Testing the effect of relative pollen productivity on the REVEALS model: A validated reconstruction of Europe-wide Holocene vegetation

• Published in: Land
• Main Author: Franco Múgica, María Fátima
• Other Authors: UAM. Departamento de Ecología
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI 2023
• Subjects: Pollen; Holocene; Land Cover; Biología y Biomedicina / Biología; Elevado; Terranova
• Online Access: http://hdl.handle.net/10486/707997; https://doi.org/10.3390/land12050986

Artículo escrito por un elevado número de autores, solo se referencian el que aparece en primer lugar, el nombre del grupo de colaboración, si le hubiere, y los autores pertenecientes a la UAM 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° × 1°) 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 This research was funded by the TERRANOVA Project, H2020 Marie Sklodowska-Curie grant agreement no. 813904