How old can we go? Evaluating the age limit for effective DNA recovery from historical insect specimens
Bibliographic record
Abstract
Abstract Historical museum specimens are valuable for exploring population genetics and evolutionary questions because they can provide snapshots of morphological and genetic characteristics from populations over space and time. Unfortunately, DNA found in older museum specimens is frequently degraded, so obtaining genotypes from many individual samples necessary for rigorous molecular population genetic studies is challenging. Previous studies have varied greatly in their success at obtaining genotypes from older preserved insect material. Many well‐intentioned collection curators have used research results showing poor preservation of DNA preserved in museum specimens to inform curatorial best practices, in some cases choosing not to allow DNA extraction by destructive sampling because, in their estimation, the likelihood of success would be low. Recent methodological advances in DNA extraction, amplification, and genotyping have allowed some researchers to include mid‐19th century samples in molecular genetic analyses. Here we present a robust, high‐throughput, and low‐cost DNA extraction and genotyping protocol for historical insect specimens employing restriction digests of PCR products followed by high sensitivity electrophoresis. Using this technique, we obtained mitochondrial haplotypes for 100% of 48 New World Junonia butterfly specimens (Nymphalidae) ranging in age from pre‐1813 to 1909 and show that the haplotype frequencies obtained are statistically indistinguishable from 20th‐century and contemporary reference populations of Junonia (1632 specimens) matched by geographic region. As most extant insect specimens were collected after 1813, based on our findings we would expect that many or even most pinned specimens preserved in museum collections contain usable DNA for mitochondrial haplotyping.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".