Microhabitat effects on herbaceous nutrient concentrations at the community and species level in <scp>M</scp>editerranean open woodlands: the role of species composition
Bibliographic record
Abstract
Abstract The presence of a woody canopy in open oak woodlands affects not only the nutrition but also the species composition of the herbaceous community. Yet, the contribution of both effects (changes in nutrient concentrations and species composition) to total resources that are captured by the herbaceous community is not well understood. We assessed the mineral nutrition ( N , P , K and C a) of three herbaceous species and the herbaceous community as well as the species composition in contrasting microhabitats (beneath trees, beneath a leguminous shrub and in open spaces). Both trees and shrubs increased the nutrient concentrations of the herbaceous species that were studied, except for T araxacum officinale . Their effects were less consistent when the entire community was considered and depended upon the nutrient being analysed. Species richness and N , P and K concentrations were positively associated, suggesting that more nutrients are captured by the herbaceous community as diversity increases. Our results suggest a close relationship between species composition and nutritional value at the community level that may explain the discrepancies observed between both levels of analysis (species vs. community). Thus, farm‐level strategies based on the whole herbaceous community may overlook processes operating at the species level, which can be relevant to achieve sustainable management.
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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 | 0.000 |
| 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) | 0.001 | 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".