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

在Lisa Sattenspiel博士的指导下，Amy Warren将研究人类种群与环境之间复杂的相互作用，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。纵观人类历史，应对资源压力的一个关键适应策略是不同规模的人口流动。先前对人口流动的研究通常集中在应对突然的、戏剧性的气候变化的大规模遗弃上。相反，这项研究侧重于气候条件的变化、人口动态和人口在很长一段时间内的流动。这项研究的结果可以外推，对人类对气候和资源压力的适应做出概括的陈述，因为这些问题在现代世界仍然是重要的考虑因素，可以潜在地为理解和减轻当代小规模农业群体的生存压力提供信息。此外，与其他研究人员分享这个项目的方法、数据和结果，可以进一步研究考古学中长期重要的问题。这个项目还包含了一些活动，让感兴趣的公众甚至年轻学生自己进行与人与环境相互作用有关的实验。从美国西南部多年的考古和人类学调查中收集的详细的环境、人口和人种学数据将与基于代理的计算机模拟模型结合使用，以调查这些问题。在这个项目中开发的计算机模型允许对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，使研究人员能够在研究期间（公元800年至公元1350年）估计美国西南部四个角落地区两个相邻地区的年农业生产力，即长屋谷和黑台地。将这些数据纳入模型提供了一个真实的环境，在这个环境中，模拟的人口可以进行基本的活动。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，并收集有关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方法，而传统的生物考古方法无法产生这些数据。通过人种学和考古学研究开发的若干理论模型提供了关于各种人口流动战略的规模和持续时间的基本资料，以应对资源紧张。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区域之间人口流动的性质以及这些流动对研究区域人口结构的影响。这些模型的实施不仅可以更细致地了解该地区人口动态变化所涉及的一些因素，而且还可以考虑其他因素，如冲突和暴力、疾病和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.