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Maxent Data Mining Technique and Its Comparison with a Bivariate Statistical Model for Predicting the Potential Distribution of Astragalus Fasciculifolius Boiss. in Fars, Iran

Author

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  • Marjaneh Mousazade

    (Department of Natural Resources and Environmental Engineering, School of Agriculture, Shiraz University, Shiraz 7144165186, Iran)

  • Gholamabbas Ghanbarian

    (Department of Natural Resources and Environmental Engineering, School of Agriculture, Shiraz University, Shiraz 7144165186, Iran)

  • Hamid Reza Pourghasemi

    (Department of Natural Resources and Environmental Engineering, School of Agriculture, Shiraz University, Shiraz 7144165186, Iran)

  • Roja Safaeian

    (Department of Natural Resources and Environmental Engineering, School of Agriculture, Shiraz University, Shiraz 7144165186, Iran)

  • Artemi Cerdà

    (Soil Erosion and Degradation Research Group, Departament de Geografia, Universitat de València, Blasco Ibàñez 28, 46010 Valencia, Spain)

Abstract

The identification of geographical distribution of a plant species is crucial for understanding the importance of environmental variables affecting plant habitat. In the present study, the spatial potential distribution of Astragalus fasciculifolius Boiss. as a key specie was mapped using maximum entropy (Maxent) as data mining technique and bivariate statistical model (FR: frequency ratio) in marl soils of southern Zagros, Iran. The A. fasciculifolius locations were identified and recorded by intensive field campaigns. Then, localities points were randomly split into a 70% training dataset and 30% for validation. Two climatic, four topographic, and eight edaphic variables were used to model the A. fasciculifolius distribution and its habitat potential. Maps of environmental variables were generated using Geographic Information System (GIS). Next, the habitat suitability index (HSI) maps were produced and classified by means of Maxent and FR approaches. Finally, the area under the receiver operating characteristic (AUC-ROC) curve was used to compare the performance of maps produced by Maxent and FR models. The interpretation of environmental variables revealed that the climatic and topographic parameters had less impact compared to edaphic variables in habitat distribution of A. fasciculifolius . The results showed that bulk density, nitrogen, acidity (pH), sand, and electrical conductivity (EC) of soil are the most significant variables that affect distribution of A. fasciculifolius . The validation of results showed that AUC values of Maxent and FR models are 0.83 and 0.76, respectively. The habitat suitability map by the better model (Maxent) showed that areas with high and very high suitable classes cover approximately 22% of the study area. Generally, the habitat suitability map produced using Maxent model could provide important information for conservation planning and a reclamation project of the degraded habitat of intended plant species. The distribution of the plants identifies the water, soil, and nutrient resources and affects the fauna distribution, and this is why it is relevant to research and to understand the plant distribution to properly improve the management and to achieve a sustainable management.

Suggested Citation

  • Marjaneh Mousazade & Gholamabbas Ghanbarian & Hamid Reza Pourghasemi & Roja Safaeian & Artemi Cerdà, 2019. "Maxent Data Mining Technique and Its Comparison with a Bivariate Statistical Model for Predicting the Potential Distribution of Astragalus Fasciculifolius Boiss. in Fars, Iran," Sustainability, MDPI, vol. 11(12), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3452-:d:242315
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    References listed on IDEAS

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