Forest structure predicts aboveground biomass better than community-weighted mean of traits, functional diversity, topography, and soil in a tropical forest across spatial scales
Bibliographic record
Abstract
Aboveground biomass of vegetation plays an important role in the global carbon cycle and climate change mitigation. Both abiotic and biotic factors can influence aboveground biomass directly, as well as indirectly, and these effects can depend on the spatial scale in which data are measured. We explored the direct and indirect effects of site topography, soil properties, forest structure, and functional traits on aboveground biomass at two spatial scales (i.e., 20 m × 20 m and 50 m × 50 m) in a tropical seasonal rainforest. We found that the relative importance of biotic factors was greater than that of abiotic factors across scales. Forest structure consistently had the greatest positive influence on aboveground biomass at both spatial scales. The mass ratio effect could act in driving aboveground biomass at the small spatial scale, while we found no evidence to support the niche complementarity effect at either scale. The relative importance of soil properties on aboveground biomass decreased with increasing spatial scale, while that of topography increased. The total effects of topography and soil properties on aboveground biomass were consistently positive across scales. We conclude that considering spatial scale is important to fully understand how biotic and abiotic factors drive aboveground biomass. Our results highlight the importance of focusing on forest structure and the scale dependence of the drivers of aboveground biomass in the context of forest management and restoration to improve forest carbon sequestration and mitigate climate change .
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".