Resistance and resilience of root fungal communities to water limitation in a temperate agroecosystem
Bibliographic record
Abstract
Abstract Understanding crop resilience to environmental stress is critical in predicting the consequences of global climate change for agricultural systems worldwide, but to date studies addressing crop resiliency have focused primarily on plant physiological and molecular responses. Arbuscular mycorrhizal fungi (AMF) form mutualisms with many crop species, and these relationships are key in mitigating the effects of abiotic stress in many agricultural systems. However, to date there is little research examining whether (1) fungal community structure in agroecosystems is resistant to changing environmental conditions, specifically water limitation and (2) resilience of fungal community structure is moderated by agricultural management systems, namely the integration of trees into cropping systems. Here, we address these uncertainties through a rainfall reduction field experiment that manipulated short‐term water availability in a soybean‐based (Glycine max L. Merr.) agroforest in Southern Ontario, Canada. We employed terminal restriction fragment length polymorphism analysis to determine the molecular diversity of both general fungal and AMF communities in soybean roots under no stress, stress (rainfall shelters added), and poststress (rainfall shelters removed). We found that general fungal and AMF communities sampled from soybean roots were resistant to rainfall reduction in a monoculture, but not in an agroforest. While AMF communities were unchanged after stress removal, general fungal communities were significantly different poststress in the agroforest, indicating a capacity for resiliency. Our study indicates that generalist fungi and AMF are responsive to changes in environmental conditions and that agroecosystem management plays a key role in the resistance and resilience of fungal communities to water limitation.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".