A keystone avian predator faces elevated energy expenditure in a warming Arctic
Bibliographic record
Abstract
Climate change is transforming bioenergetic landscapes, challenging behavioral and physiological coping mechanisms. A critical question involves whether animals can adjust behavioral patterns and energy expenditure to stabilize fitness given reconfiguration of resource bases, or whether limits to plasticity ultimately compromise energy balance. In the Arctic, rapidly warming temperatures are transforming food webs, making Arctic organisms strong models for understanding biological implications of climate change-related environmental variability. We examined plasticity in the daily energy expenditure (DEE) of an Arctic seabird, the little auk (Alle alle) in response to variability in climate change-sensitive drivers of resource availability, sea surface temperature (SST) and sea ice coverage (SIC), and tested the hypothesis that energetic ceilings and exposure to mercury, an important neurotoxin and endocrine disrupter in marine ecosystems, may limit scope for plasticity. To estimate DEE, we used accelerometer data obtained across years from two colonies exposed to distinct environmental conditions (Ukaleqarteq [UK], East Greenland; Hornsund [HS], Svalbard). We proceeded to model future changes in SST to predict energetic impacts. At UK, high flight costs linked to low SIC and high SST drove DEE from below to above 4 × basal metabolic rate (BMR), a proposed energetic threshold for breeding birds. However, DEE remained below 7 × BMR, an alternative threshold, and did not plateau. Birds at HS experienced higher, relatively invariable SST, and operated above 4 × BMR. Mercury exposure was unrelated to DEE, and fitness remained stable. Thus, plasticity in DEE currently buffers fitness, providing resiliency against climate change. Nevertheless, modeling suggests that continued warming of SST may promote accelerating increases in DEE, which may become unsustainable.
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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 | 0.000 |
| 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".