Sexual segregation and stable pregnancy rates in the Gulf of St. Lawrence’s minke whales Balaenoptera acutorostrata amidst environmental changes
Bibliographic record
Abstract
Evaluating the health of baleen whale populations is crucial for understanding how environmental changes impact these top predators. Methodological advances, particularly in endocrine profiling, have enabled us to measure reproductive rates of populations as a proxy for population health. The Gulf of St. Lawrence (GSL), Canada, is an important summer feeding ground for various North Atlantic baleen whale species and has undergone major ecosystem changes in recent decades. To explore the potential impacts on population health of minke whales Balaenoptera acutorostrata, we combined genetic analyses, endocrine profiling, and environmental data on prey availability to investigate population demographics and possible drivers of pregnancy rates between 2007 and 2015. Biopsy samples collected between May and October were sexed (n = 187) using PCR, revealing a strong female bias (88.2%). Pregnancy status was determined through blubber progesterone quantification, with progesterone concentrations of 0.061-8.04 ng g-1 for non-pregnant individuals and 10.02-359.73 ng g-1 for pregnant individuals. High annual pregnancy rates were observed, ranging from 60 to 89% (mean: 74 ± 10%), with no consistent trend detected over the study period. Generalised linear model results suggested species-specific prey availability in the year prior to pregnancy did not explain annual variation in pregnancy rates. We posit that this is due to the generalist feeding behaviour of minke whales. The results presented here indicate minke whales in the GSL exhibit sex-specific and reproductive spatial segregation. These pregnant females are likely using the area as a feeding ground prior to giving birth, with sufficient behavioural plasticity to withstand fluctuating food availability.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".