Taxonomic diversity predicts aboveground biomass mainly through functional dominance across subtropical forest succession
Bibliographic record
Abstract
Abstract Studies on diversity–biomass relationships (DBRs) provide insights into the mechanisms underlying ecosystem functioning and services. While manipulative experiments indicate that both functional diversity and functional dominance influence biomass, with functional diversity often becoming the stronger predictor over time, their relative contributions during natural forest succession remain unclear. Here, we analysed tree data from 2010 to 2020 across early, middle and late-successional forests in subtropical China to investigate how the effect of taxonomic diversity on aboveground biomass (AGB) is related to shifts in the roles of functional diversity and functional dominance of five functional traits, corresponding to the complementarity and biomass ratio hypothesis. Our results showed that mean AGB increased with succession, reaching its highest at the middle stage. Taxonomic diversity influenced AGB primarily through its impact on functional properties rather than directly. From early to late-successional stages, functional dominance consistently emerged as the stronger predictor of AGB compared to functional diversity. Specifically, in earlier stages, the dominance of species with fast leaf economic traits directly and negatively impacted AGB, whereas, in the late stage, the dominance of tall species had a direct positive impact. Although functional diversity contributed increasingly to AGB in a positive manner during succession, its effect was primarily indirect, largely mediated through functional dominance. Overall, our findings support the biomass ratio hypothesis as the primary mechanism underlying DBRs throughout succession. This highlights the importance of functional dominance in driving forest biomass production and emphasizes the need to consider dominant species’ traits in forest management and restoration strategies.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".