University of Isfahan Taxonomy and Biosystematics 3115-9001 8 28 2016 09 22 Application of Cocktail method in vegetation classification کاربرد روش ترکیب گونه‌های معرف در طبقه‌بندی پوشش گیاهی 21 38 20984 10.22108/tbj.2016.20984 EN Hamed Asadi Department of Forest Science, Faculty of Natural Resources and Marin Sciences, Tarbiat Modares University, Noor, Iran Omid Esmailzadeh Department of Forest Science, Faculty of Natural Resources and Marin Sciences, Tarbiat Modares University, Noor, Iran Seyed Mohsen Hosseini Department of Forest Science, Faculty of Natural Resources and Marin Sciences, Tarbiat Modares University, Noor, Iran Younes Asri Botany Research Division, Research Institute of Forests and Rangelands, Tehran, Iran 0000-0002-4566-1756 Habib Zare Herbarium of Noshahr Botanical Garden, Research Center of Agricultural and Natural Resources of Mazandaran, Noshahr, Iran Journal Article 2016 11 26 This study intends to assess the application of Cocktail method in the classification of large vegetation databases. For this purpose, Buxus hyrcana dataset consisted of 442 relevés with 89 species were used and by the modified TWINSPAN. For running the Cocktail method, first primarily classification was done by modified TWINSPAN, and by performing phi analysis in the groups resulted five species were selected which had the highest fidelity value. Then sociological species groups were formed by examining co-occurrence of these 5 species with other species in the database. 21 plant communities belongs to 6 variant, 17 sub associations, 11 associations, 4 alliance, 1 order and 1 class were recognized by assigning 379 releves to the sociological species groups by using logical formulas. Also, 63 releves by the logical formula were not assigned to any sociological species groups, by FPFI index were assigned to the sociological species groups which had the most index value. According to 91% classification agreement with Brown-Blanquet classification and Cocktail classification, we suggest Cocktail method to vegetation scientists as an efficient alternative of Braun-Blanquet method to classify large vegetation databases. https://tbj.ui.ac.ir/article_20984_38300177ea7a02a6883bc30273a46485.pdf