The distinction between the effective size of a population (Ne) and the effective size of its neighborhoods (Nn) has sometimes become blurred. Ne reflects the effect of random sampling on the genetic composition of a population of size N, whereas Nn is a measure of within-population spatial genetic structure and depends strongly on the dispersal characteristics of a species. Although Nn is independent of Ne, the reverse is not true. Using simulations of a population of annual plants, it was found that the effect of Nn on Ne was well approximated by Ne=N/(1-FIS), where FIS (determined by Nn) was evaluated population wide. Nn only had a notable influence of increasing Ne as it became smaller (⩽16). In contrast, the effect of Nn on genetic estimates of Ne was substantial. Using the temporal method (a standard two-sample approach) based on 1000 single-nucleotide polymorphisms (SNPs), and varying sampling method, sample size (2-25% of N) and interval between samples (T=1-32 generations), estimates of Ne ranged from infinity to <0.1% of the true value (defined as Ne based on 100% sampling). Estimates were never accurate unless Nn and T were large. Three sampling techniques were tested: same-site resampling, different-site resampling and random sampling. Random sampling was the least biased method. Extremely low estimates often resulted when different-site resampling was used, especially when the population was large and the sample fraction was small, raising the possibility that this estimation bias could be a factor determining some very low Ne/N that have been published.

An evolutionary perspective can help unify disparate observations and make testable predictions. We consider an evolutionary model in relation to two mechanistic frameworks of cancer biology: multistage carcinogenesis and the hallmarks of cancer. The multistage model predicts that cancer risk increases with body size and longevity; however, this is not observed across species (Peto's paradox), but the paradox is resolved by invoking the evolution of additional genetic mechanisms to suppress cancer in large, long-lived species. It is when cancer cells overcome these defence mechanisms that they exhibit the hallmarks of cancer, driving the ongoing evolution of these defences, which in turn is expected to create the differences observed in the genetics of cancer across species and tissues. To illustrate the utility of an evolutionary model we examined some recently published data linking stem-cell divisions and cancer incidence across a range of tissues and show why the original analysis was faulty, and demonstrate that the data are consistent with a multistage model varying from three to seven mutational hits across different tissues. Finally, we demonstrate how an evolutionary model can both define patterns of inherited (familial) cancer and explain the prevalence of cancer in post-reproductive years, including the dominance of epithelial cancers.

Environmental stress generally exacerbates the harmful effects of inbreeding and it has been proposed that this could be exploited in purging deleterious alleles from threatened inbred populations. However, understanding what factors contribute to variability in the strength of inbreeding depression (ID) observed across adverse environmental conditions remains a challenge. Here, we examined how the nature and timing of stress affects ID and the potential for purging using inbred and outbred Drosophila melanogaster larvae exposed to biotic (larval competition, bacteria infection) and abiotic (ethanol, heat) stressors compared with unstressed controls. ID was measured during (larval survival) and after (male mating success) stress exposure. The level of stress imposed by each stressor was approximately equal, averaging a 42% reduction in outbred larval survival relative to controls. All stressors induced on average the same ID, causing a threefold increase in lethal equivalents for larval survival relative to controls. However, stress-induced ID in larval success was followed by a 30% reduction in ID in mating success of surviving males. We propose that this fitness recovery is due to 'intragenerational purging' whereby fitness correlations facilitate stress-induced purging that increases the average fitness of survivors in later life history stages. For biotic stressors, post-stress reductions in ID are consistent with intragenerational purging, whereas for abiotic stressors, there appeared to be an interaction between purging and stress-induced physiological damage. For all stressors, there was no net effect of stress on lifetime ID compared with unstressed controls, undermining the prediction that stress enhances the effectiveness of population-level purging across generations.