A novel environmental DNA (eDNA) sampling method for aye-ayes from their feeding traces
Bibliographic record
Abstract
Abstract Non-invasive sampling is an important development in population genetic monitoring of wild animals. Particularly, the collection of environmental DNA (eDNA) which can be collected without needing to encounter the target animal, facilitates the genetic analysis of cryptic and threatened species. One method that has been applied to these types of sample is target capture and enrichment which overcomes the issue of high proportions of exogenous (non-host) DNA from these lower quality samples. We tested whether target capture of mitochondrial DNA from sampled feeding traces of wild aye-ayes would yield mitochondrial DNA sequences for population genetic monitoring. We sampled gnawed wood from feeding traces where aye-ayes excavate wood-boring insect larvae from trees. We designed RNA probes complementary to the aye-aye’s mitochondrial genome and used these to isolate aye-aye DNA from other non-target DNA in these samples. We successfully retrieved six near-complete mitochondrial genomes from two sites within the aye-aye’s geographic range that had not been sampled previously. This method can likely be applied to alternative foraged remains to sample species other than aye-ayes. Our method demonstrates the application to next-generation molecular techniques to species of conservation concern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".