Modeling and mapping diversity of pathogenic fungi of wheat fields using geographic information systems (GIS)

- The Shannon-Wiener index was suitable for rare species.
- The Simpson model is more appropriate for a single dominant pathogenic fungus.
- Rainfall, zinc and nitrogen content determined pathogenic fungal diversity.
- The Shannon-Wiener model could reveal the richness aspect of species diversity.

In this study, geographic information systems and remote sensing tools were integrated and used to investigate the 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. The relationships between these indices and a number of independent variables such as topographic, soil and climatic variables were investigated to understand the relationships between fungal pathogen diversity and the agroecosystem while 67 georeferenced wheat field data using 0.25 m2 quadrats were used to calculate the indices. Mallow's Cp statistical method was employed to select the most suitable factors while multiple regressions served to select model development, with two diversity maps being produced using the developed models. Residual maps were used to test the validity of the models. Rainfall and soil factors (zinc and nitrogen) formed the most important components that 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 with higher rainfall at higher altitudes which were considered to be the most endangered areas. Considering the different responses of the models, Shannon-Wiener was found to be suitable for rare species while the Simpson model might be appropriate for a single dominant pathogenic fungus in the study area.