Abstract
The new diatom-based sea-surface salinity (SSS) estimation has been applied to a collection of 27 taxa in 48 present-day sediment and surface water samples recovered in the Baltic Sea and Kattegat. The sediment core 303610-12 (2005) from the Eastern Gotland was chosen for the study of the Holocene sequence spanning the past 8160 yrs BP. The Artificial Neuronal Network (ANN) method provided an estimation of spring (March-April) SSS values ranging between 7.04-8.25 ‰. The low amplitude of salinity change might be caused by mixing of fresh water with upper surface layer of the Baltic Sea due to high precipitation and riverine input. These findings were compared with independent geochemical proxies for salinity (K, Ti and S) derived from XRF Core Scanner record. Significant correlation between salinity and sulphur records and an inverse correlation between K and Ti demonstrate that the ANN method, when combined with quantitative and qualitative analyses of diatoms, provides a useful tool for palaeo-salinity reconstructions from the Holocene sediments of the Baltic Sea.
Keywords: diatoms (Bacillariophyta), artificial neuronal network, salinity reconstruction, reference data set
DOI:10.5200/baltica.2014.27.22
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