Large spatial variations in coastal ¹⁴C reservoir age – a case study from the Baltic Sea

Abstract

Coastal locations are highly influenced by input from freshwater river runoff, including sources of terrestrial carbon, which can be expected to modify the 14C reservoir age, or R (t), associated with marine water. In this Baltic Sea case study, pre-bomb museum collection mollusc shells of known calendar age, from 30 locations across a strategic salinity transect of the Baltic Sea, were analysed for 14C, δ13C and δ18O. R (t) was calculated for all 30 locations. Seven locations, of which six are within close proximity of the coast, were found to have relatively higher R (t) values, indicative of hard-water effects. Whenever possible, the Macoma genus of mollusc was selected from the museum collections, in order to exclude species specific reservoir age effects as much as possible. When the Macoma samples are exclusively considered, and samples from hard-water locations excluded, a statistically significant correlation between Macoma R (t) and average salinity is found, indicating a two end-member linear mixing model between 14Cmarine and 14Crunoff. A map of Baltic Sea Macoma aragonite R (t) for the late 19th and early 20th centuries is produced. Such a map can provide an estimate for contemporary Baltic Sea Macoma R (t), although one must exercise caution when applying such estimates back in time or to 14C dates obtained from different sample material. A statistically significant correlation is found between δ18Oaragonite and Macoma R (t), suggesting that δ18Oaragonite can be used to estimate Macoma palaeo-R (t), due to the δ18Oaragonite signal being dominated by the salinity gradient of the Baltic Sea. A slightly increased correlation can be expected when δ18Oaragonite is corrected for temperature fractionation effects. The results of this Baltic Sea case study, which show that R (t) is affected by hydrographic conditions and local carbon inputs, have important consequences for other coastal and estuarine locations, where R (t) is also likely to significantly vary on spatial and temporal bases.