Application of geometric morphometrics to the analysis of cut mark morphology on different bones of differently sized animals. Does size really matter?

Maté-González, Miguel Ángel; Courtenay, Lloyd A.; Aramendi, Julia; Yravedra, José; Mora, Rocío; González‐Aguilera, Diego; Domı́nguez-Rodrigo, Manuel

doi:10.1016/j.quaint.2019.01.021

Cited by 25 publications

(17 citation statements)

References 99 publications

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“…From one perspective, investigation is needed to confront whether the size of prey be a conditioning factor in tooth mark morphology, as has been observed in the case of other types of carnivore produced damage 42–44 . While previous efforts regarding other types of archaeological bone surface modifications have argued the size of the animal to not be a conditioning factor in mark morphology 45 , this question is still to be confronted in the case of carnivore tooth marks. If variances were to be observed, the data provided in this study would be applicable only to wolf and dog prey of a large size (including bovids and equids), while further experimental reference collections would be needed for other animal sizes.…”

Section: Discussionmentioning

confidence: 97%

The use of canid tooth marks on bone for the identification of livestock predation

Yravedra

Maté-González

Courtenay

et al. 2019

Sci Rep

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Historically wolves and humans have had a conflictive relationship which has driven the wolf to extinction in some areas across Northern America and Europe. The last decades have seen a rise of multiple government programs to protect wolf populations. Nevertheless, these programs have been controversial in rural areas, product of the predation of livestock by carnivores. As a response to such issues, governments have presented large scale economic plans to compensate the respected owners. The current issue lies in the lack of reliable techniques that can be used to detect the predator responsible for livestock predation. This has led to complications when obtaining subsidies, creating conflict between landowners and government officials. The objectives of this study therefore are to provide a new alternative approach to differentiating between tooth marks of different predators responsible for livestock predation. Here we present the use of geometric morphometrics and Machine Learning algorithms to discern between different carnivores through in depth analysis of the tooth marks they leave on bone. These results present high classification rates with up to 100% accuracy in some cases, successfully differentiating between wolves, dogs and fox tooth marks.

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Section: Discussionmentioning

confidence: 97%

The use of canid tooth marks on bone for the identification of livestock predation

Yravedra

Maté-González

Courtenay

et al. 2019

Sci Rep

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“…BSM identification remains a subjective endeavour, given that the categorization of variables change according to the analyst 9 , and that interpretations on BSM variability are usually based on geometric and metric approaches that are so extremely variable that the same actor-effector-trace can produce statistically significant differences within the same sample 10 . Cut marks may vary metrically depending on bone portion and tool if using simple bivariate (depth-width) methods 8 or not, if using multivariate geometric-morphometric methods 11 . Regardless, if metric variability does not impact on microscopic properties of BSM, these can be accurately identifiable if the analytical method is based on such properties 10 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks

Cifuentes-Alcobendas

Domı́nguez-Rodrigo

2019

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Accurate identification of bone surface modifications (BSM) is crucial for the taphonomic understanding of archaeological and paleontological sites. Critical interpretations of when humans started eating meat and animal fat or when they started using stone tools, or when they occupied new continents or interacted with predatory guilds impinge on accurate identifications of BSM. Until now, interpretations of Plio-Pleistocene BSM have been contentious because of the high uncertainty in discriminating among taphonomic agents. Recently, the use of machine learning algorithms has yielded high accuracy in the identification of BSM. A branch of machine learning methods based on imaging, computer vision (CV), has opened the door to a more objective and accurate method of BSM identification. The present work has selected two extremely similar types of BSM (cut marks made on fleshed an defleshed bones) to test the immense potential of artificial intelligence methods. This CV approach not only produced the highest accuracy in the classification of these types of BSM until present (95% on complete images of BSM and 88.89% of images of only internal mark features), but it also has enabled a method for determining which inconspicuous microscopic features determine successful BSM discrimination. The potential of this method in other areas of taphonomy and paleobiology is enormous.

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“…The progressive dehydration produces a significant loss in organic substances, affecting the properties of osteological materials (Fyhrie, 2010). Such components are likely to affect the morphology of any damage to the bone, depending on the hardness of the cortical when produced (Maté‐González et al ., 2019). This is supported by similar observations that have already noticed the loss of organic material to increase the fragility of osteological materials (Blasco et al ., 2008), and is also likely to be conditioned by other features dependent on biostratinomy, sedimentation and diagenetic processes that fossil remains undergo throughout time.…”

Section: Discussionmentioning

confidence: 99%

Scratches and grazes: a detailed microscopic analysis of trampling phenomena

Courtenay

Huguet

Yravedra

2020

Journal of Microscopy

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Summary Sedimentary abrasion and postdepositional damage to fossil remains are of great interest if considering the possible distortion they could produce in the archaeological and paleontological record. Since their discovery, natural agents such as trampling phenomena have been a topic of great taphonomic interest. Nevertheless, the majority of investigation into these traces has focused almost exclusively on their differentiation from other anthropic agents such as cut marks. In recent years, advances into bone surface modification analysis via geometric morphometrics have proven useful for in‐depth characterization of different taphonomic traces; including cut, tooth and percussion marks. Through this, a preliminary study of trampling marks using advanced 3D digital microscopy was able to detect differences between what have since been known as scratch and graze marks. The present study expands from this, developing a more detailed analysis of these traces. Here, we use advanced data science techniques to provide a means of understanding trampling mark variations, contributing to our knowledge of site formation processes. Our results show how scratch and graze marks are a product of progressional decay and changes in cortical hardness, providing a new means of understanding taphonomic processes. Lay Description The study of microscopic bone surface modifications in archaeology and palaeontology is of great importance, allowing for a detailed reconstruction of the formation of a site and providing a means of interpreting the fossil register. The damage that sedimentary abrasion can produce, however, is likely to distort and influence these studies, thus requiring a detailed understanding of the different traces that can be found on different materials. Here, we use advanced 3D digital microscopy and pattern recognition algorithms to analyse the different marks produced in different sedimentological contexts, also controlling for other variables such as the state of the bone when buried, the type of bone and the time exposed to these types of damages. Through this detailed microscopic analysis of these types of damages, we are able to conclude that morphological variations in trampling marks are product of the state of decay when the bones are buried.

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Application of geometric morphometrics to the analysis of cut mark morphology on different bones of differently sized animals. Does size really matter?

Cited by 25 publications

References 99 publications

The use of canid tooth marks on bone for the identification of livestock predation

The use of canid tooth marks on bone for the identification of livestock predation

Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks

Scratches and grazes: a detailed microscopic analysis of trampling phenomena

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