The microbiota of intertidal macroalgae <scp> <i>Fucus distichus</i> </scp> is site‐specific and resistant to change following transplant
Bibliographic record
Abstract
It is unclear how host-associated microbial communities will be affected by future environmental change. Characterizing how microbiota differ across sites with varying environmental conditions and assessing the stability of the microbiota in response to abiotic variation are critical steps towards predicting outcomes of environmental change. Intertidal organisms are valuable study systems because they experience extreme variation in environmental conditions on tractable timescales such as tide cycles and across small spatial gradients in the intertidal zone. Here we show a widespread intertidal macroalgae, Fucus distichus, hosts site-specific microbiota over small (meters to kilometres) spatial scales. We demonstrate stability of site-specific microbial associations by manipulating the host environment and microbial species pool with common garden and reciprocal transplant experiments. We hypothesized that F. distichus microbiota would readily shift to reflect the contemporary environment due to selective filtering by abiotic conditions and/or colonization by microbes from the new environment or nearby hosts. Instead, F. distichus microbiota was stable for days after transplantation in both the laboratory and field. Our findings expand the current understanding of microbiota dynamics on an intertidal foundation species. These results may also point to adaptations for withstanding short-term environmental variation, in hosts and/or microbes, facilitating stable host-microbial associations.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".