Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. This is the first comprehensive study of COVID-19 in Iran; and it carries out 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 modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled risk map. 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 the World Health Organisation (WHO) data, Iran's fatality rate (deaths/0.1M 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. The fatality rate for China is 0.32 (deaths/0.1M pop). Over time, the heatmap of the infected areas 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 were separate from the others. The heatmap of countries of the world shows 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 evidence of turning. A polynomial relationship was identified between the coronavirus infection rate and the province population density. Also, 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 world's, but Iran's shows lower volatility. Change detection of COVID-19 risk maps with a random forest model for the period from March 11 to March 18 showed an increasing trend of 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 the distance from bus stations, bakeries, hospitals, mosques, ATMs (automated teller machines), banks, and the minimum temperature of the coldest month. We believe that this study's risk maps are the primary, fundamental step to take for managing and controlling COVID-19 in Iran and its provinces.

The identification of geographical distribution of a plant species is crucial for understanding the importance of environmental variables affecting plant habitat. In the present study, the spatial potential distribution of Astragalus fasciculifolius Boiss. as a key specie was mapped using maximum entropy (Maxent) as data mining technique and bivariate statistical model (FR: frequency ratio) in marl soils of southern Zagros, Iran. The A. fasciculifolius locations were identified and recorded by intensive field campaigns. Then, localities points were randomly split into a 70% training dataset and 30% for validation. Two climatic, four topographic, and eight edaphic variables were used to model the A. fasciculifolius distribution and its habitat potential. Maps of environmental variables were generated using Geographic Information System (GIS). Next, the habitat suitability index (HSI) maps were produced and classified by means of Maxent and FR approaches. Finally, the area under the receiver operating characteristic (AUC-ROC) curve was used to compare the performance of maps produced by Maxent and FR models. The interpretation of environmental variables revealed that the climatic and topographic parameters had less impact compared to edaphic variables in habitat distribution of A. fasciculifolius. The results showed that bulk density, nitrogen, acidity (pH), sand, and electrical conductivity (EC) of soil are the most significant variables that affect distribution of A. fasciculifolius. The validation of results showed that AUC values of Maxent and FR models are 0.83 and 0.76, respectively. The habitat suitability map by the better model (Maxent) showed that areas with high and very high suitable classes cover approximately 22% of the study area. Generally, the habitat suitability map produced using Maxent model could provide important information for conservation planning and a reclamation project of the degraded habitat of intended plant species. The distribution of the plants identifies the water, soil, and nutrient resources and affects the fauna distribution, and this is why it is relevant to research and to understand the plant distribution to properly improve the management and to achieve a sustainable management.

Medicinal plants hold immense potential for drug discovery, with indigenous knowledge being crucial for their identification and utilization. This study investigated medicinal plant use in rural communities of Lar, Gerash, and Lamerd counties, southern Iran. A total of 200 respondents from three ethnic groups in 27 local communities were interviewed over a two-year period (2020–2022) and the data was analyzed using the ethnobotanyR package in R software. Quantitative ethnobotanical indices including (FC), (FL), (RFC), (ICF), (UR), (UV), (CI), (CVs), (CV), (FUV), and (PPV) were calculated. The conservation status of the plants was assessed based on the IUCN Red List. A total of 171 plant species from 53 families were documented, with Lamiaceae and Fabaceae being the most common. Leaves were the most utilized plant part in the preparation of medicines. Decoction was the most prevalent method of preparation, and oral administration was the most common route of consumption. Gastrointestinal diseases have the highest ICF value. Notably, the study documented new medicinal applications for 70 plant species, expanding the region’s ethnobotanical knowledge. The study highlights the need for documenting and disseminating traditional knowledge, as it provides fundamental data for further research and conservation efforts.

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 analysis of essential oils has been shown that the main identified components are myrtenol (34.66%), borneol (18.17%), trans-verbenol (5.7%), bornyl acetate (4.31%), cis-verbenol (3.29%) and 1,8-cineole (2.42%), respectively. It seems that borneol, transverbenol and 1,8-cineole play as an anti-quality factor in animal feed. This issue confirms non-palatability of Pl. aucheri in the flowering stage. Hence, one of the rangeland management principles is prevention of entrance non-native and exotic livestock to the rangelands with toxic plants.

This article analyzes the traditional ethnoveterinary knowledge of medicinal plants among the Bikheii, Korosh, and Achomi tribes in Fars, Iran. Ethnoveterinary data collected from 200 informants in 27 local communities were analyzed using the ethnobotanyR package. The analysis identified 31 plant species from 21 families used in ethnoveterinary practices. The most commonly used plant families were Rosaceae and Fabaceae. Ferula assa-foetida and Astragalus fasciculifolius had the highest use reports (URs) and were noted as having particular importance in the daily lives of tribal people in the south of Fars in Iran. A key focus of this study is a regional comparison with other documented ethnoveterinary practices, primarily within Iran, to identify conserved knowledge and novel findings. This study contributes to the conservation and sustainable use of traditional ethnoveterinary knowledge, which has previously been limited to herders and aged community members. The findings also provide a basis for future phytochemical and pharmacological studies to validate the efficacy of these medicinal plants for veterinary purposes.