Re-analysis of meta-analysis: support for the stress-gradient hypothesis

1 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. 2 In light of this sweeping conclusion, we re-examined the analytical approach and explored the general synthetic power of meta-analysis. 3 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. 4 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along stress gradients, did not identify a stress gradient within the study, focused on invasive species or were not peer reviewed.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. 5 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. 6 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.