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Peer-reviewedCC BY 4.0Biology Open

One dataset, four meta-analyses: synthesising mean effects and heterogeneity within and among populations

  • Haley Lacza1
  • Ayumi Mizuno1
  • Coralie Williams2,3
  • Malgorzata Lagisz1,2
  • Shinichi Nakagawa1,2
  1. 1Collaboration for Open Science and Synthesis in Ecology and Evolution (COSSEE), Department of Biological Sciences, Faculty of Science, University of Alberta, Edmonton, AB T6G 2E9, Canada
  2. 2Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia
  3. 3School of Mathematics and Statistics, The University of New South Wales, Sydney, NSW 2052, Australia
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Abstract

Ecological syntheses (meta-analyses) usually ask ‘what is the average effect?’ But many questions also depend on whether outcomes become more or less variable and whether effects are predictable across contexts. We show how one dataset can support a workflow that synthesises both mean effects and within-group variability while also separating heterogeneity among study-level effects from residual heterogeneity among effect sizes within studies. Using an organic versus conventional crop-yield dataset, we analyse mean effects with the log response ratio (lnRR; Model 1) and within-group variability with the log variability ratio (lnVR) and log coefficient of variation ratio (lnCVR; Model 2), all calculated from the same summary statistics (means, s.d.s. and sample sizes). We then use multilevel location–scale meta-regression to test whether fertiliser type shifts expected lnRR and lnVR/lnCVR effects (Models 3 and 4) and whether residual and study-level heterogeneity differ between contexts. These heterogeneity components respectively inform consistency among represented comparisons and transferability to a new study. Analysing mean effects, within-group variability and context-dependent heterogeneity together can provide a more informative description of ecological responses than focusing on average effects alone.

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Cite

BibTeXRIS
BibTeX
@article{lacza2026dataset,
  title = {{One dataset, four meta-analyses: synthesising mean effects and heterogeneity within and among populations}},
  author = {Haley Lacza and Ayumi Mizuno and Coralie Williams and Malgorzata Lagisz and Shinichi Nakagawa},
  journal = {Biology Open},
  year = {2026},
  month = oct,
  doi = {10.1242/bio.062872},
  url = {https://doi.org/10.1242/bio.062872},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - One dataset, four meta-analyses: synthesising mean effects and heterogeneity within and among populations
AU  - Haley Lacza
AU  - Ayumi Mizuno
AU  - Coralie Williams
AU  - Malgorzata Lagisz
AU  - Shinichi Nakagawa
PY  - 2026
DA  - 2026/10/13
JO  - Biology Open
DO  - 10.1242/bio.062872
UR  - https://doi.org/10.1242/bio.062872
AB  - Ecological syntheses (meta-analyses) usually ask ‘what is the average effect?’ But many questions also depend on whether outcomes become more or less variable and whether effects are predictable across contexts. We show how one dataset can support a workflow that synthesises both mean effects and within-group variability while also separating heterogeneity among study-level effects from residual heterogeneity among effect sizes within studies. Using an organic versus conventional crop-yield dataset, we analyse mean effects with the log response ratio (lnRR; Model 1) and within-group variability with the log variability ratio (lnVR) and log coefficient of variation ratio (lnCVR; Model 2), all calculated from the same summary statistics (means, s.d.s. and sample sizes). We then use multilevel location–scale meta-regression to test whether fertiliser type shifts expected lnRR and lnVR/lnCVR effects (Models 3 and 4) and whether residual and study-level heterogeneity differ between contexts. These heterogeneity components respectively inform consistency among represented comparisons and transferability to a new study. Analysing mean effects, within-group variability and context-dependent heterogeneity together can provide a more informative description of ecological responses than focusing on average effects alone.
SN  - 2046-6390
ER  -