Relative importance of competition and plant–soil feedback, their synergy, context dependency and implications for coexistence
Bibliographic record
Abstract
Plants interact simultaneously with each other and with soil biota, yet the relative importance of competition vs. plant-soil feedback (PSF) on plant performance is poorly understood. Using a meta-analysis of 38 published studies and 150 plant species, we show that effects of interspecific competition (either growing plants with a competitor or singly, or comparing inter- vs. intraspecific competition) and PSF (comparing home vs. away soil, live vs. sterile soil, or control vs. fungicide-treated soil) depended on treatments but were predominantly negative, broadly comparable in magnitude, and additive or synergistic. Stronger competitors experienced more negative PSF than weaker competitors when controlling for density (inter- to intraspecific competition), suggesting that PSF could prevent competitive dominance and promote coexistence. When competition was measured against plants growing singly, the strength of competition overwhelmed PSF, indicating that the relative importance of PSF may depend not only on neighbour identity but also density. We evaluate how competition and PSFs might interact across resource gradients; PSF will likely strengthen competitive interactions in high resource environments and enhance facilitative interactions in low-resource environments. Finally, we provide a framework for filling key knowledge gaps and advancing our understanding of how these biotic interactions influence community structure.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".