Determining the Potential Habitat of Astragalus verus using the CART Regression Decision Tree Algorithm (Case Study: West of Isfahan Province)

Abstract

In the present study, the relationship between the distribution of Astragalus verus, climate, soil and topography in Isfahan province was investigated using the CART (Classification and Regression Trees) non-parametric regression. According to the vegetation types dominated by Astragalus verus, 287 sites were selected using stratified-random sampling and 106 presence points were recorded. Non-normally distributed soil variables were normalized according to the skewness type by data transformation. For variables that were not normalized by the data transformation, the inverse distance weighted and for normally distributed variables, kriging methods were used for mapping. To investigate the spatial continuity of these variables, the best variogram was selected. Using the principle component analysis (PCA) and correlation matrix, the most effective factors on distribution were clay percentage, mean temperature of the coldest (Bio11), and driest (Bio9) quarter, minimum temperature of the coldest month (Bio6), annual mean temperature (Bio1), saturated moisture and organic carbon, respectively. Model evaluation using replacement method and using independent data indicated high accuracy of the model. According to the optimum threshold of 0.41 and fuzzy-based habitat suitability map, the suitable habitat for the species was 34.5 % of the area. The results are used in sustainable rangeland management, conservation and their restoration.

Keywords

• habitat suitability
• geostatistics
• decision tree
• variogram
• principal component analysis