TAILIEUCHUNG - Mapping and analysing the diversity of the genus Acantholimon taxa in Turkey by geographic information systems (GIS)
We describe the spatial distribution of the genus Acantholimon Boiss. (Plumbaginaceae) taxa in Turkey, and assess the role that environmental variables may be playing on this distribution. We collected a large number of specimens from 418 geo-referenced sampling sites between 2000 and 2004, and established a point database using geographic information systems (GIS) software. | H. M. DOĞAN, M. DOĞAN, G. AKAYDIN, F. CELEP Research Article Turk J Bot 35 (2011) 91-110 © TÜBİTAK doi: Mapping and analysing the diversity of the genus Acantholimon taxa in Turkey by geographic information systems (GIS) Hakan Mete DOĞAN1,*, Musa DOĞAN2, Galip AKAYDIN3, Ferhat CELEP2 1 Geographic Information Systems and Remote Sensing Unit of Soil Science Department, Gaziosmanpaşa University, Taşlıçiftlik 60240, Tokat - TURKEY 2 Department of Biological Sciences, Middle East Technical University 06531, Ankara - TURKEY 3 Department of Biology Education, Hacettepe University, Beytepe 06800, Ankara- TURKEY Received: Accepted: Abstract: We describe the spatial distribution of the genus Acantholimon Boiss. (Plumbaginaceae) taxa in Turkey, and assess the role that environmental variables may be playing on this distribution. We collected a large number of specimens from 418 geo-referenced sampling sites between 2000 and 2004, and established a point database using geographic information systems (GIS) software. As a result, we identified and mapped 67 taxa; 43 of the determined taxa appear to be endemic. We re-evaluated the current conservation status of the taxa at a national level using recent IUCN Red List categories. In addition, we extracted the corresponding environmental variables of each determined point from the updated and available environmental raster map layers of Turkey and analysed the obtained taxa and environmental data by Hierarchical Clustering and Canonical Correspondence Analysis (CCA). Hierarchical Clustering delineated the subgroups, which have similarities at various levels in respect to environmental variables. The CCA results indicated that 8 environmental variables including longitude, distance to sea, maximum temperature, mean temperature, minimum temperature, potential evapotranspiration, elevation, and precipitation are the most effective in explaining the spatial distribution of the 18 .
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