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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.

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Heterogeneity can be studied for any dynamic or fixed environmental factors over time. However, determining the extent of heterogeneity occurrence in terms of habitat suitability, variability of dynamic and fixed factors, as well as landform role is an issue that has received less attention. This study aimed to investigate the distribution of Moringa peregrina at two climate change scenarios, to identify the Region of High Heterogenetic (ROHH) of the habitats in those scenarios and to ascertain the heterogeneity of habitat variables of the species in southern Iran. The current and potential distributions of the species in mild and severe climate change scenarios of 2050 and 2070, respectively, were modeled through the Ensemble technique using the climatic and topographic (topoclimatic) variables. The current distribution with four predictions of mild to severe Representative Concentration Pathways (RCP2.5, RCP4.5, RCP6.0 and RCP8.5) were entered into the principal component analysis (PCA) each year to achieve the heterogeneity of distribution. Then, the ROHH was calculated for areas with fluctuations of more than 50%. The topoclimatic variables in the ROHH were compared with the value of each variable in the current distribution in different landforms. The climatic variables of temperature seasonality and mean diurnal range had the greatest impact on M. peregrina distribution. There was more than 90% spatial agreement between the species current and potential distributions under different climate change scenarios (minimum Kappa = 0.9). In climate change scenarios, increase in species distribution is mainly limited by reduced rainfall, high temperature and altitude. The heterogeneity of habitat variables varied greatly in the ROHH and current presence points, indicating the species attempt to occupy new ecological niches. The highest distribution of the species was in the canyons and mountain tops, and the species seeks to occupy these areas in the ROHH. The magnitude of fluctuations of habitat variables at the presence points and the ROHH was different, indicating the species crossing the current niche range to establish in new niche. The mean diurnal range (Bio2) and annual precipitation (Bio12) variables had the lowest heterogeneity in 2050 and 2070 scenarios. This study reports that the fluctuation of habitat variables in areas with high heterogeneity was different from the current distribution range of M. peregrina. No significant fluctuation was found in the distribution range of the species in climate change scenarios.

Background Suitable habitat and landscape structure play a pivotal role in the success of forest restoration projects. This study aimed to model the habitat suitability of wild almond (Amygdalus scoparia Spach) using three individual species distribution models (SDMs), i.e., backpropagation artificial neural network (BP-ANN), maximum entropy (MaxEnt), generalized linear model (GLM), as well as the ensemble technique along with measuring the landscape metrics and analyzing the relationship between the distribution of the suitable habitat of the species in different landform classes in Fars Province, southern Iran. Results There was no clear difference in the prediction performance of the models. The BP-ANN had the highest accuracy (AUC = 0.935 and k= 0.757) in modeling habitat suitability of A. scoparia, followed by the ensemble technique, GLM, and MaxEnt models with the AUC values of 0.890, 0.887, and 0.777, respectively. The highest discrimination capacity was associated to the BP-ANN model, and the highest reliability was related to the ensemble technique. Moreover, evaluation of variable importance showed that the occurrence of A. scoparia was strongly dependent on climatic variables, particularly isothermality (Bio 3), temperature seasonality (Bio 4), and precipitation of driest quarter (Bio 17). Analysis of the distribution of species habitat in different landform classes revealed that the canyon, mountain top, upland drainage, and hills in valley classes had the highest suitability for the species establishment. Conclusions Considering the importance of landform in the establishment of plant habitats, the combination of the outputs of the SDMs, landform, and the use of landscape metrics could provide both a clear view of habitat conditions and the possibility of analyzing habitat patches and their relationships that can be very useful in managing the remaining forests in semi-arid regions. The canyon, mountain top, and upland drainage classes were found to be the most important landforms to provide the highest suitable environmental conditions for the establishment of A. scoparia. Therefore, such landforms should be given priority in restoration projects of forest in the study area.

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