Landscape reconstructions for Europe during the late Last Glacial (60–20 ka BP): A pollen-based REVEALS approach

Abstract. Vegetation change during the Last Glacial period in Europe plays a crucial role in better understanding the ecosystem dynamics response to abrupt climate change. Yet, quantitative reconstructions of land-cover primarily focus on the Holocene period and aim to disentangle the impact of anthropogenic and climatic stress on the vegetation. Here, we present temporally continuous land-cover reconstructions from Europe for the latter half of the Last Glacial period (60–20 ka BP) using the “Regional Estimates of Vegetation Abundance from Large Sites” (REVEALS) model. The pollen-based REVEALS model uniquely factors in plant-specific parameters, such as relative pollen productivity (RPP) and pollen fall speed to model pollen dispersal and thus provides more accurate representation of past vegetation cover than fossil pollen data. We compiled a total of 61 datasets from Europe and its bordering regions to model land-cover estimates across 60 time steps in 1000 year increments. By grouping the 38 analysed taxa into 5 land-cover types (LCTs), we simplify the interpretation of our results and demonstrate this using three crucial periods during the Last Glacial: Greenland Interstadial 14 (GI-14), Greenland Stadial 9 (GS-9), and the Last Glacial Maximum (LGM). These periods provide insight into stadial-interstadial vegetation variability as well as extreme glacial conditions, which seem to play a fundamental role in the demographic developments of Palaeolithic hunter-gatherers. Additionally, we compare the REVEALS land-cover estimates to raw pollen data and also provide REVEALS standard errors and discuss the reliability of our results as well as potential avenues to further improve the reliability of REVEALS estimates. To facilitate the use and interpretation of our data for a wide scientific audience, we developed the browser-based application PALVEG (https://oakern.shinyapps.io/PALVEG/), which requires no prior programming experience and dynamically generates maps based on user input.