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      Effects of immune challenge on expression of life-history and immune trait expression in sexually reproducing metazoans—a meta-analysis

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          Abstract

          Background

          Life-history theory predicts a trade-off between investment into immune defence and other fitness-related traits. Accordingly, individuals are expected to upregulate their immune response when subjected to immune challenge. However, this is predicted to come at the expense of investment into a range of other traits that are costly to maintain, such as growth, reproduction and survival. Currently, it remains unclear whether the magnitude of such costs, and trade-offs involving immune investment and other traits, manifests consistently across species and sexes. To address this, we conducted a meta-analysis to investigate how changes in sex, ontogenetic stage and environmental factors shape phenotypic trait expression following an immune challenge.

          Results

          We explored the effects of immune challenge on three types of traits across sexually reproducing metazoans: life-history, morphological and proximate immune traits (235 effect sizes, 53 studies, 37 species [21 invertebrates vs. 16 vertebrates]). We report a general negative effect of immune challenge on survival and reproduction, a positive effect on immune trait expression, but no effect on morphology or development time. The negative effects of immune challenge on reproductive traits and survival were larger in females than males. We also report a pronounced effect of the immune treatment agent used (e.g. whether the treatment involved a live pathogen or not) on the host response to immune challenge, and find an effect of mating status on the host response in invertebrates.

          Conclusion

          These results suggest that costs associated with immune deployment following an immune challenge are context-dependent and differ consistently in their magnitude across the sexes of diverse taxonomic lineages. We synthesise and discuss the outcomes in the context of evolutionary theory on sex differences in life-history and highlight the need for future studies to carefully consider the design of experiments aimed at disentangling the costs of immune deployment.

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          Most cited references67

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          Inference from Iterative Simulation Using Multiple Sequences

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            Effect size, confidence interval and statistical significance: a practical guide for biologists.

            Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.
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              Ecological immunology: costly parasite defences and trade-offs in evolutionary ecology

              In the face of continuous threats from parasites, hosts have evolved an elaborate series of preventative and controlling measures - the immune system - in order to reduce the fitness costs of parasitism. However, these measures do have associated costs. Viewing an individual's immune response to parasites as being subject to optimization in the face of other demands offers potential insights into mechanisms of life history trade-offs, sexual selection, parasite-mediated selection and population dynamics. We discuss some recent results that have been obtained by practitioners of this approach in natural and semi-natural populations, and suggest some ways in which this field may progress in the near future.
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                Author and article information

                Contributors
                magdalena.nystrand@monash.edu
                damian.dowling@monash.edu
                Journal
                BMC Biol
                BMC Biol
                BMC Biology
                BioMed Central (London )
                1741-7007
                7 October 2020
                7 October 2020
                2020
                : 18
                : 135
                Affiliations
                GRID grid.1002.3, ISNI 0000 0004 1936 7857, School of Biological Sciences, , Monash University, ; Clayton, Victoria 3800 Australia
                Author information
                http://orcid.org/0000-0002-8854-3928
                https://orcid.org/0000-0003-2209-3458
                Article
                856
                10.1186/s12915-020-00856-7
                7541220
                31898513
                7ac529fe-bcb9-4688-b890-167d936d7067
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 21 July 2020
                : 25 August 2020
                Funding
                Funded by: Australian Research Council
                Award ID: DP110104965
                Funded by: FundRef http://dx.doi.org/10.13039/501100001779, Monash University;
                Award ID: Margaret Clayton Women in Research Postdoctoral Fellowship
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Life sciences
                immune challenge,phenotype,context-dependence,life-history trade-offs,sexually dimorphic,sex-specific

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