Effects of early life history traits and warming on Arctic cod prewinter length and recruitment
Bibliographic record
Abstract
The Arctic cod (Boreogadus saida) is a key species in Arctic marine ecosystems, adapted to extreme seasonality and cold environments. The overwintering survival and recruitment of age-0 Arctic cod heavily depend on achieving a sizable prewinter length (PWL) in their first year. Over the growth period, PWL is influenced by early life history traits, such as hatch date and size-at-hatch, and by environmental conditions, such as temperature and food availability. However, our knowledge of these interacting aspects of Arctic cod ecology is extremely limited. Here we coupled an individual-based transport and bioenergetic model with a sea ice-ocean model and simulated larval dispersal and growth under current environmental conditions. In addition, we tested two alternative scenarios of higher temperatures, with +2°C, and lower daily ration by 25% over the growth period. Our modeled PWL aligned well with field data on age-0 Arctic cod lengths by the end of summer. Largest PWLs resulted from winter spawns and were associated with more days with ice cover and shorter embryonic development. Under the high-temperature scenario, average PWL increased in Baffin Bay, Chukchi Sea, and Laptev Sea but declined in Svalbard, suggesting that a portion of age-0 Arctic cod are currently at their thermal tolerance limit. The recruitment success into the juvenile stage, defined as reaching a juvenile threshold length by the end of summer, was maximized in all winter spawns under the high-temperature scenario but decreased to zero in nearly all April spawns across all regions. Under the low-food scenario, reduced prey availability halved the recruitment success in all regions, indicating potentially severe consequences for future Arctic cod growth and survival. Our study illustrates how much changes in sea ice, temperature, and food availability influence the early development of Arctic cod and could impact their recruitment, highlighting the species’ increasingly uncertain future amid rapid environmental changes in the Arctic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".