Effects of tree functional diversity and environmental gradients on belowground biomass in a natural old-growth forest ecosystem
Bibliographic record
Abstract
The positive effects of biodiversity on aboveground biomass in natural terrestrial ecosystems have been well documented, whereas the relationships between tree biodiversity and belowground biomass remain largely unexplored. Traditionally, two sets of hypotheses based on the functional trait approach, niche complementarity (NC) and mass ratio (MR), have been proposed to explain the positive effects of biodiversity. Whereas NC emphasizes that functional discrepancy enhances the collective functioning of a given ecosystem, MR states that ecosystem functioning is mainly regulated by the functional traits of dominant species. This study explored the relative importance of these two hypotheses and the effects of forest stand and environmental characteristics on belowground biomass in an old-growth broad-leaved evergreen forest. The mean coarse-root biomass, fine-root biomass, and fine-root necromass were 117.78 ± 54.000, 4.09 ± 0.85, and 0.60 ± 0.31 Mg·ha−1, respectively. We found positive effects of functional diversity on belowground biomass; however, the community-weighted mean trait values were more relevant, indicating that MR exhibited more explanatory power than NC. The combination of informative environmental factors explained 62.0%, 53.2%, and 37.8% of the variation of coarse-root biomass, fine-root biomass, and fine-root necromass, respectively. Our results suggest that the functional identity of dominant tree species exerts more influence than functional diversity on the belowground biomass in old-growth forest ecosystems and that forest stand characteristics and topographic and edaphic factors also play important roles in shaping belowground biomass patterns in old-growth forest ecosystems.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".