Long‐term trends and drivers of larval phenology and abundance of dominant brachyuran crabs in the Gulf of St. Lawrence (Canada)
Bibliographic record
Abstract
Abstract Climate change has led to major shifts in the timing of biological events, with many studies demonstrating earlier phenology in response to warming. However, few of these studies have investigated the effects of climate change on the phenology of larvae in marine species. Phenological shifts can result in mismatches between consumers and prey and hence affect growth and survival of individuals, and ultimately population demography. We investigated the temporal changes in phenology and abundance of the larvae of dominant brachyuran crabs in the southern Gulf of St. Lawrence (eastern Canada) based on plankton collections spanning 1982–2012. The Gulf of St. Lawrence has warmed since the early 1990s, and our analyses revealed that larvae of snow crab (Chionoecetes opilio) and toad crabs (Hyas spp.) exhibited a significant trend towards earlier phenology over the 30‐year study period. This shift in phenology appeared to be a consequence of the effect of climate warming on both the timing of hatching and larval development rate. Larval abundance responded differently by crab taxon to climate warming, likely due to differences in thermal tolerance. The warming trend was unfavourable to snow crab, which is the most cold‐adapted and stenothermic of the taxa examined in this study. The abundance of snow crab larvae was lower when sea ice retreat occurred earlier than day 110 of the year and sea surface temperature was higher than 8.5°C. On the other hand, larval abundance of rock crab (Cancer irroratus), which prefers higher temperatures, was positively related to surface temperature.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".