Impacts of spring environmental conditions on energetic demand in an Arctic-breeding seabird
Bibliographic record
Abstract
Life-history investment theory predicts that some individuals may be better at using physiological and behavioural mechanisms to meet the energetic demands imposed by environmental challenges to maximize breeding success. However, field-testing these seemingly straightforward predictions is complicated by the extreme challenge of quantifying environmentally induced flexibility in mechanisms before breeding investment. Nonetheless, these questions are especially vital to answer in places such as the Arctic, which is experiencing climate change rates 3-4 times the global average. Here we use a 16-year dataset from Canada’s largest colony of Arctic-breeding common eiders (Somateria mollissima), located at East Bay Island, Nunavut, to field-test these investment decision predictions. Specifically, from 2006-2023 (missing 2020-21 due to COVID-19), we captured almost 3000 pre-laying females following their arrival on the breeding grounds and collected physiological data on energetic demand (baseline corticosterone) and fattening rates (plasma triglycerides). We then followed individuals to assess two key reproductive decisions known to impact breeding success: whether birds invested in reproduction, and if so, when they initiated laying. Our goal is to use this dataset to examine whether individuals with certain physiological phenotypes can better overcome spring environment challenges (i.e., low ambient temperatures) to not only invest in breeding in a given year, but lay as early as possible to maximize lifetime breeding success in these highly seasonal environments. Filling in these challenging life history gaps is vital for predicting whether individuals and the populations they make up can persist and succeed in the face of rapidly increasing change in the north.
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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 | 0.001 | 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) | 0.001 | 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".