Tree rings as a proxy for seasonal precipitation variability and Early Neolithic settlement dynamics in Bavaria, Germany
Public Library of Science (PLoS) -- PLOS ONE
DOI 10.1371/journal.pone.0210438
Abstract(s)

Studying the dynamic of Neolithic settlement on a local scale and its connection to climate variability is often difficult due to missing on-site climate reconstructions from natural archives. Here we bring together archaeological settlement data and a regional climate reconstruction from precipitation-sensitive trees. Both archives hold information about regional settlement dynamics and hydroclimate variability spanning the time of the first farming communities, the so called Linearbandkeramik (LBK) in Bavaria, Germany. Precipitation-sensitive tree-ring series from subfossil oak are used to develop a spring-summer precipitation reconstruction (5700–4800 B.C.E.) representative for southern Germany. Early Neolithic settlement data from Bavaria, mainly for the duration of the LBK settlement activities, are critically evaluated and compared to this unique regional hydroclimate reconstruction as well as to reconstructions of Greenland temperature, summer sea surface temperature, delta 18O and global solar irradiance to investigate the potential impact of climate on Neolithic settlers and their settlement dynamic during the LBK. Our hydroclimate reconstruction demonstrates an extraordinarily high frequency of severe dry and wet spring-summer seasons during the entire LBK, with particularly high year-to-year variability from 5400 to 5101 B.C.E. and with lower fluctuations until 4801 B.C.E. A significant influence of regional climate on the dynamic of the LBK is possible (e.g. around 4960 B.C.E.), but should be interpreted very carefully due to asynchronous trends in settlement dynamics. Thus, we conclude that even when a climate proxy such as tree rings that has excellent spatio-temporal resolution is available, it remains difficult to establish potential connections between the settlement dynamic of the LBK and climate variability.