The diversity of mycorrhiza-associated fungi and trees shape subtropical mountain forest ecosystem functioning
Bibliographic record
Abstract
Aim: Mycorrhiza play key roles in ecosystem structure and functioning in forests. However, how different mycorrhizal types influence mountain forest biodiversity-ecosystem functioning relationships is largely unknown. We evaluate how the diversity of distinct mycorrhiza-associated fungi and trees shape forest carbon storage along elevational gradients. Location: Gaoligong Mountains within Hengduan Mountains, Southwest China. Taxon: Seed plants and mycorrhizal fungi. Methods: We used the data from 31 subtropical forest plots along elevational gradients on two aspects (east and west) of the mountain. We quantified species richness of trees and symbiotic fungi and assigned both to their mycorrhizal type (arbuscular mycorrhiza (AM), ectomycorrhiza (EcM) and ericoid mycorrhiza (ErM)). We then examined the diversity effects of mycorrhiza-associated fungi and trees on above-ground carbon stored in trees and organic carbon stored in soils. Results: Species richness was highest for AM trees (79.5%), followed by ErM trees (13.4%) and then EcM trees (7.1%). Species richness of AM-associated trees and fungi decreased with increasing elevation, while ErM-associated trees and fungi showed an opposite trend. EcM-associated diversity followed a hump-shaped relationship with elevation. Positive relationships between diversity and above-ground carbon were detected in all three mycorrhizal associations, but despite low species number, canopy-dominating EcM trees comprised 64.4% of the amount of above-ground carbon. Furthermore, community-weighted means of height exhibited positive correlations with forest above-ground carbon, indicating that positive selection effects occur. Soil organic carbon was positively related to EcM-associated fungi diversity, above-ground carbon mass and soil nitrogen availability, with the latter having the strongest direct effects. Main conclusions: The distributions of forest biodiversity and carbon storage can be modulated by distinct mycorrhizal fungi and trees. Moreover, future global changes (e.g., climate warming, intensifying nitrogen deposition) could alter the mycorrhizal-mediated biodiversity-ecosystem functioning relationships in mountain forests.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".