Environmental DNA surveys help to identify winter hibernacula of a temperate freshwater turtle
Bibliographic record
Abstract
Abstract Background and aims Overwintering is a critical part of the annual cycle of animals living at high latitudes, and selection of overwintering sites (hibernacula) is important to population persistence. Identifying the overwintering sites of aquatic species is challenging in areas where water bodies are frozen for significant parts of the year. We tested whether environmental DNA (eDNA) approaches could help to locate them. Materials and methods We conducted environmental DNA surveys of underwater overwintering sites of the northern map turtle (Graptemys geographica), a species of conservation concern in Canada. We collected water samples under the ice in winter across a mid‐sized temperate lake and used quantitative PCR with a species‐specific probe to quantify concentrations of map turtle eDNA. Results and discussion We found localized eDNA signals consistent with known overwintering sites and one previously suspected site. The latter was further confirmed using underwater remote operated vehicle (ROV) visual surveys. Conclusions Our study confirms that eDNA can offer insights on a critical part of the annual cycle of aquatic species, for which we know very little.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".