Energetic limitations and mass mortality of Bering Sea snow crab: Interacting effects of warming and density on collapse and recovery
Bibliographic record
Abstract
Marine heatwaves can result in mass mortality events, but the mechanisms underlying population collapse and recovery dynamics are often poorly understood. Here, we employed a comparative analysis between collapsing and noncollapsing portions of the Bering Sea snow crab population to evaluate linkages between energetic condition and population abundance during and after a recent collapse. We show that abundance declines during the collapse were associated with dramatic declines in energetic condition, and the negative impact of high population density on energetic reserves was intensified by warming during a marine heatwave. Elevated energetic condition coincided with strong recruitment post-collapse, suggesting rapid initial population recovery in the eastern Bering Sea. However, we show that cold-water habitat (≤0 °C) is critical for supporting high snow crab density in rebuilding towards a pre-collapse state. These results suggest that warming and loss of sea ice will exacerbate the risk of collapse in snow crab through energetic constraints on survival. Furthermore, we highlight the validation of an indirect energetic condition metric that will facilitate continued energetics monitoring and rapid integration into management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".