CAREER: An integrative genomic and biodemographic analysis of prehistoric human-environment interactions in an island ecosystem, with bioinformatics capacity development

这个CAREER项目将应用新的生物和计算机科学方法来研究生活在同一栖息地的人类、驯化的农业物种和濒临灭绝的本土动物的种群规模的最新变化。这些结果将有助于我们了解人口增长如何影响野生物种的自然种群，以及人类、驯养动物和环境之间复杂的相互作用。这项研究将通过开发新的基因组方法和用于分析基因组数据的计算“大数据”分析（生物信息学）来推进科学发展。这些方法将首先应用于对马达加斯加的人类、他们的牲畜和两种濒危狐猴物种的研究。马达加斯加是一个理想的研究地点，因为人类第一次到达该岛相对较晚（几千年前）。在世界其他地区，包括人与环境相互作用开始较早的北美，对人与环境相互作用的研究也将受益于这些方法。该项目的教育部分将包括每年向不同的受众举办生物信息学培训讲习班；参与的学生将发展生物和计算机科学技能，这些技能单独或一起将具有广泛的科学和工业培训适用性。该提案的科学目标包括重建高分辨率的马达加斯加人、他们共同驯养的牛和两种广泛分布的特有狐猴物种的人口统计历史，所有这些都具有与该岛最近人类历史时间表相关的时间分辨率。这些结果将通过应用由首席研究员开发的新型种群基因组方法而成为可能，这些结果将相互比较并与现有的古气候数据进行比较，以开发一个关于人为影响马达加斯加生物多样性历史的综合经验模型。马达加斯加是研究人类与环境相互作用过程和后果的重要岛屿。马达加斯加人有一段有趣的历史，因为他们相对较晚才来到这个岛屿，他们是南岛人和非洲人的混血祖先，因为他们与世界上最具生物多样性、最具地方特色和最受威胁的生态系统之一有关系。这项工作将从经验上改变我们对马达加斯加生物多样性的人为影响历史的理解，并建立一个综合研究框架，用于世界其他地区，这些地区的全新世资源利用集约化和人类与环境的相互作用同样是主要感兴趣的主题。该项目的教育部分的核心是每年提供互动式生物信息学培训讲习班，该讲习班建立在生物和计算科学的整合基础上，这是研究的核心。最先进的工作坊将通过“黑客马拉松”与项目的科学目标相结合。在本研究中产生的基因组数据的分析计算工具的风格的发展。该项目的联合资助由美国国家科学基金会国际科学与工程办公室（OISE）提供。

This CAREER project will apply new biological and computer science methods to study recent changes in the population sizes of humans, domesticated agricultural species, and endangered native animals all living in the same habitat. The results will help us understand how human population growth affects natural populations of wild species, and the complex interactions among people, domesticated animals, and the environment. The study will advance science through the development of new genomic methods and computational "big data" analyses (bioinformatics) for analyzing genomic data. These methods will initially be applied to a study of people, their cattle, and two endangered lemur species in Madagascar, which is an ideal site because humans first arrived to the island relatively recently (several thousand years ago). Research on human-environment interactions in other regions of the world, including North America where human-environment interaction started earlier, will also benefit from these methods. The educational component of the project will involve the annual delivery of bioinformatics training workshops to diverse audiences; participating students will develop biological and computer science skills that separately and together will have wide scientific and industrial training applicability.The scientific aims of this proposal include reconstructing high-resolution demographic histories for Malagasy people, their commensal domesticated cattle, and two widely-distributed endemic lemur species, all with temporal resolution relevant to the recent timeline of human history on the island. These results, made possible by the application of novel population genomic methods developed by the principal investigator, will be compared to each other and to available paleoclimate data to develop an integrated empirical model of the history of anthropogenic effects on Madagascar's biodiversity. Madagascar is an important island for studying the processes and consequences of human-environment interaction. The people of Madagascar have an interesting history because of their relatively recent arrival to the island, their mixed Austronesian and African ancestry, and because of their relationship to one of the world's most biologically diverse, endemic and threatened ecologies. This work will empirically transform our understanding of the history of anthropogenic effects on Madagascar's biodiversity and establish an integrative research framework for application to other regions of the world where Holocene resource use intensification and human-environment interaction are likewise topics of major interest. A centerpiece of the educational component of this project is the annual delivery of interactive-style bioinformatics training workshops that build on the integration of biological and computational science that is central to the research. The most advanced workshop will be integrated with the project's scientific aims through ?hackathon?-style development of computational tools for analyses of the genomic data generated in this study. Joint funding for this project is provided by the National Science Foundation's Office of International Science and Engineering (OISE).