Data from: Testing the ectomycorrhizal dominance hypothesis for ecosystem multifunctionality in a subtropical mountain forest
Bibliographic record
Abstract
Mycorrhizal associations are key mutualisms that shape the structure of forest communities and multiple ecosystem functions. However, we lack a framework for predicting the varying dominance of distinct mycorrhizal associations in an integrated proxy of multifunctionality across ecosystems. Here, we used the datasets containing diversity of mycorrhizal associations and 18 ecosystem processes related to supporting, provisioning, and regulating services to examine how the dominance of EcM associations affects ecosystem multifunctionality in subtropical mountain forests in Southwest China. Meanwhile, we synthesized the prevalence of EcM dominant effects on ecosystem functioning in forest biomes. Our results demonstrated that elevation significantly modified the distributions of EcM trees and fungal dominance, which in turn influenced multiple functions simultaneously. Multifunctionality increased with increasing proportion of EcM associations, supporting the ectomycorrhizal-dominance hypothesis. Meanwhile, we observed that the impacts of EcM dominance on individual ecosystem functions exhibited different relationships among forest biomes. Our findings highlight the importance of ectomycorrhizal dominance in regulating multifunctionality in subtropical forests. However, this ectomycorrhizal feedback in shaping ecosystem functions cannot necessarily be generalized across forests. Therefore, we argue that the predictions for ecosystem multifunctionality in response to the shifts of mycorrhizal composition could vary across space and time.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".