Climate change has a great impact on the species distribution range and many endangered plant species. Fritillaria imperialis as a species that is native to Central Zagros, Iran is a medicinal plant with great ecological and commercial profits. Its population has decreased considerably and the species would be endangered in later decades. Understanding the habitat needs of this species, evaluating habitat conditions, and forecasting its potential habitat are important for protecting F. imperialis. The presence of F. imperialis points recorded from our field surveys in Chaharmahal-va-Bakhtiari province as a part of Central Zagros, Iran in spring 2017. In order to model its distribution based on correlation analysis, two topographic variables and eight bioclimatic ones as the input of Maximum Entropy model (MaxEnt) were used. The results showed that temperature seasonality (55.1%) and precipitation of driest quarter (22.9%) were important factor drivers of F. imperialis suitable habitat. The accuracy of the maximum entropy model in predicting the distribution of the studied species was high (AUC=0.91) as 2.33% (37986 ha) in Chaharmahal-va-Bakhtiari Province for the F. imperialis, which has had suitability. About 18% and 16.5% of F. imperialis habitats in the area may be lost due to climate change by 2070 under two climate warming scenarios (RCP4.5 and RCP8.5, given by the IPCC). As shown by the model, under the current climatic conditions, the suitable habitat would be rendered to an unsuitable one in the future resulting in local extinction. The results of this study can be used to identify sites with high extinction probability of F. imperialis and protect susceptible habitats against the effects of climate change. پرونده مقاله

Fire occurrence may lead to a significant impact in many terrestrial ecosystems. This basin scale study attempted to evaluate the effects of fire on the water balance components in the Central Zagros, Iran in 2019. Two modeling frameworks including WetSpass-M spatial model and Bayesian Belief Networks were used to investigate the effect of fire on the amount of runoff, groundwater recharge and evapotranspiration. The first part of the study was water balance simulation at a monthly scale. In addition, the Bayesian belief networks were applied to explore and understand key issues effect on water balance after the fire. Calibration and validation of the WetSpass-M model were performed without considering the effect of fire (2000-2014) and then, the modelwas run againfor the fire scenario by reducing manning roughness coefficient and increasing the θ coefficient. Subsequently, the water balance components of each class, i.e. sparse forest, sparse, semi- dense and dense rangelands were calculated. The percentage of changes in the water balance components was used for comparison. Calibration and validation were performed before finalizing the simulation. A Nash-Sutcliff coefficient of 0.61 and 0.58 was obtained during the calibration and validation, respectively. The analysis of the water balance components results depicted that fire had increased the amount of runoff (13.5%) and it has reduced the amount of groundwater recharge (2.52%) and reduced the amount of actual evaporation (4.45%). The highest increase in runoff belonged to the sparse forest (15.8%), followed by dense (14.5%), semi- dense (13.7%) and sparse rangelands (12.5%), respectively. The results showed that runoff acts as the major factor affecting soil water balance (50.36%) followed by land use (10.49%), and infiltration (10.12%). پرونده مقاله