Tracking terrestrial wildlife with environmental <scp>DNA</scp> : Methods designed by and for Indigenous organizations
Bibliographic record
Abstract
Abstract Context . Environmental DNA (eDNA) could be a great addition to the toolbox of Indigenous organizations for wildlife monitoring. Knowledge gained through prolonged presence on the land, along with culture and tradition, combines with new technologies to support Indigenous stewardship of ancestral lands. However, the reliability of eDNA for the detection of terrestrial wildlife needs to be improved, and protocols that are suitable for Indigenous contexts are required. The objective of this community‐driven research was to develop and test eDNA‐based methods for the detection and monitoring of terrestrial species by Indigenous organizations. Methods . The study was carried out on Abitibiwinni Aki, the ancestral land of the Abitibiwinni First Nation (boreal Quebec, Canada). Protocols were co‐developed and detection probabilities were compared across a variety of substrates and sampling methods for three species of Cervidae held in captivity. Results . Snow sampling provided the highest detection probability, followed by dust and invertebrate sampling. Water sampling yielded less consistent results and is not recommended for the detection of terrestrial wildlife unless locally validated. Synthesis and applications . The protocols that emerged from this collaborative project are available to Indigenous and non‐Indigenous organizations around the world to support their land monitoring efforts. This research also contributes to the scientific community by providing a robust and unique comparison of eDNA sampling methods for the detection of terrestrial species.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".