Early spring egg hatching by the American lobster (Homarus americanus) linked to rising water temperature in autumn
Bibliographic record
Abstract
Abstract Increasing ocean temperatures may affect life cycles of organisms whose biological processes are temperature-dependent. Our objective was to determine whether hatching time of American lobster (Homarus americanus), which has a 2-year reproductive cycle, has advanced in the southern Gulf of St Lawrence, Canada, in response to rising temperature. We investigated temporal trends in hatching time 1989–2014 using fisheries monitoring data. We considered two metrics: the first week of the year when ovigerous females with prehatch or hatching clutches were observed [onset-of-hatching (OH)] and the rate of change in the ratio of females with prehatch/hatching vs. developing clutches each spring fishing season [rate of clutch development (RCD)]. OH advanced by 5 weeks and RCD increased by 40% on average. Comparisons of OH and RCD to cumulative degree-days going back 2 years prior to hatching suggested an effect of higher fall temperatures during early ovarian and embryonic development. The advancement of hatching time in response to environmental conditions 6–18 months before hatching occurs could lead to a mismatch with larval prey species with shorter life cycles. These findings highlight the importance of monitoring phenology of fished species and the need for further research into potential impacts of phenological changes.
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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 | 0.000 | 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".