Monitoring planktivorous seabird populations: validating surface counts of crevice-nesting auklets using mark–resight techniques
Bibliographic record
Abstract
Least Auklets ( Aethia pusilla (Pallas, 1811)) are the most abundant species of seabird in the Bering Sea and offer a relatively efficient means of monitoring secondary productivity in the marine environment. Counting auklets on surface plots is the primary method used to track changes in numbers of these crevice-nesters, but counts can be highly variable and may not be representative of the number of nesting individuals. We compared average maximum counts of Least Auklets on surface plots with density estimates based on mark–resight data at a colony on St. Lawrence Island, Alaska, during 2001–2004. Estimates of breeding auklet abundance from mark–resight averaged 8 times greater than those from maximum surface counts. Our results also indicate that average maximum surface counts are poor indicators of breeding auklet abundance and do not vary consistently with auklet nesting density across the breeding colony. Estimates of Least Auklet abundance from mark–resight were sufficiently precise to meet management goals for tracking changes in seabird populations. We recommend establishing multiple permanent banding plots for mark–resight studies on colonies selected for intensive long-term monitoring. Mark–resight is more likely to detect biologically significant changes in size of auklet breeding colonies than traditional surface count techniques.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".