Evaluating livetrapping and camera-based indices of small-mammal density
Bibliographic record
Abstract
Density estimates are integral to wildlife management, but they can be costly to obtain. Indices of density may provide efficient alternatives, but calibration is needed to ensure the indices accurately reflect density. We evaluated several indices of small-mammal density using livetrapping and motion-activated cameras in the Cascade Mountains of Washington (USA). We used linear regression to compare spatially explicit capture–recapture density estimates of mice (genus Peromyscus Gloger, 1841), voles (genera Microtus Schrank, 1798 and Myodes Pallas, 1811), and chipmunks (genus Neotamias A.H. Howell, 1929) with four indices. Two indices were based on livetrapping (minimum number alive (MNA) and number of captures per 100 trap-nights) and two indices were based on photos from motion-activated cameras (proportion of cameras detecting a species and number of independent detections). We evaluated how the accuracy of trap-based indices increased with trapping effort using subsets of the full dataset (n = 7 capture occasions per site). Most indices provided reliable indicators of small-mammal density, and livetrapping indices (R2 = 0.64–0.98) outperformed camera-based indices (R2 = 0.24–0.86). All indices performed better for more abundant species. The effort required to estimate each index varied and indices that required more effort performed better. These findings should help managers, conservation practitioners, and researchers select small-mammal monitoring methods that best fit their needs.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".