Prediction of Potential Distribution of Prosopis Farcta L. in Marginal Rangelands of Niatak River of Sistan

Document Type : Original Article

Authors

1 MSc Graduated of Range management, Range and Watershed department, Water and Soil College, University of Zabol, Zabol, Iran.

2 Associate professor, Soil and Water College, Range and Watershed Department, University of Zabol, Zabol, Iran.

3 Ph D Graduated of environmental science, University of Malayer, Hamedan, Iran.

4 Assistant professor, Soil and Water College, Range and Watershed Department, University of Zabol, Zabol, Iran.

Abstract

The present study aimed at preparing the potential distribution map and identifiying the environmental requirement of Prosopis farcta L. using tree-based and regression methods in the marginal rangeland of Niatak river in Sistan region. For this purpose, species presence data was recorded randomly. Environmental variables were prepared using field sampling and digital elevation model. In order to achieve the pseudo-absence points, first habitat modeling was performed using the domain model, then pseudo-absence points were prepared using the prediction map obtained from this method. Species distribution modelling was conducted using random forest (RF), classification and regression trees (CART) and generalized additive model (GAM). The accuracy of the models used was evaluated using the area under curve criterion. Result showed that the RF with area under curve 0.98 has the highest accuracy. Generalized additive models and classification and regression trees were ranked after RF. The highest and lowest values of kappa index were assigned to the RF with 0.75, and GAM with 0.43 Kappa value. Accordingly, the RF model is the most accurate model in predicting the potential habitat distribution. Analysis of the variable’s importance showed that in the studied scale, edafic factors and distance from the river have greater effect on species distribution than other factors. So that, in all models used, acidity and electrical conductivity were identified as the most important variables. In general, it is suggested that habitat development plans for Prosopis farcta should be planned in the central and marginal parts of the Niatek river due to better suitability of these regions for species distribution.

Keywords

Main Subjects


  1. Barbet-Massin, M., Jiguet, F., Albert, C. H., & Thuiller, W. (2012). Selecting pseudo-absences for species distribution models: how, where and how many? Methods in Ecology and Evolution, 3(2), 327-338.
  2. Breiman, L., Cutler, A. (2004). Random Forests. Department of Statistics: University of Berkeley.
  3. Coudun, C. H., & Gégout, J. C. (2006). The derivation of species response curves with Gaussian logistic regression models is sensitive to sampling intensity and curve characteristics. Ecological Modelling, 199,164-175.
  4. Dobrowski, S. Z., Thorne, J. H., Greenberg, J. A., Safford, H. D., Mynsberge, A. R., Crimmins, S. M., & Swanson, A. K. (2011). Modeling plant ranges over 75 years of climate change in California, USA: temporal transferability and species traits. Ecological Monographs, 81(2), 241-257.
  5. El-Amier, Y. A. (2016). Vegetation structure and soil characteristics of five common geophytes in desert of Egypt. Basic and Applied Science, 3, 172-186.
  6. Elith, J., 2017. (2017). Invasive species: risk assessment and management. Cambridge: Cambridge University Press.
  7. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing Environment, 202, 18-27.
  8. Guisan, A., & Thuiller, W. (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letter, 8, 993- 1009.
  9. Haidarian Aghakhani, M., Tamartash, R., Jafarian, Z., Tarkesh Esfahani, M., & Tatian, M. R. (2017). Forecasts of climate change effects on Amygdalus scoparia potential distribution by using ensemble modeling in Central Zagros. RS and GIS for Natural Resources, 8(3), 1-14. (in Farsi)
  10. Henderson, E. B., Ohmann, J. L., Gregory, M. J., Roberts, H. M., & Zald, H. (2014). Species distribution modelling for plant communities: stacked single species or multivariate modelling approaches? Applied Vegetation Science, 17(3), 516-527.
  11. Hengl, T., Nussbaum, M., Wright, M. N., Heuvelink, G. B., & Gräler, B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. Peer Journals, Life and Environment, 6, 1-49.‏
  12. Jafari, M., Zare Chahouki, M. A., & Tavili. A., & Kouhandel A. (2007). Soil-vegetation relationships in rangelands of Qom province, Pajouhesh –va- Sazandegi, 19(3), 110-116. (in Farsi)
  13. Jafarian , Z., & Kargar, M. (2017). Distribution modeling of protective and valuable plant species in the tourist area of Polour using generalized linear model (GLM) and generalized additive model (GAM), Geography and Development Iranian Journal, 15(46), 117-132. (in Farsi)
  14. Jahantigh, M. (2017). Study on relations of vegetation and soil in river banks of dryland regions‎. Plant Ecosystem Conservation, 4(9), 181-194. (in Farsi)
  15. Javadi, S.A., Khanarmooyi, A., & Jafari, M. (2016). Investigation of relationship between vegetation factors and soil properties (Case Study: Khojir National Park). Rangeland and Watershed Management, 69(2), 353-366. (in Farsi)
  16. Kargar, M., Jafarian, Z., Tamartash., & Alavi, S. J. (2017). Comparison of non-parametric and parametric species distribution models (SDM) in determining the habitat of dominant rangeland species (Case study: Khetteh Riz Rangelands). Range and Desert Research, 25(3), 512-523. (in Farsi)
  17. Karimi, Gh., Heidari Sharif abad, H., & Assareh, M. H. (2005). The effects of salinity stress on seed germination, seedling growth and proline content in Atriplex verrucifera, Rangeland and Forest Plant Breeding and Gentic Research, 12(2), 419-432. (in Farsi)
  18. Keyghobadi, M., Piri Sahragard, H., Pahlavan Rad, M. R., Karami, P., Yari, R. (2020). Application of generalized additive model (GAM) and classification and regression tree (CART) to estimate the potential habitat distribution of rangeland plant species (Case study: Khazri Rangelands of Beyaz Plain, Southern Khorasan). Range and Desert Research, 27(3), 561-576. (in Farsi)
  19. Kozak, K. H., Graham, C. H., & Wiens, J. J. (2008). Integrating GIS-based environmental data into evolutionary biology. Trends in Ecology and Evolution, 23(3), 141-48.
  20. Liu, C., White, M., & Newell, G. (2011). Measuring and comparing the accuracy of species distribution models with presence–absence data. Ecography, 34, 232-243.
  21. Mahmoudi A. A., Zahedi Gh., & Etemad, V. (2013). The investigation on the relationship between soil physical and chemical properties and succulence of natural and planted saxaul (Haloxylon spp) (Case study: Hosseinabad plain, Southern Khorasan province). Forest, 4(4), 289-299. (in Farsi)
  22. McCune,, & Keon, D. (2002). Equations for potential annual direct incident radiation and heat load. Vegetation Science, 13(4), 603-606.
  23. McCullagh, P., & Nelder, J. A. (1989). Generalized linear models. Chapman and Hall. 2nd edition: London.
  24. Milanesi, P., Holderegger, R., Caniglia, R., Fabbri, E., & Randi, E. (2016). Different habitat suitability models yield different least-cost path distances for landscape genetic analysis. Basic and Applied Ecology, 17(1), 61-71.
  25. Morovati, M., Karami, P., Bahadori Amjas, F. (2020). Accessing habitat suitability and connectivity for the westernmost population of Asian black bear (Ursus thibetanus gedrosianus, Blanford, 1877) based on climate changes scenarios in Iran. PloS ONE, 15(11), 1-22.
  26. Peters, J., Baets, B. D., Verhoest, N. E. C., Samson, R., Degroeve, S., & Becker, P. D. (2007). Random forests as a tool for ecohydrological distribution modelling. Ecological Modelling, 207(2-4), 304-18.
  27. Peterson, A.T., Sober J., Pearson, R. G., Anderson, R. P., Martinez-Meyer, E., Nakamura, M., & Bastos, M. (2011). Ecological niches and geographic distributions. New Jersey: Princeton University Press.
  28. Piri Sahragard, H., Ajorlo, M., & Karami, P. (2018). Modeling habitat suitability of range plant species using random forest method in arid mountainous rangelands. Mountain Science, 15(10), 2159- 2171.‏
  29. Piri Sahragard, H., & Zare Chahouki, M. A. (2015). Modeling of Artemisia sieberi Besser habitat distribution using maximum entropy method in desert rangelands. Rangeland Science, 6(2), 93-101.‏
  30. Piri, H., & Ansari, H. (2013). Study of Sistan plain drought and its impact on wetlands international Hamoon. Wetlands, 4(15), 63-74. (in Farsi)
  31. Schaefer, H. (2019). Predicting spawning habitat for lake whitefish coregonus clupeaformis and cisco coregonus artedi in the Lake Erie and Lake Ontario regions using classification and regression tree (CART) and random forest models. Doctoral dissertation, University of Michigan: Michigan.
  32. Sutton, L.J., & Puschendorf, R. (2020). Climatic niche of the Saker Falcon Falco cherrug: predicted new areas to direct population surveys in Central Asia. Ibis, 162(1), 27-41.
  33. Wang, H. H., Wonkka, C.L., Treglia, M. L., Grant, W. E., Smeins, F. E., & Rogers, W. E. (2015). Species distribution modeling for conservation of an endangered endemic orchid. AoB PLANTS, 7, 39.
  34. Williams, J. N., Seo, C., Thorne, J., Nelson, J. K., Erwin, S., O’Brien, J. M., & Schwartz, M. W. (2009). Using species distribution models to predict new occurrences for rare plants. Diversity and Distributions, 15(4), 565-576.
  35. Zare Chahouki M. A., Piri Sahragard, H., & Naghilou, M. (2016). Determination of occurrence optimal thresholds in the predictive models of plant species distribution (Case study: Rangelands of Nir region of Yazd province). Desert Ecosystem Engineering Journal, 5(10), 1-12. (in Farsi)
  36. Zare Chahouki, M. A., & Piri Sahragard, H. (2016). Maxent modelling for distribution of plant species habitats of rangelands (Iran). Polish Journal of Ecology, 64(4), 453-467.
  37. Zhang, K., Yongzhong, S., Wang, T., & T. (2016). Soil properties and herbaceous characteristics in an age sequence of Haloxylon ammodendron plantations in an oasis-desert ecotone of northwestern China. Arid Land, 8(6), 960-972.