Shorter Migration Distance and Breeding Latitude Correlate With Earlier Egg‐Laying Across the Northeastern Pacific Ocean Range of the Rhinoceros Auklet (<i>Cerorhinca monocerata</i>)
Bibliographic record
Abstract
ABSTRACT Models of migratory behavior predict trade‐offs between fitness costs and benefits with respect to migration distance. Shorter migration distances may confer a fitness benefit by facilitating earlier breeding, however this is rarely investigated. We tested this hypothesis using a large‐scale geolocation (GLS) dataset from 109 rhinoceros auklets (Cerorhinca monocerata), a differentially migrating seabird, that was tagged at 12 breeding colonies along the Pacific Coast of North America, spanning southern California to the eastern Aleutian Islands, Alaska. Using GLS‐based position estimates, we determined the geographic centroid of the pelagic areas occupied by birds in winter (1 January–28 February) and then calculated the distance between their wintering centroids and colony of origin. We then used GLS light‐intensity and salt‐water immersion (wet/dry) data to determine each individual's date of egg‐laying the following spring. Rhinoceros auklets were very widely distributed across the northeastern Pacific Ocean in winter. Among all individuals, the distance between winter centroids and breeding colonies ranged from < 100 to > 2500 km, being greater among individuals originating from colonies at higher latitudes. As predicted, migration distance and colony latitude were positively related to lay date: after accounting for colony‐level differences in phenology, individuals that migrated shorter distances tended to lay their eggs earlier, a pattern that emerged across all populations. Our study links the migration distance of rhinoceros auklets to a fitness‐related outcome, underscoring the selective pressure that migration exerts on subsequent breeding activity.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".