Distributions of bacterial and archaeal membrane lipids in surface sediments reflect differences in input and loss of terrestrial organic carbon along a cross-shelf Arctic transect

This paper was published in Organic Geochemistry, and is available open-access through the journal website and the MMU e-space repository. In the paper we take a detailed look at lipid biomarkers along a transect from the Kolyma River to the Arctic Ocean.

The data used in this paper is a subset of the data from across the East Siberian Artic Shelf (ESAS) published shortly afterwards in Biogeosciences. In this paper we took a closer look at the offshore trends seen in material delivered to the ocean by the Kolyma River, the easternmost of the Great Russian Arctic Rivers. The Kolyma River catchment is entirely underlain by continuous permafrost, which makes this are an extreme endmember in terms of permafrost systems. The main sources of organic matter from the Kolyma region are river erosion, mostly from top few metres of soil, the active layer that freezes and thaws each year, and coastal erosion from the “yedoma” cliffs along the shoreline.

Sample locations for the Kolyma River - ESAS transect
Sample locations for the Kolyma River – ESAS transect

These samples had previously been analysed for bulk properties (total organic carbon content, carbon isotope ratios) and some basic biomarker measurements, but we added complex lipid analyses to the story. We measured both GDGT and BHP lipids, these are found in microbes and can be analysed using LCMS. Amongst the many applications of these lipids, they can be used to trace the amount of soil found in offshore sediments. Each group of molecules has an index associated with it: GDGTs are used in the BIT index and BHPs are used in the R’soil index. Values of 0 would have no soil input and 100% marine carbon, while values towards 1 would be dominated by soil.

Offshore trends in the BIT and R'soil indices
Offshore trends in the BIT and R’soil indices

Usually these indices show the same offshore trends, which would be expected since they are both supposed to be tracing the same number – the proportion of carbon coming from soil. However, as the figure above illustrates, the two indicies have very different patterns from the river mouth (0 km) across the shelf (500 km). The BIT index drops quickly offshore, making a curved offshore profile, but the R’soil index forms a straight line offshore. Therefore two different techniques, supposedly measuring the same thing, don’t show the same results.

We think that this is due to the source of lipids used to make each index. Branched GDGTs (from soils) are common in sediments close to the river mouth, but their concentration drops quickly offshore. Marine GDGT concentrations increase across the shelf and this combination causes the BIT index to decrease rapidly. Branched GDGT concentrations in soils and lakes on land are high, but they are very rare in the coastal permafrost cliffs. Therefore any coastal erosion is not really affecting the BIT index.

On the other hand, soil marker BHP molecules are found in river sediment and coastal permafrost, and so there are two terrestrial sources. The concentration of soil marker BHPs drops much slower offshore than for the GDGTs. Also different, the concentration of the marine BHP marker doesn’t increase offshore. This combination means that the R’soil index drops much slower than the BIT index.

In the end, what this paper mainly shows is that when using biomarkers as proxy measurements for something else one single result is probably not enough. Proxy measurements are valuable tools, but they depend on measuring one thing to discover another. Combining multiple proxies together adds value and reliability to a study, either by confirming a hypothesis or bringing new insights.