Biodiversity and ecosystem functioning: Have our experiments and indices been underestimating the role of facilitation?
Bibliographic record
Abstract
Abstract After 25 years of biodiversity experiments, it is clear that higher biodiversity (B) plant communities are usually more productive and often have greater ecosystem functioning (EF) than lower diversity communities. However, the mechanisms underlying this positive biodiversity – ecosystem functioning (BEF) relationship are still poorly understood. The vast majority of past work in BEF research has focused on the roles of mathematically partitioned complementarity and selection effects. While these mathematical approaches have provided insights into underlying mechanisms, they have focused strongly on competition and resource partitioning. Importantly, mathematically partitioned complementarity effects include multiple facilitative mechanisms, including dilution of species‐specific pathogens, positive changes in soil nutrient cycling, associational defence and microclimate amelioration. Synthesis . This Special Feature takes an experimental and mechanistic approach to teasing out the facilitative mechanisms that underlie positive BEF relationships. As an example, we demonstrate diversity‐driven changes in microclimate amelioration. Articles in this Special Feature explore photoinhibition, experimental manipulations of microclimate, lidar examinations of plant canopy effects and higher‐order trophic interactions as facilitative mechanisms behind classic BEF processes. We emphasize the need for future BEF experiments to disentangle the facilitative mechanisms that are interlinked with niche complementarity to better understand the fundamental processes by which diversity regulates life on Earth.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".