Insights from comparative analyses of aging in birds and mammals

Abstract

Many laboratory models used in aging research are inappropriate for understanding senescence in mammals, including humans, because of fundamental differences in life history, maintenance in artificial environments, and selection for early aging and high reproductive rate. Comparative studies of senescence in birds and mammals reveal a broad range in rates of aging among a variety of taxa with similar physiology and patterns of development. These comparisons suggest that senescence is a shared property of all vertebrates with determinate growth, that the rate of senescence has been modified by evolution in response to the potential life span allowed by extrinsic mortality factors, and that most variation among species in the rate of senescence is independent of commonly ascribed causes of aging, such as oxidative damage. Individuals of potentially long-lived species, particularly birds, appear to maintain high condition to near the end of life. Because most individuals in natural populations of such species die of aging-related causes, these populations likely harbor little genetic variation for mechanisms that could extend life further, or these mechanisms are very costly. This, and the apparent evolutionary conservatism in the rate of increase in mortality with age, suggests that variation in the rate of senescence reflects fundamental changes in organism structure, likely associated with the rate of development, rather than physiological or biochemical processes influenced by a few genes. Understanding these evolved differences between long-lived and short-lived organisms would seem to be an essential foundation for designing therapeutic interventions with respect to human aging and longevity.

Figure 1

Examples of mortality rate and survival as a function of age for Weibull and Gompertz functions with parameters chosen to match closely (Weibull: m₀ = 0.02, α = 0.0001, β = 3; Gompertz: m₀ = 0.02, γ = 0.02).

Figure 2

Initial mortality rate (m₀, left) and rate of increase in mortality rate (ω, right) in wild and domesticated representatives of 10 lineages of mammals. Lineage is a significant effect for ln(ω) (F_9,9 = 5.6, P = 0.008) but not for ln(m₀) (F_9,9 = 2.2, P = 0.13); the difference between wild and domesticated is significant for ln(m₀) (F_1,9 = 6.3, P = 0.03; means, −1.30 vs. −0.67), but not for ln(ω) (F_1,9 = 0.3, P = 0.57; means, −0.79 vs. −0.84). m₀ decreases with domestication in all 10 species (P = 0.001), but ω increases in half the species and decreases in the other half. The correlation coefficient (r) between m₀ and ω was 0.77 (P = 0.01) for the wild populations and 0.37 (P = 0.29) for the domesticated populations (Ricklefs and Scheuerlein, unpubl. data).

Figure 3

Left: Logarithmic relationship between the rate of actuarial senescence in natural populations of birds (open symbols) and mammals (filled symbols) as a function of adult body mass. The relationship, including the difference between birds and mammals, accounts for 37% of the variance in ω. Numbered species of mammals that lie below the regression line for birds are: 1, Common Pipistrelle Bat Pipistrellus pipistrellus; 2, Ermine Mustela erminea; 3, Senegal Bushbaby Galago senegalensis; 4, Rhesus Macaque Macaca mulatta; 5, Chimpanzee Pan troglodytes; 6, Common or Harbor Seal Phoca vitulina. Right: Logarithmic relationship of the rate of actuarial aging to the initial (extrinsic) mortality rate. The regression accounts for 42% of the total variance in ω. Data from various sources, mostly summarized by Ricklefs (1998) and by Lynch and Fagan (2009).

Figure 4

Decrease in the proportion of aging-related mortality in natural populations of birds and mammals as a function of increasing extrinsic mortality (m₀). The slope of the relationship is −0.435 ± 0.037 proportion of deaths per 10-fold increase in m₀ (F_1,57 = 136, P < 0.0001, r² = 0.704). Birds and mammals are not distinguishable statistically. The species with the greatest longevity (upper left) are the African Bush Elephant Loxodonta africana and the Wandering Albatross Diomedea exulans. P_S based on Weibull functions fitted to survival data compiled by Ricklefs (1998) and Lynch and Fagan (2009).

Figure 5

The increase in fitness (λ) as a function of absolute and relative decreases in the scaling parameter (α) of the Weibull aging function, shown as a function of the initial mortality rate in the population, given the relationship between m₀ and ω portrayed by the regression line in Figure 3.

Figure 6

Left: Individuals decline in condition until death (*), which is often a direct consequence of reduced performance in old age. Right: Individuals maintain a high level of condition into old age, but die with increasing probability at older ages from catastrophic causes. Ages at death are the same in both panels.

Figure 6

Left: Individuals decline in condition until death (*), which is often a direct consequence of reduced performance in old age. Right: Individuals maintain a high level of condition into old age, but die with increasing probability at older ages from catastrophic causes. Ages at death are the same in both panels.

Figure 7

Heritability of age at death in captive (zoo) populations of birds and mammals plotted as a function of the probability (P) that heritability differs significantly from 0. The six species of mammal with P < 0.10 were, from the lowest value, the Lion (Panthera leo), Addax (Addax nasomaculatus), Golden Lion Tamarin (Leontopithecus rosalia), Domestic Goat (Capra hircus), Red Kangaroo (Macropus rufus), and Cheetah (Acinonyx jubatus). From data in Ricklefs and Cadena (2008).