Approaches and Paradigms


At some level all organisms experience constraints: genetic, energetic, physiological, developmental, and ecological. As a result, life-history theory is founded on a basic paradigm of tradeoffs. For example, given a set amount of energy, organisms must tradeoff the investment of that limited energy between growth and reproduction. Similarly, genetic correlations can cause changes in one trait (such as growth rate) due to selection on another trait (such as fecundity). Thus, a genetic correlation can limit the evolutionary response in both traits.

Phenotypic Models

The most common approach to modeling life-history patterns is optimality theory. This approach is based on the idea that natural selection will favor individuals or genes that confer the greatest fitness and thus asks which of all possible phenotypes is expected to confer the greatest fitness. Of course, this approach only makes sense in light of the tradeoffs or constraints discussed above. Otherwise a 'Darwinian Demon' (an organism that matures immediately, produces an infinite number of tiny offspring, and lives forever) would be predicted. Typically, phenotypic models examine a single tradeoff (e.g., between growth and reproduction) and predict the trait or traits expected to maximize fitness for that set of circumstances. The most common approach has been to examine how traits such as age or size at maturity (through their effect on birth rates) influence population growth rate (often referred to as the Malthusian parameter r) and find the life-history pattern that maximizes r. This links life history explicitly to population dynamics.

Another type of phenotypic model that has been influential in evolutionary ecology is game theory. This approach to modeling also predicts the phenotype that will be favored by natural selection but considers the situation where fitness depends on the traits of other individuals in the population. For example, if offspring survival depends on relative rather than absolute size, the size of offspring that maximizes individual fitness will depend on the size of offspring produced by other individuals in the population.

Both phenotypic approaches have been criticized for assuming any phenotype is possible, that organisms will be optimally adapted to their environment, and for ignoring the underlying genetic basis and dynamics of trait evolution. Despite these simplifying assumptions, optimality and game theory have successfully predicted observed patterns.

Genetic Models

In contrast, genetic models focus on how selection and the underlying genetic basis of traits influence life-history evolution. Population genetics examines how the differential fitness of genotypes (resulting from a few alleles and loci) leads to changes in allele frequencies over time. Quantitative genetics assumes that many genes of small effect contribute to the traits under consideration and that phenotypes are the result of an interaction between genetic and environmental factors. One advantage of quantitative genetics for life-history theory is its ability to examine the evolution of multiple correlated traits and to consider how various forms of environmental variation and genetic constraints affect the evolution of traits. In addition, the use of quantitative genetics has a strong empirical component where key parameters can be measured empirically and predictions can be readily compared to observed patterns in the lab and field (though many of the underlying genetic parameters are not known and are difficult to measure in wild populations). Research by Derek Roff and colleagues has illustrated the immense power of using quantitative genetic models and empirical studies in combination to illuminate our understanding of life-history evolution.

Summary of Phenotypic and Genetic Models

While phenotypic and genetic approaches each have strengths and weaknesses, they have also both made important contributions to our understanding of life-history patterns. We have learned the most about the evolution and ecology of life-history patterns by applying multiple methods to the same general questions.

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