Influence of tree functional diversity and stand environment on fine root biomass and necromass in four types of evergreen broad-leaved forests
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Positive effects of tree diversity on above-ground biomass have been well documented, whereas the relationships between tree functional diversity and fine root biomass and necromass remain unclear. This study explored the variation in fine root biomass and necromass among different evergreen broad-leaved forest types, the relative importance of the niche complementarity and the mass ratio hypotheses in driving biodiversity effects, as well as forest stand attributes and environmental factors causing variation in fine root biomass and necromass. We detected no significant differences between most forest types, and the prominently lower amount of fine root biomass and necromass in monsoon evergreen broad-leaved forests may be ascribed to the accelerated turnover rate caused by the elevated temperature. Conversely, the functional divergence showed marginally positive effects on fine root necromass, hence the effects of functional diversity may be negligible; however, community-weighted mean trait values, i.e. specific leaf area and leaf phosphorus concentration, demonstrated significantly negative effects on them. Basal area and stem density showed significant influence in regulating fine root biomass. The optimal GAM models explained 79.5% and 54.4% of the variation of fine root biomass and necromass, respectively. Our results suggest that fine root biomass and necromass may be determined by the functional characteristics of dominant tree species rather than collective functional diversity and closely linked to forest stand, topographic and edaphic factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 it