Prebreeding populations and the importance of life history for conserving the world’s imperiled seabirds
Bibliographic record
Abstract
Abstract Seabird conservation often focuses on nestlings and breeding adults. Yet imperiled seabird populations also contain prebreeders, including juveniles and subadults, that wait several years before breeding at colonies. We use previously-published data on reproductive and survival rates for 84 species to quantify the conservation relevance of prebreeding seabirds. We find, first, that prebreeders average about half of seabird populations (median 47.4%, range 11.2%–66.7%). Second, while seabird population growth is much more sensitive to adult survival than prebreeder survival, human-driven changes may shift the importance of prebreeders for future population stability. Third, lowering the breeding age is a powerful, but underexplored, route to increasing population growth. Managing prebreeders could thus play a key role in protecting seabirds. This task may require answering fundamental questions about the behavior of young birds. Broadly, we suggest that life history characteristics (e.g., breeding age) actively shape both obstacles to, and opportunities for, successful conservation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".