FURTHER INVESTIGATION OF TECTONIC DEFORMATION IN FOSSILS USING GEOMETRIC MORPHOMETRIC SIMULATIONS
In general, the amount of variance in a dataset increased rapidly as deformation increased, and the covariance structure of the deformed dataset became progressively less similar to that of the original. It usually was difficult to reject the hypothesis that the ontogenetic signal in the deformed and original datasets was the same, but this seemed to result in part from the increased noisiness of the deformed datasets. MANOVAs usually could not discriminate the original and deformed datasets at low levels of deformation, but discrimination improved slightly with greater deformation. An exception to this generalization was observed in the datasets with limited variation in the orientation of specimens relative to the direction of applied stress. This may result from the fact that the deformation-induced variance reflected the non-random bias relative to cases where the specimens were randomly oriented relative to the applied stress. Attempts to remove the effects of deformation using four published methods generally were not successful. All the retro-deformation methods reduce the total amount of variance in the data, but they uniformly suffer from either removing too much or too little. Furthermore, none significantly improve the similarity of the covariance structure of the deformed dataset relative to the original.