Optimizing extraction and targeted capture of ancient environmental DNA for reconstructing past environments using the PalaeoChip Arctic-1.0 bait-set
Bibliographic record
Abstract
Abstract Sedimentary ancient DNA (sedaDNA) has been established as a viable biomolecular proxy for tracking taxon presence through time in a local environment, even in the total absence of surviving tissues. SedaDNA is thought to survive through mineral binding, facilitating long-term biomolecular preservation, but also challenging DNA isolation. Two common limitations in sedaDNA extraction are the carryover of other substances that inhibit enzymatic reactions, and the loss of authentic sedaDNA when attempting to reduce inhibitor co-elution. Here, we present a sedaDNA extraction procedure paired with targeted enrichment intended to maximize DNA recovery. Our procedure exhibits a 7.7–19.3x increase in on-target plant and animal sedaDNA compared to a commercial soil extraction kit, and a 1.2–59.9x increase compared to a metabarcoding approach. To illustrate the effectiveness of our cold spin extraction and PalaeoChip capture enrichment approach, we present results for the diachronic presence of plants and animals from Yukon permafrost samples dating to the Pleistocene-Holocene transition, and discuss new potential evidence for the late survival (~9700 years ago) of mammoth ( Mammuthus sp. ) and horse ( Equus sp. ) in the Klondike region of Yukon, Canada. This enrichment approach translates to a more taxonomically diverse dataset and improved on-target sequencing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".