Some Assyriella Species in South East Anatolia in Turkey Differentiated by Multivariate Statistical Analysis (Gastropoda: Pulmonata, Helicidae)

Turk J Zool 25 (2001) 1-9 © T†BÜTAK

Hasan AKBAYIN, Murat HEVEDANLI Science Education Department, Education Faculty, Dicle University, DiyarbakÝr, TURKEY RÝdvan ÞEÞEN Biology Department, Science and Art Faculty, Dicle University, DiyarbakÝr, TURKEY

Received: 16.05.2000

Abstract: This study was performed on 3 species of the genus Assyriella (A. escheriana , A. guttata and A. thospitis) collected from DiyarbakÝr province in Southeast Anatolia. Two derived and 6 shell variables of 50 specimens were examined for each species. Hierarchical clustering and discriminant analysis were used to evaluate the data in order to clarify and contribute to some systematic problems of these species. It was shown that WardÕs method with Euclidean distance is the most appropriate hierarchical clustering method to cluster these species. A. escheriana is clearly differentiated from A. guttata and A. thospitis by both WardÕs method and discriminant analysis. The results of discriminant analysis and WardÕs method reveal that A. thospitis and A. guttata may have a close evolutionary relationship. Also, the most important shell characters (variables) that differentiate these species from each other were determined by discriminant analysis and the success efficiency of these techniques in systematic problems was observed. Consequently, the results of this study showed that more precise identification can be made in classification of A. escheriana, A. guttata and A. thospitis if classical criteria are used together with the group classification functions obtained from discriminant analysis. Key Words: Assyriella, Biometry, Numeric Taxonomy

TŸrkiye, GŸneydoÛu Anadolu BšlgesiÕnde Bulunan BazÝ Assyriella TŸrlerinin (Gastropoda, Pulmonata, Helicidae), ‚ok DeÛißkenli Üstatistik Metodlarla AyÝrÝmÝnÝn YapÝlmasÝ

…zet: Bu •alÝßma, GŸneydoÛu Anadolu Bšlgesi, DiyarbakÝr ili civarÝndan toplanmÝß Assyriella genusunun Ÿ• tŸrŸ (A. escheriana , A. guttata ve A. thospitis) Ÿzerinde ger•ekleßtirilmißtir. Her bir tŸr i•in 50 šrnek Ÿzerinde, iki tŸretilmiß ve altÝ kabuk deÛißkeni incelendi. Bu Ÿ• tŸrŸn bazÝ sistematik problemlerinin •ŸzŸmŸne katkÝda bulunmak amacÝyla, veriler, hiyerarßik gruplama ve diskriminant analizi teknikleri ile deÛerlendirildi ve bu tekniklerin sistematikte ne derecede baßarÝlÝ olarak kullanÝlabilecekleri gšzlendi. …klit uzaklÝÛÝ kullanÝlarak, Ward metodunun bu tŸrleri gruplamada daha uygun bir hiyerarßik gruplama metodu olduÛu gšrŸlmŸßtŸr. Hem Ward metodu hem de diskriminant analizi A. escherianaÕyÝ A. guttata ve A. thospitisÕten belirgin bir ßekilde ayÝrmaktadÝr. Diskriminant analizi ve Ward metodunun sonu•larÝ, A. guttata ve A. thospitisÕin evrimsel olarak daha yakÝn akraba olabilecekleri gšstermektedir. AyrÝca, bu tŸrleri biribirinden ayÝran en šnemli kabuk karakteristikleri (deÛißkenleri) diskriminant analizi ile belirlenmiß ve bu tekniklerin sistematikteki etkinlikleri gšzlenmißtir. Sonu• olarak, bu •alÝßmanÝn bulgularÝ A. escheriana, A. guttata ve A. thospitisÕin sÝnÝflandÝrÝlmasÝnda, geleneksel kriterler, diskriminant analizinden elde edilen grup sÝnÝflandÝrma fonksiyonlarÝ ile birlikte kullanÝlÝrsa, daha doÛru teßhislerin mŸmkŸn olabileceÛini gšstermektedir. Anahtar SšzcŸkler: Assyriella, Biyometri, Numerik Taksonomi

Introduction on shell features and genital apparatus. Recently, a SchŸtt (1) stated that the species of the genus revision was made on the species of this genus. A total of Assyriella spread in the Middle East, Near East and some 2 new and 15 known species have been described by islands such as Rhodes and Cyprus originally developed in SchŸtt and Subai (2). Southeastern Anatolia. Their classification is mainly based

1 Some Assyriella Species in South East Anatolia in Turkey Differentiated by Multivariate Statistical Analysis (Gastropoda: Pulmonata, Helicidae)

Due to morphological and anatomical similarity, them to non-technical users and demonstrated their Assyriella species have many synonyms. For instance, power for a range of data. McGill et al. (6) implemented Assyriella thospitis (SchŸtt and Subai, 1996) which was confidence intervals on the medians of several groups in described as a new species in 1996 (2), was previously box plots. If the intervals around 2 medians do not considered to be Helix ergilensis by Galland in 1885. overlap, one can be about 95 % confident that the Assyriella guttata (Olivier 1804) and Assyriella escheriana population medians are different and so the confidence (Bourguignat, 1864) were considered to be Helix guttata intervals are useful for judging difference between by Olivier in 1804 and Helix escheriana by Bourguignat groups. More information about constructing and in 1864, respectively. interpreting box plots can be found in Hamilton (7). The shell features of A.guttata and A.escheriana and Hierarchical clustering the comparative anatomy of their reproduction systems There are many clustering methods and similarity were studied by AgŸloÛlu and BalcÝ (3). coefficients. Many of them can be found in Sneath and The present study was carried out on 3 species of the Sokal (8), Hartigan (9); Abbot et al. (10) and Everit (11). genus Assyriella, A. escheriana, A. guttata and A. Discriminant Analysis thospitis, collected from DiyarbakÝr province in South- eastern Anatolia in order to clarify and contribute to the Discriminant functions were developed independently taxonomic status of these species by employing by Fisher (12), Mahaonobis (13) and Hotelling (14), hierarchical clustering and discriminant analysis whose primary interests were in different areas. The techniques and the efficiency of these techniques was theoretical base of this analysis can be found in detail in observed. Some shell characters (variables) were used in Chatfield and Colins (15), Johnson and Wichern (16) the multivariate statistical techniques since these and in the publications dealing with multivariate statistics. characters are the basis of the original species In summary, discriminant analysis is a technique for description. finding functions so as to discriminate several groups previously defined. It is therefore of considerable interest to those wishing to classify specimens, on each of which Materials and Methods a number of measurements have been made and which are to be collected in previously defined groups. This Materials analysis was employed for the same purpose in this study. This study was performed on the specimens of 3 species (A. escheriana, A. guttata, A. thospitis), collected A large number of discriminant analysis studies and from DiyarbakÝr province in Southeast Anatolia in Turkey hierarchical clustering applications have been described between 1995 and 1999 and identified according to the such as in AkbayÝn et al. (17), Togan et al. (18), …zbay traditional taxonomic characters used by SchŸtt and Subai and AkbayÝn (19), Kristensen and Christensen (20), and (2). These specimens are kept at the Dicle University Mukaratirva et al. (21). Science Faculty Museum (DUM). Fifty specimens were Measurements randomly drawn for each of the 3 species. The selected Two derived and 6 shell variables shown in Figure 1 specimens of A. escheriana A. guttata, and A. thospitis and Table 1 were measured on the selected 150 were numbered [ 1-50 ], [ 51-100 ] and [101-150 ], specimens in millimeters by a calliper compass. In this respectively. way, we obtained a 150x8 data 3 with three categories. Methods The data matrix with descriptive statistics by categories is given in Table 2. STATISTICA (22) vwas used for the Box plots statistical analysis. Box plots provide a simple graphical summary of a group (groups) of cases defined by a categorical Descriptive statistics, hierarchical clustering methods (grouping) variable. Tukey (4) originally presented them and discriminant analysis were used to evaluate the data as schematic plots. Vellman and Hoalgin (5) introduced matrix in order to clarify some systematic problems of these species.

2 H. AKBAYIN, R. ÞEÞEN, M. HEVEDANLI

with Euclidean distance is a more appropriate method Table 1. Measured variables of Assyriella species than the others due to its producing clusters at high Shell variable (Character) Abbreviation similarity level. The dendrogram obtained by WardÕs method is given in Figure 3., Shell Diameter (mm) ShD The data matrix given in Table 2 was subjected to Shell Height (mm) ShH discriminant analysis taking species as grouping variables. Ratio of shell diameter to shell height ShD/ShH Eigen values and discriminant functions associated with Aperture Height (mm) ApH these eigen values were obtained. These functions were Aperture Width (mm) ApW tested by chi-square statistics. The results of these Ratio of Aperture Height to aperture width ApH/ApW operations are given in Table 3. The test statistics shown Spire Height (mm) SpH that the 2 discriminant functions were statistically Truncation Width (mm) TrW significant (p< 0.001) in discrimination of the 3 species. The scatter plot of the first canonical variate (CV1) by the second canonical variate (CV2) is presented in Figure Results 4. The multiple box plot given in Figure 2 was used to The group classification functions are used to determine the classificatory values of the measured determine to which group (category) a new specimen characteristics of Assyriella species in question. most likely belongs. A specimen is classified into the Therefore, the data matrix was categorised with respect group for which it has the largest classification function to the species. score. The group classification functions are given in If Figure 2 is reviewed, the variables ShD/ShH, Table 4. ApH/ApW and TrW have no classificatory value because of Group classification functions were calculated for the the approximately equal distribution of these variables. data matrix (see Table 2) to see how our specimens are So these variables were removed when the cluster classified by these functions. Observed memberships analysis was performed. On the other hand, the against those predicted by these functions are given in remaining variables have more or less classificatory value. Table 5. In the present study, the hierarchical methods with Canonical loadings are correlations between variables different similarity coefficients found in the STATISTICA and discriminant functions. These loadings are used to (22) clustering module were applied to the data matrix determine how much and in which direction a variable given in Table 2 by removing the variables (ShD / ShH, contributes to discrimination. The canonical loading ApH / ApW and TrW) that have no classificatory values. produced during the application of discriminant analysis The results of these operations were observed. During are given in Table 6 in decreasing order of importance. the process, it was determined that WardÕs (23) method

a Figure 1. The location where measurements of h shell variables were taken on each g specimen of Assyriella. f e ab: Shell Height (ShH) ac: Spire Height (SpH) d Ý c dg: Shell Diameter ShD) ef: Truncation Width (TrW) b eg: Aperture Width (ApW) hÝ: Aperture Height (ApH)

3 Some Assyriella Species in South East Anatolia in Turkey Differentiated by Multivariate Statistical Analysis (Gastropoda: Pulmonata, Helicidae) 8 A. escheriana A. guttata A. thospitis Table 2. Data matrix obtained from the randomly selected specimens.* *For abbreviation see Table 1 and Figure 1. Sn ShD12 ShH3 ShD/ShH 30.64 32.0 ApH5 9.3 33.46 13.2 32.2 ApW7 3.290 12.2 32.4 2.424 ApH/ApW8 17.3 34.1 2.738 21.89 10.4 29.9 18.5 SpH 1.86110 13.1 15.3 28.3 20.4 3.11511 14.6 10.8 31.7 23.2 TrW 2.603 1.42512 17.0 30.8 14.4 13.5 1.267 2.76913 16.8 30.7 16.0 Sn 13.2 1.200 1.965 16.214 18.2 31.6 10.6 19.8 1.381 1.981 13.7 12.015 16.2 33.1 ShD 1.901 17.9 0.742 14.2 14.116 18.4 32.7 2.2 2.241 18.0 0.815 1.7 12.2 10.917 20.4 32.6 ShH 19.1 2.225 1.076 1.4 11.6 14.018 16.7 33.1 51 18.2 2.713 0.877 12.5 ShD/ShH 52 1.7 14.2 14.719 27.0 18.3 2.819 1.078 15.9 36.2 53 1.9 15.4 11.720 32.8 ApH 18.0 1.528 2.296 34.7 17.0 54 1.6 15.9 11.521 35.6 21.4 1.145 2.149 34.7 17.6 17.2 55 1.8 14.4 11.8 ApW22 32.8 17.9 19.9 1.076 1.698 34.8 13.0 16.6 56 1.7 12.923 31.7 16.6 21.1 ApH/ApW 1.047 2.278 2.057 34.0 14.4 16.4 57 1.6 10.3 1.93924 29.2 17.4 18.3 1.289 2.760 35.4 2.0 12.9 SpH 16.2 58 14.1 2.090 20.425 31.2 18.1 18.2 1.213 3.184 35.8 1.7 12.3 20.4 14.7 59 13.6 2.00026 32.8 17.3 16.4 1.302 2.248 34.9 60 2.0 14.2 TrW 19.5 20.0 17.7 12.2 1.87827 29.3 17.2 16.2 1.245 17.2 2.147 33.5 61 1.8 13.6 17.7 17.9 15.3 2.046 34.528 39.3 16.8 14.7 1.028 16.4 2.557 62 1.8 1.046 Sn 14.2 21.0 17.0 10.9 2.081 35.329 1.186 31.5 16.9 18.7 0.916 17.5 2.144 63 2.0 14.4 20.9 16.6 17.4 10.9 2.077 34.730 1.220 34.2 20.2 0.953 15.6 19.0 2.688 64 2.3 15.3 18.8 ShD 15.0 17.3 15.0 15.7 1.982 34.331 1.011 30.0 18.8 0.886 17.4 3.606 65 1.9 14.5 1.983 20.1 14.2 16.8 15.8 16.0 34.332 1.105 31.8 2.4 17.4 1.247 18.6 2.100 66 2.3 12.0 2.040 20.7 16.5 2.6 17.6 14.0 ShH 16.4 35.333 1.201 33.4 20.0 1.423 17.3 22.1 2.165 67 1.6 11.4 2.065 ShD/ShD 17.7 2.0 16.8 12.9 16.4 101 33.134 1.011 31.4 17.4 1.139 19.7 20.1 2.143 68 1.2 13.3 ApH 1.949 102 20.0 2.0 16.4 12.7 16.7 18.5 34.035 1.162 34.9 19.2 0.983 20.6 2.465 69 1.9 14.3 2.042 103 15.3 2.1 17.4 12.3 16.6 17.9 35.4 37.336 1.051 34.4 18.1 1.000 19.4 2.630 70 1.4 12.7 36.3 ApW 2.152 104 1.195 17.2 2.3 17.2 17.9 15.4 16.1 35.737 27.9 21.2 1.137 20.6 2.553 71 2.2 10.3 36.6 1.902 105 1.123 20.3 2.4 16.2 13.7 17.7 15.9 19.1 34.638 35.0 19.7 1.116 19.8 1.950 72 1.7 12.4 17.7 ApH/ApW 16.2 36.4 2.000 1.977 106 1.280 17.4 2.3 18.6 15.5 18.3 34.539 SpH 33.2 17.9 0.892 2.051 20.2 2.511 73 1.5 12.5 16.4 16.3 37.0 2.302 107 1.016 13.0 2.6 17.7 13.2 16.9 35.940 25.2 21.4 16.1 1.218 2.232 19.7 1.800 2.2 74 1.7 12.1 18.2 16.0 36.5 1.919 108 1.126 15.3 16.8 20.9 13.4 15.9 36.241 35.4 TrW 19.1 1.515 2.000 21.2 2.652 2.6 75 2.1 12.5 17.1 17.2 36.4 1.955 109 1.172 14.5 18.0 20.5 14.1 18.0 16.8 35.942 32.1 20.8 1.170 2.164 110 20.3 2.478 2.2 76 2.0 14.2 17.5 15.6 37.7 2.054 20.5 1.270 14.9 17.4 20.6 12.3 18.7 33.443 28.7 17.8 1.110 2.086 111 18.9 1.787 2.4 77 1.8 13.2 18.1 16.3 38.2 1.994 21.2 1.094 16.9 16.1 19.7 14.1 1.274 17.8 36.244 27.2 15.8 35.6 1.282 2.011 112 20.1 2.878 2.5 78 1.6 16.4 18.4 1.020 15.9 2.080 18.1 1.134 17.6 17.1 21.8 14.4 18.2 35.545 28.8 19.9 36.0 1.231 2.049 113 20.2 2.277 2.4 16.0 79 1.6 12.9 18.0 0.967 14.6 2.230 20.2 1.140 13.0 17.1 20.4 13.3 17.1 17.2 36.046 31.9 17.5 21.0 36.2 1.011 2.122 114 21.4 1.993 2.4 80 2.3 12.1 1.138 16.1 1.953 19.1 1.038 17.4 16.7 19.5 17.5 2.034 19.1 15.7 35.647 28.0 17.8 20.9 36.6 1.215 2.3 115 20.3 2.045 2.1 81 1.5 12.9 0.975 16.5 2.117 19.7 1.175 17.3 16.9 21.6 13.3 2.022 2.3 16.8 15.3 34.448 31.9 17.4 19.3 36.1 1.144 116 21.4 1.646 2.1 20.4 82 1.9 14.8 1.141 16.4 2.126 20.0 1.058 15.1 17.5 15.0 2.080 2.6 18.2 16.9 34.549 36.7 17.9 21.6 36.3 1.214 117 21.9 2.398 2.5 20.5 83 2.1 13.7 1.036 16.6 2.130 18.0 1.274 15.4 16.5 14.5 2.045 2.4 18.8 17.0 34.650 34.6 17.5 19.6 37.6 1.384 20.5 118 18.9 1.867 2.4 20.1 84 2.3 13.8 0.975 15.5 2.034 1.115 16.6 17.5 10.9 2.063 2.3 16.6 17.3 35.3 29.4 17.3 19.3 33.0 1.253 18.5 119 20.7 2.200 2.1 20.2 85 1.4 12.9 1.200 16.0 2.085 1.138 16.5 17.8 12.3 2.098 2.0 17.3 16.7 35.7 27.9 19.0 0.995 20.0 37.3 1.301 21.1 120 20.2 3.367 2.1 20.5 86 1.9 10.3 16.4 1.971 1.319 14.7 17.9 16.1 1.979 2.1 17.1 15.4 34.2Mean 17.3 1.108 14.3 35.6 1.188 17.3 121 21.5 2.813 2.3 21.8 87 1.4 14.7 15.9 1.944 16.3 1.092 15.0 16.7 13.7 1.908 2.4 17.8 34.3St. Dev. 18.1 0.953 17.3 36.5 31.73 1.313 19.2 122 21.0 1.826 2.3 21.1 88 1.8 12.4 16.5 2.718 1.972 14.9 1.211 16.5 18.2 2.061 2.4 16.2 34.2 17.8 1.168 15.4 35.5 1.333 21.1 123 19.1 2.036 2.2 20.9 89 1.3 2.4 16.4 13.65 2.138 9.0 16.6 1.135 16.9 16.3 2.000 1.922 19.0 34.3 17.7 1.068 11.6 36.7 0.867 21.8 124 19.8 2.5 20.7 90 1.3 13.9 2.1 16.3 1.879 16.2 1.327 17.1 16.3 2.062 16.8 35.2 2.38 17.1 1.033 19.2 36.1 0.455 1.024 18.9 125 20.2 1.9 20.4 91 11.6 2.2 16.5 2.104 15.4 1.105 15.6 16.4 1.9 2.076 20.3 34.7 17.6 0.968 37.5 0.901 18.0 126 19.4 2.0 22.0 92 1.7 15.3 2.6 16.0 2.098 17.1 1.137 15.1 17.6 2.373 2.085 18.47 17.4 35.2 17.8 1.106 36.9 0.744 19.3 127 19.2 2.3 21.3 1.7 11.1 2.6 16.6 2.091 16.3 0.975 16.9 93 2.028 19.7 34.9 1.665 18.7 16.35 1.150 34.9 1.272 17.9 128 20.4 2.3 20.7 94 2.2 13.1 2.0 16.6 2.000 14.9 1.161 17.5 2.00

4 H. AKBAYIN, R. ÞEÞEN, M. HEVEDANLI

40 Figure 2. Multiple box plot of the measured variables by Assyriella species. ShD 35 ShD ShD

25 ApH ApH ApH ShH

20 ShH ShH ShH ShH ShH Measurement (mm)

15 ApW ApW ApW 10 ShD/ShH 5 TrW TrW TrW ShD/ShH ShD/ShH ApH/ApW ApH/ApW ApH/ApW

0 A. escheriana A. guttata A. thospitis

*For abbreviation see Table 1 and Figure 1.

Discussion differentiated from Clusters II and III, which include A. WardÕs method with Euclidean distance based on thospitis and A. guttata respectively. However, some these variables is the most appropriate hierarchical specimens, which were originally described as A. thospitis clustering method for these species (A. escheriana, A. or A. guttata, fall into the same clusters. So there may be guttata, A. thospitis) because it produces clusters at high an evolutionarily close relationship between A. guttata similarity level and the obtained clusters are in accord and A. thospitis rather than A. escheriana (See Figure 3). with the classification determined by the traditional In the case of a similarity level greater than 87.5 %, method (See Figure 2 and Table 2). Cluster I is segregrated into 2 subclusters. This result is Clusters I, II and III were constituated at an 87.5 % interpreted as A. escheriana possibly comprising 2 similarity level by WardÕs method. Cluster I, which varieties. The impression of 2 varieties may be considered includes specimens of A. escheriana, is clearly a subject for new research (Figure 3).

5 Some Assyriella Species in South East Anatolia in Turkey Differentiated by Multivariate Statistical Analysis (Gastropoda: Pulmonata, Helicidae)

Table 3. Raw coefficients and constants for Canonical Variables.* Table 5. Observed classification (rows) against predicted (columns). Variable CV1 CV2 Percent of ShD 0.0693 0.3505 Correct ShH -1.2464 -0.3207 Species Classification A. escheriana A. guttata A. thospitis ShD/ShH -4.2137 -2.4573 ApH -0.5160 -1.4631 A. escheriana 98 49 1 0 ApW 0.1105 2.1397 A. guttata 84 0 42 8 ApH/ApW 5.2743 24.8986 A. thospitis 84 0 8 42 SpH -0.5799 -0.4077 Total 88.66 49 51 50 TrW -1.1122 -1.5015 Constant 40.3478 -29.2369

Explained Cumulative Chi-squre P-level variance percent of Statistics Table 6. Canonical loadings in decreasing importance.* (Eigen value) explained variance Variable CV1 Variable CV2

9.7390 0.9792 367.69125 0.00000 SpH -0.493313 ApW 0.686959 0.2073 1.0000 27.03905 0.00033 ShH -0.480596 TrW -0.407456

*For abbreviation see Table 1 and Figure 1. ShD -0.346135 ApH/ApW -0.396973 CV1: The first canonical variable, CV2: The second canonical variable TrW -0.311651 SpH -0.386501 ApW -0.275063 ShD 0.320868 Table 4. Group classification functions and constants.* ApH -0.201802 ShD/ShH 0.187056

Variable A. escheriana A. guttata A. thospitis ShD/ShH 0.187102 ApH 0.024679 ApH/ApW 0.051266 ShH -0.018809 ShD -69.87 -70.52 -70.21 ShH 201.60 209.26 210.21 *For abbreviation see Table 1 and Figure 1. ShD/ShH 1080.85 1107.68 1109.38 ApH -251.28 -247.20 -248.27 ApW 290.38 288.27 290.50 ApH/ApW 4344.68 4296.30 4318.04 SpH 27.85 31.59 31.74 Figure 4 reveals that CV1 essentially serves to TrW 25.53 33.19 32.70 distinguish A. escheriana from A. guttata and A. thospitis, Constant -4291.02 -4504.24 -4580.43 while A. guttata and A. thospitis are differentiated by CV2. *For abbreviation see Table 1 and Figure 1. In the present study based on shell morphology, A. escheriana can be clearly differentiated from A. thospitis The discriminant analysis as illustrated by the point of and A. guttata although the variability of the canonical CV1 to CV2 mainly shows 2 clusters; 1 consists of scores of A. escheriana was greater than that of A. specimens originally classified as A. escheriana and the thospitis and A. guttata. On the other hand, the other includes specimens which were classified as A. convergence of the canonical scores of A. thospitis and A. thospitis and A. guttata. The latter cluster seems to guttata support the idea that these two species may have consist of 2 subclusters: 1 mainly consists of A. guttata a close evolutionary relationship (See Figure 4). and the other subcluster mainly includes A. thospitis (See Group classification functions are used to determine Figure 4). to which group a new specimen most likely belongs. One

6 H. AKBAYIN, R. ÞEÞEN, M. HEVEDANLI

1 7 23 8 29 11 22 41 17 18 32 35 42 38 45 50 2 34 31 23 44 10 37 33 4 40 43 9 24 Cluster I 12 27 15 16 28 3 14 30 39 13 20 47 19 36 48 26 5 6 46 21 49 31 114 72 130 104 111 136 68 130 138 106 122 144 69 113 118 125 109 143 120 134 83 110 112 135

Cluster II 103 102 123 128 115 107 119 147 105 141 108 139 132 116 124 142 52 126 58 93 99 64 67 87 88 93 56 89 61 146 79 74 98 78 84 54 82 65 71 86 77 94 127 57 98 145 63 70 85 90

Cluster III 137 53 62 80 91 66 73 76 100 101 55 81 131 97 133 129 60 92 121 148 140 149 59 75 117 100 87.5 75.0 0.00 Percent of similarity

Figure 3. Dendrogram obtained by WardÕs method

7 Some Assyriella Species in South East Anatolia in Turkey Differentiated by Multivariate Statistical Analysis (Gastropoda: Pulmonata, Helicidae)

for our specimens. The results of this operation are given 3 in Table 5. The largest percentage of specimens used in 2 the present study were predicted correctly by group classification functions. We hope that the methods tried 1 here on a limited number of individuals will be equally 0 successful when further material is examined.

-1 The order of importance of the characteristics (variables) for discriminating these species is shown in A. escheriana -2 A. guttata Table 6 in decreasing importance. SpH, ShH, ShD and Second cannonical variable (CV2) A. thospitis TrW are most differentiating characters for A. escheriana -3 -4 -2 0 2 4 6 8 from A. thospitis and A. guttata. The 4 most First canonical variable (CVI) differentiating characters between A. guttata and A. thospitis are ApW, TrW, ApH / ApW and SpH in decreasing importance. Figure 4. The scatter plot of CV1 by CV2 Consequently, the results of this study show that can apply these functions to new data and assign each more precise identification can be made in the case to the group with the largest function value for that classification of A. escheriana, A. thospitis and A. guttata case. These functions given in Table 4 were applied to the if the classical criteria are used together with the group data matrix (Table 2) to see how these assignments work classification functions.

References

1. SchŸtt, H., Landschnecken der TŸrkei. Acta Biologica Benrodis 11. Everith,B., Cluster Analysis, Reineman Educational Book Ltd., Suppl. 4, 482p., 1996. London, 1974. 2. SchŸtt, H. and Subai P., Revision der Gattung Assyriella P. 12. Fisher, R.A., The use of multiple measurements in taxonomic HESSE 1908 (Gastropoda: Pulmonata: Helicidae: Helicinae. problems, Ann. Eugenics, 7: 179-188, 1936. Arch. Molluskenkunde, 125: (1/2), 117-161, 1996. 13. Mahalanobis, P.C., On the generalised distance in statistics, 3. AgŸloÛlu, B., BalcÝ K., The shell features of Assyriella guttata and Proceedings of the Natural Institute of Science of India, 12: 49- Asyyriela escheriana and comparative anatomy of their 55, 1936. reproduction systems. Proceedings of XII. National Biology 14. Hotelling, H., The generalisation of StudentÕs ratio, Ann. Math. Congress, 6-8 July, 40-46, Edirne / Turkey, 1994. Statist. 2: 360-378, 1931. 4. Tukey, J. W., Expletory Data Analysis, Reading Mass Addison- 15. Chatfield, C. and Colins, A.J., Introduction to the Multivariate Wesley, 1977. analyses, Chapman and Hall, London, New York, 1980. 5. Velleman, P.F. and Hoalgin, D.C., Applications Basics and 16. Johnson, R.A. and Wichern, D.W., Applied Multivariate Statistical Computing of Expletory Data Analysis, Boston: Duxbury Analysis, Third Edition, Prentice-Hall, Inc., A Simon and Schuster Press.,1981. Company, Englewood Cliffs, NJ 07632, 1992. 6. McGill, R., Tukey, J.W., and Larsen, W.A., Variations of Box 17. AkbayÝn, H., Demir, R., and Ertekin, A.S., Solution of some Plots, The American Statistician, 32: 12-16, 1978. taxonomic problems in Medicago rigudula (L.) All. Varieties by 7. Hamilton, L.C., Modern Data Analysis: A first course in applied using discriminant analysis technique, Tr. J. Of Botany 18: 481- statistics, Brooks / Cole Publishing Comp., California, 1990. 488, 1994. 8. Sneath, P.H.A. and Sokal, R.R., Numerical Taxonomy, W.H. 18. Togan, I., Aydem, N. and Kence, A., A Numerical Taxonomic Freeman and Company, San Francisco, 1973. Study on Carthamus L. Taxa in Turkey, NATO ASI series, GI, 1983. 9. Hartigan, J.A., Clustering Algorithms, New York, Wiley and Sons Inc., 1975. 19. …zbay, C. and AkbayÝn, H., A Numerical taxonomic Study of Vespidae (Hymenoptera) Species from South-East Anatolia in 10. Abbot, L.A., Bisby, A.F. and Rogers, D.J., Taxonomic Analysis in Turkey., Tr. J. Of Biology, 21: 189-196, 1997. Biology, Colombia Univ. Press, New York, 1985.

8 H. AKBAYIN, R. ÞEÞEN, M. HEVEDANLI

20. Kristensen, T.K., Christensen, A.G., Bulinus africanus- Group 22. Statistica, Statistica for Windows Computer Program manual, species in West Africa Differentiated by Morphometric Analysis Tulsa OK: StatSoft Inc., 1995. (Gastropada, Planorbidae). J. Moll. Stud. 55: 103 Ð 110, 1989. 23. Ward, J.H., Hierarchical grouping to optimize an objective 21. Mukaratirwa, S, Kristensen T.K., Siegismund, H.R., Chandiwana, function., J. Am. Statist. Ass, 58: 236-244, 1936. S.K., Genetic and morphological variation of populations belonging to the Bulinus truncatus Tropicus complex (Gastropoda: Planorbidae) in southwestern Zimbabwe. J. Moll. Stud. 64: 435-446, 1998.