Neutral theory for life histories and individual variability in fitness components

Abstract

Individuals within populations can differ substantially in their life span and their lifetime reproductive success but such realized individual variation in fitness components need not reflect underlying heritable fitness differences visible to natural selection. Even so, biologists commonly argue that large differences in fitness components are likely adaptive, resulting from and driving evolution by natural selection. To examine this argument we use unique formulas to compute exactly the variance in life span and in lifetime reproductive success among individuals with identical (genotypic) vital rates (assuming a common genotype for all individuals). Such individuals have identical fitness but vary substantially in their realized individual fitness components. We show by example that our computed variances and corresponding simulated distribution of fitness components match those observed in real populations. Of course, (genotypic) vital rates in real populations are expected to differ by small but evolutionarily important amounts among genotypes, but we show that such differences only modestly increase variances in fitness components. We conclude that observed differences in fitness components may likely be evolutionarily neutral, at least to the extent that they are indistinguishable from distributions generated by neutral processes. Important consequences of large neutral variation are the following: Heritabilities for fitness components are likely to be small (which is in fact the case), small selective differences in life histories will be hard to measure, and the effects of random drift will be amplified in natural populations by the large variances among individuals.L ongitudinal studies of humans and other species show that individuals within a population often differ greatly in age at death and lifetime reproduction (1-4). For example, the SD of human age at death was ∼40% of life expectancy (∼35 y) in the early 1800s and ∼20% in the late 1900s even with a much higher life expectancy (∼70 y) (3). Lifetime reproduction of human females in high-fertility societies had a SD-to-mean ratio [coefficient of variation (CV)] of ∼0.5-1, and there was much greater variability in male lifetime reproduction (5, 6). Such variation obviously leads us to ask, Do the longest-lived individuals (or the most prolific reproducers) differ from other individuals because of heritable genetic differences? Many researchers assume the answer is "yes" (7-10), because both age at death and lifetime reproduction should be related to fitness (although they are not fitness, which is a property of genotypes as we discuss below) (Box 1). However, the desired genetic variation need not exist. For example, populations of genetically identical nematodes in the laboratory display substantial variation in age at death with CV ∼ 0.2. This variation is clearly not due to genetic differences, but is generated by stochastic variation during development (11). In this and other examples, individual fates are shaped by a sequence of stochastic ev...

Lifetime reproduction and the second demographic transition: Stochasticity and individual variation

“…Individual stochasticity is inherent in any set of mortality and fertility rates, and given those rates, we can calculate the consequences of these stochastic events (Caswell 2009(Caswell , 2011(Caswell , 2014aCaswell and Kluge 2015). Individual stochasticity has been found to be a major contributor to variance in LRO in many species (Caswell 2011;Tuljapurkar, Steiner, and Orzack 2009;Steiner and Tuljapurkar 2012).…”

“…Of course, the vital rates in Finland in the 18th and 19th century are different from those of Europe during the second demographic transition, but this crude comparison suggests that the opportunity for selection is to a large part determined by heterogeneity, with only a relatively small contribution from individual stochasticity. Such comparisons are a valuable tool for understanding observed variance in LRO (Tuljapurkar, Steiner, and Orzack 2009;Steiner and Tuljapurkar 2012;van Daalen and Caswell 2015). Our approach makes it possible to explore the addition of heterogeneity to the model.…”

“…Our results do not incorporate heterogeneity among individuals in mortality or fertility. They could be interpreted as baseline results in comparison to measurements of actual lifetime reproduction (Caswell 2011;Steiner and Tuljapurkar 2012). Adding further dimensions of heterogeneity in addition to age may increase or decrease the variance in LRO (Caswell 2014b).…”

“…To calculate the consequences of this heterogeneity requires a model that incorporates the heterogeneity, as frailty models do in the study of mortality (e.g., Vaupel, Manton, and Stallard 1979;Caswell 2014a). The calculated variance due to individual stochasticity can serve as a null model for comparison with observed measures of variance (Tuljapurkar, Steiner, and Orzack 2009;Steiner and Tuljapurkar 2012).…”

Successful by Chance? The Power of Mixed Models and Neutral Simulations for the Detection of Individual Fixed Heterogeneity in Fitness Components

“…Recent publications (Tuljapurkar et al 2009;Steiner et al 2010;Orzack et al 2011;Steiner and Tuljapurkar 2012) have argued forcefully that invoking fixed differences among individuals (i.e., fixed heterogeneity) in fitness components is rarely required to explain the observed heterogeneity in LRS. Instead, they emphasize that due to the stochasticity of individual life histories, individual heterogeneity is expected even in populations of identical individuals (Caswell 2011).…”