Meta‐analyses in ecology and evolution require special attention due to certain study characteristics in these fields. First, the primary articles in these fields usually report results that are observed from studies conducted with different species, and the phylogeny among the species violates the independence assumption. Second, articles frequently allow the computation of multiple effect sizes which cannot be accounted for by conventional meta‐analytic models. While both issues can be dealt with by utilizing a multilevel model that accounts for phylogeny, the performance of such a model has not been examined extensively. In this article, we investigate the performance of this model in comparison with some simpler models. We conducted an extensive simulation study where data with different hierarchical structures (in terms of study and species levels) were generated and then various models were fitted to examine their performance. The models we used include the conventional random effects and multilevel random‐effects models along with more complex multilevel models that account for species‐level variance with different variance components. Furthermore, we present an illustrative application of these models based on the data from a meta‐analysis on sizeassortative mating and comment on the results in light of the findings from the simulation study. Our simulation results show that, when the phylogenetic relationships among the species are at least moderately strong, only the most complex model that decomposes the species‐level variance into nonphylogenetic and phylogenetic components provides approximately unbiased estimates of the overall mean and variance components and yields confidence intervals with an approximately nominal coverage rate. Contrarily, removing the phylogenetic or non‐phylogenetic component leads to biased variance component estimates and an increased risk for incorrect inferences about the overall mean. These findings are supported by the results derived from the illustrative application. Based on our results, we suggest that meta‐analyses in ecology and evolution should use the model that accounts for both the nonphylogenetic and phylogenetic species‐level variance in addition to the multilevel structure of the data. Any attempts to simplify this model, such as using only the phylogenetic variance component, may lead to erroneous inferences from the data.

liability for schizophrenia and childhood adversity influences daily-life emotion dysregulation and psychosis proneness.Objective: To test whether polygenic risk score for schizophrenia (PRS-S) interacts with childhood adversity and daily-life stressors to influence momentary mental state domains (negative affect, positive affect, and subtle psychosis expression) and stress-sensitivity measures. Methods: The data were retrieved from a general population twin cohort including 593 adolescents and young adults. Childhood adversity was assessed using the Childhood Trauma Questionnaire. Daily-life stressors and momentary mental state domains were measured using ecological momentary assessment. PRS-S was trained on the latest Psychiatric Genetics Consortium schizophrenia metaanalysis. The analyses were conducted using multilevel mixed-effects tobit regression models. Results: Both childhood adversity and daily-life stressors were associated with increased negative affect, decreased positive affect, and increased subtle psychosis expression, while PRS-S was only associated with increased positive affect. No gene-environment correlation was detected. There is novel evidence for interaction effects between PRS-S and childhood adversity to influence momentary mental states [negative affect (b = 0.07, P = 0.013), positive affect (b = À0.05, P = 0.043), and subtle psychosis expression (b = 0.11, P = 0.007)] and stress-sensitivity measures. Conclusion: Exposure to childhood adversities, particularly in individuals with high PRS-S, is pleiotropically associated with emotion dysregulation and psychosis proneness.

The poolr package provides an implementation of a variety of methods for pooling (i.e., combining) p values, including Fisher's method, Stouffer's method, the inverse chisquare method, the binomial test, the Bonferroni method, and Tippett's method. More importantly, the methods can be adjusted to account for dependence among the tests from which the p values have been derived assuming multivariate normality among the test statistics. All methods can be adjusted based on an estimate of the effective number of tests or by using an empirically-derived null distribution based on pseudo replicates that mimics a proper permutation test. For the Fisher, Stouffer, and inverse chi-square methods, the test statistics can also be directly generalized to account for dependence, leading to Brown's method, Strube's method, and the generalized inverse chi-square method. In this paper, we describe the various methods, discuss their implementation in the package, illustrate their use based on several examples, and compare the poolr package with several other packages that can be used to combine p values.

Meta‐regression can be used to examine the association between effect size estimates and the characteristics of the studies included in a meta‐analysis using regression‐type methods. By searching for those characteristics (i.e., moderators) that are related to the effect sizes, we seek to identify a model that represents the best approximation to the underlying data generating mechanism. Model selection via testing, either through a series of univariate models or a model including all moderators, is the most commonly used approach for this purpose. Here, we describe alternative model selection methods based on information criteria, multimodel inference, and relative variable importance. We demonstrate their application using an illustrative example and present results from a simulation study to compare the performance of the various model selection methods for identifying the true model across a wide variety of conditions. Whether information‐theoretic approaches can also be used not only in combination with maximum likelihood (ML) but also restricted maximum likelihood (REML) estimation was also examined. The results indicate that the conventional methods for model selection may be outperformed by information‐theoretic approaches. The latter are more often among the set of best methods across all of the conditions simulated and can have higher probabilities for identifying the true model under particular scenarios. Moreover, their performance based on REML estimation was either very similar to that from ML estimation or at times even better depending on how exactly the REML likelihood was computed. These results suggest that alternative model selection methods should be more widely applied in meta‐regression.

Meta-analyses in ecology and evolution typically include multiple estimates from the same study and based on multiple species. The resulting dependencies in the data can be addressed by using a phylogenetic multilevel meta-analysis model. However, the complexity of the model poses challenges for accurately estimating model parameter. We therefore carried out a simulation study to investigate the performance of models with different degrees of complexities. While the overall mean was estimated with little to no bias irrespective of the model, only the model that accounted for the multilevel structure and that incorporates both a non-phylogenetic and a phylogenetic variance component provided confidence intervals with approximately nominal coverage rates. We therefore suggest that meta-analysts in ecology and evolution use the phylogenetic multilevel meta-analysis model as the de facto standard when analyzing multi-species datasets.