SAMPLING DESIGN AND BIAS IN DNA-BASED CAPTURE–MARK–RECAPTURE POPULATION AND DENSITY ESTIMATES OF GRIZZLY BEARS
Bibliographic record
Abstract
Over a 3-year period, we assessed 2 sampling designs for estimating grizzly bear (Ursus arctos) population size using DNA capture–mark–recapture methods on a population of bears that included radiomarked individuals. We compared a large-scale design (with 8 × 8-km grid cells and sites moved for 4 sessions) and a small-scale design (5 × 5-km grid cells with sites not moved for 5 sessions) for closure violation, capture-probability variation, and estimate precision. We used joint telemetry/capture–mark–recapture (JTMR) analysis and traditional closure tests to analyze the capture–mark–recapture data with each design. A simulation study compared the performance of each design for robustness to heterogeneity bias caused by reduced capture probabilities of cubs. Our results suggested that the 5 × 5-km grid cell design was more precise and more robust to potential sample biases, but the risk of closure violation due to smaller overall grid size was greater. No design exhibited complete closure as estimated by JTMR. The results of simulation studies suggested that CAPTURE heterogeneity models are relatively robust to probable forms of capture-probability variation when capture probabilities are >0.2. Only the 5 × 5-km designs exhibited this capture-probability level, suggesting that this design is preferred to ensure estimator robustness when population size is <100. The power of the CAPTURE model selection routine to detect capture probability variation was low regardless of sampling design used. Our study illustrated the trade-off between intensive sampling to ensure robustness and adequate precision of estimators while being extensive enough to avoid closure violation bias.
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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 | 0.062 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".