Linking understory species diversity, community‐level traits and productivity in a Chinese boreal forest
Bibliographic record
Abstract
Abstract Question The consensus has been growing over the past decade that functional traits and diversity are better to explain forest overstory diversity–productivity relationships (DPRs) than species diversity. Although the understory accounts for the majority of plant diversity in forests, it remains unclear how understory aboveground biomass production (UABP) is influenced by its species diversity and community‐level functional traits. Location Great Xing'an Mountains of ortheastern China. Methods We quantified the effects of species richness, community aggregated traits (community weighted mean trait values,CWM) and functional diversity (functional dispersion,FDis) onUABPusing structural equation modeling (SEM), which simultaneously accounted for the effects of overstory tree basal area, stand age, and soil fertility. Results In the full model, species richness had a negative direct, a positive indirect and no total effect onUABP. Furthermore,CWMandFDis, respectively, exhibited positive and no effect onUABP. Among the covariates, soil fertility, stand age, and overstory tree basal area had, respectively, positive, negative, and no effect onUABP. In the model without species richness, all trait variables had similar effects onUABPto those in the full model. In the richness‐only model without traits, species richness, soil fertility and stand age had no effect onUABP. Conclusions Our results suggest that the selection effect largely determined understoryDPRs due to the stronger effects ofCWMonUABPthanof FDis. Soil fertility exhibited the strongest influence on understoryDPRs due to its parallel influences on traits, diversity, and productivity. The increase in resource availability induced by overstory tree litter‐fall likely promoted soil fertility as the main driver of the understoryDPRs. Stand age exhibited a negative effect onUABP, which may have contributed to the increases in shrub dominance and decreases in production due to limited resources.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".