Declining reproductive success in the Gulf of St. Lawrence’s humpback whales (<i>Megaptera novaeangliae</i>) reflects ecosystem shifts on their feeding grounds
Bibliographic record
Abstract
Climate change has resulted in physical and biological changes in the world's oceans. How the effects of these changes are buffered by top predator populations, and therefore how much plasticity lies at the highest trophic levels, are largely unknown. Here endocrine profiling, longitudinal observations of known individuals over 15 years between 2004 and 2018, and environmental data are combined to examine how the reproductive success of a top marine predator is being affected by ecosystem change. The Gulf of St. Lawrence, Canada, is a major summer feeding ground for humpback whales (Megaptera novaeangliae) in the North Atlantic. Blubber biopsy samples (n = 185) of female humpback whales were used to investigate variation in pregnancy rates through the quantification of progesterone. Annual pregnancy rates showed considerable variability, with no overall change detected over the study. However, a total of 457 photo-identified adult female sightings records with/without calves were collated, and showed that annual calving rates declined significantly. The probability of observing cow-calf pairs was related to favourable environmental conditions in the previous year; measured by herring spawning stock biomass, Calanus spp. abundance, overall copepod abundance and phytoplankton bloom magnitude. Approximately 39% of identified pregnancies were unsuccessful over the 15 years, and the average annual pregnancy rate was higher than the average annual calving rate at ~37% and ~23% respectively. Together, these data suggest that the declines in reproductive success could be, at least in part, the result of females being unable to accumulate the energy reserves necessary to maintain pregnancy and/or meet the energetic demands of lactation in years of poorer prey availability rather than solely an inability to become pregnant. The decline in calving rates over a period of major environmental variability may suggest that this population has limited resilience to such ecosystem change.
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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 | 0.000 |
| 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 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".