Implications for meta-analysis of scale dependence in biodiversity change Seminar
- Time:
- 12:00
- Date:
- 18 June 2020
- Venue:
- Via Teams
Event details
Geography & Environmental Science Seminar
Empirical studies of biodiversity have well-recognised sensitivities to spatial and temporal scale. Meta-analyses typically collate studies that vary widely in replication and scale of observed or estimated numbers or densities of species. Any scale dependence in the study-level biodiversity metrics then influences estimates of meta-effect size and confidence interval, with an as yet unquantified potential for bias. Here we sample from simulated communities of old-growth and secondary forests to evaluate sources of inaccuracy in study-estimates and meta-estimates of effect sizes, caused by within-study plot and sample sizes, biodiversity measure, and choice of effect-size metric. We find multiplicative influences of all these parameters on the magnitude and direction of change in effect-size accuracy with precision (1/variance, the study-level weighting). Across scales, species richness with log response ratio yields better accuracy than species density and Hedges’ g , which dangerously combines higher precision with persistent bias. Using empirical datasets, we demonstrate the challenge of detecting scale-dependence, which is governed by the weighting scheme and the effect-size metric, due to covariation between replication, variance and plot size. We propose solutions to reducing bias, urge empirical studies to publish raw data to allow meta-analysts to evaluate covariation, and generally recommend against using Hedges’ g in biodiversity meta-analyses.
Speaker Information
Dr Becks Spake , Postdoc in Applied Biogeography, School of Geography and Environmental Science, University of Southampton