Document Type : Research Paper


1 Associate Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 PhD Student, Faculty of Natural Resources, University of Gorgan, Karaj, Iran

3 Professor, Faculty of Natural Resources, University of Tehran, Karaj, Iran


The aim of this study was providing plant species predictive habitat models by using logistic
regression method. For this purpose, study area conducted in north east rangelands of Semnan
modeling vegetation data in addition to site condition in formation including topography, and soil was
prepared. sampling was done within each unit of sampling parallel transects and 1 vertical transect
with 750m length, each containing 15 quadrates (according to vegetation variations) were established.
Quadrate size was determined for each vegetation type using the minimal area method. Soil samples
were taken from 0-20 cm and 20-80 cm in starting and ending points of each transect. Logestic
regression (LR) techniques were implemented for plant species predictive modeling. To plant
predictive mapping, it is necessary to prepare the maps of all affective factors of models. To mapping
soil characteristics, geostatistical method was used based on obtained predictive models for each
species (through LR method). The accuracy of the predicted maps was tested with actual vegetation
maps. In this study, the adequacy of vegetation type mapping was evaluated using kappa statistics.
Predictive maps of Astragalus spp. ( κ =0.86), Halocnemum strobilaceum ( κ =0.51), Zygophylum
eurypterum ( κ =0.58) and Seidlitzia rosmarrinus ( κ =0.6) with narrow amplitude is as the same of
actual vegetation map prepared for the study area. Predictive model of Artemisia sieberi ( κ =0.33),
due to its ability to grow in most parts of north east rangeland of Semnan with relatively different
habitat condition, is not possible.