Embryo development and ageing in birds and mammals

Abstract

The rate of ageing is a genetically influenced feature of an individual's life history that responds to selection on lifespan. Various costs presumably constrain the evolution of prolonged life, but these have not been well characterized and their general nature is unclear. The analyses presented here demonstrate a correlation among birds and mammals between rates of embryonic growth and ageing-related mortality, which are quantified by the exponents of fitted power functions. This relationship suggests that rapid early development leads to accelerated ageing, presumably by influencing some aspect of the quality of the adult individual. Although the mechanisms linking embryo growth rate and ageing are not known, a simple model of life-history optimization shows that the benefits of longer life can be balanced by connected costs of extended development.

Figure 1

Power functions fitted to the relationship of embryo mass and adult survival to time. (a) Embryo growth curves for zebra finch, Taeniopygia guttata (b=1.98), Rhode Island Red chicken, Gallus domesticus (3.25) and gadwall, Anas strepera (4.47). (b) Survival curves for zebra finch (β=1.17) to red jungle fowl females, Gallus gallus (3.63) and gadwall (4.17). The straight lines are the ageing-related components of hazard functions for power (Weibull) models fitted to the survival curves.

Figure 2

Relationship between the exponents of power functions for embryo growth, b, and ageing-related mortality, β. (a) Birds (F_1,21=9.7, p=0.005, slope=0.74±0.24, R²=0.32). Several groups of birds are distinguished to show that the relationship is generalized across taxa. (b) Birds and mammals together. Among mammals, ageing β was uniquely related only to embryo b (F_1,18=6.7, p=0.019, slope=0.33±0.13, R²=0.27). The slopes for birds and mammals did not differ significantly and their common slope was 0.48±0.13 (F_1,40=16.9, p=0.0002); intercepts (1.79 for mammals and 1.20 for birds) differed by 0.59±0.28 (F_1,40=4.4, p=0.043; overall R² =0.35). Lines represent least-squares regressions.

Figure 3

The proportion of embryos surviving to birth as a function of embryo growth parameters a and b, illustrated for m=0.01 d⁻¹ and w_n=10 g.

Figure 4

Optimization of embryo growth balances embryo and adult survival. (a) Relative fitness (the product of embryo survival (s_n, fecundity) and average adult lifespan) as a function of ageing mortality exponent (β) for m=0.02 d⁻¹, neonate mass=10 g, and the scaling constants a (embryo growth) and α (ageing-related mortality) equal to 0.0001. I set b=β/0.8 (see figure 2a). For this figure, embryo survival was calculated as in figure 3b. Average lifespan was calculated as Σxl_x/Σl_x, where l_x is survivorship to age x, iterated over 0.1 year intervals. The open triangles represent the maximum relative fitness and optimized β for each value of extrinsic mortality, m₀, and correspond to the same symbols for a single line in figure part (b). (b) Optimum β as a function of extrinsic adult mortality and nest mortality rate (m) having descending values as in (a).

Figure 5

The proportion of a population's mortality due to ageing-related causes (P_S) indicates the strength of selection to extend lifespan by delaying senescence. P_S increases with lower extrinsic mortality (and longer lifespan). The combination of a steep increase in mortality at higher values of α and β with high P_S in long-lived birds (low m₀, uppermost surface) suggests that further reduction of ageing-related mortality is strongly constrained.