The context dependence of beneficiary feedback effects on benefactors in plant facilitation
Bibliographic record
Abstract
Facilitative effects of some species on others are a major driver of biodiversity. These positive effects of a benefactor on its beneficiary can result in negative feedback effects of the beneficiary on the benefactor and reduced fitness of the benefactor. However, in contrast to the wealth of studies on facilitative effects in different environments, we know little about whether the feedback effects show predictable patterns of context dependence. We reanalyzed a global data set on alpine cushion plants, previously used to assess their positive effects on biodiversity and the nature of the beneficiary feedback effects, to specifically assess the context dependence of how small- and large-scale drivers alter the feedback effects of cushion-associated (beneficiary) species on their cushion benefactors using structural equation modelling. The effect of beneficiaries on cushions became negative when beneficiary diversity increased and facilitation was more intense. Local-scale biotic and climatic conditions mediated these community-scale processes, having indirect effects on the feedback effect. High-productivity sites demonstrated weaker negative feedback effects of beneficiaries on the benefactor. Our results indicate a limited impact of the beneficiary feedback effects on benefactor cushions, but strong context dependence. This context dependence may help to explain the ecological and evolutionary persistence of this widespread facilitative system.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".