Sex-ratio imbalances significantly reduce the effective population size
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Peer-reviewed literature demonstrates that standard population genetics formulas and wildlife studies confirm sex-ratio imbalances reduce the effective population size.
SummaryTo estimate the effective size (Ne) of populations with unequal sex ratio, a well‐known formula, Ne=4NmNf/(Nm + Nf), has been frequently used, where Nm and Nf are the numbers of male and female parents, respectively. In this paper, the formula was examined under typical mating systems in animals. It was shown that the formula holds only when there are no variations in the numbers of mates (mating success) of parents of each sex. More appropriate equations were developed by accounting for the variation in mating success. It was found that for animal populations with harem mating system, an equation Ne=4NmNf/(2Nm+Nf) gives a more accurate estimate than the well‐known formula. The effective population sizes of several wild, experimental and domestic animals are estimated by applying the derived equations to the published demographic and ecological data.
Sequential hermaphroditism, where males change to females (protandry) or the reverse (protogyny), is widespread in animals and plants, and can be an evolutionarily stable strategy (ESS) if fecundity rises faster with age in the second sex. Sequential hermaphrodites also generally have sex ratios skewed towards the initial sex, and standard theory based on fixed sexes indicates that this should reduce effective population size (Ne) and increase the deleterious effects of genetic drift. We show that despite having skewed sex ratios, populations that change sex at the ESS age do not have reduced Ne compared with fixed-sex populations with an even sex ratio. This implies that the ability of individuals to operate as both male and female allows the population to avoid some evolutionary constraints imposed by fixed sexes. Furthermore, Ne would be maximized if sex change occurred at a different (generally earlier) age than is selected for at the individual level, which suggests a potential conflict between individual and group selection. We also develop a novel method to quantify the strength of selection for sex reversal.
Harvesting and culling are methods used to monitor and manage wildlife diseases. An important consequence of these practices is a change in the genetic dynamics of affected populations that may threaten their long‐term viability. The effective population size (Ne) is a fundamental parameter for describing such changes as it determines the amount of genetic drift in a population. Here, we estimate Ne of a harvested wild reindeer population in Norway. Then we use simulations to investigate the genetic consequences of management efforts for handling a recent spread of chronic wasting disease, including increased adult male harvest and population decimation. The Ne/N ratio in this population was found to be 0.124 at the end of the study period, compared to 0.239 in the preceding 14 years period. The difference was caused by increased harvest rates with a high proportion of adult males (older than 2.5 years) being shot (15.2% in 2005–2018 and 44.8% in 2021). Increased harvest rates decreased Ne in the simulations, but less sex biased harvest strategies had a lower negative impact. For harvest strategies that yield stable population dynamics, shifting the harvest from calves to adult males and females increased Ne. Population decimation always resulted in decreased genetic variation in the population, with higher loss of heterozygosity and rare alleles with more severe decimation or longer periods of low population size. A very high proportion of males in the harvest had the most severe consequences for the loss of genetic variation. This study clearly shows how the effects of harvest strategies and changes in population size interact to determine the genetic drift of a managed population. The long‐term genetic viability of wildlife populations subject to a disease will also depend on population impacts of the disease and how these interact with management actions.
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