DEVELOPING PREDICTIVE DISTRIBUTION MAP OF PLANT SPECIES HABITATS USING LOGISTIC REGRESSION (CASE STUDY: KHALAJESTAN RANGELANDS OF QUM PROVINCE)

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PIRI SAHRAGARD HOSSEIN; ZARE CHAHUOKI MOHAMMAD ALI; AZARNIVAND HOSSEIN

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Journal: Journal of Rangeland Year:2015 | Volume:9 | Issue:3 Page(s): 222-233

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Information Journal Paper

Title

DEVELOPING PREDICTIVE DISTRIBUTION MAP OF PLANT SPECIES HABITATS USING LOGISTIC REGRESSION (CASE STUDY: KHALAJESTAN RANGELANDS OF QUM PROVINCE)

Author(s)

PIRI SAHRAGARD HOSSEIN | ZARE CHAHUOKI MOHAMMAD ALI | AZARNIVAND HOSSEIN | Issue Writer Certificate

Keywords

PREDICTION MODELQ1

OPTIMAL THRESHOLDQ2

LOGISTIC REGRESSION METHODQ2

EQUAL SENSITIVITY AND SPECIFICITYQ2

KAPPAQ1

Abstract

This study was conducted to evaluate the ability of logistic regression to specify the environmental condition affecting the presence of selected plant species, and identifying suitable areas for the establishment of these species. Some sites with relatively homogeneous ecological conditions were identified by overlaying slope, aspect, elevation and geology maps (1: 25000 scales). Vegetation sampling was carried out using random- systematic method, and 60 plots were established along four transects with 200-1000 meters length in each site. The appropriate plot sizes were determined from 2 to 25 m2 using Minimal Area Method. Soil samples were collected from eight soil profiles in each site from 0-30 cm and 30-80 cm depths from soil surface. Predictive maps of plant species habitats were produced using LOGISTIC REGRESSION METHOD. Optimal environmental condition of selected plant species were determined and predictive performance of the produced potential maps were assessed using KAPPA coefficient and the True skill Static. According to the results, geological formation, percent gravel, soil texture, acidity and lime contents were identified as the most important factors controlling distribution of plant communities in the study area. The accuracy of produced predictive maps for Amygdalus scoparia was very good, for Scariola orientalis- Stipa barbata and Pteropyrum olivieri- Stipa barbata were good and for Artemisia aucheri- Astragalus glaucacanthus vegetation habitats was moderate. The results showed that the logistic regression models, provides high accuracy predictive model for Amygdalus scopariahabitat due to its exclusive habitat conditions. The accuracies of the produced maps for other vegetation habitats were lower than Amygdalus scoparia because they had more wider ecological spectrum.

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APA: Copy

PIRI SAHRAGARD, HOSSEIN, ZARE CHAHUOKI, MOHAMMAD ALI, & AZARNIVAND, HOSSEIN. (2015). DEVELOPING PREDICTIVE DISTRIBUTION MAP OF PLANT SPECIES HABITATS USING LOGISTIC REGRESSION (CASE STUDY: KHALAJESTAN RANGELANDS OF QUM PROVINCE). RANGELAND, 9(3 ), 222-233. SID. https://sid.ir/paper/136314/en

Vancouver: Copy

PIRI SAHRAGARD HOSSEIN, ZARE CHAHUOKI MOHAMMAD ALI, AZARNIVAND HOSSEIN. DEVELOPING PREDICTIVE DISTRIBUTION MAP OF PLANT SPECIES HABITATS USING LOGISTIC REGRESSION (CASE STUDY: KHALAJESTAN RANGELANDS OF QUM PROVINCE). RANGELAND[Internet]. 2015;9(3 ):222-233. Available from: https://sid.ir/paper/136314/en

IEEE: Copy

HOSSEIN PIRI SAHRAGARD, MOHAMMAD ALI ZARE CHAHUOKI, and HOSSEIN AZARNIVAND, “DEVELOPING PREDICTIVE DISTRIBUTION MAP OF PLANT SPECIES HABITATS USING LOGISTIC REGRESSION (CASE STUDY: KHALAJESTAN RANGELANDS OF QUM PROVINCE),” RANGELAND, vol. 9, no. 3 , pp. 222–233, 2015, [Online]. Available: https://sid.ir/paper/136314/en

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