Does complementary resource use enhance ecosystem functioning? A model of light competition in plant communities
Bibliographic record
Abstract
Recent experiments on grassland ecosystems have shown that biodiversity can enhance ecosystem processes such as plant biomass production. Functional complementarity is generally regarded as the main class of mechanisms generating these effects of biodiversity on ecosystem functioning. Although intuitively appealing and supported by some data, the complementarity hypothesis has been little explored theoretically using mechanistic approaches. Here, we present a simple dynamical model for a light-limited terrestrial ecosystem to assess the effects of species diversity on light competition and total biomass in plant communities. Our model shows that competitive relaxation (reduction in average light competition intensity) due to differences in foliar architecture among species enhances total plant biomass in mixtures, but that competitive imbalance (generated by the variance of the average light competition intensity experienced by different species) can either reinforce the effect of competitive relaxation or counteract it and contribute to reducing total plant biomass. Thus, complementary resource use is not enough to increase total plant biomass in species-rich communities; competitive balance among species also plays an important role. We propose an operational measure of light-use complementarity using empirical field data on light absorption to test the presence of complementarity in natural plant communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".