Understanding the constraints on sex ratio adaptation using artificial neural networks

使用人工神经网络了解性别比例适应的限制

• 批准号: BB/E013430/1
• 负责人: Peter Mayhew
• 金额: $ 34.23万
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FE013430%2F1
• 关键词: Understanding; constraints; sex; ratio; adaptation

How perfect should behaviour be? There is little doubt that, given sufficient time, and genetic variation, natural selection can produce organisms displaying startlingly precise adaptations to their surroundings. At the same time, a variety of processes can constrain the ability of populations to reach adaptive peaks. Determining the relative importance of these processes is a major challenge still facing evolutionary biology. We will work on a model system that will allow us to say how obtaining and processing information can limit adaptation. We will work on parasitic wasps, which can choose the sex of their offspring by deciding whether or not to fertilize their eggs. The choice of offspring sex in this group has been the subject of much previous work, and the wasps generally show a good but imperfect fit to predicted behaviour. We will ask if we can better understand the behaviour shown in this group of organisms by incorporating the limitations of sensory and nervous systems into our predictions using a modeling approach known as artificial neural networks. The new predictions will then be tested against existing datasets and against new data, obtained by manipulating the information available to a parasitic wasp in the laboratory.

行为应该有多完美？毫无疑问，只要有足够的时间和遗传变异，自然选择就能产生对环境表现出惊人的精确适应的生物。与此同时，各种各样的过程可能会限制种群达到适应性峰值的能力。确定这些过程的相对重要性仍然是进化生物学面临的一个重大挑战。我们将研究一个模型系统，它将使我们能够说明获取和处理信息如何限制适应。我们将研究寄生蜂，它们可以通过决定是否使卵受精来选择后代的性别。这一群体中后代性别的选择一直是以前许多工作的主题，黄蜂通常表现出良好但不完美的预测行为。我们会问，我们是否可以更好地理解这组生物体中所表现出的行为，通过将感官和神经系统的局限性纳入我们的预测，使用一种称为人工神经网络的建模方法。然后，新的预测将针对现有的数据集和新的数据进行测试，这些数据是通过操纵实验室中寄生蜂可用的信息获得的。