Intraspecific variation in plant–soil feedback depends on plant dominance while interspecific variation is unrelated to plant community structure
Bibliographic record
Abstract
Abstract Plants interact with a variety of soil biota; the accumulation of which can affect their growth and that of subsequent plants. This plant–soil feedback (PSF) can both positively and negatively affect plant populations. Diverse plant communities should dilute pathogens and increase beneficial soil biota, which can mitigate negative PSF. Plant dominance, conversely, should result in reduced microbial diversity and increased pathogens or mutualists of the dominant plant, enhancing negative or positive PSF. Genetic diversity within the dominant species may dilute PSF, yet it is unclear whether species and genetic diversity can have additive effects. Using field‐conditioned soils from Medicago sativa production systems varying in dominance and species diversity, we inoculated multiple plant species and Medicago cultivars to assess effects on PSF. In the field, we measured multiple aspects of the biotic and abiotic environment, including sequencing bacteria, fungi, arbuscular mycorrhizal fungi and oomycetes. Using structural equation modelling, we linked the dominance and diversity of the plant community to intraspecific and interspecific (community‐wide) means and variances in PSF via changes in microbiome community composition and diversity. Intraspecific PSF was more negative and variable as Medicago dominance increased, whereas the mean and variance in interspecific PSF were largely unlinked to plant composition. While the microbiome was strongly linked to both the mean and variance of intra‐ and interspecific PSF, only the oomycete community had similar effects within and among species, suggesting they are important generalist pathogens and drivers of plant population and community dynamics. Nonetheless, each microbiome component was linked to the mean PSF of either the community or Medicago. The diversity of the eukaryotic microbiome, however, was more important for determining variability in PSF within and among species. Synthesis . Plant dominance had stronger effects on microbiome assembly and plant–soil feedback (PSF) than plant diversity. Although plant diversity did not reduce negative PSF, independent variation in PSF within and among species suggests additive benefits of genetic and species diversity for dilution of plant responses to pathogens. Understanding this variation, however, requires quantifying microbiome components beyond bacteria and fungi.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".