Sympatric kelp species share a large portion of their surface bacterial communities
Bibliographic record
Abstract
Kelp forest ecosystems are biodiversity hotspots, providing habitat for dense assemblages of marine organisms and nutrients for marine and terrestrial food webs. The surfaces of kelps support diverse microbial communities that facilitate the transfer of carbon from algal primary production to higher trophic levels. We quantified the diversity of bacteria on the surfaces of eight sympatric kelp species from four sites in British Columbia. Kelp-associated bacterial communities are significantly different from their environment, even though 86% of their bacterial taxa are shared with seawater and 97% are shared with rocky substrate. This differentiation is driven by differences in relative abundance of the bacterial taxa present. Similarly, a large portion of bacterial taxa (37%) is shared among all eight kelp species, yet differential abundance of bacterial taxa underlies differences in community structure among species. Kelp-associated bacterial diversity does not track host phylogeny; instead bacterial community composition is correlated with the life-history strategy of the host, with annual and perennial kelps supporting divergent bacterial communities. These data provide the first community-scale investigation of kelp forest-associated bacterial diversity. More broadly, this study provides insight into mechanisms that may structure bacterial communities among closely related sympatric host species.
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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 | 0.001 |
| 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".