Exploring the impact of microbial manipulation on the early development of kelp ( <i>Saccharina latissima</i> ) using an ecological core microbiome framework
Bibliographic record
Abstract
ABSTRACT Kelp cultivation is a rapidly expanding economic sector, as kelp are valued for a wide range of commercial products and for restoration of kelp forest ecosystems. Microbes associated with kelp and other macroalgae play a critical role in processes such as nutrient exchange, chemical signaling, and defense against pathogens. Thus, manipulating the microbiome to enhance macroalgal growth and resilience is a promising, but largely untested, tool in sustainable kelp cultivation. The core microbiome hypothesis suggests that bacteria that are consistently found on a host (the core microbes) are likely to have a disproportionate impact on host biology, making them an attractive target for microbiome manipulation. Here, we surveyed wild Saccharina latissima and their surrounding environment to identify core bacterial taxa, compared to cultivated kelp, and experimentally tested the effect of cultured bacterial isolates on kelp development. We find that core bacteria are nearly absent in cultivated juvenile sporophytes in nurseries but eventually colonized after outplanting kelp to ocean farm sites. We find that bacterial inoculants can have both positive and negative effects on kelp development. In line with predictions from the core microbiome hypothesis, we find a positive correlation between the frequency of the bacterial genus in the wild and the bacterial effect on the number of sporophytes in kelp co-culture experiments. IMPORTANCE The core microbiome hypothesis suggests that symbiotic microorganisms consistently associated with hosts have functional effects on host biology and health. However, there is a lack of evidence to either support or refute this idea. This study surveys the distribution of bacteria on wild and cultivated kelp to identify the core microbiome and tests the ability of bacterial isolates cultured from the surface of wild kelp to influence kelp growth and development in laboratory microbial manipulation experiments. The frequency of bacterial genera on wild kelp was positively correlated with influence on kelp development in laboratory experiments, providing support for the core microbiome hypothesis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".