Tree diversity shapes the spectral signature of light transmittance in developing forests
Bibliographic record
Abstract
Greater tree diversity often increases forest productivity by increasing the fraction of light captured and the effectiveness of light use at the community scale. However, light may shape forest function not only as a source of energy or a cause of stress but also as a context cue: Plant photoreceptors can detect specific wavelengths of light, and plants use this information to assess their neighborhoods and adjust their patterns of growth and allocation. These cues have been well documented in laboratory studies, but little studied in diverse forests. Here, we examined how the spectral profile of light (350-2200 nm) transmitted through canopies differs among tree communities within three diversity experiments on two continents (200 plots each planted with one to 12 tree species, amounting to roughly 10,000 trees in total), laying the groundwork for expectations about how diversity in forests may shape light quality with consequences for forest function. We hypothesized-and found-that the species composition and diversity of tree canopies influenced transmittance in predictable ways. Canopy transmittance-in total and in spectral regions with known biological importance-principally declined with increasing leaf area per ground area (LAI) and, in turn, LAI was influenced by the species composition and diversity of communities. For a given LAI, broadleaved angiosperm canopies tended to transmit less light with lower red-to-far-red ratios than canopies of needle-leaved gymnosperms or angiosperm-gymnosperm mixtures. Variation among communities in the transmittance of individual leaves had a minor effect on canopy transmittance in the visible portion of the spectrum but contributed beyond this range along with differences in foliage arrangement. Transmittance through mixed species canopies often deviated from expectations based on monocultures, and this was only partly explained by diversity effects on LAI, suggesting that diversity effects on transmittance also arose through shifts in the arrangement and optical properties of foliage. We posit that differences in the spectral profile of light transmitted through diverse canopies serve as a pathway by which tree diversity affects some forest ecosystem functions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".