Abstract
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.
Highlights
Habitat and biodiversity loss are global concerns related to climate change—especially drought—and serve as an enormous warnings for the future [1,2,3]
The findings of our study suggest the habitat suitability map generated by the Support vector machine (SVM) model has the largest suitable area for future cultivation of Juniperus
SVM and Maximum entropy (MaxEnt) were comparatively used to spatially model landslide occurrence, and the results show that the highest areal percentage was allocated to the high susceptibility class by the SVM model, whereas the MaxEnt model allocated the lowest areal percentage to the high susceptibility class [45]
Summary
Habitat and biodiversity loss are global concerns related to climate change—especially drought—and serve as an enormous warnings for the future [1,2,3]. Statistical modeling and geographic information systems (GIS) have been widely used in recent years to evaluate the ecological theories in the field of ecosystem and resource conservation and to predict suitable regions for future cultivation in accordance with climate change [8,11,12]. MaxEnt is a machine learning algorithm with a high capability in artificial fitting rules or functional connections (e.g., nonlinear relation) according to appearance information, usage of species’ presence, and background data for the prediction of species distribution and habitat suitability [8,16,17,18]. MaxEnt and SVMs yielded a good performance with the original data, indicating their sufficient regulation of multicollinearity in spatial distributions studies [25,27] Their suitability for the assessment of species distribution and habitat suitability models has led them to become popular methods for evaluating habitat requirements in recent years. Some species of juniper trees are distributed in Iran, with a geographical distribution throughout different regions
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