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A tool for determining duration of mortality events in archaeological assemblages using extant ungulate microwear.

Rivals F, Prignano L, Semprebon GM, Lozano S - Sci Rep (2015)

Bottom Line: The seasonality of human occupations in archaeological sites is highly significant for the study of hominin behavioural ecology, in particular the hunting strategies for their main prey-ungulates.We propose a new tool to quantify such seasonality from tooth microwear patterns in a dataset of ten large samples of extant ungulates resulting from well-known mass mortality events.The tool is tested on a selection of eleven fossil samples from five Palaeolithic localities in Western Europe which show a consistent classification in the three categories.

View Article: PubMed Central - PubMed

Affiliation: Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.

ABSTRACT
The seasonality of human occupations in archaeological sites is highly significant for the study of hominin behavioural ecology, in particular the hunting strategies for their main prey-ungulates. We propose a new tool to quantify such seasonality from tooth microwear patterns in a dataset of ten large samples of extant ungulates resulting from well-known mass mortality events. The tool is based on the combination of two measures of variability of scratch density, namely standard deviation and coefficient of variation. The integration of these two measurements of variability permits the classification of each case into one of the following three categories: (1) short events, (2) long-continued event and (3) two separated short events. The tool is tested on a selection of eleven fossil samples from five Palaeolithic localities in Western Europe which show a consistent classification in the three categories. The tool proposed here opens new doors to investigate seasonal patterns of ungulate accumulations in archaeological sites using non-destructive sampling.

No MeSH data available.


Related in: MedlinePlus

Boundary lines of the three regions with the error probability (heat map).Points correspond to the test dataset: triangles stand for seasonal or shorter events; the black square is an event of deaths distributed over a year; the white square is an unknown case. All the triangles are located in region A, while the squares are in region B and no point falls in region C. Labels refer to the samples ID (Table 1).
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f6: Boundary lines of the three regions with the error probability (heat map).Points correspond to the test dataset: triangles stand for seasonal or shorter events; the black square is an event of deaths distributed over a year; the white square is an unknown case. All the triangles are located in region A, while the squares are in region B and no point falls in region C. Labels refer to the samples ID (Table 1).

Mentions: Taking this observation as a starting point, we have developed a classification methodology that estimates the boundaries between the three regions based on the naïve Bayes classifier37. Details on the methodology are provided in the Methods section. The resulting division of the SD-CV plane into three regions is shown in Fig. 6. This plane division allows for the classification of any new case as a function of its position in the SD-CV plane.


A tool for determining duration of mortality events in archaeological assemblages using extant ungulate microwear.

Rivals F, Prignano L, Semprebon GM, Lozano S - Sci Rep (2015)

Boundary lines of the three regions with the error probability (heat map).Points correspond to the test dataset: triangles stand for seasonal or shorter events; the black square is an event of deaths distributed over a year; the white square is an unknown case. All the triangles are located in region A, while the squares are in region B and no point falls in region C. Labels refer to the samples ID (Table 1).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663483&req=5

f6: Boundary lines of the three regions with the error probability (heat map).Points correspond to the test dataset: triangles stand for seasonal or shorter events; the black square is an event of deaths distributed over a year; the white square is an unknown case. All the triangles are located in region A, while the squares are in region B and no point falls in region C. Labels refer to the samples ID (Table 1).
Mentions: Taking this observation as a starting point, we have developed a classification methodology that estimates the boundaries between the three regions based on the naïve Bayes classifier37. Details on the methodology are provided in the Methods section. The resulting division of the SD-CV plane into three regions is shown in Fig. 6. This plane division allows for the classification of any new case as a function of its position in the SD-CV plane.

Bottom Line: The seasonality of human occupations in archaeological sites is highly significant for the study of hominin behavioural ecology, in particular the hunting strategies for their main prey-ungulates.We propose a new tool to quantify such seasonality from tooth microwear patterns in a dataset of ten large samples of extant ungulates resulting from well-known mass mortality events.The tool is tested on a selection of eleven fossil samples from five Palaeolithic localities in Western Europe which show a consistent classification in the three categories.

View Article: PubMed Central - PubMed

Affiliation: Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.

ABSTRACT
The seasonality of human occupations in archaeological sites is highly significant for the study of hominin behavioural ecology, in particular the hunting strategies for their main prey-ungulates. We propose a new tool to quantify such seasonality from tooth microwear patterns in a dataset of ten large samples of extant ungulates resulting from well-known mass mortality events. The tool is based on the combination of two measures of variability of scratch density, namely standard deviation and coefficient of variation. The integration of these two measurements of variability permits the classification of each case into one of the following three categories: (1) short events, (2) long-continued event and (3) two separated short events. The tool is tested on a selection of eleven fossil samples from five Palaeolithic localities in Western Europe which show a consistent classification in the three categories. The tool proposed here opens new doors to investigate seasonal patterns of ungulate accumulations in archaeological sites using non-destructive sampling.

No MeSH data available.


Related in: MedlinePlus