Efficiency and fitness consequences of two trapping methods for recapturing ground‐nesting songbirds
Bibliographic record
Abstract
Capturing nesting songbirds is a core component of many field studies. However, avoidance of traps and mist-nets by birds can reduce capture efficiency and bias study results, particularly when individuals need to be recaptured multiple times. We describe a novel capture method—the noose-line—for an alpine population of Horned Larks (Eremophila alpestris) studied during three breeding seasons (2015–2017) in northern British Columbia, Canada. Our objective was to develop a safe, efficient method to recapture individuals that exhibited trap avoidance. We compared the capture efficiency (trap success relative to capture effort) and fitness consequences (nest survival and nest attentiveness) of the noose-line (non-selective method) to those of a more traditional bownet trap (selective method) for both naïve (not previously captured) and previously captured Horned Larks. Mean trapping success for the noose-line was high for both naïve (89.7%) and previously captured (62.9%) birds, whereas mean trapping success for the more visible bownet was strongly influenced by bird experience (naïve = 41.4%, previously captured = 12.1%). However, mean capture effort (time required for successful capture) was greater for noose-lines than the bownet (45.3 min vs. 17.5 min) and noose-lines were more likely to capture non-targeted individuals. The trap type used to capture birds did not influence nest survival. Overall, our results suggest that noose-lines can be an effective option for capturing ground-nesting songbirds, particularly for studies where birds must be recaptured, e.g., to retrieve tracking devices or repeatedly measure body condition.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".