Statistical inference of the demographic consequences of agriculture worldwide

Duke University, Durham NC

Investigators

Abstract

Human population growth during the Holocene has affected modern human genetic variation and redefined global ecosystems. Was the shift to agricultural societies the cause of this population growth? This project proposes a large-scale quantitative examination of the demographic consequences of this worldwide agricultural revolution. The investigators will develop a new statistical framework that jointly infers prehistoric population growth from genetic and archeological data in order to test a long-standing hypothesis suggesting that the transition to agriculture spurred rapid human population growth because of stable food sources and sedentism. The project will develop interdisciplinary methods and open-access software that can be used by geneticists, archeologists, paleontologists, and others interested in prehistory. The project also will support hands-on boot-camps in statistical methods and data analysis for upper level undergraduates and early graduate students, and train two interdisciplinary graduate students in quantitative methods, focusing on integration of large-scale genomic and archeological data, as well as software development. This proposal develops a novel method to infer population history, combining next generation sequence data with large archeological datasets, including important developments in computational archaeology. Incorporation of data from multiple fields allows for higher resolution inferences, and tests for concordance between disciplines. The method will then be applied to study one of the most impactful events in human history: the agricultural transition, testing classic hypotheses about the massive social and demographic rearrangements thought to accompany the shift to agriculture using new large-scale databases. Comparisons between multiple continents will provide independent tests of the consequences of the agricultural transition, while also providing novel insight into the population histories of understudied regions of the world. The method will be of use to researchers in a range of fields as these types of big datasets become increasingly common. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.