Mitochondrial cox1 sequencing datasets, cox1 haplotype sequences and microsatellite genotyping datasets for Saccharina latissima (3 files in total); mitochondrial cox1 sequencing datasets, cox1 haplotype sequences and microsatellite genotyping datasets for Laminaria digitata (3 files in total); cox1 sequencing datasets, cox1 haplotype sequences and mitochondrial SNP genotyping datasets for Alaria esculenta (3 files in total).
Bibliographic record
Abstract
Intraspecific genetic variability matters for species adapting to climate change and yet it is generally overlooked in projections of biodiversity impacts, particularly for cold-water benthic environments. Here, we used molecular datasets to delimit two divergent genetic lineages between the Northeast and Northwest Atlantic for the cold-temperate kelps Saccharina latissima, Laminaria digitata and Alaria esculenta. Building on such genetic insights, we applied ecological niche modelling and found that lineage-level modelling provided more realistic projections than species-level and identified sea-surface temperature as the dominant factor shaping kelp distribution ranges. Lineage-level projections revealed distinct northward expansion and southern-edge contraction of habitat range in the Northwest vs. the Northeast Atlantic in each kelp species, leading to a net increase in the Northwest and decrease in the Northeast Atlantic, respectively. Many populations of S. latissima and L. digitata on the south of c. 48°N in the Northwest (e.g. the Canadian Maritimes and Maine) and those on the south of c. 67°N in the Northeast Atlantic (e.g. France, the UK, and Germany), are projected to lose unique and endemic genetic variation. Unexpectedly, A. esculenta is predicted to lose unique genetic diversity on both sides of the North Atlantic in the 2050s. These striking changes in distribution range and genetic characteristics highlight the necessity to conserving and managing distinct gene pools to avoid regional extinction of cold-temperate kelp forest ecosystems under climate 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.038 |
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".