. High contents of borneol, bornyl acetate, 1,8-cineol and linalool in the leaves and flowers of T. dumosum show its potential for use in the food and perfumery industry.