Sexual size dimorphism (SSD) is common in both plants and animals, and current evidence suggests that it reflects the adaptation of males and females to their different reproductive roles. When species are compared within a clade, SSD is frequently found to vary with body size. This allometry is detected as β ≠ 1, where β is the slope of a model II regression of log(male size) on log(female size). Most frequently, β exceeds 1, indicating that SSD increases with size where males are the larger sex, but decreases with size where females are larger, a trend formalized as “Rensch's rule.” Exceptions are uncommon and associated with female-biased SSD. These trends are derived from a sample of 40 independent clades of terrestrial animals, primarily vertebrates. Their extension to plants and aquatic animals awaits quantitative assessments of allometry for SSD within these groups. Many functional hypotheses have been proposed to explain the evolution of allometry for SSD, most featuring sexual selection on males or reproductive selection on females. Of these, the hypothesis that allometry evolves because of correlational selection between the sexes appears most promising as a general model but remains untested.

Trade‐offs are a core component of many evolutionary models, particularly those dealing with the evolution of life histories. In the present paper, we identify four topics of key importance for studies of the evolutionary biology of trade‐offs. First, we consider the underlying concept of ‘constraint’. We conclude that this term is typically used too vaguely and suggest that ‘constraint’ in the sense of a bias should be clearly distinguished from ‘constraint’ in the sense of proscribed combinations of traits or evolutionary trajectories. Secondly, we address the utility of the acquisition–allocation model (the ‘Y‐model’). We find that, whereas this model and its derivatives have provided new insights, a misunderstanding of the pivotal equation has led to incorrect predictions and faulty tests. Thirdly, we ask how trade‐offs are expected to evolve under directional selection. A quantitative genetic model predicts that, under weak or short‐term selection, the intercept will change but the slope will remain constant. Two empirical tests support this prediction but these are based on comparisons of geographic populations: more direct tests will come from artificial selection experiments. Finally, we discuss what maintains variation in trade‐offs noting that at present little attention has been given to this question. We distinguish between phenotypic and genetic variation and suggest that the latter is most in need of explanation. We suggest that four factors deserving investigation are mutation‐selection balance, antagonistic pleiotropy, correlational selection and spatio‐temporal variation, but as in the other areas of research on trade‐offs, empirical generalizations are impeded by lack of data. Although this lack is discouraging, we suggest that it provides a rich ground for further study and the integration of many disciplines, including the emerging field of genomics.

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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.

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