Obtaining new resolutions in carnivore tooth pit morphological analyses: A methodological update for digital taphonomy

Abstract

Modern day investigation in fields of archaeology and palaeontology can be greatly characterised by an exponential growth of integrated new technologies, nevertheless, while these advances are of great significance to multiple lines of research, their evaluation and update over time is equally as important. Here we present an application of inter and intra-observer analysis in taphonomy based geometric morphometrics, employing robust non-parametric statistical analyses for the study of experimental carnivore tooth pit morphologies. To fully understand the influence of measurement errors in the collection of this data, our statistical assessment was performed on fully superimposed, partially superimposed and raw landmark coordinates collected from 3D surface scanning. Experimental samples used to assess these errors includes wolf and dog tooth pits used in modern day ecological livestock predation analysis. Results obtained from this study highlight the importance of landmark type in the assessment of error, emphasising the value of semi-landmark models over the use of ambiguous Type III landmarks. In addition to this, data also reveals the importance of observer experience for the collection of data alongside an interesting increase in error when working with fully superimposed landmarks due to the "Pinocchio Effect". Through this study we are able to redefine the geometric morphometric models used for tooth pit morphological analyses. This final hybrid Type II fixed landmark and semi-landmark model presents a significant reduction in human induced error, generating a more metrically reliable and replicable method that can be used for data pooling in future inter-institutional research. These results can be considered a fundamental step forward for carnivore inspired studies, having an impact on archaeological, palaeontological, modern-day ecological research as well as applications in other forensic sciences.

Developments in data science solutions for carnivore tooth pit classification

“…In recent years, GM have been applied to a wide array of different applications outside of biology. Among these applications, these tools have shown promising results when applied to the study of BSMs 8,[20][21][22][23][24][25]37 . While subsequent analyses have identified some issues with these techniques for carnivore BSM applications 21 , the present study has shown that high quality results are still realistically obtainable (Accuracy > 90 %, AUC > 0.8, κ > 0.8, Fig.…”

“…Likewise, in a recent study the original landmark model proposed 24 was found to present important margins of error product of landmark quality. These observations infer that analyst experience condition the quality of results 21 .…”

“…The landmark configuration used for this study consists of a total of 30 landmarks (LMs) 21 ; 5 fixed Type II landmarks 18 and a 5 × 5 patch of semilandmarks 69 (Fig. S2).…”

“…This in turn facilitates the comparison of landmark configurations, as well as hypothesis testing, using multivariate statistical analyses. Nevertheless, considering observations made by Courtenay et al 20,21,25 revealed tooth mark size to be an important conditioning factor in their morphology, prior analyses in allometry were also performed 72 . From this perspective, allometric analyses first considered the calculation of centroid sizes across all individuals; the square root of the sum of squared distances of all landmarks of an object from their centroid 18 .…”

“…Under this premise, the present study uses an updated version of the landmark model using semi-landmarks 21 , and a much larger sample size to expand on the current referential samples available for taphonomic analyses. These efforts aim to provide high quality data that can aid in the understanding of modern carnivore taxa that are frequently found across Eurasia, Africa and the Americas.…”