Neighbourhood‐mediated shifts in tree biomass allocation drive overyielding in tropical species mixtures
Bibliographic record
Abstract
Variations in crown forms promote canopy space-use and productivity in mixed-species forests. However, we have a limited understanding on how this response is mediated by changes in within-tree biomass allocation. Here, we explored the role of changes in tree allometry, biomass allocation and architecture in shaping diversity-productivity relationships (DPRs) in the oldest tropical tree diversity experiment. We conducted whole-tree destructive biomass measurements and terrestrial laser scanning. Spatially explicit models were built at the tree level to investigate the effects of tree size and local neighbourhood conditions. Results were then upscaled to the stand level, and mixture effects were explored using a bootstrapping procedure. Biomass allocation and architecture substantially changed in mixtures, which resulted from both tree-size effects and neighbourhood-mediated plasticity. Shifts in biomass allocation among branch orders explained substantial shares of the observed overyielding. By contrast, root-to-shoot ratios, as well as the allometric relationships between tree basal area and aboveground biomass, were little affected by the local neighbourhood. Our results suggest that generic allometric equations can be used to estimate forest aboveground biomass overyielding from diameter inventory data. Overall, we demonstrate that shifts in tree biomass allocation are mediated by the local neighbourhood and promote DPRs in tropical forests.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".