The evaluation of climate change impact on lakes typically relies on statistical methods like the reorganisation of organism communities (beta diversity) or transfer functions. A new method uses the silicification of diatoms that correlates with temperature and nutrients. The so-called silicification value (SiVa) overcomes problems of descriptive statistics or absent indicator species. Averaged over diatom communities, it related inversely to lake surface temperatures in mountain lakes. Hence, its change over time (δ SiVa) in a lake was hypothesised to reflect global change-driven lake warming quantitatively, which supposedly climaxes in shallow lakes. Sixteen different δ SiVa calculation approaches were tested. They (1) included or excluded planktic diatoms, (2) integrated fixed or variable time series referring to climate data or changes in diatom assemblages, (3) employed a top-bottom or regression approach and (4) expressed the δ SiVa as relative or absolute values. Subfossil diatom assemblages from 24 sediment cores from Bavarian and north Tyrolian mountain lakes served as sample set. All possible approaches were evaluated for their explanatory power for lake characteristics using GLMs. The top-bottom benthic approach with fixed climate data-based time series appeared to be the best model based on AIC and the extent of variable integration. In line with the hypothesis, the strongest decrease of δ SiVa was evident in most shallow lakes. Segmented regression further highlighted a positive correlation with depth if shallower than 10 m. By referring to the negative SiVa-summer temperature relation, δ SiVa also enabled the quantification of lake warming within the last decades, which ranged mainly between 0.1 °C and 1.1 °C per decade, consistent with existing literature. Additionally, a 100 year temperature reconstruction from a varved sediment core successfully validated the approach. Further studies may focus and extend its application to deeper lakes, but it can already serve as a powerful tool in palaeolimnological studies of shallow lakes like hard-water mountain lakes.
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The evaluation of climate change impact on lakes typically relies on statistical methods like the reorganisation of organism communities (beta diversity) or transfer functions. A new method uses the silicification of diatoms that correlates with temperature and nutrients. The so-called silicification value (SiVa) overcomes problems of descriptive statistics or absent indicator species. Averaged over diatom communities, it related inversely to lake surface temperatures in mountain lakes. Hence, i...
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