Kelp‐associated Microbiota are Structured by Host Anatomy<sup>1</sup>
Bibliographic record
Abstract
Seaweed-associated microbiota are essential for the health and resilience of nearshore ecosystems, marine biogeochemical cycling, and host health. Yet much remains unknown about the ecology of seaweed-microbe symbioses. In this study, we quantified fine-scale patterns of microbial community structure across distinct anatomical regions of the kelp Laminaria setchellii. These anatomical regions represent a gradient of tissue ages: perennial holdfasts can be several years old, whereas stipe epicortex and blades are younger annual structures. Within blades, new growth occurs at the base, while the blade tips may be several months old and undergoing senescence. We hypothesized that microbial communities will differ across anatomical regions (holdfast, stipe, blade base, and blade tip), such that younger tissues will harbor fewer microbes that are more consistent across replicate individuals. Our data support this hypothesis, with the composition of bacterial (16S rRNA gene) and microeukaryote (18S rRNA gene) communities showing significant differences across the four anatomical regions, with the surfaces of older tissues (holdfast and blade tips) harboring significantly greater microbial richness compared to the younger tissues of the meristematic region. Additional samples collected from the surfaces of new L. setchellii recruits (<1y old) also showed differences in microbial community structure across anatomical regions, which demonstrates that these microbial differences are established early. We also observed this pattern in two additional algal species, suggesting that microbial community structure across host anatomy may be a common feature of the seaweed microbiome.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".