Mycorrhizal Fungi Associated With Juniper and Oak Seedlings Along a Disturbance Gradient in Central Mexico
Bibliographic record
Abstract
Competition for resources between arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) plants can alter belowground mycorrhizal communities, but few studies have investigated host effects on both AM and ECM communities. In Central Mexico, the AM plant Juniperus deppeana is frequently used for reforesting areas affected by soil erosion, while the surrounding native forests are dominated by ECM oak trees. Oaks are capable of associating with both AM and ECM fungi during part of their life cycle (a feature known as dual mycorrhization) but it is unclear whether junipers possess such ability. To assess how juniper planting may affect belowground fungal interactions with oaks, we investigated mycorrhizal associations in J. deppeana and Quercus rugosa seedlings along a disturbance gradient: a native oak forest, a mixed Juniperus-Quercus population in secondary vegetation and a juniper site severely degraded by mining extraction. We measured root colonization and identified fungal communities using soil and root meta-barcoding of the ITS2 rDNA region. ECM fungal community composition was strongly affected by disturbance (regardless of host), while the community composition of AM fungi was mostly host-dependent, with a higher AM fungal richness in J. deppeana . Importantly, the fungal communities associated with Q. rugosa seedlings significantly changed in the vicinity of juniper trees, while those of J. deppeana seedlings were not affected by the presence of oak trees. Even though ECM fungal richness was higher in Q. rugosa and in the native forest, we detected a variety of ECM fungi associated exclusively with J. deppeana seedlings, suggesting that this plant species may be colonized by ECM fungi. Our results indicate that J. deppeana can alter ECM native fungal communities, with implications for its use in reforestation of mixed oak 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".