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Understanding 'it depends' in ecology: a guide to hypothesising, visualising and interpreting statistical interactions

Understanding 'it depends' in ecology: a guide to hypothesising, visualising and interpreting statistical interactions
Understanding 'it depends' in ecology: a guide to hypothesising, visualising and interpreting statistical interactions
Ecologists routinely use statistical models to detect and explain interactions among ecological drivers, with a goal to evaluate whether an effect of interest changes in sign or magnitude in different contexts. Two fundamental properties of interactions are often overlooked during the process of hypothesising, visualising and interpreting interactions between drivers: the measurement scale – whether a response is analysed on an additive or multiplicative scale, such as a ratio or logarithmic scale; and the symmetry – whether dependencies are considered in both directions. Overlooking these properties can lead to one or more of three inferential errors: misinterpretation of (i) the detection and magnitude (Type-D error), and (ii) the sign of effect modification (Type-S error); and (iii) misidentification of the underlying processes (Type-A error). We illustrate each of these errors with a broad range of ecological questions applied to empirical and simulated data sets. We demonstrate how meta-analysis, a widely used approach that seeks explicitly to characterise context dependence, is especially prone to all three errors. Based on these insights, we propose guidelines to improve hypothesis generation, testing, visualisation and interpretation of interactions in ecology.
antagonistic, effect size, generalised linear models; Hedges' g, log response ratio, meta-regression, statistical interaction, synergistic, synthesis, transformation
1464-7931
983-1002
Spake, Rebecca
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Bowler, Diana E.
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Callaghan, Corey T.
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Blowes, Shane A.
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Doncaster, C. Patrick
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Antao, Laura H.
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Nakagawa, Shinichi
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McElreath, Richard
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Chase, Jonathan M.
e7fee8ac-54e6-4540-86a0-a1fcee549143
Spake, Rebecca
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Bowler, Diana E.
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Callaghan, Corey T.
742a49d2-1e69-4a80-8194-03ffc35c56fd
Blowes, Shane A.
3746e760-7dfb-46ea-8014-12895e67b030
Doncaster, C. Patrick
0eff2f42-fa0a-4e35-b6ac-475ad3482047
Antao, Laura H.
45e758ea-8478-4a5a-9309-034193397901
Nakagawa, Shinichi
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McElreath, Richard
e5927581-4cc9-4c1e-aed2-4f2695069c17
Chase, Jonathan M.
e7fee8ac-54e6-4540-86a0-a1fcee549143

Spake, Rebecca, Bowler, Diana E., Callaghan, Corey T., Blowes, Shane A., Doncaster, C. Patrick, Antao, Laura H., Nakagawa, Shinichi, McElreath, Richard and Chase, Jonathan M. (2023) Understanding 'it depends' in ecology: a guide to hypothesising, visualising and interpreting statistical interactions. Biological Reviews, 98 (4), 983-1002. (doi:10.1111/brv.12939).

Record type: Article

Abstract

Ecologists routinely use statistical models to detect and explain interactions among ecological drivers, with a goal to evaluate whether an effect of interest changes in sign or magnitude in different contexts. Two fundamental properties of interactions are often overlooked during the process of hypothesising, visualising and interpreting interactions between drivers: the measurement scale – whether a response is analysed on an additive or multiplicative scale, such as a ratio or logarithmic scale; and the symmetry – whether dependencies are considered in both directions. Overlooking these properties can lead to one or more of three inferential errors: misinterpretation of (i) the detection and magnitude (Type-D error), and (ii) the sign of effect modification (Type-S error); and (iii) misidentification of the underlying processes (Type-A error). We illustrate each of these errors with a broad range of ecological questions applied to empirical and simulated data sets. We demonstrate how meta-analysis, a widely used approach that seeks explicitly to characterise context dependence, is especially prone to all three errors. Based on these insights, we propose guidelines to improve hypothesis generation, testing, visualisation and interpretation of interactions in ecology.

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Biological Reviews - 2023 - Spake - Understanding it depends in ecology a guide to hypothesising visualising and - Version of Record
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Accepted/In Press date: 7 February 2023
e-pub ahead of print date: 1 March 2023
Published date: August 2023
Additional Information: Funding Information: R. S. was funded by the German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig. C. T. C. was supported by a Marie Skłodowska‐Curie Individual Fellowship (no. 891052). L. H. A. was funded by the Academy of Finland (grant 340280). We thank I. Oliver for supplying the ant data for Fig. 4 . We thank D. Craven for motivating Figure 9 with their blog post on nonlinear properties of response ratios. We are grateful to B. Bolker and N.G. Yoccoz for reviewing and improving an earlier version of this manuscript. Funding Information: R. S. was funded by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. C. T. C. was supported by a Marie Skłodowska-Curie Individual Fellowship (no. 891052). L. H. A. was funded by the Academy of Finland (grant 340280). We thank I. Oliver for supplying the ant data for Fig. 4. We thank D. Craven for motivating Figure 9 with their blog post on nonlinear properties of response ratios. We are grateful to B. Bolker and N.G. Yoccoz for reviewing and improving an earlier version of this manuscript. Publisher Copyright: © 2023 The Authors. Biological Reviews published by John Wiley & Sons Ltd on behalf of Cambridge Philosophical Society.
Keywords: antagonistic, effect size, generalised linear models; Hedges' g, log response ratio, meta-regression, statistical interaction, synergistic, synthesis, transformation

Identifiers

Local EPrints ID: 477560
URI: http://eprints.soton.ac.uk/id/eprint/477560
ISSN: 1464-7931
PURE UUID: e9689814-2c92-4b8e-a3dd-7d304510b6e5
ORCID for C. Patrick Doncaster: ORCID iD orcid.org/0000-0001-9406-0693

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Date deposited: 08 Jun 2023 16:44
Last modified: 06 Jun 2024 01:35

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Contributors

Author: Rebecca Spake
Author: Diana E. Bowler
Author: Corey T. Callaghan
Author: Shane A. Blowes
Author: Laura H. Antao
Author: Shinichi Nakagawa
Author: Richard McElreath
Author: Jonathan M. Chase

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