Support vector machine (SVM) and maximum entropy (MaxEnt) machine learning techniques are well suited to model the habitat suitability of species. In this study, SVM and MaxEnt models were developed to predict the habitat suitability of Juniperus spp. in the Southern Zagros Mountains of Iran. In recent decades, drought extension and climate alteration have led to extensive changes in the geographical occurrence of this species and its growth and regeneration are extremely limited in this area. This study evaluated the habitat suitability of Juniperus through spatial modeling and predicts appropriate regions for future cultivation and resource conservation. We modeled the natural habitat of Juniperus for an area of 700 ha in Sepidan Area in the Fars province using (1) data regarding the presence of the species (295 samples) collected through field surveys and GPS, (2) habitat soil information and indices derived from 60 soil samples collected in the study area, and (3) climatic and topographic datasets collected from various sources. In total, 15 conditioning factors were used for this spatial modeling approach. Receiver operator characteristic (ROC) curves were applied to estimate the accuracy of the habitat suitability models produced by the SVM and MaxEnt techniques. Results indicated logical and similar area under the curve (AUC)-ROC values for the SVM (0.735) and MaxEnt (0.728) models. Both the SVM and MaxEnt methods revealed a significant relationship between the Juniperus spp. distribution and conditioning factors. Environmental factors played a vital role in evaluating the presence of Juniperus sp. as Max and Min temperatures and annual mean rainfall were the three most important factors for habitat suitability in the study area. Finally, an area with high and very high suitability for the future cultivation of Juniperus sp. and for landscape conservation was suggested based on the SVM model.

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Juniperus seravschanica is the southernmost population of Juniperus that has a limited habitat in the world near the equator. In Iran, the lone habitat of this species in the Genow mountains has been endangered with thin foliage, abscissing needles, and dried shoots. The current study investigated the effects of climatic, genetic factors, and physiologic indices on the distribution of J. seravschanica. Distribution was evaluated for 450 ha and physiological indices were evaluated for two groups: (A) trees with dried branches and (B) trees without dried branches. Results showed that the distribution of J. seravschanica in the Genow habitat was influenced by elevation, slope degree, aspect, and distance to stream. Results also indicated that max temperature and precipitation are two effective factors that have the highest effects on falling needles and drying branches of J. seravschanica. Chlorophyll, relative water content (RWC), and relative turgidity (RT) are significantly influenced by max temperature. Endangered trees with dried branches had a lower chlorophyll content, RWC, and RT than trees without dried branches. Vulnerability of J. seravschanica was significantly influenced by its genetic structure. Results of AMOVA showed 83% genetic variability between two groups of J. seravschanica trees.