Cliff-edge model of obstetric selection in humans

Abstract

The strikingly high incidence of obstructed labor due to the disproportion of fetal size and the mother's pelvic dimensions has puzzled evolutionary scientists for decades. Here we propose that these high rates are a direct consequence of the distinct characteristics of human obstetric selection. Neonatal size relative to the birth-relevant maternal dimensions is highly variable and positively associated with reproductive success until it reaches a critical value, beyond which natural delivery becomes impossible. As a consequence, the symmetric phenotype distribution cannot match the highly asymmetric, cliff-edged fitness distribution well: The optimal phenotype distribution that maximizes population mean fitness entails a fraction of individuals falling beyond the "fitness edge" (i.e., those with fetopelvic disproportion). Using a simple mathematical model, we show that weak directional selection for a large neonate, a narrow pelvic canal, or both is sufficient to account for the considerable incidence of fetopelvic disproportion. Based on this model, we predict that the regular use of Caesarean sections throughout the last decades has led to an evolutionary increase of fetopelvic disproportion rates by 10 to 20%.

Short‐term insurance versus long‐term bet‐hedging strategies as adaptations to variable environments

“…; Mitteroecker et al. ). The same is also true for a single individual experiencing uncertainty about its current state (i.e., regarding its phenotype or position on the x ‐axis on the fitness function) or uncertainty about its current microenvironment (i.e., the position of the fitness function on the x ‐axis relative to its phenotype).…”

“…With a skewed fitness function, the cliff-edge effect entails that if individuals with the same genotype differ stochastically in their phenotypes (or the fitness value of their phenotypes, e.g., due to inhabiting different microenvironments -see Fig. 1), their average fitness is maximized if the mean phenotype is shifted away from the peak of the fitness function, toward the less steeply decreasing side (Mountford 1968;Vercken et al 2012;Mitteroecker et al 2016). The same is also true for a single individual experiencing uncertainty about its current state (i.e., regarding its phenotype or position on the x-axis on the fitness function) or uncertainty about its current microenvironment (i.e., the position of the fitness function on the x-axis relative to its phenotype).…”

“…; Mitteroecker et al. ), and is commonly encountered as “insurance” strategies in fields such as behavioral ecology (Dall ). A well‐known example is the small bird in winter (Brodin ).…”

“…Finding the (arithmetic) mean fitness in such cases involves multiplying the phenotype-specific fitnesses with the frequencies of the different phenotypes (Mountford 1968). This phenomenon is sometimes described as the cliffedge effect (Vercken et al 2012;Mitteroecker et al 2016), and is commonly encountered as "insurance" strategies in fields such as behavioral ecology (Dall 2010). A well-known example is the small bird in winter (Brodin 2007).…”

Short-term insurance versus long-term bet-hedging strategies as adaptations to variable environments

“…Finding the (arithmetic) 64 mean fitness in such cases involves multiplying the phenotype-specific fitnesses with the frequencies of 65 the different phenotypes (Mountford 1968). This phenomenon is sometimes described as the cliff-edge 66 effect (Vercken et al 2012;Mitteroecker et al 2016), and is commonly encountered as 'insurance' 67 strategies in fields such as behavioral ecology (Dall 2010). A well-known example is the small bird in 68 winter (Brodin 2007).…”