Summary 1.Researchers frequently take repeated measurements of individuals in a sample with the goal of quantifying the proportion of the total variation that can be attributed to variation among individuals vs. variation among measurements within individuals. The proportion of the variation attributed to variation among individuals is known as repeatability and is most frequently estimated as the intraclass correlation coefficient (ICC). The goal of our study is to provide guidelines for determining the sample size (number of individuals and number of measurements per individual) required to accurately estimate the ICC. 2. We report a range of ICCs from the literature and estimate 95% confidence intervals for these estimates. We introduce a predictive equation derived by Bonett (2002), and we test the assumptions of this equation through simulation. Finally, we create an R statistical package for the planning of experiments and estimation of ICCs. 3. Repeatability estimates were reported in 1AE5% of the articles published in the journals surveyed. Repeatabilities tended to be highest when the ICC was used to estimate measurement error and lowest when it was used to estimate repeatability of behavioural and physiological traits. Few authors report confidence intervals, but our estimated 95% confidence intervals for published ICCs generally indicated a low level of precision associated with these estimates. This survey demonstrates the need for a protocol to estimate repeatability. 4. Analysis of the predictions from Bonett's equation over a range of sample sizes, expected repeatabilities and desired confidence interval widths yields both analytical and intuitive guidelines for designing experiments to estimate repeatability. However, we find a tendency for the confidence interval to be underestimated by the equation when ICCs are high and overestimated when ICCs and the number of measurements per individual are low. 5. The sample size to use when estimating repeatability is a question pitting investigator effort against expected precision of the estimate. We offer guidelines that apply over a wide variety of ecological and evolutionary studies estimating repeatability, measurement error or heritability. Additionally, we provide the R package, icc, to facilitate analyses and determine the most economic use of resources when planning experiments to estimate repeatability.

The rate of evolution of population mean fitness informs how selection acting in contemporary populations can counteract environmental change and genetic degradation (mutation, gene flow, drift, recombination). This rate influences population increases (e.g., range expansion), population stability (e.g., cryptic eco-evolutionary dynamics), and population recovery (i.e., evolutionary rescue). We review approaches for estimating such rates, especially in wild populations. We then review empirical estimates derived from two approaches: mutation accumulation (MA) and additive genetic variance in fitness (IAw). MA studies inform how selection counters genetic degradation arising from deleterious mutations, typically generating estimates of <1% per generation. IAw studies provide an integrated prediction of proportional change per generation, nearly always generating estimates of <20% and, more typically, <10%. Overall, considerable, but not unlimited, evolutionary potential exists in populations facing detrimental environmental or genetic change. However, further studies with diverse methods and species are required for more robust and general insights.

Summary1. The Non-Additive InVerses (nadiv) R software package contains functions to create and use non-additive genetic relationship matrices in the animal model of quantitative genetics. 2. This study discusses the concepts relevant to non-additive genetic effects and introduces the package. 3. nadiv includes functions to create the inverse of the dominance and epistatic relatedness matrices from a pedigree, which are required for estimating these genetic variances in an animal model. The study focuses on three widely used software programs in ecology and in evolutionary biology (ASReml, MCMCglmm and WOMBAT) and how nadiv can be used in conjunction with each. Simple tutorials are provided in the Supporting Information.

Extra-pair reproduction is widely hypothesized to allow females to avoid inbreeding with related socially paired males. Consequently, numerous field studies have tested the key predictions that extra-pair offspring are less inbred than females’ alternative within-pair offspring, and that the probability of extra-pair reproduction increases with a female's relatedness to her socially paired male. However, such studies rarely measure inbreeding or relatedness sufficiently precisely to detect subtle effects, or consider biases stemming from failure to observe inbred offspring that die during early development. Analyses of multigenerational song sparrow (Melospiza melodia) pedigree data showed that most females had opportunity to increase or decrease the coefficient of inbreeding of their offspring through extra-pair reproduction with neighboring males. In practice, observed extra-pair offspring had lower inbreeding coefficients than females’ within-pair offspring on average, while the probability of extra-pair reproduction increased substantially with the coefficient of kinship between a female and her socially paired male. However, simulations showed that such effects could simply reflect bias stemming from inbreeding depression in early offspring survival. The null hypothesis that extra-pair reproduction is random with respect to kinship therefore cannot be definitively rejected in song sparrows, and existing general evidence that females avoid inbreeding through extra-pair reproduction requires reevaluation given such biases.

Summary Quantifying and predicting microevolutionary responses to environmental change requires unbiased estimation of quantitative genetic parameters in wild populations. ‘Animal models’, which utilize pedigree data to separate genetic and environmental effects on phenotypes, provide powerful means to estimate key parameters and have revolutionized quantitative genetic analyses of wild populations.However, pedigrees collected in wild populations commonly contain many individuals with unknown parents. When unknown parents are non‐randomly associated with genetic values for focal traits, animal model parameter estimates can be severely biased. Yet, such bias has not previously been highlighted and statistical methods designed to minimize such biases have not been implemented in evolutionary ecology.We first illustrate how the occurrence of non‐random unknown parents in population pedigrees can substantially bias animal model predictions of breeding values and estimates of additive genetic variance, and create spurious temporal trends in predicted breeding values in the absence of local selection. We then introduce ‘genetic group’ methods, which were developed in agricultural science, and explain how these methods can minimize bias in quantitative genetic parameter estimates stemming from genetic heterogeneity among individuals with unknown parents.We summarize the conceptual foundations of genetic group animal models and provide extensive, step‐by‐step tutorials that demonstrate how to fit such models in a variety of software programs. Furthermore, we provide new functions in r that extend current software capabilities and provide a standardized approach across software programs to implement genetic group methods.Beyond simply alleviating bias, genetic group animal models can directly estimate new parameters pertaining to key biological processes. We discuss one such example, where genetic group methods potentially allow the microevolutionary consequences of local selection to be distinguished from effects of immigration and resulting gene flow.We highlight some remaining limitations of genetic group models and discuss opportunities for further development and application in evolutionary ecology. We suggest that genetic group methods should no longer be overlooked by evolutionary ecologists, but should become standard components of the toolkit for animal model analyses of wild population data sets.

Replicated selection experiments provide a powerful way to study how "multiple adaptive solutions" may lead to differences in the quantitative-genetic architecture of selected traits and whether this may translate into differences in the timing at which

It is assumed that dominance genetic variance contributes little to the prediction of evolutionary change in polygenic traits. This is based on the assumption that populations are large, panmictic, and randomly mating. However, the ecological contexts of most wild populations studied to date violate one, if not several, of these assumptions, and the widespread occurrence of inbreeding and inbreeding depression of phenotypic traits and fitness suggests dominance genetic effects are ubiquitous. This chapter reviews what genetic dominance represents at the level of a single locus and how this contributes to phenotypic variation and discusses how to estimate dominance variance with emphasis on the complications arising in wild populations and with inbreeding. Next, empirical estimates of dominance variance are reviewed. Since no estimates exist of dominance variance in the wild (except for humans), laboratory and agricultural populations are examined, and it is shown that dominance variance is a major contributor to phenotypic variation and in some cases contributes as much as additive genetic variance. This chapter also discusses how inbreeding and dominance affect predictions of evolutionary change, and ends with a review of some of the empirical questions for which genetic dominance is an important quantity in its own right. In this chapter, it is argued that dominance variance has been ignored for too long, may hamper the ability to predict evolutionary change, can be a major contributor to phenotypic variance, is interesting to study in its own right, and provides many avenues of research to be addressed by empirical study.

Appropriately defining and enumerating “fitness” is fundamental to explaining and predicting evolutionary dynamics. Yet, general theoretical concepts of fitness are often hard to translate into quantities that can be measured in wild populations experiencing complex environmental, demographic, genetic, and selective variation. Although the “fittest” entities might be widely understood to be those that ultimately leave most descendants at some future time, such long‐term legacies can rarely be measured, impeding evaluation of the degree to which tractable short‐term metrics of individual fitness could potentially serve as useful direct proxies. One opportunity for conceptual and empirical convergence stems from the principle of individual reproductive value ( V i ), here defined as the number of copies of each of an individual's alleles that is expected to be present in future generations given the individual's realized pedigree of descendants. As V i tightly predicts an individual's longer term genetic contribution, quantifying V i provides a tractable route to quantifying what, to date, has been an abstract theoretical fitness concept. We used complete pedigree data from free‐living song sparrows ( Melospiza melodia ) to demonstrate that individuals’ expected genetic contributions stabilize within an observed 20‐year (i.e. approximately eight generation) time period, allowing estimation of individual V i . Considerable among‐individual variation in V i was evident in both sexes. Standard metrics of individual lifetime fitness, comprising lifespan, lifetime reproductive success, and projected growth rate, typically explained less than half the variation. We thereby elucidate the degree to which fitness metrics observed on individuals concur with measures of longer term genetic contributions and consider the degree to which analyses of pedigree structure could provide useful complementary insights into evolutionary outcomes.