Complementarity as a mechanism of coexistence between functional groups of grasses
Bibliographic record
Abstract
1 Increasing functional diversity often leads to an increase in ecosystem productivity in the form of overyielding. While the mechanisms (i.e. complementarity or facilitation) that underlie overyielding provide strong insights into species coexistence and community assembly, they are rarely tested. In subalpine grasslands, traditional management through manuring and hay-making results in intermediate productivity that is associated with high functional diversity. This functional diversity results from the coexistence between conservative plant species (with slow growth rates, low specific leaf area) and exploitative species (with fast growth rates, high specific leaf area). 2 We hypothesized that overyielding occurs among these two functional groups and tested whether complementarity or facilitation can explain overyielding. Using three perennial grass species per functional group, we compared single and mixed functional group mesocosms at low and intermediate levels of fertilization to test the occurrence of overyielding. Additionally, we measured the outcomes of biotic interactions among these two functional groups by manipulating plant density. 3 After two growing seasons, we found evidence of overyielding under intermediate levels of fertility. Overyielding was associated with a reduction of competition intensity when both functional groups were grown together. These results suggest that complementarity, as evidenced by a decrease in competition intensity, rather than facilitation, explains the observed overyielding. Indeed, we found evidence for complementarity for light and modification of nutrient use as possible mechanisms for the overyielding. 4 Synthesis. Complementarity between functional groups might be an important mechanism enhancing functional diversity, particularly in harsh environments at intermediate rather than low fertility.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".