Enhanced understanding of predator–prey relationships using molecular methods to identify predator species, individual and sex
Bibliographic record
Abstract
Predator species identification is an important step in understanding predator-prey interactions, but predator identifications using kill site observations are often unreliable. We used molecular tools to analyse predator saliva, scat and hair from caribou calf kills in Newfoundland, Canada to identify the predator species, individual and sex. We sampled DNA from 32 carcasses using cotton swabs to collect predator saliva. We used fragment length analysis and sequencing of mitochondrial DNA to distinguish between coyote, black bear, Canada lynx and red fox and used nuclear DNA microsatellite analysis to identify individuals. We compared predator species detected using molecular tools to those assigned via field observations at each kill. We identified a predator species at 94% of carcasses using molecular methods, while observational methods assigned a predator species to 62.5% of kills. Molecular methods attributed 66.7% of kills to coyote and 33.3% to black bear, while observations assigned 40%, 45%, 10% and 5% to coyote, bear, lynx and fox, respectively. Individual identification was successful at 70% of kills where a predator species was identified. Only one individual was identified at each kill, but some individuals were found at multiple kills. Predator sex was predominantly male. We demonstrate the first large-scale evaluation of predator species, individual and sex identification using molecular techniques to extract DNA from swabs of wild prey carcasses. Our results indicate that kill site swabs (i) can be highly successful in identifying the predator species and individual responsible; and (ii) serve to inform and complement traditional methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".