| Abstract: | Dental variation has been used commonly to assess taxonomic composition in morphologically homogeneous fossil samples. While the coefficient of variation (CV) has been used traditionally, range-based measures of variation, such as the range as a percentage of the mean (R%) and the maximum/minimum index (Imax/min) have recently become popular alternatives. The current study compares the performance of these statistics when applied to single- and pooled-species dental samples of extant Cercopithecus species. A common methodology for such problems of species discrimination has been to simply compare the maximum value of a variation statistic observed in extant samples with that observed in the fossil sample. However, regardless of what statistic is used, this approach has an unknowable Type I error rate, and usually has low power to detect multiple species. A more appropriate method involves a formal hypothesis test. The null hypothesis is that the level of variation in the fossil sample does not exceed what might be expected in a sample drawn randomly from a reference population, taking into account sampling error and the size of the fossil sample. Previous research using this method with the CV has indicated that it offers considerable power at an acceptable Type I error rate. In the current study, the data of primary interest were posterior dental dimensions for single- and pooled species samples from extant Cercopithecus species. In addition, the study also investigated the relative performance of variation statistics when applied to highly dimorphic canine dimensions, since much recent work has employed sexually dimorphic dental dimensions for assessing single-species hypotheses. The results indicate that the CV consistently out-performed the range-based statistics when using posterior dental dimensions to test a single-species hypothesis. Regardless of which statistic was used, tests on sexually dimorphic dimensions offered minimal power. In consideration of these results and the problem of studywise Type I error rates, we recommend against the use of multiple measures of variation to test for multiple species composition, and advocate the CV as the statistic of choice when using the method of Cope & Lacy (1992). For similar reasons, we argue for careful selection of dental variables for inclusion in such analyses, and in particular recommend against including sexually dimorphic dimensions when testing for multiple species composition. |
