Ancient DNA analysis of archaeological fish remains: Methods and applications
Bibliographic record
Abstract
Despite their cultural importance, relatively few ancient DNA (aDNA) studies have focused on fish. Consequently, the methods available for the aDNA analysis of fish remains are underdeveloped relative to those available for other fauna, particularly mammals. This thesis addresses this methodological gap through a series of three projects focused on developing and applying new DNA-based methods for analysis of archaeological fish remains. The first project presents a DNA-based method for the sex identification of archaeological Pacific salmonid (Oncorhynchus spp.) remains. In this method, two PCR assays that each co-amplify fragments of the Y-linked sexually dimorphic on the Y chromosome (sdY) gene and an internal positive control (clock1a or D-loop) are used to assign sex identities to samples. This method’s reliability, sensitivity, and efficiency was evaluated by applying it to 72 modern Pacific salmonids from five species and 75 archaeological remains from six species. The results of these tests indicate this method is a reliable and efficient method for the sex identification of Pacific salmonid remains. Building on the first project, the second project modified the sex identification method developed for Pacific salmonids to make it applicable to archaeological Atlantic salmonid (Salmo spp.) and char (Salvelinus spp.) remains. This method was subsequently applied to 61 Atlantic salmon (Salmo salar) and lake trout (Salvelinus namaycush) remains from the 13th century CE Antrex site (AjGv-38) in southern Ontario, Canada. Using this method, we successfully assigned sex identities to 51 of these remains (83.61% success rate), highlighting the method’s sensitivity and efficacy. In the third project, a new two-tiered approach to the DNA-based species identification of archaeological fish remains was developed. In this approach, novel universal primers are first used to amplify a short fragment of the mitochondrial cytochrome oxidase I DNA barcode region, which is used to assign an initial taxonomic identification to samples. This initial identification is then used to guide the selection of taxon-specific primers targeting a secondary marker capable of refining the initial identification to the species-level. Application of this method whole or in part to 33 modern fish samples and 89 archaeological fish remains suggests it is an efficient species identification method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".