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Research article2023Peer reviewedOpen access

Testing the Effect of Relative Pollen Productivity on the REVEALS Model: A Validated Reconstruction of Europe-Wide Holocene Vegetation

Serge, M. A.; Mazier, F.; Fyfe, R.; Gaillard, M. -J.; Klein, T.; Lagnoux, A.; Galop, D.; Githumbi, E.; Mindrescu, M.; Nielsen, A. B.; Trondman, A. -K.; Poska, A.; Sugita, S.; Woodbridge, J.; Abel-Schaad, D.; Akesson, C.; Alenius, T.; Ammann, B.; Andersen, S. T.; Anderson, R. Scott;
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Reliable quantitative vegetation reconstructions for Europe during the Holocene are crucial to improving our understanding of landscape dynamics, making it possible to assess the past effects of environmental variables and land-use change on ecosystems and biodiversity, and mitigating their effects in the future. We present here the most spatially extensive and temporally continuous pollen-based reconstructions of plant cover in Europe (at a spatial resolution of 1 degrees x 1 degrees) over the Holocene (last 11.7 ka BP) using the 'Regional Estimates of VEgetation Abundance from Large Sites' (REVEALS) model. This study has three main aims. First, to present the most accurate and reliable generation of REVEALS reconstructions across Europe so far. This has been achieved by including a larger number of pollen records compared to former analyses, in particular from the Mediterranean area. Second, to discuss methodological issues in the quantification of past land cover by using alternative datasets of relative pollen productivities (RPPs), one of the key input parameters of REVEALS, to test model sensitivity. Finally, to validate our reconstructions with the global forest change dataset. The results suggest that the RPPs.st1 (31 taxa) dataset is best suited to producing regional vegetation cover estimates for Europe. These reconstructions offer a long-term perspective providing unique possibilities to explore spatial-temporal changes in past land cover and biodiversity.


Europe; quantitative past land cover; Holocene; pollen data; REVEALS model; relative pollen productivity; validation

Published in

2023, Volume: 12, number: 5, article number: 986Publisher: MDPI