Revisiting the ‘Gadgil effect’: do interguild fungal interactions control carbon cycling in forest soils?
Bibliographic record
Abstract
Summary In forest ecosystems, ectomycorrhizal and saprotrophic fungi play a central role in the breakdown of soil organic matter (SOM). Competition between these two fungal guilds has long been hypothesized to lead to suppression of decomposition rates, a phenomenon known as the ‘Gadgil effect’. In this review, we examine the documentation, generality, and potential mechanisms involved in the ‘Gadgil effect’. We find that the influence of ectomycorrhizal fungi on litter and SOM decomposition is much more variable than previously recognized. To explain the inconsistency in size and direction of the ‘Gadgil effect’, we argue that a better understanding of underlying mechanisms is required. We discuss the strengths and weaknesses of each of the primary mechanisms proposed to date and how using different experimental methods (trenching, girdling, microcosms), as well as considering different temporal and spatial scales, could influence the conclusions drawn about this phenomenon. Finally, we suggest that combining new research tools such as high‐throughput sequencing with experiments utilizing natural environmental gradients will significantly deepen our understanding of the ‘Gadgil effect’ and its consequences on forest soil carbon and nutrient cycling. Contents Summary 1382 I. Introduction 1382 II. Documenting the ‘Gadgil effect’ 1383 III. Generality of the ‘Gadgil effect’ 1383 IV. Mechanisms of the ‘Gadgil effect’ 1384 V. Priming and the ‘Gadgil effect’ 1386 VI. Is the ‘Gadgil effect’ context‐dependent? 1387 VII. Future research on the ‘Gadgil effect’ 1389 VIII. Conclusions 1391 Acknowledgements 1391 References 1391
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".