The control of external and internal canopy structural heterogeneity on diversity and productivity relationship in a subtropical forest
Bibliographic record
Abstract
Forest canopy structure is closely related to species diversity, crown packing efficiency, and ecological processes, while influencing ecosystem functions. However, most existing structural diversity indices only consider internal heterogeneity within the canopy but often neglect the heterogeneity of the external canopy. While, the external canopy heterogeneity can increase the exchange surface area between canopy and external atmosphere, capture more light, which is closely linked to tree growth and ecosystem functions. Comprehensively and accurately estimating canopy structural diversity is crucial in understanding the underlying mechanisms of productivity change. Therefore, in this study, we apply a topographic index – canopy surface rugosity (CSR) – to capture canopy external heterogeneity, and use crown complementary index (CCI) and other canopy structural variables to quantify canopy internal heterogeneity. We find that CSR varies significantly among quadrats (20 m × 20 m) and it decreases with species diversity and wood net primary productivity (NPP wood ). Species diversity increases NPP wood directly and indirectly through CSR and CCI. Functional diversity increases NPP wood through CSR and CCI. Community weighted mean of light demand decreases with NPP wood through CCI. The mediating effect of CSR on the relationship between species (functional) diversity and NPP wood is greater than the mediating effect of CCI. We find soil fertility positive effects on NPP wood mainly through species richness and CSR. Conversely, water availability affects NPP wood by a direct positive effect and indirect negative effects through species richness, CSR and CCI. Overall, our CSR and CCI provide strong support for a space-based niche partitioning mechanism regulating the relationship between species diversity and NPP wood . Additionally, CSR is sensitive to gap dynamics, suggesting mechanistic relationships between local disturbance, species diversity, and NPP wood . • Canopy surface rugosity (CSR) - a new structural diversity index captures the external heterogeneity of canopy structure. • Species and functional diversity promote productivity mainly through CSR and crown complementarity (CCI), and CSR performs better than CCI. • Soil organic carbon increases but soil water availability decreases with forest productivity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".