Scat detection dogs in wildlife research and management: application to grizzly and black bears in the Yellowhead Ecosystem, Alberta, Canada
Bibliographic record
Abstract
We report the development and application of a method using domestic dogs (Canis familiaris Linnaeus, 1758) to systematically locate wildlife scat over large remote areas. Detection dogs are chosen for their strong object orientation, high play drive, and willingness to strive for a reward. Dogs were trained to detect grizzly bear (Ursus arctos Linnaeus, 1758) and black bear (Ursus americanus Pallas, 1780) scats over a 5200-km 2 area of the Yellowhead Ecosystem, Alberta, Canada. DNA from scat provided the species and (for grizzly bears only) sex and individual identities of the animal at each location. Concentrations of fecal cortisol and progesterone metabolites from these same grizzly bear scats provided indices of physiological stress and reproductive activity (in females), respectively. Black and grizzly bears were most concentrated in the northern portion of the multiuse study area, where food is most abundant yet poaching-related mortality appears to be heaviest. Physiologic stress was also lowest and female reproductive activity correspondingly highest for grizzly bears in the north. The scat-based distributions corresponded to concurrently collected hair-snag data in 1999 and global positioning system radiotelemetry data (of grizzly bears) in 1999 and 2001. Results suggest that the scat dog detection methodology provides a promising tool for addressing a variety of management and research questions in the wildlife sciences.
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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) | 0.000 | 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".