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

利用进化博弈论理解现实世界中的生命史进化

基本信息

批准号：
NE/E013015/1
负责人：
Dylan Childs
金额：
$ 29.71万
依托单位：
University of Sheffield
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2008
资助国家：
英国
起止时间：
2008 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FE013015%2F1
关键词：
Using evolutionary game theory understand

项目摘要

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.

物种的生活历史是描述（1）生物生命周期中事件的速率或时机的一组特征，例如生殖的开始；（2）将资源分配给不同的生活功能，例如增长和生存。自然世界的一个重要特征是它涵盖了生活史的巨大多样性。即使是同一物种的不同种群也可能在其人生历史上表现出非常明显的差异。理解这种变化是对进化生物学家的主要重要性的一项努力，因为最好通过将它们视为自己的适应性来理解生活史特征。我们目前对生活历史进化的大部分理解是理论生物学家工作的结果。数学模型对于绘制有利于一种类型的生活历史策略的基本条件（例如，迭代性：每年复制）非常重要（例如，semparity：semelparity：一次再现一次）。这些模型中的许多模型都利用一种称为进化游戏理论的方法。从本质上讲，这旨在通过将一种策略与另一种策略相提并论并计算一个人群主导人群来确定最佳的生活历史策略。这种方法的巨大优势在于，它自然地包含了本质上常见的反馈，即特定个体的成功取决于人群中其他个体的数量和类型，而不仅仅是非生物环境的状态。尽管它是一种理论工具的成功，但很少有具体的例子，即进化游戏理论被用来了解自然环境中生活历史特征的演变。我的研究试图通过使用游戏理论来了解塑造自然种群生殖特征的选择性力量来缩小这一差距。生殖策略是生命历史的关键组成部分，以及生殖工作的时机（例如，首次繁殖年龄）和分配（例如，垃圾大小）已受到许多理论研究的约束。在野外研究这些特征是充满挑战的，因为：（1）从一年到下一年的非生物环境不变，因此随着时间的推移，任何一刻的最佳策略可能会有所不同。（2）自然种群由不同类型的个体（例如年龄较小的大型）组成的混合物组成，这些人可能会以不同的方式体验生物和非生物环境。（3）现实生活中的历史通常比理论模型的假设更为复杂，这些假设使我们对生活历史的演变有了当前的看法。我使用的数据集在他们一生中遵循的个人来构建可以使用游戏理论方法分析的数学人群模型。由于这些模型的预测本质上是定量的，而不是定性的，所以我可以用它们来摘取塑造观察到的生殖策略的选择性力量。这是通过将模型视为工具而不是终点来实现的，以便在模型系统上执行模拟实验。例如，我们可以询问如何改变死亡率际变化的量可能会影响最佳的生殖策略。这项工作令人兴奋，因为它结合了统计和数学人群生物学的最新发展，使新的见解是一些研究的动物和植物种群的发展，同时提供了分析其他系统中复杂生活历史的路线图。如果我们希望预测并可能减轻人类诱发的环境变化的影响，那么了解环境最终如何塑造物种的演变至关重要。