Structured demography, stochasticity and selection in free-living populations.

自由生活人群的结构化人口统计学、随机性和选择。

基本信息

批准号：
NE/I022027/1
负责人：
Dylan Childs
金额：
$ 60.22万
依托单位：
University of Sheffield
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2011
资助国家：
英国
起止时间：
2011 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FI022027%2F1
关键词：
Structured demography stochasticity selection free

项目摘要

Evolutionary biologists have always employed a large diversity of approaches for studying natural selection and the complex suite of species' adaptations that arise from this process. Theoretical biologists have used both simple and complex models to map out the fundamental mechanisms driving evolutionary processes and understand specific adaptations, while empirical biologists often approach this problem by carrying out careful manipulations in the laboratory or controlled field conditions. Ultimately of course, we must aim to explain the patterns and processes observed in natural populations to completely valid our theories. Evolutionary quantitative genetics (EQG) is one such approach biologists have used to meet this objective. By monitoring natural populations over long periods of time, we can build rich datasets describing the performance of individual organisms in terms of their rates of survival and reproduction (fitness), and then link these to the traits we wish to study. By employing sophisticated statistical models, it is possible to then use this data to understand how natural selection interacts with the genetics governing these traits' inheritance to predict how they should change. Although EQG has taught us much about evolutionary processes, this approach is not without problems. The assumptions of the simple model that underlies it are seldom completely met in a natural setting. For example, natural populations are often demographically structured (individuals differ as a result of processes such as ageing or growth and different generations overlap), and the environment they experience fluctuates a great deal from one year to the next. A core objective of my proposed research is to develop a methodology that can account for these complexities and improve out ability to predict and understand the traits we observe. Throughout my career I have used detailed mathematical models of natural systems to better understand how natural selection works. I have often relied on a set of mathematical tools called evolutionary game theory (often now referred to as adaptive dynamics). This approach can cope with many of issues raised above, but in doing so necessarily makes simplifying assumptions about the role of genetics. Making use of two of the world's very best long-term mammal studies (the feral Soay sheep of St Kilda, Scotland and the Yellow-bellied marmots of Gothic, Colorado), I aim to combine the best aspects of both EQG and adaptive dynamics to build a better predictive framework for studying evolution. In developing this research programme, I aim to focus much of my research on an important biological question that has interested me throughout my research career, 'How does living in an unpredictable fluctuating environment shape natural selection, and ultimately, species' traits?' We must address this question if we hope to predict how species may (or may not) respond to ongoing anthropogenic environmental change. For example, extreme weather events are predicted to become more common in the face of climate change. If my research shows that such variation is indeed important, then we will need to move beyond thinking just about changes in the average, to consider the role variation per se.

进化生物学家一直采用大量的方法来研究自然选择，以及由此过程产生的复杂物种适应套件。理论生物学家已经使用简单和复杂的模型来绘制驱动进化过程的基本机制并理解特定的适应性，而经验生物学家通常通过在实验室或受控野外条件中进行仔细的操作来解决这个问题。最终，当然，我们必须旨在解释自然种群中观察到的模式和过程，以完全有效我们的理论。进化定量遗传学（EQG）就是这样的方法生物学家用来满足这一目标的方法。通过监测长时间的自然种群，我们可以建立丰富的数据集，描述各个生物的生存和生殖率（适应性）的性能，然后将其与我们希望研究的特征联系起来。通过采用复杂的统计模型，可以使用这些数据来了解自然选择如何与控制这些特征的继承的遗传学相互作用，以预测它们应该如何改变。尽管EQG已经教会了我们很多关于进化过程的知识，但这种方法并非没有问题。在自然环境中很少完全满足其基础的简单模型的假设。例如，自然种群通常是人口结构化的（由于衰老或成长和不同的世代重叠的过程，个人有所不同），并且它们所经历的环境从一年到下一年都波动很大。我提出的研究的核心目标是开发一种可以解释这些复杂性并提高预测和理解我们观察到的特征的能力的方法。在整个职业生涯中，我都使用了自然系统的详细数学模型来更好地了解自然选择的工作原理。我经常依靠一组称为进化游戏理论的数学工具（通常称为自适应动力学）。这种方法可以应对上述许多问题，但是这样做必须简化对遗传学作用的假设。我利用世界上两项最佳的长期哺乳动物研究（苏格兰圣基尔达的野生羊和科罗拉多州的哥特式哥特式的黄色腹肌羊皮），旨在结合EQG和自适应动力学的最佳方面，以建立一个更好地研究进化的预测框架。在制定该研究计划时，我的目标是将许多研究重点放在一个重要的生物学问题上，这使我在整个研究生涯中对我感兴趣，“生活在不可预测的波动环境中如何塑造自然选择，最终是“物种特征？”我们必须解决这个问题，如果我们希望预测物种如何（或可能不会）对正在进行的人为环境变化做出反应。例如，面对气候变化，预计极端天气事件会变得更加普遍。如果我的研究表明这种变化确实很重要，那么我们将需要超越思考平均值的变化，以考虑角色变化本身。