Mohadeseh Amiri; Mohammad Shafiezadeh; Mostafa Tarkesh; Seyyed Mostafa Moslemi
Abstract
Introduction: Invasive species are currently the concern of ecologists, conservationists and natural resource managers, and decrease biodiversity due to their rapid spread. These species cause changes in ecological processes, function and structure of community in natural ecosystems. The most obvious ...
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Introduction: Invasive species are currently the concern of ecologists, conservationists and natural resource managers, and decrease biodiversity due to their rapid spread. These species cause changes in ecological processes, function and structure of community in natural ecosystems. The most obvious change in the invaded areas is the reduction of biodiversity and the creation of a pure community of invasive plants. One of the invasive species in our country is Prosopis juliflora, which is important in the field of combat desertification, biological control and stabilization of quicksand dunes in the southern regions of Iran due to its resistance to adverse environmental conditions. Material and methods: In the present study, the efficiency of five discrimination group models (GLM, GBM, ANN, SRE, RF) and a profile model (MAXENT) and their ensemble with the weighted average approach in spatial distribution of this species in Makuran region and determining the most important environmental factors affecting the invasion distribution were investigated. By recording 63 occurrence points and 100 absence points, using climatic, physiographic and human variables as environmental variables, and evaluating the performance of models by Area under Curve (AUC), True Skill Statistics (TSS), Sensitivity and Specificity, the species invasion potential was determined. Results and discussion: Among the single algorithms, according to the threshold-independent and threshold-dependent evaluation criteria, two machine learning techniques, i.e. RF and GBM, predicted the climatic habitat of this invasive species with higher accuracy. Also, the evaluation criteria in the ensemble prediction were higher than the average of all single modelling algorithms. According to the ensemble model, P. juliflora habitats occupy about 15% of the total study area. After generalization of the models to the geographical space, it was found that the invaded areas have spread in a uniform strip on the shores of Oman Sea and Persian Gulf. Evaluation of variable importance indicated that Altitude was the most important independent variable justifying about half of the changes in the ensemble model and has the greatest effect on species distribution. The variable of distance from road was the next important variable. But, aspect was mentioned the least important environmental variable affecting the scattering of the invasion. Based on response curves, the maximum probability of the species presence was observed at the altitude of 50 m above sea level and a distance less than 50 m from the road. Also, the species is most likely to be present, if the temperature in the warmest month and the coldest season of the year is more than 34 and 14 °C, respectively, and the precipitation seasonality is 100-150. Conclusion: It was found that the integrated algorithms in the framework of ensemble modelling showed higher accuracy and the maps derived from potential distribution of species invasion make it possible to restrict and manage the distribution range of invasive species by providing management solution and conserving plans to protect native species. In fact, the results of this study can be used as a basis for subsequent monitoring to prevent further spread of invasive species and to create a balance between the native vegetation protection programs of the region and desertification management measures.
