An Study of Effective Species Plant and Ecological Niche Analytical Approach of Environmental Factor for Assessing the Proper Levels of Fauna and Flora in the Northeastern Part of Shiraz

The present was mainly intended to figure out how much suitable Bamoo National Park as an important habitat for Persian gazelle’s were via an Ecological Niche Factor Analytic (ENFA) approach to evaluating the geographical distribution of the species on a large scale through presence-only data. The rationale behind this research endeavor was related to the negative influence human interference can exert on habitat fragmentation and species segregation. As a multivariate approach, ENFA has been widely applied in many fields including wildlife management, habitat assessment and prediction. Throughout the current study, bio-mapper was utilized in such a way that the presence points constituted the dependent variable, whereas predator, vegetation, altitude, distance from the trough, topography, aspect, slope and soil were considered as the independent variables of the study. The results gained from the study revealed that the Persian gazelle’s prefer 1700- 200 elevation zones and 2-5% slope dominated by such shrub species as Astaragalus and Artemisia sieberi. In addition, the global marginality turned out to be above 1 indicating that the requirements for the habitats of the study differed from the average conditions. That the global specialization coefficient was above 1 showed that the Persian gazelle’s considered as specialized species in the area occupied a narrow niche. Regarding the variables, trough, altitude, and vegetation were the most effective ones, but Predator was demonstrated to be a major threat to the habitat. According to the map, the areas were classified into three categories, namely suitable, intermediate, and unsuitable habitats, and the continuous Boyce index gained from the harmonic mean distance algorithm was indicative of the most important areas of unsuitable habitats, the highest amount of robustness, and the predictive power of the HS model.