Evolutionary consequences of microbiomes for hosts: impacts on host fitness, traits, and heritability
Bibliographic record
Abstract
An organism's phenotypes and fitness often depend on the interactive effects of its genome (Ghost), microbiome (Gmicrobe), and environment (E). These G × G, G × E, and G × G × E effects fundamentally shape host-microbiome (co)evolution and may be widespread, but are rarely compared within a single experiment. We collected and cultured Lemnaminor (duckweed) and its associated microbiome from 10 sites across an urban-to-rural ecotone. We factorially manipulated host genotype and microbiome in two environments (low and high zinc, an urban aquatic stressor) in an experiment with 200 treatments: 10 host genotypes × 10 microbiomes × 2 environments. Host genotype explained the most variation in L.minor fitness and traits, while microbiome effects often depended on host genotype (G × G). Microbiome composition predicted G × G effects: when compared in more similar microbiomes, duckweed genotypes had more similar effects on traits. Further, host fitness increased and microbes grew faster when applied microbiomes more closely matched the host's field microbiome, suggesting some local adaptation between hosts and microbiota. Finally, selection on and heritability of host traits shifted across microbiomes and zinc exposure. Thus, we found that microbiomes impact host fitness, trait expression, and heritability, with implications for host-microbiome evolution and microbiome breeding.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".