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      Maternal Testosterone and Offspring Sex-Ratio in Birds and Mammals: A Meta-Analysis

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          rotl : an R package to interact with the Open Tree of Life data

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            Constraints in the evolution of sex ratio adjustment.

            When the relative fitness of male and female offspring varies with environmental conditions, evolutionary theory predicts that parents should adjust the sex of their offspring accordingly. Qualitative and even quantitative support for this prediction is striking in some taxa but much less convincing in others. Explaining such variation across taxa in the fit of sex ratio theory remains a major challenge. We use meta-analysis to test the role of two constraints in the evolution of sex ratios. Based on analysis of sex ratio skews in birds and wasps, we show that (i) mechanisms of sex determination do not necessarily constrain the evolution of sex ratio adjustment, and (ii) parental ability to predict their offsprings' environment influences the evolution of sex ratio patterns across taxa. More generally, our results show that multiple constraints may determine the precision of adaptation.
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              Heterogeneity in ecological and evolutionary meta-analyses: its magnitude and implications.

              Meta-analysis is the gold standard for synthesis in ecology and evolution. Together with estimating overall effect magnitudes, meta-analyses estimate differences between effect sizes via heterogeneity statistics. It is widely hypothesized that heterogeneity will be present in ecological/evolutionary meta-analyses due to the system-specific nature of biological phenomena. Despite driving recommended best practices, the generality of heterogeneity in ecological data has never been systematically reviewed. We reviewed 700 studies, finding 325 that used formal meta-analysis, of which total heterogeneity was reported in fewer than 40%. We used second-order meta-analysis to collate heterogeneity statistics from 86 studies. Our analysis revealed that the median and mean heterogeneity, expressed as I(2) , are 84.67% and 91.69%, respectively. These estimates are well above "high" heterogeneity (i.e., 75%), based on widely adopted benchmarks. We encourage reporting heterogeneity in the forms of I(2) and the estimated variance components (e.g., τ(2) ) as standard practice. These statistics provide vital insights in to the degree to which effect sizes vary, and provide the statistical support for the exploration of predictors of effect-size magnitude. Along with standard meta-regression techniques that fit moderator variables, multi-level models now allow partitioning of heterogeneity among correlated (e.g., phylogenetic) structures that exist within data.
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                Author and article information

                Journal
                Evolutionary Biology
                Evol Biol
                Springer Science and Business Media LLC
                0071-3260
                1934-2845
                March 2018
                September 30 2017
                March 2018
                : 45
                : 1
                : 96-104
                Article
                10.1007/s11692-017-9432-9
                eb1ee213-1e15-48f9-b70d-a5276d336cba
                © 2018

                http://www.springer.com/tdm

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