Quantitative evidence synthesis: a practical guide on meta-analysis, meta-regression, and publication bias tests for environmental sciences

Nakagawa, S. ORCID: https://orcid.org/0000-0002-7765-5182, Yang, Y. ORCID: https://orcid.org/0000-0002-8610-4016, Macartney, E. L. ORCID: https://orcid.org/0000-0003-3866-143X, Spake, R. ORCID: https://orcid.org/0000-0003-4671-2225 and Lagisz, M. ORCID: https://orcid.org/0000-0002-3993-6127 (2023) Quantitative evidence synthesis: a practical guide on meta-analysis, meta-regression, and publication bias tests for environmental sciences. Environmental Evidence, 12. 8. ISSN 2047-2382 doi: 10.1186/s13750-023-00301-6

Abstract/Summary

Meta-analysis is a quantitative way of synthesizing results from multiple studies to obtain reliable evidence of an intervention or phenomenon. Indeed, an increasing number of meta-analyses are conducted in environmental sciences, and resulting meta-analytic evidence is often used in environmental policies and decision-making. We conducted a survey of recent meta-analyses in environmental sciences and found poor standards of current meta-analytic practice and reporting. For example, only ~ 40% of the 73 reviewed meta-analyses reported heterogeneity (variation among effect sizes beyond sampling error), and publication bias was assessed in fewer than half. Furthermore, although almost all the meta-analyses had multiple effect sizes originating from the same studies, non-independence among effect sizes was considered in only half of the meta-analyses. To improve the implementation of meta-analysis in environmental sciences, we here outline practical guidance for conducting a meta-analysis in environmental sciences. We describe the key concepts of effect size and meta-analysis and detail procedures for fitting multilevel meta-analysis and meta-regression models and performing associated publication bias tests. We demonstrate a clear need for environmental scientists to embrace multilevel meta-analytic models, which explicitly model dependence among effect sizes, rather than the commonly used random-effects models. Further, we discuss how reporting and visual presentations of meta-analytic results can be much improved by following reporting guidelines such as PRISMA-EcoEvo (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Ecology and Evolutionary Biology). This paper, along with the accompanying online tutorial, serves as a practical guide on conducting a complete set of meta-analytic procedures (i.e., meta-analysis, heterogeneity quantification, meta-regression, publication bias tests and sensitivity analysis) and also as a gateway to more advanced, yet appropriate, methods.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/121839
Identification Number/DOI	10.1186/s13750-023-00301-6
Refereed	Yes
Divisions	Life Sciences > School of Biological Sciences > Ecology and Evolutionary Biology
Publisher	BioMed Central
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