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Implications of scale dependence for cross-study syntheses of biodiversity differences

Implications of scale dependence for cross-study syntheses of biodiversity differences
Implications of scale dependence for cross-study syntheses of biodiversity differences
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.
accuracy, biodiversity, effect size, grain, meta-analysis, multilevel model, precision, scale, synthesis
1461-023X
374-390
Spake, Rebecca
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Mori, Akira S.
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Beckmann, Michael
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Martin, Philip A.
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Christie, Alec
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Duguid, Marlyse
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Doncaster, Charles
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Spake, Rebecca
1cda8ad0-2ab2-45d9-a844-ec3d8be2786a
Mori, Akira S.
f24dc459-2b2e-4e5a-8924-710770b98ad0
Beckmann, Michael
d1d1decb-276b-411c-af86-a2c56c981181
Martin, Philip A.
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Christie, Alec
0a601110-ff07-4a51-9c44-473a051165c1
Duguid, Marlyse
6b2f591c-a318-4089-bb66-bbe7c1039215
Doncaster, Charles
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Spake, Rebecca, Mori, Akira S., Beckmann, Michael, Martin, Philip A., Christie, Alec, Duguid, Marlyse and Doncaster, Charles (2021) Implications of scale dependence for cross-study syntheses of biodiversity differences. Ecology Letters, 24 (2), 374-390. (doi:10.1111/ele.13641).

Record type: Review

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.

Text
ELE-00714-2020.R1_revised_main_text_accepted - Accepted Manuscript
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More information

Accepted/In Press date: 19 October 2020
e-pub ahead of print date: 20 November 2020
Published date: 11 January 2021
Keywords: accuracy, biodiversity, effect size, grain, meta-analysis, multilevel model, precision, scale, synthesis

Identifiers

Local EPrints ID: 444760
URI: http://eprints.soton.ac.uk/id/eprint/444760
ISSN: 1461-023X
PURE UUID: cab00542-62db-4435-9c73-b16f557fcf6b
ORCID for Charles Doncaster: ORCID iD orcid.org/0000-0001-9406-0693

Catalogue record

Date deposited: 04 Nov 2020 17:30
Last modified: 17 Mar 2021 05:04

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Contributors

Author: Rebecca Spake
Author: Akira S. Mori
Author: Michael Beckmann
Author: Philip A. Martin
Author: Alec Christie
Author: Marlyse Duguid

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