Latitudinal temperature-dependent variation in timing of prey availability can impact Pacific seabird populations in Canada
Bibliographic record
Abstract
We modelled how nestling growth rates of Cassin’s Auklet (Ptychoramphus aleuticus (Pallas, 1811)) varied with timing of peak copepod prey availability at two breeding colonies in British Columbia: on Triangle Island, in the California Current Ecosystem, and Frederick Island, in the Gulf of Alaska Ecosystem. We used time series of nestling growth rates and estimated the seasonal timing of peak biomass of the copepod Neocalanus cristatus (Krøyer, 1848) using a temperature-dependent phenology equation. We developed a single model to examine intercolony differences in the effect of the timing of regional peak prey biomass on seabird nestling growth rates. This model indicated nestling growth rates on Triangle Island varied widely and were positively associated with timing of peak zooplankton biomass, such that higher growth rates were observed when the peak biomass occurred later in the breeding season. In contrast, nestling growth rates were consistently high at Frederick Island, where peak copepod biomass always occurred relatively late. If ocean climate warming results in a poleward shift of Neocalanus abundance and induces earlier and more narrow timing of availability, then episodes of poor nestling growth will increase in frequency on Triangle Island and could eventually affect auklets on more northerly colonies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".