Considerations for a threatened seabird: The impact of shoreline avian predators on at‐sea marbled murrelets
Bibliographic record
Abstract
Abstract We tested the influence of on‐shore avian predators on the at‐sea distribution and abundance of marbled murrelets ( Brachyramphus marmoratus ) during their breeding season in Haida Gwaii, British Columbia, Canada. We conducted a field experiment using deterrent predator decoy kites that mimicked flying bald eagles ( Haliaeetus leucocephalus ). In the summers of 2018 and 2019, we conducted at‐sea surveys of murrelet distributions along inshore and offshore transects with and without kites flying, and tallied real eagles along the shoreline and fish schools encountered. Kites negatively influenced overall murrelet counts (estimate = −1.19, 95% CI = −1.88 to −0.51), but we did not detect inshore to offshore movements within the study site. Our results indicate murrelet movement out of the study area in response to the kites. Overall murrelet counts were also lower when real eagle counts were higher (estimate = −0.22, 95% CI = −0.35 to −0.09). When kites were flying, a higher proportion of the murrelets remaining along inshore transects were found between rather than adjacent to kite locations (estimate = −1.61, 95% CI = −2.67 to −0.54), indicating avoidance of kites. Since avian predator populations have steadily increased in the past 30 years throughout the murrelet's breeding range, these avoidance effects could create a negative bias in long‐term shoreline count trends. Our findings highlight the importance of considering non‐lethal predator effects on murrelets when conducting at‐sea censuses and constructing conservation plans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".