Marine Mammal Strandings and Environmental Changes: A 15-Year Study in the St. Lawrence Ecosystem
Bibliographic record
Abstract
Understanding the effects of climatic variability on marine mammals is challenging due to the complexity of ecological interactions. We used general linear models to analyze a 15-year database documenting marine mammal strandings (1994-2008; n = 1,193) and nine environmental parameters known to affect marine mammal survival, from regional (sea ice) to continental scales (North Atlantic Oscillation, NAO). Stranding events were more frequent during summer and fall than other seasons, and have increased since 1994. Poor ice conditions observed during the same period may have affected marine mammals either directly, by modulating the availability of habitat for feeding and breeding activities, or indirectly, through changes in water conditions and marine productivity (krill abundance). For most species (75%, n = 6 species), a low volume of ice was correlated with increasing frequency of stranding events (e.g. R(2)adj = 0.59, hooded seal, Cystophora cristata). This likely led to an increase in seal mortality during the breeding period, but also to increase habitat availability for seasonal migratory cetaceans using ice-free areas during winter. We also detected a high frequency of stranding events for mysticete species (minke whale, Balaenoptera acutorostrata) and resident species (beluga, Delphinapterus leucas), correlated with low krill abundance since 1994. Positive NAO indices were positively correlated with high frequencies of stranding events for resident and seasonal migratory cetaceans, as well as rare species (R(2)adj = 0.53, 0.81 and 0.34, respectively). This contrasts with seal mass stranding numbers, which were negatively correlated with a positive NAO index. In addition, an unusual multiple species mortality event (n = 114, 62% of total annual mortality) in 2008 was caused by a harmful algal bloom. Our findings provide an empirical baseline in understanding marine mammal survival when faced with climatic variability. This is a promising step in integrating stranding records to monitor the consequences of environmental changes in marine ecosystems over long time scales.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".