Summary1. Meta-analysis has become a standard way of summarizing empirical studies in many fields, including ecology and evolution. In ecology and evolution, meta-analyses comparing two groups (usually experimental and control groups) have almost exclusively focused on comparing the means, using standardized metrics such as Cohen's / Hedges' d or the response ratio. 2. However, an experimental treatment may not only affect the mean but also the variance. Investigating differences in the variance between two groups may be informative, especially when a treatment influences the variance in addition to or instead of the mean. 3. In this paper, we propose the effect size statistic lnCVR (the natural logarithm of the ratio between the coefficients of variation, CV, from two groups), which enables us to meta-analytically compare differences between the variability of two groups. We illustrate the use of lnCVR with examples from ecology and evolution. 4. Further, as an alternative approach to the use of lnCVR, we propose the combined use of ln s (the log standard deviation) and ln x (the log mean) in a hierarchical (linear mixed) model. The use of ln s with ln x overcomes potential limitations of lnCVR and it provides a more flexible, albeit more complex, way to examine variation beyond two-group comparisons. Relevantly, we also refer to the potential use of ln s and lnCV (the log CV) in the context of comparative analysis. 5. Our approaches to compare variability could be applied to already published meta-analytic data sets that compare two-group means to uncover potentially overlooked effects on the variance. Additionally, our approaches should be applied to future meta-analyses, especially when one suspects a treatment has an effect not only on the mean, but also on the variance. Notably, the application of the proposed methods extends beyond the fields of ecology and evolution.

Summary 1.A general hypothesis is presented to explain interspecific differences in size-independent resting metabolic rate. This hypothesis is based on a presumed trade-off between a low resting metabolism and adaptations of metabolism during activity. 2. With such a trade-off, selection to reduce resting metabolism is less intense in active species than in species where resting metabolism constitutes a large proportion of the daily metabolic costs. Those animals that spend more energy on activity should therefore have a higher resting metabolic rate than animals that spend less energy on activity. 3. A literature review reveals that flying insects have higher resting metabolic rates than species that use energetically less demanding types of locomotion. 4. Insects producing acoustic advertisement signals can be shown to have higher mass-independent resting metabolic rates than closely related species without this energetically demanding behaviour. 5. Literature data on vertebrate resting metabolic rates are also consistent with the presented hypothesis: the more energy animals spend on activity, the higher the massindependent resting metabolic rate.

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We examined evolutionary stable sperm allocation and included stochastic variation in male mating frequency, not included in previous models examining sperm allocation strategies. We assumed sperm mixing and variation in female quality and used a genetic algorithm to analyse the evolution of male sperm allocation. Our results show that males should invest more sperm in initial copulations than in subsequent copulations as a male might fail to mate again. The inclusion of variation in female fecundity had no influence on the evolutionary stable sperm allocation strategy if males were unable to recognize female quality. If males were assumed to allocate sperm in response to female quality, the proportion of sperm allocated was positively correlated with female quality. Moreover, with increasing variance in female quality, males conserved more sperm for later copulations. Literature data on sperm allocation from diverse taxa show a good fit with the predictions given by our model.

As females of many species mate with more than one male, ejaculates often face competition from the sperm of other males. In recent years, numerous papers have been published on theoretical predictions of evolutionary, behavioural and physiological responses to variation in the strength of sperm competition (SC). These theoretical predictions have also been extensively tested. However, although predictions from SC theory are relatively straightforward, extra caution has to be paid in the design of experiments testing them. One difficulty is for example to disentangle immediate and mean SC risk and intensity. Without carefully designed experiments, it is also very easy to simultaneously increase SC risk and the probability of intense SC – a situation for which we currently have no clear predictions, as the theoretical models to date only assume variation in either SC risk or intensity. In this paper, we discuss these and some other pitfalls related to manipulations of SC risk and intensity and suggest how to avoid them.