Do hunters target auxiliary markers? An example using black brant
Bibliographic record
Abstract
ABSTRACT Auxiliary markers play an essential role in understanding migration, movement, demography, and behavior of migratory birds. Use of such markers relies on the assumption that the markers do not affect the traits of interest. Neck collars, among the most conspicuous of markers, substantially affect risk of harvest, and survival even in the absence of harvest. Effects of less‐conspicuous markers, such as colored plastic tarsal bands, are not well understood. We used 30 years (1986–2015) of banding, recovery, and recapture data from the Yukon‐Kuskokwim Delta in Alaska, USA, to assess differences in direct band recovery rates (DRRs) between black plastic and brightly colored plastic bands applied to black brant (Branta bernicla nigricans). We also assessed the effect of the color of plastic tarsal bands on annual survival, risks of natural mortality harvest, and fidelity to the breeding colony of adult female black brant. When assessing only DRRs we found that brightly colored bands were recovered at higher rates than black plastic bands in the early 2000s, but DRRs for black bands increased more rapidly through time, resulting in similar DRRs for the 2 band colors at the end of the study. Using a Burnham model structure, our results demonstrated that individuals fitted with colored bands had slightly lower hazards of dying from natural causes or hunting than individuals carrying less‐conspicuous black tarsal bands. Differences on annual probability scales were small and credible intervals broadly overlapped between band types, indicating minimal differences between individuals with different band types; however, we could not resolve all confounding in our study design and we suggest that specific studies directed at assessing marker effects are warranted. We encourage education of hunters about their roles as citizen scientists and the potentially detrimental effect of targeting birds with auxiliary markers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".