Experimental design and the outcome and interpretation of diversity–stability relations
Bibliographic record
Abstract
The nature of the relationship between diversity and stability has become the subject of intense research effort over the last few decades as the role of diversity as a major driver of ecosystem functioning and stability has come to the forefront of ecological interest. Here, we present a meta‐analysis of the impact of twelve experimental design factors on the strength and direction of relations between biotic richness and temporal variability at both the aggregate community‐ and population‐level. Based on 35 studies that report 59 community‐level and 36 population‐level relations, our results show that biotic richness has a highly general stabilizing effect on community properties that are only marginally affected by the nuances of experimental design. In contrast, experimental design factors have a highly significant effect on mean effect sizes and the resulting interpretation of relations between richness and population‐level variability. The strongest dichotomous effect was observed based on the method of calculating the response variable, such that when population variability was calculated as the mean variability of populations across all replicates, biotic richness showed a negative (stabilizing) mean effect size. In contrast, when population variability was calculated on a per replicate basis, biotic richness showed a positive (destabilizing) mean effect size. This latter result suggests that a renewed focus on the mechanisms by which populations can be stabilized (and destabilized) by diversity is needed.
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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 | 0.090 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".