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Relative abundances of 157 diatom taxa from Yakutian lake surface-sediments were investigated for their potential to indicate certain environmental conditions. Data from 206 sites from Arctic, sub-Arctic and boreal environments were included. Redundancy analyses were performed to assess the explanatory power of mean July temperature (T-July), conductivity, pH, dissolved silica concentration, phosphate concentration, lake depth and vegetation type on diatom species composition. Boosted regression tree analyses were performed to infer the most relevant environmental variables for abundances of individual taxa and weighted average regression was applied to infer their respective optimum and tolerance. Electrical conductivity was best indicated by diatom taxa. In contrast, only few taxa were indicative of Si and water depth. Few taxa were related to specific pH values. Although T-July, explained the highest proportion of variance in the diatom spectra and was, after conductivity, the second-most selected splitting variable, we a priori decided not to present indicator taxa because of the poorly understood relationship between diatom occurrences and T-July. In total, 92 diatom taxa were reliable indicators of a certain vegetation type or a combination of several types. The high numbers of indicative species for open vegetation sites and for forested sites suggest that the principal turnover is the transition from forest-tundra to northern taiga. Overall, our results reveal that preference ranges of diatom taxa for environmental variables are mostly broad, and the use of indicator taxa for the purposes of environmental reconstruction or environmental monitoring is therefore restricted to marked rather than subtle environmental transitions.
Rapidly changing climate in the Northern Hemisphere and associated socio-economic impacts require reliable understanding of lake systems as important freshwater resources and sensitive sentinels of environmental change. To better understand time-series data in lake sediment cores, it is necessary to gain information on within-lake spatial variabilities of environmental indicator data. Therefore, we retrieved a set of 38 samples from the sediment surface along spatial habitat gradients in the boreal, deep, and yet pristine Lake Bolshoe Toko in southern Yakutia, Russia. Our methods comprise laboratory analyses of the sediments for multiple proxy parameters, including diatom and chironomid taxonomy, oxygen isotopes from diatom silica, grain-size distributions, elemental compositions (XRF), organic carbon content, and mineralogy (XRD). We analysed the lake water for cations, anions, and isotopes. Our results show that the diatom assemblages are strongly influenced by water depth and dominated by planktonic species, i.e. Pliocaenicus bolshetokoensis. Species richness and diversity are higher in the northern part of the lake basin, associated with the availability of benthic, i.e. periphytic, niches in shallower waters. delta O-18(diatom) values are higher in the deeper south-western part of the lake, probably related to water temperature differences. The highest amount of the chironomid taxa underrepresented in the training set used for palaeoclimate inference was found close to the Utuk River and at southern littoral and profundal sites. Abiotic sediment components are not symmetrically distributed in the lake basin, but vary along restricted areas of differential environmental forcing. Grain size and organic matter are mainly controlled by both river input and water depth. Mineral (XRD) data distributions are influenced by the methamorphic lithology of the Stanovoy mountain range, while elements (XRF) are intermingled due to catchment and diagenetic differences. We conclude that the lake represents a valuable archive for multiproxy environmental reconstruction based on diatoms (including oxygen isotopes), chironomids, and sediment-geochemical parameters. Our analyses suggest multiple coring locations preferably at intermediate depth in the northern basin and the deep part in the central basin, to account for representative bioindicator distributions and higher temporal resolution, respectively.