Evidence map›Paper›PMID 34003857›Full record

ArticlePloS one2021

Distinguishing African bovids using Zooarchaeology by Mass Spectrometry (ZooMS): New peptide markers and insights into Iron Age economies in Zambia.

Anneke Janzen, Kristine Korzow Richter, Ogeto Mwebi, Samantha Brown, Veronicah Onduso, Filia Gatwiri, Emmanuel Ndiema, Maggie Katongo, Steven T Goldstein, Katerina Douka and 1 more

Open access · goldAbstract readHistorical Article
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
70.8field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 55 citations in OpenAlex.

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  17. Threatened North African seagrass meadows have supported green turtle populations for millennia.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  18. Paleoproteomics.Chemical reviews · 2022
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 5 countries.

Anneke JanzenDepartment of Archaeology, Max-Planck Institute for the Science of Human History, Jena, Germany.ORCID 0000-0002-6371-7469
Kristine Korzow RichterDepartment of Archaeology, Max-Planck Institute for the Science of Human History, Jena, Germany.
Ogeto MwebiDepartment of Zoology, Osteology Section, National Museums of Kenya, Nairobi, Kenya.
Samantha BrownDepartment of Archaeology, Max-Planck Institute for the Science of Human History, Jena, Germany.ORCID 0000-0001-5001-525X
Veronicah OndusoDepartment of Zoology, Osteology Section, National Museums of Kenya, Nairobi, Kenya.
Filia GatwiriDepartment of Earth Sciences, Archaeology Section, National Museums of Kenya, Nairobi, Kenya.
Emmanuel NdiemaDepartment of Earth Sciences, Archaeology Section, National Museums of Kenya, Nairobi, Kenya.
Maggie KatongoDepartment of Archaeology, Livingstone Museum, Livingstone, Zambia.
Steven T GoldsteinDepartment of Archaeology, Max-Planck Institute for the Science of Human History, Jena, Germany.
Katerina DoukaDepartment of Archaeology, Max-Planck Institute for the Science of Human History, Jena, Germany.
Nicole BoivinDepartment of Archaeology, Max-Planck Institute for the Science of Human History, Jena, Germany.
Max Planck Institute for the Science of Human History · DENational Museums of Kenya · KEHarvard University · USSmithsonian Institution · US

Funding

European Research Council 715069
6 · The paper itself

Abstract

Assessing past foodways, subsistence strategies, and environments depends on the accurate identification of animals in the archaeological record. The high rates of fragmentation and often poor preservation of animal bones at many archaeological sites across sub-Saharan Africa have rendered archaeofaunal specimens unidentifiable beyond broad categories, such as "large mammal" or "medium bovid". Identification of archaeofaunal specimens through Zooarchaeology by Mass Spectrometry (ZooMS), or peptide mass fingerprinting of bone collagen, offers an avenue for identification of morphologically ambiguous or unidentifiable bone fragments from such assemblages. However, application of ZooMS analysis has been hindered by a lack of complete reference peptide markers for African taxa, particularly bovids. Here we present the complete set of confirmed ZooMS peptide markers for members of all African bovid tribes. We also identify two novel peptide markers that can be used to further distinguish between bovid groups. We demonstrate that nearly all African bovid subfamilies are distinguishable using ZooMS methods, and some differences exist between tribes or sub-tribes, as is the case for Bovina (cattle) vs. Bubalina (African buffalo) within the subfamily Bovinae. We use ZooMS analysis to identify specimens from extremely fragmented faunal assemblages from six Late Holocene archaeological sites in Zambia. ZooMS-based identifications reveal greater taxonomic richness than analyses based solely on morphology, and these new identifications illuminate Iron Age subsistence economies c. 2200-500 cal BP. While the Iron Age in Zambia is associated with the transition from hunting and foraging to the development of farming and herding, our results demonstrate the continued reliance on wild bovids among Iron Age communities in central and southwestern Zambia Iron Age and herding focused primarily on cattle. We also outline further potential applications of ZooMS in African archaeology.

Indexed as

AnimalsArchaeologyBiomarkersBone and BonesCattleCollagenFossilsHistory, AncientPeptide MappingSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationZambiaBiomarkersCollagen

Identifiers

PMID34003857
PMCPMC8130928
OpenAlexW3161003553

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.