“Genetic Variance in Fitness Indicates Rapid Contemporary Adaptive Evolution in Wild Animals”, 2022-05-26 ():
Rapid change: Human impacts are leading to exceedingly rapid alteration of our world, from land conversion and habitat loss to climate change. Some have proposed that rapid adaptation could help some species persist in the face of these changes, but questions remain about whether adaptation could occur rapidly enough to make a difference. Bonnet et al looked at additive genetic variance, which determines the contribution of selection to genetic change that increases fitness, in long-term data from 19 species and found it to be higher than expected—often substantially higher (see the Perspective by Walsh). These results suggest that many species may have some capacity to adapt to our changing world.
The rate of adaptive evolution, the contribution of selection to genetic changes that increase mean fitness, is determined by the additive genetic variance in individual relative fitness. To date, there are few robust estimates of this parameter for natural populations, and it is therefore unclear whether adaptive evolution can play a meaningful role in short-term population dynamics.
We developed and applied quantitative genetic methods to long-term datasets from 19 wild bird and mammal populations and found that:
while estimates vary between populations, additive genetic variance in relative fitness is often substantial and, on average, twice that of previous estimates.
We show that these rates of contemporary adaptive evolution can affect population dynamics and hence that natural selection has the potential to partly mitigate effects of current environmental change.
[Walsh’s discussion:]
“…Estimating BVs [breeding values], and thus additive variance, is a common problem in modern animal breeding, built around using pedigree information. A BV exists even when the trait is not displayed, as it is a measure of how exceptional an offspring from that parent would be, if produced. In the case of milk production, information on the BV of a bull is provided by the observed yields of his mother, sisters, and daughters. The same pedigree machinery used by breeders can, in theory, be applied in natural populations to estimate the additive variance of any measured trait. Pedigrees for natural populations can be constructed using molecular markers, and closed populations of vertebrates are well suited for such analyses. Even with perfect pedigrees, the transition of pedigree methods from a large and well-structured domesticated population to a small wild population has been somewhat rocky4. Domesticated pedigrees tend to be much deeper and denser than those for natural populations, resulting in greater precision in BV estimates. Furthermore, fitness is a problematic trait for standard pedigree methods, which assume trait values are continuous and follow a Gaussian distribution, whereas fitness data are highly discrete—a parent can only have an integer number of offspring, with a large point mass at zero, that is, individuals with zero offspring. Although there have been a few attempts to estimate the additive variance in fitness in wild populations using standard pedigree methods, the failure of the Gaussian assumption suggests that these are likely rather biased.
et al 2022 extended these pedigree methods by using a discrete Poisson distribution with an inflated zero value instead of a Gaussian and provided a much better fit for the fitness data. Using the improved fitting, their resulting average estimate of the additive variance in relative fitness, VA(w), was 2–4× larger than previous values. To put it in a more tangible context, this means that if the fitness of a population drops by a third, it would take roughly 10 generations to recover back to normal fitness levels. Hence, populations with shorter generation times might have a better chance to somewhat mitigate anthropogenic changes.
In nature, the target of selection is almost certainly a constantly shifting, high-dimensional (ie. multi-trait) phenotype that may poorly project onto individual traits or even a set of traits. Most studies of adaptation are structured around some assumed edifice of traits that affects fitness. A poor choice of traits can give a misleading impression of population adaptation. Fortunately, an estimate of VA(w) provides an upper bound, and therefore a maximal possible change in any trait independent of selection. For example, a typical trait heritability of 0.3 will mean that 30% of the trait variation is due to variance in BVs, and the maximal possible change in the average value of a trait in the population is about one standard deviation every 4 generations. A more reliable way to estimate VA(w) can help to better quantify the nature of selection and the robustness of a population to major environmental changes.”