The dynamics and geographic disjunction of the kelp <i>Eisenia arborea</i> along the west coast of Canada
Bibliographic record
Abstract
Abstract Eisenia arborea has a disjunct distribution along the west coast of North America. We detail the current distribution of E. arborea and use long-term records to examine how present-day shifts in E. arborea prevalence and abundance in British Columbia (BC), relative to the dominant stipitate kelp Pterygophora californica, may be driven by interactions between changing grazing pressure and warming water. We further speculate on how the disjunction of E. arborea arose. The ancestor of E. arborea likely dispersed from Japan to North America where glaciation disrupted its distribution and speciation occurred. As glaciers retreated E. arborea likely dispersed into BC from warmer waters in the south and/or expanded from refugia off Vancouver Island and Haida Gwaii. While E. arborea is uncommon, our records extend its range into Alaska and Washington State. Along western Vancouver Island, BC, under warming conditions, E. arborea prevalence and abundance increased where once-extirpated sea otters (Enhydra lutris) removed urchins. Where otters were absent, however, reduced summer wave heights, associated with warming, apparently allowed urchins to graze shallow-water kelps, which declined. We suggest that under warming conditions, sea otters may increase kelp resilience, with E. arborea becoming more prevalent in NE Pacific kelp forests.
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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 | 0.001 |
| 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".