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 modelling of plant species habitats in the rangelands of western Taftan, southeastern Iran. Vegetation cover was sampled using a systematicrandomized method. Soils were sampled at 0-30 and 30-60 cm depths through digging of eight soil profiles. The agreement between predictive maps generated by models with documented maps of habitats indicated that logistic regression was able to predict the distribution of Artemisia aucheri and Artemisia sieberi habitats 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 was very good for Amygdalus scoparia and Artemisia aucheri habitats (kappa value = 0.82 and 0.76, respectively), and weak for Artemisi aucheri (kappa value = 0.55). This study indicates that logistic regression and maximum entropy methods had the same efficiency in distribution modelling of plant species with a limited ecological niche. However, the maximum entropy model can receive priority in distribution prediction of plant species with a limited ecological niche because it uses only presence data of plants and a small dataset.

Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models

“…The MaxEnt software (version 3.3.3 k), which is based on the ME approach, is conducted for GWR potential mapping. Groundwater potential mapping 63,64 , flood 30 , landslide 65,66 , rangeland 67,68 , earthquake 69 , species distribution 70 , springs and wells 71 , and groundwater quality 72 .…”

Assessing impacts of climate change on Campanula yaltirikii H.Duman (Campanulaceae), a critically endangered endemic species in Turkey

“…For the current model, the jackknife evaluations and percent contributions indicated that geological formation (jeo), soil groups (soil), and elevation (dem) are the main factors influencing the species' current distribution (Figures 5 and 6; Table 1). Edaphic factors are emphasized by many studies for their important contribution to the ENM of plant species (Pir Sahragard and Ajorlo, 2018). Moreover, there are a number of studies that show that altitude affects the distribution of plant species (Maltez-Mouro et al, 2005;Baudraz et al, 2017).…”

“…The species distribution model was created with the maximum entropy algorithm, Maxent v. 3.3.3 (http:// biodiversityinformatics.amnh.org/open_source/maxent/) (Phillips et al, 2004(Phillips et al, , 2006. This software was chosen because of its successful discrimination performance for presence-only data and small sample sizes (Wisz et al, 2008;Piri Sahragard and Ajorlo, 2018). The Maxent algorithm fundamentally builds species distribution models by quantifying the unknown likelihood distribution, determining the occurrence of a species across a study area without any inferring groundless information about the observed distribution (Salter and Michael, 2012).…”

Modeling the natural vegetation dynamic under climate change scenarios in coastal protected dryland of southeastern Tunisia

“…There are some species that migrate toward elevated habitats to reach more suitable conditions for their development (Parmesan & Yohe, 2003). Moreover, variables related to soil characteristics are highlighted by many researchers for their importance in plant species distribution (Piri Sahragard & Ajorlo, 2018). The MaxEnt model predicts the distribution of SAVI and shows differences from the present occurrences.…”