Larger presence of ectomycorrhizae detected from pygmy pine ecotype in the fire-frequent pine barrens ecosystem
Bibliographic record
Abstract
Pine barrens ecosystem has acidic, sandy, and nutrient-poor soil and is prone to drought and fire. In the New Jersey Pine Barrens, the predominant pitch pine (Pinus rigida) consists of two ecotypes: the regular pitch pines with heights of 4.6–12 m, and the pygmy pines of low stature (1.2–1.8 m) in the New Jersey Pine Plains. Previous ecological studies suggested that the dwarf pines in the Pine Plains that are embedded within the Pine Barrens were an evolutionary adaptation to frequent fire. Pines are obligate ectomycorrhizal (EcM) mutualists, and their root mycobiota may contribute to stress protection and plant health. However, information on the mycobiota associated with plants in the pine barrens ecosystem is lacking. To have a holistic understanding of the evolution and adaptation in this stressed environment, we used both culture-independent metabarcoding and culture-based method to characterize the mycobiota from soil and root of the two ecotypes and to identify core mycobiota. We found that Agaricomycetes, Leotiomycetes, and Mucoromycotina are predominant fungi in the New Jersey Pine Barrens ecosystem, which is rich in root mutualistic fungi. We observed that the pygmy pine roots had significantly higher density of EcM tips than the regular pine roots. This was corroborated by our metabarcoding analysis, which showed that the pygmy pine trees had higher ratio of ectomycorrhiza-forming fungi than the regular-statured pines. We hypothesize that symbiotrophic EcM fungi associated with pygmy pines are capable of mitigating high fire stress in the Pine Plains.
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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 | 0.000 |
| 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".