Biodiversity effects and transgressive overyielding
Bibliographic record
Abstract
The potential for mixtures of plant species to produce more biomass than every one of their constituent species in monoculture is still controversially discussed in the literature. Here we tested how this so-called transgressive overyielding is affected by variation between and within species in monoculture yields in biodiversity experiments. We use basic statistical principles to calculate expected maximum monoculture yield in a species pool used for a biodiversity experiment. Using a real example we show how between- and within-species variance components in monoculture yields can be obtained. Combining the two components we estimate the importance of sampling bias in transgressive overyielding analysis. The net biodiversity effect (difference between mixture and average monoculture yield) needed to achieve transgressive overyielding increases with the number of species in a mixture and with the variation between constituent species in monoculture yields. If there is no significant variation between species, transgressive overyielding should not be calculated using the best monoculture, because in this case the difference between this species and the other species could exclusively reflect a sampling bias. The sampling bias decreases with increasing variation between species. Tests for transgressive overyielding require replicated species’ monocultures. However, it can be doubted whether such an emphasis on monocultures in biodiversity experiments is justified if an analysis of transgressive overyielding is not the major goal.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".