:: 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:   (706 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.
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Type of Study: Research | Subject: Special
Received: 2021/06/17 | Accepted: 2021/08/2 | Published: 2022/03/16


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Volume 9, Issue 19 (3-2022) Back to browse issues page