Multivariate statistical methods as a tool for model-based prediction of vegetation types

The current research was carried out to find the most effective environmental factors in plant species occurrence and providing their predictive habitat models. For this purpose, study was conducted in Poshtkouh rangelands of Yazd province in the central of Iran. For modeling, vegetation data in addition to site condition information including topography, climate, geology and soil were prepared. CCA and Logistic regression (LR) techniques were implemented for plant species predictive modeling. To plants predictive mapping, it is necessary to prepare the maps of all affective factors of models. To mapping soil characteristics, geoestatistical method including variogram analysis and Kriging interpolation were used. Based on obtained predictive models for each species (through LR method) and for whole species (through CCA method) related predictive maps were prepared in GIS. The accuracy of predictive maps were tested with actual vegetation maps. Vegetation modeling results with CCA indicates that predictive map of vegetation corresponds with actual map (with high accuracy). Predictive maps of Cornulaca monachantha, Ephedra strobilacea-Zygophyllum eurypterum, Seidlitzia rosmarinus, and Tamarix ramosissima, which have narrow amplitude, has high accordance with actual vegetation map prepared for the study area. Among species of study area, predictive model of Artemisia sieberi, due to its ability to grow in most parts of Poshtkouh rangelands with relatively different habitat conditions, is not possible. Comparing CCA and LR methods showed that each technique has its advantages and drawbacks. In general, LR will provide better specific-model, but CCA will provide a broader overview of multiple species.