Plant-soil feedback: incorporating untested influential drivers and reconciling terminology
Bibliographic record
Abstract
Abstract Background Plants condition the soil in which they grow, thereby altering the performance of subsequent plants growing in this soil. This phenomenon, known as plant-soil feedback (PSF), has garnered increasing interest. Experiments are moving from single species soil pairings in the glasshouse to community-level field trials. Consequently, our knowledge of the role PSF plays in shaping ecosystem functions has advanced. However, knowledge gaps remain. Scope Here, we explore intrinsic and extrinsic abiotic and biotic drivers of PSF such as maternal effects, plant functional traits, self-DNA, plant-plant competition, herbivory, interactions between soil organisms, temperature, drought, flooding, greenhouse gases, (micro)nutrients, plant-litter-soil feedback and priority effects. These drivers have begun to feature in experiments, thereby increasing our mechanistic understanding of PSF. Nonetheless, many of these topics have received insufficient coverage to determine general principles across larger temporal and spatial scales. Further, conflicting terminology has excluded PSF studies from reviews and meta-analyses. We review terms such as soil sickness, Janzen-Connell hypothesis, soil-related invasive species work, soil legacies, allelopathy and soil-related succession that overlap with PSF but are generally not named as such. Conclusion Holistic experimental designs that consider the continual reciprocal feedback between the extrinsic environment, plants and soil, as well as the unification of terminologies are necessary if we are to realise the full potential of PSF for understanding and steering ecosystem processes. Here, we compile outstanding questions related to PSF research that emphasis the aforementioned topics and suggest ways to incorporate them into future research in order to advance plant-soil ecology.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".