Plant-soil feedback in the ‘real world’: how does fire fit into all of this?
Bibliographic record
Abstract
Abstract Aims Plant–soil feedback (PSF) is an important mechanism controlling plant growth, vegetation dynamics, and longer-term and larger-scale patterns of plant community diversity. We know that feedback between plants and soil biota depends on several external factors, such as nutrient and water availability, and interactions with neighbouring plants. We argue that in the ‘real world’, PSF are not working in isolation but instead proceed within a complex context of multiple interacting factors. Fire is one of those complex external factors which could greatly alter PSF by re-setting or re-directing plant-soil biota interactions. Methods We reviewed key literature on the effects of fire on soil biota and soil physicochemical properties with soil depth, to generate predictions on the complex effects of fire on PSF. Results We highlight that fire has strong potential to directly and indirectly affect the strength of PSF. To what extent this influences longer-term plant community trajectories depends on the interactions between fire characteristics and ecosystem type. Here, we conceptualized these effects of fire on soil properties and biota, and then discuss the main pathways through which fire should alter PSF. Conclusions We think that PSF processes should be nullified under and after fire. Average neutral PSF responses are expected to be more common in the short-term or within the timeframe required for major soil microbial players to regain their pre-fire abundances and diversity. We conclude by providing directions for future research and possible methods to study fire effects on PSF both in the field and under controlled conditions.
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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 | 0.003 | 0.009 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".