High tree diversity enhances light interception in tropical forests
Bibliographic record
Abstract
Abstract Understanding the processes that underlie the effects of tree diversity on primary production is of foremost importance to enhance climate change mitigation by tropical forests. Here, we investigated the effects of tree diversity on light interception over space and time in two tropical tree experiments, located in Panama— Sardinilla site (monocultures to 18‐species mixtures), and in Brazil— Anhembi site (20‐ to 114‐species mixtures). We assessed intercepted photosynthetically active radiation (iPAR) over horizontal grids and vertical transects (up to 5 m high in Sardinilla and up to 4 m high in Anhembi ), in plots containing different richness levels and combinations of species. Light interception increased from monocultures to 5‐species mixtures in Sardinilla and from 20‐ to 114‐species mixtures in Anhembi (during the dry season). At the Sardinilla site, five‐species mixtures showed iPAR comparable to the best performing monocultures, despite substantial differences observed among monocultures. Diversity‐iPAR relationships changed seasonally and were more pronounced during the dry season. Tree richness promoted a less variable temporal and spatial (i.e. both horizontal and vertical) distribution of light interception. Synthesis . High tree diversity (i.e. over a hundred species in mixture) maximizes the amount of light intercepted by the canopy in restored tropical forests through more even capture in space and time. Diversity‐light interception relationships should be critically considered for designing cost‐efficient large‐scale tropical forest restoration programs.
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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 | 0.000 | 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) | 0.001 | 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 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".