Phosphorus deficiencies invoke optimal allocation of exoenzymes by ectomycorrhizas
Bibliographic record
Abstract
Abstract Ectomycorrhizal (EM) fungi can acquire phosphorus (P) through the production of extracellular hydrolytic enzymes (exoenzymes), but it is unclear as to the manner and extent native EM fungal communities respond to declining soil P availability. We examined the activity of six exoenzymes (xylosidase, N-acetyl glucosaminidase, β-glucosidase, acid phosphomonoesterase, acid phosphodiesterase [APD], laccase) from EM roots of Pseudotsuga menzesii across a soil podzolization gradient of coastal British Columbia. We found that APD activity increased fourfold in a curvilinear association with declining inorganic P. Exoenzyme activity was not related to organic P content, but at a finer resolution using 31P-NMR, there was a strong positive relationship between APD activity and the ratio of phosphodiesters to orthophosphate of surface organic horizons (forest floors). Substantial increases (two- to fivefold) in most exoenzymes were aligned with declining foliar P concentrations of P. menzesii, but responses were statistically better in relation to foliar nitrogen (N):P ratios. EM fungal species with consistently high production of key exoenzymes were exclusive to Podzol plots. Phosphorus deficiencies in relation to N limitations may provide the best predictor of exoenzyme investment, reflecting an optimal allocation strategy for EM fungi. Resource constraints contribute to species turnover and the assembly of distinct, well-adapted EM fungal communities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".