Phenological cues to breeding and the differential response of Pacific auks to variation in marine productivity
Bibliographic record
Abstract
Many bird species use features of the physical environment to cue breeding activity. We show that for 2 species of Pacific auks (zooplanktivorous Cassin’s auklet Ptychoramphus aleuticus and generalist rhinoceros auklet Cerorhinca monocerata), spatio-temporal variation in marine production indicators surrounding a major breeding colony in the northeast Pacific Ocean can cue laying date. By utilizing a multi-year phenological time series and a spatio-temporal sliding-window analysis spanning November up until median lay dates in spring, chlorophyll a (chl a) concentrations in the surface ocean around the colony strongly predicted lay date. However, the response to this cue differed between species by over 2 mo, as each species was exposed to different, sequential water-masses. We show that for Cassin’s auklets, chl a levels at Triangle Island, British Columbia (Canada) in February, nearly 2 mo prior to actual laying, strongly correlated with lay date in April (average r = 0.83). At this time, the ocean environment around Triangle Island is dominated by water-masses from the south-southeast. However, for sympatric rhinoceros auklets, chl a along broad shelf-break areas in April prior to lay dates in May strongly correlated with lay date (average r = 0.76). Occurring after the spring transition, these water-masses flow primarily from the northwest. Consistent with other bird species, both auks appear to respond to information about food production, but at different spatio-temporal scales. We suggest that these different responses to environmental cues reflect species-specific differences in female migration behaviour, and an attempt by Cassin’s auklets to bet-hedge against phenological mismatches.
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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 | 0.000 | 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".