Species Distribution Modelling (SDM) is an important tool for conservation planning and resource management. Invasive species represent a good opportunity to evaluate SDMs predictive accuracy with independent data as their invasive range can expand quickly. Thus, the aim of this study was to investigate the relationships between presence of Leucanthemum vulgare Lam. and environmental variables in Fandoghlou rangeland, Ardabil, Iran using logistic regression model. Sampling was conducted in six sites as presence/absence of L. vulgare by a systematic random method in 2016. Physiographic, climatic, surface coverage and density of L. vulgare were measured in sampling sites. In the beginning, middle and end of each transect, soil samples were taken from the depth of rootstock of range plants including L. vulgare. Soil attributes were measured in the laboratory. The maps of physiographic and climate were derived from digital elevation model, and selected soil attributes were derived using Kriging interpolation method. Derived regression equation from the presence of L. vulgare was applied to map the effective environmental variables, and a prediction map was produced for the study area. The comparison between the predicted and actual maps was assessed using the Kappa coefficient. Results showed that the presence of L. vulgare had a positive relationship with temperature and volumetric soil water content factors and had a negative relationship with electrical conductivity, sodium, diffusible clay factors. Therefore, L. vulgare type is significantlyaffected by the presence of these factors (p<0.01). The Kappa coefficient was 0.55 for derived predicted map. The evaluation of the model indicated that logistic regression was able to predict the distribution of L. vulgare habitats. The results of this study gave more insight and understanding from the habitats and effective environmental factors in L. vulgare distribution. Manuscript profile

Investigation of the relationship between plant species and environmental factors plays an important role in plant ecology. The present study aimed to develop the best predicting model for distribution of Prangos uloptera DC. using logistic regression (LR) and Maximum Entropy Methods (MaxEnt) in its habitat in the southern rangeland of Ardabil province, Iran. Vegetation data (presence and absence of P. uloptera) and environmental factors (including soil, topography and climatic variables) were collected. The original vegetation type map was prepared using slope and elevation maps (1: 25000 scale) and satellite imagery (Landsat). Vegetation samples were collected in 2016. In each site, three transects of 100 m length were deployed (two transects in the direction of a gradient and one transect perpendicular to the slope direction). On each transect, ten 4m2 plots were placed along each transect, and the total canopy cover and plant density were recorded. Overall, 180 plots were sampled in six sites. Soil samples were collected at a depth of 0-30 cm at the beginning and end of each transect. The LR method was performed in the SPSS Ver. 19 software, and the Maximum Entropy method was carried out in the MaxEnt Ver. 3.1 software. The LR model showed that rainfall had the highest effect on the distribution of the P. uloptera habitat. The accuracy of the LR method for the prediction map was good (Kappa index= 0.65). The MaxEnt method showed that variables, including sand, nitrogen (N), silt, and potassium (K) had the highest effect on distribution of P. uloptera habitat. However, the accuracy of the MaxEnt method was low (Kappa index=0.35). It was concluded that modeling methods could be used as a prediction tool to determine the location of plant species. This may lead to better rangelands management and improvement in areas with similar conditions. Manuscript profile