A comparison of logistic regression and maximum entropy for distribution modeling of range plant species (a case study in rangelands of western Taftan, southeastern Iran)

Abstract

This study aimed to compare the efficiency of logistic regression and maximum entropy models for distribution modellingof plant species habitats in the rangelands of western Taftan, southeastern Iran. Vegetation cover was sampled using a systematic- randomized method. Soils were sampled at 0 30 and 30 60 cm depths through digging of eight soil profiles. The agreement betweenpredictive maps generated by models with documented maps of habitats indicated that logistic regression was able to predict thedistribution ofArtemisia aucheriandArtemisia sieberihabitats at excellent (kappa value = 0.95) and weak (kappa value = 0.39) levels,respectively. On the other hand, the agreement between predicted maps generated by maximum entropy with documented maps wasvery good forAmygdalus scopariaandArtemisia aucherihabitats (kappa value = 0.82 and 0.76, respectively), and weak forArtemisiaucheri(kappa value = 0.55). This study indicates that logistic regression and maximum entropy methods had the same efficiency indistribution modelling of plant species with a limited ecological niche. However, the maximum entropy model can receive priority indistribution prediction of plant species with a limited ecological niche because it uses only presence data of plants and a small dataset.