DIRECTLY COMPARING AN EFFECTIVE EDNA PROTOCOL TO TRADITIONAL SURVEY METHODS FOR DETECTING OCCUPANCY OF BLANDING’S TURTLES (Emydoidea blandingii)
Bibliographic record
Abstract
Blanding’s Turtles (Emydoidea blandingii; BLTU) are an at-risk semi-aquatic turtle occurring in the Great Lakes-St. Lawrence region. Population monitoring and management of this species can only occur once populations have been located. BLTU occupancy is typically determined using visual encounter (VES) or trapping (TS) surveys. However, these traditional methods can be labour intensive, costly and invasive. Environmental DNA (eDNA) is a survey technique that has been used to detect the occupancy of endangered species, including the BLTU. We set out to design an effective eDNA collection protocol to improve BLTU detection. Collection method and filter pore size had no significant effect on the proportion of positive detections. Sampling season and preservation method had a significant effect on the proportion of positive detections; sample filters collected during the active season (April-July) that were submerged in 96% ethanol and refrigerated produced the lowest proportion of false negatives. We determined that 4, 13, and 24 samples were required to achieve a 95% eDNA detection probability in the active, inactive and winter seasons at radiotelemetry confirmed locations using McArdle’s equation. We directly compared our improved eDNA protocol to visual encounter and trapping surveys at two separate sites within the species range. We found no significant difference in the proportion of positive occupancy results obtained by eDNA and VES in Ontario, or eDNA and TS in Illinois. eDNA can produce new insights on BLTU occupancy. Notably, eDNA detected BLTU at two conservation areas without any detection by TS in 808 and 632 historical trap-nights. At a 10-ha site we estimate that eDNA is the least time consuming, and that VES is the least expensive. TS were most expensive and time consuming. eDNA surveys have additional advantages in that they can be completed by technicians with minimal training, no scientific permits and at remote sites.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".