Re‐analysis of meta‐analysis: support for the stress‐gradient hypothesis
Bibliographic record
Abstract
Summary Using meta‐analysis, Maestre et al. (2005 , Journal of Ecology, 93, 748–757) recently rejected the predictions of the stress‐gradient hypothesis for arid and semi‐arid environments in entirety. It was concluded that neither positive nor negative effects of neighbours increased with abiotic stress and that different theoretical models are now needed. In light of this sweeping conclusion, we re‐examined the analytical approach and explored the general synthetic power of meta‐analysis. Detailed statistical re‐analyses demonstrated that some of the meta‐analyses of Maestre et al. were robust. However, more rigorous data selection criteria, changing gradient lengths between studies and covariance in response effects did not support their original conclusions. Additionally, application of more rigorous data selection criteria did allow us to detect a significant and consistent positive effect of neighbours, which suggests that facilitation is important at many points along stress gradients. Careful evaluation of the studies used by Maestre et al. also revealed serious limitations. Many studies included in the meta‐analyses were not conducted along stress gradients, did not identify a stress gradient within the study, focused on invasive species or were not peer reviewed. Most importantly, however, gradient lengths were not quantified and appeared to differ dramatically among studies. This crucial source of variation was not accounted for statistically nor in the interpretations. Meta‐analyses are useful tools for synthesis and description but are inherently limited by the appropriateness of the data selected. Unfortunately, in this particular instance, the data available to and selected by Maestre et al. did not adequately test the stress‐gradient hypothesis and cannot thus reject its value for understanding the organization of plant communities in arid systems. The ecological implication of our synthesis is that meta‐analytical summary statistics may not always tell the whole story. Alternative interpretations of differences in effect sizes (or lack thereof) are possible because studies will vary in their ability to test specific predictions of a hypothesis, and furthermore, a certain level of judgement is required to infer the relative importance of certain ideas to synthetic progress within a discipline.
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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 | 0.160 | 0.358 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.041 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".