Using evolutionary game theory to understand life history evolution in the real world

The life history of a species is the set of traits that describe (1) the rate or timing of events in an organism's lifecycle, such as the onset of reproduction; and (2) the allocation of resources to different life functions such as growth and survival. A prominent feature of the natural world is that it encompasses an enormous diversity of life histories. Even different populations of the same species may exhibit very obvious differences in their life history. Making sense of this variation is an endeavour of primary importance to evolutionary biologists, as life history traits are best understood by viewing them as adaptations in their own right. Much of our current understanding of life history evolution is the result of work by theoretical biologists. Mathematical models have been very important for mapping out the basic conditions that favour one type of life history strategy (e.g. iteroparity: reproduce every year) over another (e.g. semelparity: reproduce once and then die). Many of these models make use of an approach dubbed evolutionary game theory. Essentially, this seeks to determine optimal life history strategies by pitting one strategy against another and calculating which one comes to dominate the population. The great strength of this approach is that it naturally incorporates the feedbacks that are common in nature, i.e. the success of a particular individual depends on the number and type of other individuals in a population, and not simply the state of the abiotic environment. Despite its success as a theoretical tool, there are very few concrete examples where evolutionary game theory has been used to understand the evolution of life history traits in a natural setting. My research seeks to close this gap by using game theory to understand the selective forces that shape reproductive traits in natural populations. Reproductive strategies are a key component of life histories and the timing (e.g. age of first reproduction) and allocation (e.g. litter size) of reproductive effort have been subject to much theoretical research. Studying these traits in the wild is challenging because: (1) The abiotic environment is not constant from one year to the next, such that the best strategy to play at any one moment may vary through time. (2) Natural populations are made up of a mixture of different types of individual (e.g. young-old, small-large) and these may experience the biotic and abiotic environment differently. (3) Real life histories are often much more complicated than the assumptions of theoretical models that have given us our current view of life history evolution. I use datasets in which individuals have been followed over their lifetime to build mathematical population models that can be analysed using game theoretic methods. Because the predictions from these models are quantitative rather than qualitative in nature, I can use them to pick apart the selective forces that have shaped observed reproductive strategies. This is achieved by treating the model as a tool, rather than an end in itself, in order to perform simulated experiments on the model system. For example, we can ask how changing the amount inter-annual variation in mortality might affect the optimal reproductive strategy. This work is exciting because it combines recent developments in statistics and mathematical population biology to bring new insight into the evolution of some of the very best studied animal and plant populations, while providing a roadmap for analysing complex life histories in other systems. Understanding how the environment ultimately shapes the evolution of a species is essential if we hope to predict and perhaps mitigate the effect of human induced environmental change.