Prediction of wild pistachio ecological niche using machine learning models |
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| Institution: | 1. Department of Natural Resources Engineering, Faculty of Agriculture & Natural Resources, University of Hormozgan, Bandar Abbas, Iran;2. Department of Rehabilitation of Arid and Mountainous, Faculty of Natural Resources, University of Tehran, Karaj, Iran;3. Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov, 500123 Brasov, Romania;4. School of Forest, Fisheries and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA;5. Faculty of Civil Engineering, Transilvania University of Brasov, 900152 Brasov, Romania;1. Anhui Province Engineering Laboratory for Mine Ecological Remediation, Anhui University, Hefei 230601, China;2. Huaibei Bureau of Natural Resources and Planning, Huaibei 235000, China;3. Department of Engineering Management, Hefei College of Finance and Economics, Hefei 231299, China;1. Department of Geography and Environment, Western University, 1151 Richmond St, London, Ontario N6A 3K7, Canada;2. School of Geography and Sustainable Development, Irvine Building, University of St Andrews, North Street, St Andrews, KY16 9AL Scotland, United Kingdom;3. The Alan Turing Institute, British Library, 2QR, John Dodson House, 96 Euston Rd, London NW1 2DB, United Kingdom;4. Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, Norwida 25, 50-375 Wrocław, Poland;5. School of Earth and Environment, University of Canterbury, 20 Kirkwood Avenue, Upper Riccarton, Christchurch 8041, New Zealand;6. British Geological Survey, Research Ave South, Riccarton, Edinburgh, EH14 4AP Scotland, United Kingdom;1. School of Computer Science and Engineering, Central South University, Changsha 410083, China;2. ChangSha XiangFeng Intelligent Equipment Co., Ltd, Changsha 410083, China |
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| Abstract: | Observing vegetation dynamics and determining optimum conditions for tree species are important for the long-term habitat conservation. In this study we evaluate the environmental drivers that may explain the development and geographic distribution of Pistacia atlantica Desf. (wild pistachio) in Northeastern Iran. The study uses seven machine learning models to predict the habitats of P. atlantica: multivariate adaptive regression splines (MARS), flexible discriminant analysis (FDA), boosted regression tree (BRT), maximum entropy (MaxEnt), random forest (RF), support vector machine (SVM), generalized linear model (GLM), and their ensembles (ESMs). In total, 1477 P. atlantica sites were identified, described and mapped. The most relevant determinants of the species habitat were included as 28 bioclimatic, topographic, edaphic, and geologic components. While all the models returned high accuracies, the ESMs achieved the highest AUC, TSS, and Kappa values, suggesting a good predictive performance. The most important parameters explaining the species habitat were found to be the mean diurnal temperature range, annual precipitation and slope. These results support the higher performance of ESMs to predict the spatial distribution of P. atlantica. In turn, this model may support species conservation and decision-making at the regional and national levels. |
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