In the trap: detectability of fixed hair trap DNA methods in grizzly bear population monitoring
Bibliographic record
Abstract
A significant challenge to monitoring wildlife that are secretive, wide ranging, and at low densities is the need to achieve adequate detection rates. Knowledge of spatial patterns in occupancy and the spatial and/or temporal patterns in detectability allows for stratification of traps and improved detection rates. This study investigated how local variation in habitats affected the detectability of grizzly bears in west—central Alberta when monitored with a fixed DNA hair snag design. Bear hair samples were collected in 2011 at 60 sites across 1500 km 2 over six sessions, each 14 days in length, between June and August. Microsatellite analysis of hair samples revealed grizzly bear detections at 25 of 60 sites and 21 individual bears. We investigated occupancy and detectability of grizzly bears at the patch and landscape scales using detection histories and program PRESENCE. At the patch scale, grizzly bear detection was highest when sites were placed near streams with clover and in intermediate levels of forest crown closure. At the landscape scale, probability of detection increased near streams and oil and gas wellsites, especially when food resources and wellsite density in the surrounding area was low. Our results highlight the importance of considering local food resources and habitat conditions during placement of fixed DNA hair snag sites. Detectability was not found to vary over time, suggesting that sampling in west—central Alberta can occur at any time between June and August.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".