رزگار داروند

In this research, the capability of indicator species analysis with the emphasis of the group combination concept for determining the pre-classified plant communities was investigated.

For this purpose, the yew ecological groups of sites in the Jahan Nama Protected Area (JNPA) yew forests were firstly classified by TWINSPAN method based of vegetation abundance data and then the indicator species of each ecological groups were derived by using group-equalized indicator value analysis as well as the groups combination concept. The indicator value of the species-group was analyzed by ‘multipatt’ function and its statistical significance was evaluated using the permutation test in the ‘indicspecies’ package. Finally, the importance of indicator species analysis results by considering all possible combinations of JNPA yew groups of sites in order to assigning each ecological groups to pre- classified yew communities in the Braun-Blanquet hierarchical synoptic table were illustrated.

Combining groups of sites in the indicator species analysis not only provide indicator species in target combined groups but also presenting the zero fidelity of those species to non-target groups of sites and allows the differentiation and distinction of plant communities/groups of sites based on both features of prescence and absent of indicator species. While, in the typical indicator special analysis only species-target groups of sites association is taken into account. Results also showed that indicator species analysis by combining groups of sites not only provided the pre- classified JNPA yew communities which were determined by Braun-Blanquet synoptic table but also provides their hierarchical order and also higher syntaxa at the next hierarchical rank in Braun-Blanquet synoptic tables as well as dendrogram.

Consequently, the results of this research reiterate that indicator species analysis by combining groups of sites compared with individual ones provides the more reliable indicator species lists for characterizing plant communities in phytosociological studies. So, we believe that applying indicator species analysis by combining groups of sites approach could be useful in the automatic (expert system) classification of plant communities which is inevitable while an extensive database of vegetation composition is available. Consideration of combining groups of sites in indicator species analysis due to providing common occurrence of indicator species at different hierarchical plant community classification, will improve the capability of expert system method in characterizing of the next higher syntaxa than association as the basic unit in the braun-blanquet synoptic table method.

In this study, the efficiency of the two nearest neighbor (KNN) and weighted average (WA) methods was evaluated for indirect estimation of environmental variables in plant communities. For this purpose, vegetation composition data of 324 relevés with an area 400 m2 of the Hyrcanian yew forests database were used. Then, environmental variables in each relevés were indirectly estimated by using KNN and WA methods based on two kind of vegetation data (incidence based and abundance based of floristic data) as well as the initial values of that environmental variables.Validation of the models were evaluated using determinant coefficient of linear regression analysis, which done based on the initial values and followed by estimated one of each environmental variables as the predictor and response variables. Results showed that using KNN method based on abundance data due to having the highest determination coefficient value has the priority in comparison to another three algorithms. The main reason of the differences between KNN and WA was influenced by different approaches of interpolation (KNN) and extrapolation (WA) in the process of environmental variables point estimation. The better performance of the KNN compared with WA in the point estimating of environmental variables is due to using the environmental data of the only adjacent plot data with the most similarly floristically features to each points in the KNN, while the results of the WA are globally affected by the range of each environmental variables at the whole of the dataset.