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While all models of sexual selection assume that the development and expression of enlarged secondary sexual traits are costly, males with larger ornaments or weapons generally show greater survival or longevity. These studies have mostly been performed in species with high sexual size dimorphism, subject to intense sexual selection. Here we examined the relationships between horn growth and several survival metrics in the weakly dimorphic Pyrenean chamois (Rupicapra pyrenaica). In this unhunted population living at high density, males and females were able to grow long horns without any apparent costs in terms of longevity. However, we found a negative relationship between horn growth and survival during prime age in males. This association reduces the potential evolutionary consequences of trophy hunting in male chamois. We also found that females with long horns tended to have lower survival at old ages. Our results illustrate the contrasting conclusions that may be drawn when different survival metrics are used in analyses. The ability to detect trade-off between the expression of male secondary sexual traits and survival may depend more on environmental conditions experienced by the population than on the strength of sexual selection.

Fisher’s principle states that natural selection favours an equal number of male and female births at the population level, unless there are sex differences in rearing costs or sex differences in mortality before the end of the period of parental investment. Sex differences in rearing costs should be more pronounced in low- than in high-resource settings. We, therefore, examined whether human development index and sex differences in child mortality contribute to the natural variation in human sex ratio at birth across the globe. As predicted by Fisher’s principle, the proportion of male births increased with both increasing male-biased childhood mortality and level of development of each country. However, these relationships were absent after accounting for spatial autocorrelation in the residuals, which our inference is conditioned on. This work shows how the failure to account for residual spatial autocorrelation can lead to incorrect conclusions regarding support for predictions from sex allocation theory.