Multivariate statistical methods as a tool for model-based prediction of vegetation types |
| |
Authors: | M A Zare Chahouki H Azarnivand M Jafari A Tavili |
| |
Institution: | (1) Ministry of Water and Livestock Development, Veterinary Investigation Centre, P.O. Box 1068, Arusha, Tanzania;(2) TBD consultant, Vetagro Tanzania Ltd, P.O. Box 13188, Arusha, Tanzania;(3) Crop Research Institute, Drnovska 507, Prague-6, Czech Republic;(4) Utrecht Centre for Tick-borne Diseases, Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 1, 3584 CL Utrecht, The Netherlands;(5) Department of Veterinary Tropical Diseases, Faculty of Veterinary Science, University of Pretoria, Private Bag X04, Onderstepoort, 0110, South Africa |
| |
Abstract: | The current research was carried out to find the most effective environmental factors in plant species occurrence and providing
their predictive habitat models. For this purpose, study was conducted in Poshtkouh rangelands of Yazd province in the central
of Iran. For modeling, vegetation data in addition to site condition information including topography, climate, geology and
soil were prepared. CCA and Logistic regression (LR) techniques were implemented for plant species predictive modeling. To
plants predictive mapping, it is necessary to prepare the maps of all affective factors of models. To mapping soil characteristics,
geoestatistical method including variogram analysis and Kriging interpolation were used. Based on obtained predictive models
for each species (through LR method) and for whole species (through CCA method) related predictive maps were prepared in GIS.
The accuracy of predictive maps were tested with actual vegetation maps. Vegetation modeling results with CCA indicates that
predictive map of vegetation corresponds with actual map (with high accuracy). Predictive maps of Cornulaca monachantha, Ephedra strobilacea-Zygophyllum eurypterum, Seidlitzia rosmarinus, and Tamarix ramosissima, which have narrow amplitude, has high accordance with actual vegetation map prepared for the study area. Among species of
study area, predictive model of Artemisia sieberi, due to its ability to grow in most parts of Poshtkouh rangelands with relatively different habitat conditions, is not possible.
Comparing CCA and LR methods showed that each technique has its advantages and drawbacks. In general, LR will provide better
specific-model, but CCA will provide a broader overview of multiple species. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|