Presentation
Analysis of Sorting Errors of Animal Remains from Shell Middens: Lessons Learned from the Čḯxwicən Project
The 82nd Annual Meeting of the Society for American Archaeology
(2018)
Abstract
Zooarchaeologists routinely analyze assemblages that were initially sorted into major animal type (birds, mammals, fish, invertebrates) by students or lab technicians with little experience in zooarchaeology. Sorting errors are likely made, which affect taxonomic representation and understanding of human-animal relationships. Recent study of the immense faunal assemblage (over 500,000 NSP) from Čḯxwicən, a 2800-year-old Lower Elwha Klallam village located on the coast of Washington (USA), allows us to systematically analyze trends in sorting errors. For example, 22.6% of the bird bones included in our sample were initially mis-sorted into other classes, primarily mammal (13.1%), but also fish (7.5%) and shell (2%). Fish bones were less frequently mis-sorted, but certain taxa with unusual elements were affected (e.g. ratfish dental plates, dogfish spines, skate dermal denticles, cod otoliths). 34% of all mammal bone chips (from tool production) were mis-sorted. Failure to recognize and mediate these errors could lead to significant biases. Projects should recognize the potential for sorting error at the beginning and train lab technicians in the kinds of faunal remains they will be encountering, including distinctive elements. Collaborative researchers need to develop protocols for transferring specimens, and those working with "legacy collections" should not assume the collections were sorted correctly.
Keywords
- Shell middens,
- Zooarchaeology,
- Subsistence and food-ways
Disciplines
Publication Date
2018
Location
Washington, D.C.
Citation Information
Analysis of Sorting Errors of Animal Remains from Shell Middens: Lessons Learned from the Čḯxwicən Project. Kristine Bovy, Virginia L. Butler, Sarah K. Campbell, Michael A. Etnier. Presented at The 82nd Annual Meeting of the Society for American Archaeology, Washington, DC. 2018 ( tDAR id: 442827)