Stand age and sampling year effect on the fungal fruit body community in <i>Pinus pinaster</i> forests in central Spain
Bibliographic record
Abstract
The process of fungal community succession in forests is still insufficiently known, especially in Mediterranean ecosystems. To reveal successional patterns, we studied changes in aboveground sporocarp composition in managed Pinus pinaster Ait. forests across a stand age chronosequence from 5 to more than 90 years in Soria province (Central Spain). We evaluated the effect of yearly conditions and stand age on species richness, composition, and trophic groups. All aboveground sporocarps were collected and identified in 21 plots, each measuring 150 m2, placed in a random stand age-stratified survey. Sampling was performed weekly in the autumn during 6 years (1997–2002). The effect of year and stand age on species richness was analysed using repeated measures analysis of variance and change in composition. Changes in community composition were evaluated via canonical correspondence analysis (CCA). The response of different trophic groups was analyzed using ANOVA with age as a fixed factor. Fungal community richness increased with stand age. This increase was related to the rise in humicolous saprotroph and mycorrhizal species. The presence of parasitic and lignicolous fungi was always very low, probably as a result of forest management. Yearly conditions exerted a stronger effect than stand age on aboveground sporocarp community richness and composition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".