Massive barnacle recruitment on the Gulf of St. Lawrence coast of Nova Scotia (Canada) in 2024 linked to increased sea surface temperature
Bibliographic record
Abstract
With the ongoing climate and oceanographic change, an increasing number of studies are reporting dramatic population losses caused by thermal extremes in intertidal habitats. Under moderate warming, however, populations can fare better in places where species normally experienced suboptimal temperatures. This article reports the massive recruitment of the barnacle Semibalanus balanoides on the Gulf of St. Lawrence coast of Nova Scotia (Canada) in 2024. As recruits appear mostly during May in this region, coastal sea surface temperature (SST) in April is critical for the ecological performance of larvae, as they are pelagic and live in the water column for weeks before intertidal settlement. Thus, a study that spanned 12 years (2005 to 2016) on this coast found that annual barnacle recruitment was positively correlated to April SST. In April 2024, coastal SST was 116% higher than for the same month averaged over those 12 years (4.1 vs. 1.9 °C). This SST spike was followed by an elevated recruitment that was 111% higher than the average for those 12 years (1,278 vs. 607 recruits dm−2). Overall for the studied years, the amount of variation in annual barnacle recruitment statistically explained by April SST was 51%. While the southern distribution limit of S. balanoides has moved northwards in recent decades due to lethal warming, our results support the notion of improving reproductive success with seawater warming on colder northern shores.
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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 | 0.001 |
| 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".