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:: Volume 9, Issue 19 (3-2022) ::
PEC 2022, 9(19): 261-279 Back to browse issues page
Prediction of potential habitat distribution of Artemisia sieberi Besser using data-driven methods in Poshtkouh rangelands of Yazd province
Hossein Piri Sahragard * , Mohammad Ali Zare Chahouki2
University of Zabol , hpirys@gmail.com
2- Tehran University
Abstract:   (2220 Views)
The present study aimed to model potential habitat distribution of A. sieberi, and its ecological requirements using generalized additive model (GAM) and classification and regression tree (CART) in in the Poshtkouh rangelands of Yazd province. For this purpose, pure habitats of the species was delineated and the species presence data was recorded by the systematic-randomize sampling method. Using DEM and geostatistical method, digital layers of environmental variables (soil and physiographic variables) were prepared with the same spatial resolution (pixel size 30×30 meter). Plant distribution modeling was conducted using CART and GAM models in the R.3.3.1 software environment. The prediction performance of the models was evaluated by the AUC (Area Under the Curve) In addition, The TSS (True Skill Statistic) was used to determine the optimal threshold limitof species presence. The classification accuracy of the presence/ absence map was investigated using the Kappa index. Based on the results, The CART model had a better predictive performance than the GAM models (AUC=0.97 and 0.89, respectively). Furthermore, the Kappa coefficient of the CART model was higher than GAM, based on the obtained Kappa coefficient values (0.97 and 0.89, respectively). This study concludes that the CART model were more accurate in estimating the distribution range of A. sieberi in comparison with the GAM model. The analysis of the importance of variables showed that the electrical conductivity (EC) and acidity (pH) of the first soil depth had the highest effect on the distribution of A. sieberi. In general, it can be concluded that application of data-driven methods, such as CART model, can be useful for accurate estimation of the potential habitat distribution of plant species on a local scale. Therefore, the application of these models to introduce the suitable species in vegetation reclamation plans of Iran's desert rangelands is recommended.
Keywords: Spatial Distribution, Habitat Requirement, Classification and Regression Tree Generalized Additive Model, Desert Rangelands.
Full-Text [PDF 1126 kb]   (388 Downloads)    
Type of Study: Research | Subject: Special
Received: 2021/06/17 | Accepted: 2021/08/2 | Published: 2022/03/16
References
1. Ahmadi, M., Nezami Balouchi, B., Jowkar, H., Hemami, M.R., Fadakar, D., Malakouti-Khah, S.H., Ostrowski, S. 2017. Combining landscape suitability and habitat connectivity to conserve the last surviving population of cheetah in Asia. Diversity and Distributions, 23(6): 592-603. https://doi.org/10.1111/ddi.12560.
2. Ahmed, N., Atzberger, C., Zewdie, W. 2021. Species Distribution Modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia. Ecological Process 10, 18. https://doi.org/10.1186/s13717-021-00285-6.
3. Allouche, O.A., Tsoar, Kadmon, R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43(6):1223-1232.
4. Ashraf, U., Chaudhry, M.N., Ahmad, SR., Ashraf, I., Arslan, M., Noor, H., Jabbar, M. 2018. Impacts of climate change on Capparis spinosa L. based on ecological niche modeling. PeerJ, 6:e5792 https://doi.org/10.7717/peerj.5792.
5. Carter, G.M., Stolen, E.D., Breininger, D. R. 2006. A rapid approach to modeling species–habitat relationships. Biological conservation, 127: 237-244.
6. Death, G., Fabricius, K.E. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81: 3178-3192.
7. El-Amier, Y.A. 2016. Vegetation structure and soil characteristics of five common geophytes in desert of Egypt. Egyptian journal of basic and applied science, 3: 172-186.
8. Elith, J., Graham, C. 2009. Do they? How do they? Why do they differ? On finding reasons for differing performances of species distribution models. Ecography, 32: 66-77.
9. Elith, J., Leathwick, J.R. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution and Systematics, 40: 677- 697.
10. Erfanian, M.B., Sagharyan, M., Memariani, F., Ejtehadi, H. 2021. Predicting range shifts of three endangered endemic plants of the Khorassan-Kopet Dagh floristic province under global change. Scientific Reports,11: 9159 https://doi.org/10.1038/s41598-021-88577-x.
11. Evans, J.S., Murphy, M.A., Holden, Z.A., Cushman, S.A. 2011. Modeling species distribution and change using Random Forest. In: Drew C.A., Wiersma Y.F. and Huettmann F. (eds.), Predictive Species and Habitat Modeling in Landscape Ecology, Springer New York, 139-159.
12. Franklin, J. 2010. Mapping species distributions: spatial inference and prediction. Cambridge: Cambridge University Press.
13. Hastie, T., Tibshirani, R. 1990. Nonparametric logistic and proportional odds regression. Applied statistics: 260-276.
14. Hosseini S.Z., Kappas, M., Zare Chahouki, M.A., Gerold, G., Erasmi S., Rafiei Emam, A. 2013. Modelling potential habitats for Artemisia sieberi and Artemisia aucheri in Poshtkouh area, central Iran using the maximum entropy model and geostatistics. Ecological Informatics, 18: 61-68.
15. Hijmans, R.J., Elith, J. 2019. Spatial distribution models. https://rspatial.org/sdm/SDM.pdf. Accessed date 10 June 2020.
16. Kaky, E., Nolan, W., Alatawi, A., Gilbert, F. 2020. A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants. Ecological Informatics, 60: 101150, https://doi.org/10.1016/j.ecoinf.2020.101150.
17. Lei, Z., Shirong, L., Pengsen, S., Wang, T. 2011. Comparative evaluation of multiple models of the effects of climate change on the potential distribution of Pinus massoniana. Chinese Journal of Plant Ecology, 35(11):1091-1105.
18. Liu, C., Newell, G., White, M. 2016. On the selection of thresholds for predicting species occurrence with presence‐only data. Ecology and Evolution, 6(1): 337-348.
19. Mi, C., Huettmann, F., Guo, Y., Han, X., Wen, L. 2017. Why choose Random Forest to predict rare species distribution with few samples in large under sampled areas? Three Asian crane species models provide supporting evidence. PeerJ 5:e2849; DOI 10.7717/peerj.2849.
20. Naimi, B., Araújo, M.B. 2016. Sdm: a reproducible and extensible R platform for species distribution modelling. Ecography, 39:368-375. https://doi.org/10.1111/ecog.01881.
21. Ng, W.T, Silva, A.C.O., Rima, P., Atzberger, C., Immitzer, M. 2018. Ensemble approach for potential habitat mapping of invasive Prosopis spp. in Turkana, Kenya. Ecology and Evolution, 8(23):11921-11931. https://doi.org/10.1002/ece3.4649.
22. Piri Sahragard, H., Zare Chahouki, M.A. 2015. An evaluation of predictive habitat models performance of plant species in Hoze soltan rangelands of Qom province. Ecological Modelling, 309-310: 64-71.
23. Piri Sahragard, H., Keshtegar, B., Zare Chahouki, M.A. 2019. Modeling spatial distribution of plant species using autoregressive logistic regression method-based conjugate search direction. Plant Ecology, 220 (2): 267-278. doi: 10.1007/s11258-019-00911-6.
24. Randin, C.F., Dirnböck, T., Dullinger, S., Zimmermann, N.E., Zappa, M., Guisan, A. 2006. Are niche-based species distribution models transferable in space? Journal of Biogeography, 33(10):1689-1703.
25. Renner, I.W., Warton, D.I. 2013. Equivalence of MaxEnt and Poisson point process models for species distribution modeling in ecology. Biometrics 69, 274e281.
26. Rogan, J., Franklin, J., Stow, D., Miller, J., Woodcock, C., Roberts, D. 2008. Mapping land-cover modification over large areas: a comparison of machine learning algorithms. Remote Sens Environment, 112:2272–2283.
27. Sor, R., Park, Y.S., Boets, P. 2017. Effects of species prevalence on the performance of predictive models. Ecological Modelling, 354, 11-19. DOI: 10.1016/j.ecolmodel.2017.03.006.
28. Stohlgren, T.J., Ma, P., Kumar, S., Rocca, M., Morisette, J.T., Jarnevich, C.S., Benson, N. 2010. Ensemble habitat mapping of invasive plant species. Risk Analysis, 30: 224-235.
29. Sutton, C.D. 2005. Classification and regression trees, bagging, and boosting. In: Rao CR, Wegman EJ, Solka JL (eds) Handbook of statistics: data mining and data visualization, 24. Elsevier, Amsterdam.
30. Tarkesh, M., Jetschke, G. 2012. Comparison of six correlative models in predictive vegetation mapping on a local scale. Environmental and Ecological Statistics, 19: 437-457.
31. Williams, J.N., Seo, C.H., Thorne, J., Nelson, J.K., Erwin, S., Brien, J.M.O., Schwartz, M.W. 2009. Using species distribution models to predict new occurrences for rare plants. Diversity and Distributions, 15: 565-576.
32. Zare Chahouki, M.A., Khalasi Ahvazi, L., Azarnivand, H. 2012. Comparison of three modeling approaches for predicting plant species distribution in mountainous scrub vegetation (Semnan rangelands, Iran). Polish Journal of Ecology, 60(2): 105-117.
33. 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.
34. Zare Chahouki, M.A., Azarnivand, H., Jafari, M., Tavili, A., 2010. Multivariate statistical methods as a tool for model-based prediction of vegetation types. Russian Journal of Ecology, 41(1): 84-94.
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Piri Sahragard H, Zare Chahouki M A. Prediction of potential habitat distribution of Artemisia sieberi Besser using data-driven methods in Poshtkouh rangelands of Yazd province. PEC 2022; 9 (19) :261-279
URL: http://pec.gonbad.ac.ir/article-1-801-en.html


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Volume 9, Issue 19 (3-2022) Back to browse issues page
مجله حفاظت زیست بوم گیاهان Journal of Plant Ecosystem Conservation
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