Data from: Soil microbes alter plant fitness under competition and drought
Bibliographic record
Abstract
Plants exist across varying biotic and abiotic environments, including variation in the composition of soil microbial communities. The ecological effects of soil microbes on plant communities are well known, whereas less is known about their importance for plant evolutionary processes. In particular, the net effects of soil microbes on plant fitness may vary across environmental contexts and among plant genotypes, setting the stage for microbially mediated plant evolution. Here we assess the effects of soil microbes on plant fitness and natural selection on flowering time in different environments. We performed two experiments in which we grew Arabidopsis thaliana genotypes replicated in either live or sterilized soil microbial treatments, and across varying levels of either competition (isolation, intraspecific competition, or interspecific competition) or watering (well-watered or drought). We found large effects of competition and watering on plant fitness as well as the expression and natural selection of flowering time. Soil microbes increased average plant fitness under interspecific competition and drought, and shaped the response of individual plant genotypes to drought. Finally, plant tolerance to either competition or drought was uncorrelated between soil microbial treatments suggesting that the plant traits favoured under environmental stress may depend on the presence of soil microbes. In summary, our experiments demonstrate that soil microbes can have large effects on plant fitness, which depend on both the environment and individual plant genotype. Future work in natural systems is needed for a complete understanding of the evolutionary importance of interactions between plants and soil microorganisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.063 | 0.009 |
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".