This study identifies and critically assesses the social and physical consequences of, and possible responses to, sea-level rise and loss of land in the area that is now covered by the southern North Sea and English Channel/La Manche during the Mesolithic period. It suggests that Mesolithic studies still, despite debate on the matter, frame hunter fisher gatherers in economic terms. In this way, nature is seen as a separate entity to culture, the changing environment, therefore, becomes an external force against which people struggle. However, as an alternative, this thesis advocates an understanding of Mesolithic hunter fisher gatherers as an integral part of their changing world, suggesting that they would have had a fundamental awareness of these changes through a sensorial engagement, and acted accordingly. That said, it also suggests that, while not all people living in the area were equally affected by sea-level rise, the associated loss of land could have profoundly impacted people’s sense of place and being. It also highlights that, although sea-level rise and climate change occurred globally and on a millennial-scale, it unfolded and was experienced at a local and generational level. It therefore makes a case that to understand the human experience of early Holocene sea-level rise, it must be studied at the local-scale. This provides us with a better understanding of the effects of sea-level rise – a sense of the experience of it, rather than simply recording it as an abstract concept. Further, the local scale can identify problems that are not necessarily obvious from the larger scale. In this way, this thesis captures some of the nuances of environmental change that are frequently missing from the archaeological literature, and highlights the intense relationship between humans and their environment, providing a fresh approach to Mesolithic environment relations and a richer and more complex story of the effects of early Holocene sea-level rise.

Yes / The value of artificial intelligence and machine learning applications for use in heritage research is increasingly appreciated. In specific areas, notably remote sensing, datasets have increased in extent and resolution to the point that manual interpretation is problematic and the availability of skilled interpreters to undertake such work is limited. Interpretation of the geophysical datasets associated with prehistoric submerged landscapes is particularly challenging. Following the Last Glacial Maximum, sea levels rose by 120 m globally, and vast, habitable landscapes were lost to the sea. These landscapes were inaccessible until extensive remote sensing datasets were provided by the offshore energy sector. In this paper, we provide the results of a research programme centred on AI applications using data from the southern North Sea. Here, an area of c. 188,000 km2 of habitable terrestrial land was inundated between c. 20,000 BP and 7000 BP, along with the cultural heritage it contained. As part of this project, machine learning tools were applied to detect and interpret features with potential archaeological significance from shallow seismic data. The output provides a proof-of-concept model demonstrating verifiable results and the potential for a further, more complex, leveraging of AI interpretation for the study of submarine palaeolandscapes.