The Role of Climate Change in Early Human Behavioural Evolution

人们普遍认为，所有长期的物种进化都受到气候和环境条件的影响。在人类进化史的大部分时间里，这些因素相对知之甚少，部分原因是缺乏综合。生态位建模（ENM）为考古学家提供了一种方法来生成全球，大陆和区域规模的人类生态位开发预测模型，为主要的进化趋势提供了广泛的背景，并将考古遗址的不同数据合成为一个有凝聚力的框架。这项研究将建立非洲和欧亚大陆早期人类居住的模型，重点是中更新世人类进化和气候变化的关键时期。中更新世包含了人类行为和生物学的定义性变化，这些变化支撑了后来智人的出现，包括技术创新，重复使用火，延长儿童学习，大脑尺寸的增加和向更高纬度的扩张。这是在一个重大的气候剧变时期之后，通过ENM，这项研究将研究气候变化，栖息地变化和人类反应之间的相关性，提高我们对中更新世气候变化对我们进化影响的理解。虽然ENM已经在生物科学中建立，但其在考古数据中的应用需要进一步探索和完善，以更好地理解模型中固有的不确定性及其对解释范围的影响。本研究将研究时间不确定性带来的挑战，并探讨如何增加模型的复杂性可能会影响这些延长的时间跨度的可靠性。

It is widely recognised that all long-term species evolution is influenced by climatic and environmental conditions. These factors are relatively poorly understood for much of human evolutionary history, partly due to a lack of synthesis. Ecological Niche Modelling (ENM) offers archaeologists a method to generate global, continental and regional scale predictive models of human niche exploitation, providing the broad context for major evolutionary trends and synthesising disparate data from archaeological sites into a cohesive framework. This research will build models of early human occupation across Africa and Eurasia, focusing on a key period of human evolution and climate change within the Middle Pleistocene. The Middle Pleistocene encompasses defining changes in human behaviour and biology underpinning the later emergence of Homo sapiens including; technological innovations, repeated use of fire, extended childhood learning, increases in brain size and expansion in to higher latitudes. This follows a period of significant climatic upheaval, and through ENM this research will examine correlations between climate change, habitat change, and human responses, improving our understanding of the impact of Middle Pleistocene climate change on our evolution. Although ENMs have become established in the biological sciences, their application to archaeological data requires further exploration and refinement to better understand the inherent uncertainty in the models with implications for the scope of interpretations. This research will examine the challenges raised by chronological uncertainty, and explore how increasing model complexity may affect reliability in these extended time-spans.