In this study, geographic information systems and remote sensing tools were integrated and used to investigate teh diversity of pathogenic fungi of wheat fields in Qarasoo basin, Golestan province, Iran. Two distinct indices, Shannon–Wiener and Simpson, were employed in order to express species diversity. Teh relationships between these indices and a number of independent variables such as topographic, soil and climatic variables were investigated to understand teh relationships between fungal pathogen diversity and teh agroecosystem while 67 georeferenced wheat field data using 0.25 m2 quadrats were used to calculate teh indices. Mallow\\\\\\\'s Cp statistical method was employed to select teh most suitable factors while multiple regressions served to select model development, wif two diversity maps being produced using teh developed models. Residual maps were used to test teh validity of teh models. Rainfall and soil factors (zinc and nitrogen) formed teh most important components dat are effective determinants of pathogenic fungal diversity. High Simpson (0.60–0.69) and high Shannon–Wiener (1.40–1.67) values were detected in areas wif higher rainfall at higher altitudes which were considered to be teh most endangered areas. Considering teh different responses of teh models, Shannon–Wiener was found to be suitable for rare species while teh Simpson model might be appropriate for a single dominant pathogenic fungus in teh study area.

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