Publication details

Relative pollen productivity estimates for vegetation reconstruction in central-eastern Europe inferred at local and regional scales



Year of publication 2019
Type Article in Periodical
Magazine / Source Holocene
MU Faculty or unit

Faculty of Science

Keywords Czech Republic; ERV model; Gaussian plume model; Lagrangian stochastic model; modern pollen spectra; REVEALS; vegetation cover
Description Understanding pollen-vegetation relationships is crucial for accurate land-cover and climate reconstructions, yet important parameters for quantifying past vegetation abundance are mostly unknown for large parts of Europe harbouring temperate thermophilous ecosystems. We collected pollen and vegetation data in central-eastern Europe, a region covered by patchy cultural landscapes of high biodiversity to estimate relative pollen productivity (RPP) for important pollen-equivalent taxa. Our study area was situated in the south-western part of the White Carpathians (Czechia-Slovakia borderland), where we collected 40 modern moss pollen samples scattered over 250 km(2) and mapped vegetation within 100 m around each pollen site. Additional vegetation data were compiled from Forest management plans, Natura 2000 habitat mapping and floristic inventories over the entire area. We calculated RPP (referenced to Poaceae) by testing two approaches: the extended R-value (ERV) model by estimating relevant source area of pollen and the REVEALS-based productivity using regional scale vegetation estimates. Two models were applied to depict pollen dispersal: Lagrangian stochastic and the Gaussian plume (Prentice) models. We estimated RPP for 16 taxa using the ERV model and an additional nine taxa using REVEALS. Both approaches found Plantago lanceolata-type to be a high pollen producer, Quercus medium-to-high, Asteraceae subf. Cichorioideae, Anthemis-type, Ranunculus acris-type and Rubiaceae low-to-medium and Brassicaceae and Senecio-type as low pollen producers. Results for other, mainly tree taxa, significantly differed in both approaches mainly due to largely uneven representation in both local and regional vegetation. In comparison with other studies, our data demonstrate a high variability in the estimated RPPs which could be influenced by climatic conditions or potentially vegetation structure. We suggest that the accuracy of RPP estimates could be enhanced by comparing modern pollen data with large-scale vegetation data in the future.

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