Mohammad Ali Zare Chahouki; Lyla Khalsi Ahvazi; Hossein Azarnivand
Abstract
The aim of this study was providing plant species predictive habitat models by using logisticregression method. For this purpose, study area conducted in north east rangelands of Semnanmodeling vegetation data in addition to site condition in formation including topography, and soil wasprepared. sampling ...
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The aim of this study was providing plant species predictive habitat models by using logisticregression method. For this purpose, study area conducted in north east rangelands of Semnanmodeling vegetation data in addition to site condition in formation including topography, and soil wasprepared. sampling was done within each unit of sampling parallel transects and 1 vertical transectwith 750m length, each containing 15 quadrates (according to vegetation variations) were established.Quadrate size was determined for each vegetation type using the minimal area method. Soil sampleswere taken from 0-20 cm and 20-80 cm in starting and ending points of each transect. Logesticregression (LR) techniques were implemented for plant species predictive modeling. To plantpredictive mapping, it is necessary to prepare the maps of all affective factors of models. To mappingsoil characteristics, geostatistical method was used based on obtained predictive models for eachspecies (through LR method). The accuracy of the predicted maps was tested with actual vegetationmaps. In this study, the adequacy of vegetation type mapping was evaluated using kappa statistics.Predictive maps of Astragalus spp. ( κ =0.86), Halocnemum strobilaceum ( κ =0.51), Zygophylumeurypterum ( κ =0.58) and Seidlitzia rosmarrinus ( κ =0.6) with narrow amplitude is as the same ofactual vegetation map prepared for the study area. Predictive model of Artemisia sieberi ( κ =0.33),due to its ability to grow in most parts of north east rangeland of Semnan with relatively differenthabitat condition, is not possible.
