Maximum-likelihood identification of fossils: taxonomic identification of Quaternary marmots (Rodentia, Mammalia) and id
We applied a Maximum-Likelihood (ML) criterion to the problem of identifying unknown specimens using a database of specimens whose identity was known. Our approach was based on shape, quantified using two-dimensional Cartesian landmarks. We applied the te
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14.1 Abstract We applied a Maximum-Likelihood (ML) criterion to the problem of identifying unknown specimens using a database of specimens whose identity was known. Our approach was based on shape, quantified using two-dimensional Cartesian landmarks. We applied the technique to two specific problems: (1) identifying Quaternary marmot skulls (Marmota, Sciuridae, Rodentia) to species, and (2) identifying the position of individual elements within the vertebral column of the Red-tailed pipesnake, Cylindrophis rufJus (Serpentes, Alethinophidia). The ML criterion finds the best identity by choosing the sample that best fits the unknown. Cross-validation tests indicated that identifications of unknown marmots were correct about 80%-90% of the time. Fossil marmots from two sites (Meyer Cave, Illinois and Little Box Elder Cave, Wyoming) could be assigned to species (M monax and M flaviventris respectively), but marmots from several other localities could not be assigned to a species-level taxon. Snake vertebrae could be allocated to their proper columnar interval more than 80% of the time, with incorrect assignments rarely being more than 10% out of place. Our technique is widely applicable in palaeontology, where the problem of identifying isolated morphological elements can be acute but is often ignored. Our approach allows palaeontologists to base their identifications securely on their morphological data, and to recognize conditions under which a confident identification can or cannot be made.
A. M. T. Elewa (ed.), Morphometrics © Springer-Verlag Berlin Heidelberg 2004
198
P. D. Polly and Jason J. Head
Keywords: Maximum-likelihood, Marmota, Cylindrophis, classification, vertebrae, skull.
14.2 Introduction The identification of palaeontological specimens can often be problematic, especially in the face of morphological variation. Whether the problem is assigning a specimen to a taxon or determining which anatomical element a specimen represents, identifications can be compromised by subjectivity and "small sample" typology. Consequently, assignments are often influenced by external, a priori considerations such as geographic and stratigraphic provenance. Many automatic identification methods are available, including neural networks, nearest neighbor classification, and linear discriminant analysis (Ripley 1994; Yang 2002; Behnke 2003). In this paper, we applied a maximum-likelihood (ML) criterion and landmark representations of shape to the problem of identifying unknown specimens using a database of specimens whose identity was known (Hastie and Tibshirani 1996). Our goal was to explore an objective method for identifying fossil materials, and to assess the extent to which the method was able to make accurate identifications. We applied this approach to two problems. The first was species-level identification of fossil marmots based on their skulls. Marmots (woodchucks, Alpine marmots) have a geographic distribution that covers much of Holarctica (Hoffmann et al. 1997; Barash 1989; Steppan et al. 1999). Most liv
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