Alll organisms require energy to survive, grow, and reproduce. Metabolic rate gives us an indication of the rate at which energy required for all biological function is used, and is one of the most widely measured physiological traits. Over the last century, physiologists have sought to understand what drives patterns in metabolic rate – such as the scaling relationship between body mass and metabolic rate, but also why so much variation in metabolic rate (from the within-individual to among-species level) exists.
So far, the approach to understanding variation in metabolic rate has been through a largely mechanistic perspective, for example, metabolic rate is thought be highly correlated with body mass. Yet, a 2–3 fold difference in basal metabolic rate can be observed among individuals of the same species. These hypothesis-driven approaches are often tested under laboratory conditions and link metabolic rate to some measure of performance (such as lifespan, running speed, or growth), and so can only offer limited insights into how we might expect metabolic rates to evolve. A much more direct tool for understanding variation in traits such as metabolic rate is through the use of a quantitative genetics framework.
(A) Directional selection: in this example the linear coefficient of selection β is positive. Over generations, the population mean of trait 1 (t1) is expected to increase. (B) Stabilizing selection: where the quadratic coefficient γ is negative. Over generations, the population variance will decrease, forming a single optimum for t1. (C) Disruptive selection: where the quadratic coefficient γ is positive. Over generations, the population variance will decrease, forming two optima for t1. (D) Positive correlational selection on t1 and trait 2 (t2) (where γ is positive) produces an increase in the covariance between t1 and t2. (E) Negative correlational selection on t1 and t2 (where γ is negative) produces a decrease in the covariance between t1 and t2.
Quantitative genetics studies the inheritance of continuous traits, such as body mass and metabolic rate, by measuring both selection (i.e the relative fitness of individuals in a population with variation in a trait), and heritability, in order to predict how traits are likely to evolve. Quantitative genetics was originally developed for animal breeding almost a century ago, and has since been implemented by evolutionary biologists. Unlike measures of performance, estimates of selection and heritability are standardised, which means direct comparisons can be made across studies, environments and species. Quantitative genetics approaches also allow for formal consideration of the genetic correlations between traits – while higher metabolic rates may be favoured early in the life history, evolution of higher metabolic rates may be constrained if it is genetically correlated with metabolic rates at other stages in the life history. Hence, quantitative genetics considers the complex nature of traits, and provides a formal, predictive, comparative, and powerful framework for understanding variation in any trait that shows continuous variation (e.g. body mass, metabolic rate).
Here, we advocate for further adoption of a quantitative genetics approach in measures of metabolic rate. Further, in order to gain realistic measures of how metabolic rates evolve, more measures of selection on metabolic rate under field conditions are needed. By directly linking metabolic rate to fitness (lifetime reproductive output of an individual), and measuring the heritability of that trait, we can directly measure how metabolic rate should evolve, and why we might expect variation in metabolic rate to persist.
This research was published in the Journal of Experimental Biology.
Centre for Geometric Biology 