Restoring high value medicinal and industrial plant species requires identifying habitat potentials of the target species. This study aimed to generate Astragalus verus potential distribution map in Fereydunshahr rangeland, Isfahan Province. First, soil maps were obtained from 70 soil profiles and climatic maps were produced from 10 climatic stations data. Geostatistical methods were used to convert the obtained data to digital maps with 90 by 90 meter resolutions. Finally potential distribution map of the species in the study area, was produced using presence-absence data of 100 sites and performing Logistic regression method. According to results, annual mean precipitation, clay, mean temperature of warmest season and slope were the most important environmental factors influencing the distribution of the species. According to model output, 21 and 27 percent of the whole study area had excellent and fair habitat condition for the species, respectively. The produced species distribution model had good accuracy as calculated Kappa coefficient and ROC area under curve plots were 0.75 and 0.5, respectively. This modeling approach enables to allocate appropriate areas to protect and restore valuable native plant species such as Astragalus verus.
Safaei, M., Tarkesh, M., Basiri, M., & Bashari, H. (2013). Determining the potential habitat of Astragalus verus Olivier using the geostatistical and logistic regression methods. Journal of Arid Biome, 3(1), 42-55.
MLA
M. Safaei; M. Tarkesh; M. Basiri; H. Bashari. "Determining the potential habitat of Astragalus verus Olivier using the geostatistical and logistic regression methods". Journal of Arid Biome, 3, 1, 2013, 42-55.
HARVARD
Safaei, M., Tarkesh, M., Basiri, M., Bashari, H. (2013). 'Determining the potential habitat of Astragalus verus Olivier using the geostatistical and logistic regression methods', Journal of Arid Biome, 3(1), pp. 42-55.
VANCOUVER
Safaei, M., Tarkesh, M., Basiri, M., Bashari, H. Determining the potential habitat of Astragalus verus Olivier using the geostatistical and logistic regression methods. Journal of Arid Biome, 2013; 3(1): 42-55.
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