Doctoral Dissertation Improvement Grant: Simulating Interactions Among Environment, Population Dynamics And Behavioral Response

艾米·沃伦在丽莎·斯滕斯皮尔博士的指导下，将研究人类种群与其环境之间的复杂相互作用，以更深入地了解资源压力和人类适应行为对史前人类种群结构的影响。在整个人类历史上，应对资源压力的一项关键的适应性战略是人口在不同规模上的流动。以前关于人口流动的研究通常集中在大规模的遗弃，以应对突然的、戏剧性的气候变化。相反，这项研究的重点是气候条件的变化、人口动态和长时间内的人口迁移。这项研究的结果可以外推，得出关于人类适应气候和资源压力的概括性声明，因为这些问题仍然是现代世界的重要考虑因素，可能有助于理解和缓解当代小规模农业群体的生存压力。此外，与其他研究人员共享这个项目的方法、数据和结果，可以进一步调查考古学中长期存在的重要问题。该项目还包括一些活动，允许感兴趣的公众成员甚至年轻学生进行他们自己的与人与环境互动有关的实验。从美国西南部多年的考古和人类学调查中挑选出来的详细的环境、人口和人种学数据将与基于代理的计算机模拟模型一起用于调查这些问题。在这个项目中开发的计算机模型允许对影响史前人口动态的各种因素进行现实的实验。细粒度的环境数据和对特定气候条件下特定土壤类型农业生产力的研究使研究人员能够估计研究期间(公元800年至公元1350年)美国西南部四角地区长屋山谷和黑台地两个相邻地区的年农业生产力。将这些数据合并到模型中提供了一个现实的场景，模拟的人群可以在上面进行基本的活动。利用对生育率和死亡率的经验估计，人们可以模拟人口增长和下降的模式，并收集关于史前人口可能结构的数据。这提供了一种产生史前人口统计数据的手段，而这些数据不能用传统的生物考古方法产生。通过人种学和考古学研究开发的几个理论模型，提供了关于应对资源压力的各种人口流动战略的规模和持续时间的基本信息。将这些理论模型与计算机模拟相结合，可以深入探索两个研究地区之间人口流动的性质，以及这些流动对研究地区人口结构的影响。这些模型的实施不仅使人们能够更加细致入微地了解该区域人口动态变化所涉及的一些因素，而且还能考虑到其他因素，如冲突和暴力、疾病、社会政治复杂性及其对史前人类群体的影响。

Amy Warren, under the guidance of Dr. Lisa Sattenspiel, will investigate the complex interactions between human populations and their environments to gain greater insight into the effects of resource stress and human adaptive behaviors on the structure of prehistoric human populations. A key adaptive strategy in response to resource stress throughout human history has been population movement at various scales. Prior research on population movements typically focused on large-scale abandonments in response to sudden, dramatic climatic shifts. This study instead focuses on changing climatic conditions, population dynamics, and population movements over a long span of time. The results of this research can be extrapolated to make generalized statements about human adaptations to climate and resource stress and, because these issues remain important considerations in the modern world, can potentially inform efforts to understand and mitigate subsistence stress among contemporary small-scale farming groups. In addition, sharing the methods, data, and results of this project with other researchers permits further investigation of questions of long-standing importance in archaeology. This project also incorporates activities that allow interested members of the public and even young students to perform their own experiments related to human-environment interactions. Detailed environmental, demographic, and ethnographic data culled from years of archaeological and anthropological investigations in the American Southwest will be used in conjunction with agent-based computer simulation models to investigate these issues. The computer models developed in this project allow for realistic experimentation with various factors that contribute to prehistoric population dynamics. Fine-grained environmental data and research on the agricultural productivity of specific soil types under specific climatic conditions allows researchers to estimate annual agricultural productivity in two neighboring areas in the Four Corners region of the American Southwest, Long House Valley and Black Mesa during the study period (AD 800 to AD 1350). Incorporating this data into the models provides a realistic landscape on which the simulated populations can carry out essential activities. Using empirically derived estimates for fertility and mortality, one can simulate patterns of population growth and decline and collect data regarding possible structures of the prehistoric populations. This provides a means to generate prehistoric demographic data that cannot be produced using traditional bioarchaeological approaches. Several theoretical models, developed through ethnographic and archaeological research, provide basic information about the magnitude and duration of various population movement strategies in response to resource stress. Incorporating these theoretical models with the computer simulations allows for in-depth exploration of the nature of population movements between the two study areas and the effects of these movements on the structure of populations in the study areas. Implementation of these models permits not only a more nuanced understanding of some of the factors involved in changing population dynamics in the region but also allows for consideration of additional factors such as conflict and violence, disease, and sociopolitical complexity and their effects on prehistoric human groups.