Not all parents are the same: Diverse strategies of symbiont transmission in seaweeds
Bibliographic record
Abstract
Different marine seaweed species have been shown to harbour specific bacterial communities, however, the extent to which vertical symbiont transmission from parents to offspring contributes to host-specificity is unclear. Here we use fluorescence and electron microscopy as well as 16S rRNA gene-based community analysis to investigate symbiont transmission in members of the three major seaweed groups (green Chlorophyta, red Rhodophyta and brown Phaeophyceae). We found seaweeds employ diverse strategies to transfer symbionts to their progeny. For instance, the green Ulva australis does not appear to have the capacity for vertical transmission. In contrast, the brown Phyllospora comosa adopts a non-selective vertical transmission. The red Delisea pulchra demonstrates weak selectivity in symbiont transmission, while the brown Hormosira banksii exhibits a strongly selective symbiont transfer. Mucilage on the gametes appears to facilitate vertical transmission and transferred bacteria have predicted properties that could support early development of the seaweeds. Previous meta-analysis has indicated that vertical transmission is rare in aquatic compared to terrestrial environments, however, our results contribute to the growing evidence that this might not be the case and that instead vertical transmission with various degrees of symbiont selection occurs in the ecologically important group of seaweeds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".