A Bayesian Approach to Inferring the Strength of Coevolution

推断协同进化强度的贝叶斯方法

• 批准号: 1450653
• 负责人: Scott Nuismer
• 金额: $ 25.1万
• 依托单位: Regents of the University of Idaho
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1450653&HistoricalAwards=false
• 关键词: Bayesian; Approach; Inferring; Strength; Coevolution

Interactions between species play an important role in the function of biological communities and have important implications for human health and agriculture. For instance, interactions between pathogens and their hosts influence the stability of natural communities and shape the spread of infectious disease within human populations. Similarly, interactions between plants and pollinators are essential for the proper functioning of natural communities and also for the efficient and economical operation of agricultural systems. For these reasons, predicting how species interactions change over time is important for understanding biological communities, managing agricultural systems, and minimizing infectious disease. Unfortunately, our ability to predict how species interactions change over time is poor, in part because the forces driving evolution of interacting species is not well understood. This research will develop new mathematical and statistical methods for estimating the strength of coevolution, a force long hypothesized to play a key role in the evolution of species interactions. The research will also provide significant opportunities for graduate student training at the interface of mathematics, statistics, computation, and biology and thus contribute to human resource development in STEM. Ultimately, the mathematical and statistical tools developed by the research will be made available to the scientific community through development of free computer software; this software will allow the strength of coevolution to be easily estimated in a broad range biological systems. Two broad classes of techniques currently exist for inferring the strength of coevolutionary selection in natural populations: (1) Direct techniques that make robust inferences but are limited to specific types of systems and small numbers of populations, and (2) indirect techniques that can be applied to a broad range of systems and large numbers of populations but which provide inferences of questionable robustness. Even under the best case scenario, both techniques generally yield only qualitative results and thus cannot provide crucial quantitative information on the intensity of coevolutionary selection in natural populations. This research will capitalize on recent advances in Bayesian statistics to develop novel methods for estimating parameters of well-established coevolutionary models using data routinely collected as part of broad scale studies of trait matching in species interactions. Once these new statistical methods have been thoroughly tested using simulated data, they will be used to estimate the strength of coevolutionary selection in a textbook example of coevolution - the interaction between toxic newts and their garter snake predators.

物种之间的相互作用在生物群落的功能中发挥着重要作用，对人类健康和农业具有重要意义。例如，病原体与其宿主之间的相互作用会影响自然群落的稳定性，并影响传染病在人群中的传播。同样，植物和传粉者之间的相互作用对于自然群落的正常运作以及农业系统的有效和经济运作至关重要。由于这些原因，预测物种间的相互作用如何随时间变化，对于了解生物群落、管理农业系统和最大限度地减少传染病非常重要。不幸的是，我们预测物种间相互作用如何随时间变化的能力很差，部分原因是驱动相互作用物种进化的力量还没有得到很好的理解。这项研究将开发新的数学和统计方法来估计共同进化的强度，长期以来，人们一直假设这种力量在物种相互作用的进化中发挥着关键作用。该研究还将为数学，统计，计算和生物学接口的研究生培训提供重要机会，从而有助于STEM的人力资源开发。最终，研究开发的数学和统计工具将通过开发免费的计算机软件提供给科学界;该软件将允许在广泛的生物系统中容易地估计共同进化的强度。目前存在两大类技术来推断自然种群中的协同进化选择的强度：（1）直接技术，使强大的推论，但限于特定类型的系统和少量的人口，和（2）间接技术，可以应用于广泛的系统和大量的人口，但提供的推论的鲁棒性值得怀疑。即使在最好的情况下，这两种技术通常只产生定性的结果，因此不能提供重要的定量信息的强度在自然种群的共同进化选择。本研究将利用贝叶斯统计的最新进展，开发新的方法来估计参数的完善的共同进化模型，使用常规收集的数据作为大规模研究的一部分，在物种相互作用的性状匹配。一旦这些新的统计方法已经使用模拟数据进行了彻底的测试，它们将被用来估计共同进化的教科书中的共同进化选择的强度-有毒蝾螈和它们的束带蛇捕食者之间的相互作用。