Living under Arctic kelp forests: linking soft-bottom communities to kelp cover in the Canadian Arctic
Bibliographic record
Abstract
Kelp forests are dominant habitats along Canadian Arctic coastlines. While their extent and productivity are expected to change dramatically due to global warming, their role in supporting Arctic coastal biodiversity remains poorly explored. Leveraging an extensive data set encompassing core samples, video transects, and environmental data, this study explores the patterns and drivers of benthic diversity, particularly focusing on kelp cover, across 4 Eastern Canadian Arctic regions. We show that the widespread soft bottoms in many subtidal coastal Arctic areas are associated with high densities of the kelp Saccharina latissima, often growing in mixed stands with Laminaria solidungula and Alaria esculenta. These kelps enhance the diversity of invertebrate communities thriving in the sediment below, increasing densities in specific groups and promoting subsurface feeding activity. Interregional comparisons indicate that high turbidity conditions typically lead to low macroalgal cover and low invertebrate richness, whereas extensive sea ice cover can favor high macroalgal abundance and unique diverse communities. Conditions of intermediate open-water duration and high water clarity support tall kelp forests, hosting approximately 70% of local rare taxa. Based on these surveys, we provide a list of Arctic invertebrate taxa according to their level of selectivity for kelp forests as habitats. Despite variation among regions, kelp forests enhance biodiversity and drive unique benthic communities in the Canadian Arctic. Due to their ecological significance and potential vulnerability, we recommend efforts to integrate kelp forests into marine protected areas and minimize human-induced damaging activities within or near these habitats.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".