Modeling the potential distribution of Juniperus excelsa using environmental factors in Alborz Mountains

Document Type : Original Article

Authors

1 Department of Combat Desertification, Faculty of desert studies, Semnan University, Semnan, Iran

2 Department of Range & Watershead Management, Faculty of Natural Resources, Isfahan University of Technology, Isfahan, Iran

3 Department of Forest Researches, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

Abstract

Introduction:
Species distribution modeling is a common method for understanding the relationships between a species and its environment and is used to predict the changes in distribution due to environmental changes. A lot of research has been done around the world based on these models. In this study, the optimal habitat of Juniperus excelsa, an indicator species, and the most important tree species in Irano-Turanian forests in the Alborz Mountains have been investigated using 38 environmental parameters and domain and logistic regression models.
Material and methods:
The study area consists of parts of the Alborz Mountains located in the north of Iran with an area of 14656 km2. In the present study, a stratified random sampling method was used to determine the sampling sites. Finally, 390 occurrence sites (240 presence sites and 150 absentee sites) of J. excelsa at a 30-second scale (approximately 1 km×1 km) as the input model was studied. Also, 38 environmental parameters were considered as predictive variables for implementing two models of the domain and logistic regression.
Results and discussion:
The results showed that the domain model had a high performance for predicting the habitat of J. excelsa with AUC =0.97, Kappa =0.730, and TSS = 0.91. Based on the results, the areas with the lowest potential for the presence of J. excelsa were 5665.95 km2, the moderate potential was 2033.1 km2, the good potential was 3076.38 km2, the very good potential was 3063.42 km2, and the high potential was 817.29 km2. The results obtained from the implementation of the logistic regression model indicated that 5084.37 km2 of the studied area was in the class of least potential, 2539.35 km2 had moderate potential, 1410.21 km2 had good potential, 1104. 84 km2 had very good potential, and 4517.37 km2 had high potential. Also, the results showed that the suitable habitats for J. excelsa were regions with a mean diurnal range of 13 °C and 15.5 °C, annual precipitation of 120-220 mm, precipitation of 64-90 mm, precipitation of coldest quarter of 35-60 mm, the altitude from the sea level of 3100-1800 m, and a slope of 30-10% in the southern, southeast, and east directions. Also, the probability of species occurrence was more in areas near the snow and water line as well as on calcic rocks. Also, in the habitat of J. excelsa the NDVI index varied between 0.38 and 0.20. The results of the performance evaluation of the models showed that the domain model had higher performance in predicting the suitable habitat of J. excelsa than the logistic regression model in the study area.
Conclusion:
Species distribution models can provide a suitable management plan for different parts of the habitat. Areas with low potential of suitable habitat are usually partial habitats and populations in these areas are more vulnerable than the others, whereas areas with very good potential are excellent for a protected area. Areas with good to the excellent potential that do not have significant tree cover are suitable for forestry and native ecosystem restoration. The present study showed that the domain model, despite using only presence data, can be a useful method for predicting the suitable habitat of J. excelsa in the Alborz Mountains. Therefore, it can be stated that species distribution models, with acceptable accuracy, have enough performance in the evaluation of species distribution and can be used to execute conservation strategies.

Keywords

References

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Environmental Sciences
Volume 18, Issue 2
July 2020
Pages 237-254

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