Implications of scale dependence for cross-study syntheses of biodiversity differences
Author(s)
Type
Journal Article
Abstract
Biodiversity studies are sensitive to well-recognised temporal and spatial scale dependencies. Cross-study syntheses may inflate these influences by collating studies that vary widely in the numbers and sizes of sampling plots. Here we evaluate sources of inaccuracy and imprecision in study-level and cross-study estimates of biodiversity differences, caused by within-study grain and sample sizes, biodiversity measure, and choice of effect-size metric. Samples from simulated communities of old-growth and secondary forests demonstrated influences of all these parameters on the accuracy and precision of cross-study effect sizes. In cross-study synthesis by formal meta-analysis, the metric of log response ratio applied to measures of species richness yielded better accuracy than the commonly used Hedges' g metric on species density, which dangerously combined higher precision with persistent bias. Full-data analyses of the raw plot-scale data using multilevel models were also susceptible to scale-dependent bias. We demonstrate the challenge of detecting scale dependence in cross-study synthesis, due to ubiquitous covariation between replication, variance and plot size. We propose solutions for diagnosing and minimising bias. We urge that empirical studies publish raw data to allow evaluation of covariation in cross-study syntheses, and we recommend against using Hedges' g in biodiversity meta-analyses.
Editor(s)
Chase, Jonathan
Date Issued
2021-02-01
Date Acceptance
2020-10-19
Citation
Ecology Letters, 2021, 24 (2), pp.374-390
ISSN
1461-023X
Publisher
Wiley
Start Page
374
End Page
390
Journal / Book Title
Ecology Letters
Volume
24
Issue
2
Copyright Statement
© 2020 The Authors. Ecology Letters published by John Wiley & Sons Ltd This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000590609800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
accuracy
analysis
biodiversity
DIVERSITY
Ecology
effect size
EFFECT SIZE
Environmental Sciences & Ecology
FOREST
grain
Life Sciences & Biomedicine
MANAGEMENT
meta‐
METAANALYSIS
MODELS
multilevel model
POPULATION
precision
scale
Science & Technology
synthesis
Publication Status
Published
Coverage Spatial
England
Date Publish Online
2020-11-20