Molting strategies of Arctic seals drive annual patterns in metabolism
Bibliographic record
Abstract
Abstract Arctic seals, including spotted (Phoca largha), ringed (Pusa hispida) and bearded (Erignathus barbatus) seals, are directly affected by sea ice loss. These species use sea ice as a haul-out substrate for various critical functions, including their annual molt. Continued environmental warming will inevitably alter the routine behavior and overall energy budgets of Arctic seals, but it is difficult to quantify these impacts as their metabolic requirements are not well known—due in part to the difficulty of studying wild individuals. Thus, data pertaining to species-specific energy demands are urgently needed to better understand the physiological consequences of rapid environmental change. We used open-flow respirometry over a four-year period to track fine-scale, longitudinal changes in the resting metabolic rate (RMR) of four spotted seals, three ringed seals and one bearded seal trained to participate in research. Simultaneously, we collected complementary physiological and environmental data. Species-specific metabolic demands followed expected patterns based on body size, with the largest species, the bearded seal, exhibiting the highest absolute RMR (0.48 ± 0.04 L O2 min−1) and the lowest mass-specific RMR (4.10 ± 0.47 ml O2 min−1 kg−1), followed by spotted (absolute: 0.33 ± 0.07 L O2 min−1; mass-specific: 6.13 ± 0.73 ml O2 min−1 kg−1) and ringed (absolute: 0.20 ± 0.04 L O2 min−1; mass-specific: 7.01 ± 1.38 ml O2 min−1 kg−1) seals. Further, we observed clear and consistent annual patterns in RMR that related to the distinct molting strategies of each species. For species that molted over relatively short intervals—spotted (33 ± 4 days) and ringed (28 ± 6 days) seals—metabolic demands increased markedly in association with molt. In contrast, the bearded seal exhibited a prolonged molting strategy (119 ± 2 days), which appeared to limit the overall cost of molting as indicated by a relatively stable annual RMR. These findings highlight energetic trade-offs associated with different molting strategies and provide quantitative data that can be used to assess species-specific vulnerabilities to changing conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".