The influence of Holocene forest dynamics on the chironomid fauna of a boreal lake (Flocktjärn, northeast Sweden)

There is a notable lack of palaeoecological records, particularly quantitative palaeoenvironmental reconstructions, for northeast Sweden. Here I use a lake sediment record from lake Flocktjärn to reconstruct Holocene terrestrial vegetation change and lake ecosystem dynamics, and to study the relationship between these components of the environment. After a period in which the vegetation around the lake is characterised by boreal forest, thermophilous arboreal taxa such as Ulmus establish in the Flocktjärn area from 6000 cal. a BP onward. Picea becomes abundant from 2930 cal. a BP onward, in line with results from other records from the region. The chironomid fauna of the lake shows high turnover before 7700 cal. a BP, after which an assemblage dominated by taxa indicative of shallow, warm and relatively nutrient rich conditions establishes. A transition to chironomid taxa that are indicative of slightly lower temperatures and lower nutrient levels occurs at 5260 cal. a BP. A chironomid-inferred mean July air temperature record shows unreliable inferences for the record pre-dating 7700 cal. a BP, a Holocene Thermal Maximum with temperatures around 14 °C between 7700-5260 cal. a BP, and temperatures around 12.5 °C between 5260 cal. a BP and the present. A numerical comparison between upland vegetation change and chironomid faunal dynamics for the first time shows that either vegetation change directly impacted on the chironomid fauna of the lake, or alternatively an external factor impacted on both the terrestrial and the aquatic ecosystem, resulting in concurrent changes in both parts of the ecosystem. This novel evidence of ecosystem connectivity is of vital importance to landscape management, as ongoing climate and land use change is likely to lead to increased pressure on lake ecosystems

Clustering misaligned dependent curves applied to varved lake sediment for climate reconstruction

In this paper we introduce a novel functional clustering method, the Bagging Voronoi K-Medoid Aligment (BVKMA) algorithm, which simultaneously clusters and aligns spatially dependent curves. It is a nonparametric statistical method that does not rely on distributional or dependency structure assumptions. The method is motivated by and applied to varved (annually laminated) sediment data from lake Kassjön in northern Sweden, aiming to infer on past environmental and climate changes. The resulting clusters and their time dynamics show great potential for seasonal climate interpretation, in particular for winter climate changes