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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

Hypothetical scenarios illustrating how two short events concentrated in separated seasons (“EARLY FALL + SPRING” and “SEPT + FEB” in red and blue, respectively) can only be reliably differentiated from a homogeneous distribution of deaths over the whole year.(“YEAR”, in grey) when considering both SD and CV variability measures. FIT corresponds to the Gaussian fitting implicitly applied when calculating the SD and CV of red and blue bipartite distributions. Panel (A) CV does not distinguish two concrete events occurring in spring and early fall, from a homogeneous distribution of deaths over the year. On the contrary, the SD value is different for the two scenarios. Panel (B) Opposite to example in Panel (A). In this case, only the CV value makes it possible to distinguish two short events concentrated in February and September from deaths evenly distributed through the year.
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f4: Hypothetical scenarios illustrating how two short events concentrated in separated seasons (“EARLY FALL + SPRING” and “SEPT + FEB” in red and blue, respectively) can only be reliably differentiated from a homogeneous distribution of deaths over the whole year.(“YEAR”, in grey) when considering both SD and CV variability measures. FIT corresponds to the Gaussian fitting implicitly applied when calculating the SD and CV of red and blue bipartite distributions. Panel (A) CV does not distinguish two concrete events occurring in spring and early fall, from a homogeneous distribution of deaths over the year. On the contrary, the SD value is different for the two scenarios. Panel (B) Opposite to example in Panel (A). In this case, only the CV value makes it possible to distinguish two short events concentrated in February and September from deaths evenly distributed through the year.

Mentions: This complementarity between SD and CV suggests that better classification accuracy could be achieved through a 2D mapping combining both variability measures. Moreover, such a bi-dimensional approach could differentiate a third sort of scenario (i.e. besides single seasonal and longer death events), namely two short events occurring in non-consecutive, different seasons (i.e. spring-autumn and winter-summer, independently of the actual year). In many cases, datasets corresponding to this sort of scenario present the same SD or CV as an equivalent longer death event, but not both variability measures simultaneously. Two illustrative examples are provided in Fig. 4.


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)

Hypothetical scenarios illustrating how two short events concentrated in separated seasons (“EARLY FALL + SPRING” and “SEPT + FEB” in red and blue, respectively) can only be reliably differentiated from a homogeneous distribution of deaths over the whole year.(“YEAR”, in grey) when considering both SD and CV variability measures. FIT corresponds to the Gaussian fitting implicitly applied when calculating the SD and CV of red and blue bipartite distributions. Panel (A) CV does not distinguish two concrete events occurring in spring and early fall, from a homogeneous distribution of deaths over the year. On the contrary, the SD value is different for the two scenarios. Panel (B) Opposite to example in Panel (A). In this case, only the CV value makes it possible to distinguish two short events concentrated in February and September from deaths evenly distributed through the year.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Hypothetical scenarios illustrating how two short events concentrated in separated seasons (“EARLY FALL + SPRING” and “SEPT + FEB” in red and blue, respectively) can only be reliably differentiated from a homogeneous distribution of deaths over the whole year.(“YEAR”, in grey) when considering both SD and CV variability measures. FIT corresponds to the Gaussian fitting implicitly applied when calculating the SD and CV of red and blue bipartite distributions. Panel (A) CV does not distinguish two concrete events occurring in spring and early fall, from a homogeneous distribution of deaths over the year. On the contrary, the SD value is different for the two scenarios. Panel (B) Opposite to example in Panel (A). In this case, only the CV value makes it possible to distinguish two short events concentrated in February and September from deaths evenly distributed through the year.
Mentions: This complementarity between SD and CV suggests that better classification accuracy could be achieved through a 2D mapping combining both variability measures. Moreover, such a bi-dimensional approach could differentiate a third sort of scenario (i.e. besides single seasonal and longer death events), namely two short events occurring in non-consecutive, different seasons (i.e. spring-autumn and winter-summer, independently of the actual year). In many cases, datasets corresponding to this sort of scenario present the same SD or CV as an equivalent longer death event, but not both variability measures simultaneously. Two illustrative examples are provided in Fig. 4.

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