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      Phylogenetic meta-analysis reveals system-specific behavioural type–behavioural predictability correlations

      research-article
      1 , 2 , , 3 , 4 , 1 , 2
      Royal Society Open Science
      The Royal Society
      animal personality, behavioural type, behavioural predictability, covariation, phylogenetic meta-analysis

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          Abstract

          The biological significance of behavioural predictability (environment-independent within-individual behavioural variation) became accepted recently as an important part of an individual's behavioural strategy besides behavioural type (individual mean behaviour). However, we do not know how behavioural type and predictability evolve. Here, we tested different evolutionary scenarios: (i) the two traits evolve independently (lack of correlations) and (ii) the two traits' evolution is constrained (abundant correlations) due to either (ii/a) proximate constraints (direction of correlations is similar) or (ii/b) local adaptations (direction of correlations is variable). We applied a set of phylogenetic meta-analyses based on 93 effect sizes across 44 vertebrate and invertebrate species, focusing on activity and risk-taking. The general correlation between behavioural type and predictability did not differ from zero. Effect sizes for correlations showed considerable heterogeneity, with both negative and positive correlations occurring. The overall absolute (unsigned) effect size was high (Zr = 0.58), and significantly exceeded the null expectation based on randomized data. Our results support the adaptive scenario: correlations between behavioural type and predictability are abundant in nature, but their direction is variable. We suggest that the evolution of these behavioural components might be constrained in a system-specific way.

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          Most cited references117

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          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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            Measuring inconsistency in meta-analyses.

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              Quantifying heterogeneity in a meta-analysis.

              The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: SupervisionRole: ValidationRole: Writing – original draft
                Journal
                R Soc Open Sci
                R Soc Open Sci
                RSOS
                royopensci
                Royal Society Open Science
                The Royal Society
                2054-5703
                September 6, 2023
                September 2023
                September 6, 2023
                : 10
                : 9
                : 230303
                Affiliations
                [ 1 ] Department of Systematic Zoology and Ecology, Institute of Biology, ELTE Eötvös Loránd University, , Pázmány Péter sétány 1/C, 1117 Budapest, Hungary
                [ 2 ] ELKH-ELTE-MTM Integrative Ecology Research Group, , Pázmány Péter sétány 1/C, 1117 Budapest, Hungary
                [ 3 ] Centre for Ecological Research, Institute of Ecology and Botany, , Alkotmány u. 2-4, 2163 Vácrátót, Hungary
                [ 4 ] National Laboratory for Health Security, Centre for Ecological Research, , Budapest, Hungary
                Author notes

                Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.6771564.

                Author information
                http://orcid.org/0000-0002-0485-333X
                Article
                rsos230303
                10.1098/rsos.230303
                10480700
                37680498
                3b8572bb-8a73-452f-b890-8c594c602c85
                © 2023 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : March 16, 2023
                : July 24, 2023
                Funding
                Funded by: Ministry of Economy and Competitiveness in Spain;
                Award ID: CGL2015-70639-P
                Funded by: Ministry of Human Capacities;
                Award ID: ÚNKP-17-3-III-ELTE-14
                Funded by: NKFIH;
                Award ID: K-115970
                Award ID: K-129215
                Award ID: PD-132041
                Award ID: SNN-125627
                Categories
                1001
                14
                70
                60
                Organismal and Evolutionary Biology
                Research Articles

                animal personality,behavioural type,behavioural predictability,covariation,phylogenetic meta-analysis

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