Albatrosses respond adaptively to climate variability by changing variance in a foraging trait
Bibliographic record
Abstract
The ability of individuals and populations to adapt to a changing climate is a key determinant of population dynamics. While changes in mean behaviour are well studied, changes in trait variance have been largely ignored, despite being assumed to be crucial for adapting to a changing environment. As the ability to acquire resources is essential to both reproduction and survival, changes in behaviours that maximize resource acquisition should be under selection. Here, using foraging trip duration data collected over 7 years on black-browed albatrosses (Thalassarche melanophris) on the Kerguelen Islands in the southern Indian Ocean, we examined the importance of changes in the mean and variance in foraging behaviour, and the associated effects on fitness, in response to the El Niño Southern Oscillation (ENSO). Using double hierarchical models, we found no evidence that individuals change their mean foraging trip duration in response to a changing environment, but found strong evidence of changes in variance. Younger birds showed greater variability in foraging trip duration in poor conditions as did birds with higher fitness. However, during brooding, birds showed greater variability in foraging behaviour under good conditions, suggesting that optimal conditions allow the alteration between chick provisioning and self-maintenance trips. We found weak correlations between sea surface temperature and the ENSO, but stronger links with sea-level pressure. We suggest that variability in behavioural traits affecting resource acquisition is under selection and offers a mechanism by which individuals can adapt to a changing climate. Studies which look only at effects on mean behaviour may underestimate the effects of climate change and fail to consider variance in traits as a key evolutionary force.
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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".