Ecological niche partitioning in two Pacific puffins
Bibliographic record
Abstract
Ecological theory predicts that closely related species can coexist if they segregate in space, time, or diet to reduce competitive overlap when resources are limited. These differences in ecological niche are presumably driven by concomitant differences in morphology. The link between form and functional segregation may only occur at 1 period of the year, and while examining behavioural differences among closely related species across the whole annual cycle can answer this question, it is rarely tested. Here, we investigated niche partitioning between sister species within the puffin clade: tufted puffins Fratercula cirrhata with high wing loading and rhinoceros auklets Cerorhinca monocerata with low wing loading, breeding in sympatry on Middleton Island, Alaska. We collected data for horizontal movement, dive depth, and trophic ecology. Tufted puffins dived deeper, foraged closer to the colony during breeding, and migrated shorter distances relative to rhinoceros auklets. Examination of allometric patterns further revealed that puffins exceeded the predicted dive duration limit with exceptionally long dives. However, isotopic niches of the 2 species were very similar. Rhinoceros auklet breeding phenology was about 3 wk earlier than tufted puffin phenology, and breeding success during the study period was always higher for auklets than puffins. We conclude that the 2 species of sympatric puffin partitioned both in space and time throughout the annual cycle, illustrating how such partitioning can facilitate the coexistence of seabirds sharing apparently similar space.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".