Successional dynamics of the cultivated kelp microbiome
Bibliographic record
Abstract
Kelp are important primary producers that are colonized by diverse microbes that can have both positive and negative effects on their hosts. The kelp microbiome could support the burgeoning kelp cultivation sector by improving host growth, stress tolerance, and resistance to disease. Fundamental questions about the cultivated kelp microbiome still need to be addressed before microbiome-based approaches can be developed. A critical knowledge gap is how cultivated kelp microbiomes change as hosts grow, particularly following outplanting to sites that vary in abiotic conditions and microbial source pools. In this study we assessed if microbes that colonize kelp in the nursery stage persist after outplanting. We characterized microbiome succession over time on two species of kelp, Alaria marginata and Saccharina latissima, outplanted to open ocean cultivation sites in multiple geographic locations. We tested for host-species specificity of the microbiome and the effect of different abiotic conditions and microbial source pools on kelp microbiome stability during the cultivation process. We found the microbiome of kelp in the nursery is distinct from that of outplanted kelp. Few bacteria persisted on kelp following outplanting. Instead, we identified significant microbiome differences correlated with host species and microbial source pools at each cultivation site. Microbiome variation related to sampling month also indicates that seasonality in host and/or abiotic factors may influence temporal succession and microbiome turnover in cultivated kelps. This study provides a baseline understanding of microbiome dynamics during kelp cultivation and highlights research needs for applying microbiome manipulation to kelp cultivation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".