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Abstract
Many species of Criconemoides are very similar morphologically and morphometrically, and can be distinguished only by a difficult comparison of various combinations of some morphometric and morphologic features. A high degree of variability within morphologic characters and morphometric data leads to considerable overlap among species and increases the potential for misidentification. Hierarchical cluster analysis based on both morphological characters and morphometrical data (in minimum and maximum measurements for morphometrics) means including stylet length, total number of body annules (R), number of annules between anterior end of body and excretory pore (Rex), number of annules between posterior end of body and vulva (Rv), number of annules between vulva and anus (Rvan), number of annules on tail, distance from head end to vulva as percentage of body length (V), distance between vulva posterior end of body divided by body width at vulva (VL/VB), total body length (L), anastomoses, annule margin, vagina, anterior vulval lip, tail shape and sub-median lobes were used examine the relationships between more than 120 published Criconemoides species plus six populations from Tabriz and its suburb via creating dendrograms by using SPSS 18.0.0 software. Cluster analysis dendrograms visually illustrated the grouping of the populations and species. It provided a computerised statistical approach to assist by helping to identify species by indicating both morphologic and morphometric relationships among species and by assisting with new and/or unknown species diagnosis. The preliminary species identification can be accomplished by running cluster analysis for unknown species together with the data matrix of published Criconemoides species.