Location‐Scale Meta‐Analysis and Meta‐Regression as a Tool to Capture Large‐Scale Changes in Biological and Methodological Heterogeneity: A Spotlight on Heteroscedasticity.
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| Title: | Location‐Scale Meta‐Analysis and Meta‐Regression as a Tool to Capture Large‐Scale Changes in Biological and Methodological Heterogeneity: A Spotlight on Heteroscedasticity. |
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| Authors: | Nakagawa, Shinichi1,2 (AUTHOR) s.nakagawa@unsw.edu.au, Mizuno, Ayumi1,2 (AUTHOR), Morrison, Kyle2 (AUTHOR), Ricolfi, Lorenzo2 (AUTHOR), Williams, Coralie2,3 (AUTHOR), Drobniak, Szymon M.2,4 (AUTHOR), Lagisz, Malgorzata1,2 (AUTHOR), Yang, Yefeng2 (AUTHOR) yefeng.yang1@unsw.edu.au |
| Source: | Global Change Biology. May2025, Vol. 31 Issue 5, p1-14. 14p. |
| Subject Terms: | *Global environmental change, Publication bias, Heteroscedasticity, Homoscedasticity, Heterogeneity |
| Abstract: | Heterogeneity is a defining feature of ecological and evolutionary meta‐analyses. While conventional meta‐analysis and meta‐regression methods acknowledge heterogeneity in effect sizes, they typically assume this heterogeneity is constant across studies and levels of moderators (i.e., homoscedasticity). This assumption could mask potentially informative patterns in the data. Here, we introduce and develop a location‐scale meta‐analysis and meta‐regression framework that models both the mean (location) and variance (scale) of effect sizes. Such a framework explicitly accommodates heteroscedasticity (differences in variance), thereby revealing when and why heterogeneity itself changes. This capability, we argue, is crucial for understanding responses to global environmental change, where complex, context‐dependent processes may shape both the average magnitude and the variability of biological responses. For example, differences in study design, measurement protocols, environmental factors, or even evolutionary history can lead to systematic shifts in variance. By incorporating hierarchical (multilevel) structures and phylogenetic relationships, location‐scale models can disentangle the contributions from different levels to both location and scale parts. We further attempt to extend the concepts of relative heterogeneity and publication bias into the scale part of meta‐regression. With these methodological advances, we can identify patterns and processes that remain obscured under the constant variance assumption, thereby enhancing the biological interpretability and practical relevance of meta‐analytic results. Notably, almost all published ecological and evolutionary meta‐analytic data can be re‐analysed using our proposed analytic framework to gain new insights. Altogether, location‐scale meta‐analysis and meta‐regression provide a rich and holistic lens through which to view and interpret the intricate tapestry woven with ecological and evolutionary data. The proposed approach, thus, ultimately leads to more informed and context‐specific conclusions about environmental changes and their impacts. [ABSTRACT FROM AUTHOR] |
| Copyright of Global Change Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 185452613 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Location‐Scale Meta‐Analysis and Meta‐Regression as a Tool to Capture Large‐Scale Changes in Biological and Methodological Heterogeneity: A Spotlight on Heteroscedasticity. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nakagawa%2C+Shinichi%22">Nakagawa, Shinichi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> s.nakagawa@unsw.edu.au</i><br /><searchLink fieldCode="AR" term="%22Mizuno%2C+Ayumi%22">Mizuno, Ayumi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Morrison%2C+Kyle%22">Morrison, Kyle</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ricolfi%2C+Lorenzo%22">Ricolfi, Lorenzo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Williams%2C+Coralie%22">Williams, Coralie</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Drobniak%2C+Szymon+M%2E%22">Drobniak, Szymon M.</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lagisz%2C+Malgorzata%22">Lagisz, Malgorzata</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Yefeng%22">Yang, Yefeng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> yefeng.yang1@unsw.edu.au</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Global+Change+Biology%22">Global Change Biology</searchLink>. May2025, Vol. 31 Issue 5, p1-14. 14p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Global+environmental+change%22">Global environmental change</searchLink><br /><searchLink fieldCode="DE" term="%22Publication+bias%22">Publication bias</searchLink><br /><searchLink fieldCode="DE" term="%22Heteroscedasticity%22">Heteroscedasticity</searchLink><br /><searchLink fieldCode="DE" term="%22Homoscedasticity%22">Homoscedasticity</searchLink><br /><searchLink fieldCode="DE" term="%22Heterogeneity%22">Heterogeneity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Heterogeneity is a defining feature of ecological and evolutionary meta‐analyses. While conventional meta‐analysis and meta‐regression methods acknowledge heterogeneity in effect sizes, they typically assume this heterogeneity is constant across studies and levels of moderators (i.e., homoscedasticity). This assumption could mask potentially informative patterns in the data. Here, we introduce and develop a location‐scale meta‐analysis and meta‐regression framework that models both the mean (location) and variance (scale) of effect sizes. Such a framework explicitly accommodates heteroscedasticity (differences in variance), thereby revealing when and why heterogeneity itself changes. This capability, we argue, is crucial for understanding responses to global environmental change, where complex, context‐dependent processes may shape both the average magnitude and the variability of biological responses. For example, differences in study design, measurement protocols, environmental factors, or even evolutionary history can lead to systematic shifts in variance. By incorporating hierarchical (multilevel) structures and phylogenetic relationships, location‐scale models can disentangle the contributions from different levels to both location and scale parts. We further attempt to extend the concepts of relative heterogeneity and publication bias into the scale part of meta‐regression. With these methodological advances, we can identify patterns and processes that remain obscured under the constant variance assumption, thereby enhancing the biological interpretability and practical relevance of meta‐analytic results. Notably, almost all published ecological and evolutionary meta‐analytic data can be re‐analysed using our proposed analytic framework to gain new insights. Altogether, location‐scale meta‐analysis and meta‐regression provide a rich and holistic lens through which to view and interpret the intricate tapestry woven with ecological and evolutionary data. The proposed approach, thus, ultimately leads to more informed and context‐specific conclusions about environmental changes and their impacts. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Global Change Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/gcb.70204 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Global environmental change Type: general – SubjectFull: Publication bias Type: general – SubjectFull: Heteroscedasticity Type: general – SubjectFull: Homoscedasticity Type: general – SubjectFull: Heterogeneity Type: general Titles: – TitleFull: Location‐Scale Meta‐Analysis and Meta‐Regression as a Tool to Capture Large‐Scale Changes in Biological and Methodological Heterogeneity: A Spotlight on Heteroscedasticity. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nakagawa, Shinichi – PersonEntity: Name: NameFull: Mizuno, Ayumi – PersonEntity: Name: NameFull: Morrison, Kyle – PersonEntity: Name: NameFull: Ricolfi, Lorenzo – PersonEntity: Name: NameFull: Williams, Coralie – PersonEntity: Name: NameFull: Drobniak, Szymon M. – PersonEntity: Name: NameFull: Lagisz, Malgorzata – PersonEntity: Name: NameFull: Yang, Yefeng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13541013 Numbering: – Type: volume Value: 31 – Type: issue Value: 5 Titles: – TitleFull: Global Change Biology Type: main |
| ResultId | 1 |
