Coping with novel environments may be facilitated by plastic physiological responses that enable survival during environmentally sensitive life stages. We tested the capacity for embryos of the common wall lizard (Podarcis muralis) from low altitude to cope with low-oxygen partial pressure (hypoxia) in an alpine environment. Developing embryos subjected to hypoxic atmospheric conditions (15-16% O sea-level equivalent) at 2,877 m above sea level exhibited responses common to vertebrates acclimatized to or evolutionarily adapted to high altitude: suppressed metabolism, cardiac hypertrophy, and hyperventilation. These responses might have contributed to the unaltered incubation duration and hatching success relative to the ancestral, low-altitude, condition. Even so, hypoxia constrained egg energy utilization such that larger eggs produced hatchlings with relatively low mass. These findings highlight the role of physiological plasticity in maintaining fitness-relevant phenotypes in high-altitude environments, providing impetus to further explore altitudinal limits to ecological diversification in ectothermic vertebrates.

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The distribution of fitness effects (DFE) of new mutations has been of interest to evolutionary biologists since the concept of mutations arose. Modern population genomic data enable us to quantify the DFE empirically, but few studies have examined how data processing, sample size and cryptic population structure might affect the accuracy of DFE inference. We used simulated and empirical data (from Arabidopsis lyrata) to show the effects of missing data filtering, sample size, number of single nucleotide polymorphisms (SNPs) and population structure on the accuracy and variance of DFE estimates. Our analyses focus on three filtering methods—downsampling, imputation and subsampling—with sample sizes of 4–100 individuals. We show that (1) the choice of missing‐data treatment directly affects the estimated DFE, with downsampling performing better than imputation and subsampling; (2) the estimated DFE is less reliable in small samples (<8 individuals), and becomes unpredictable with too few SNPs (<5000, the sum of 0‐ and 4‐fold SNPs); and (3) population structure may skew the inferred DFE towards more strongly deleterious mutations. We suggest that future studies should consider downsampling for small data sets, and use samples larger than 4 (ideally larger than 8) individuals, with more than 5000 SNPs in order to improve the robustness of DFE inference and enable comparative analyses.