Calibration-in-time versus calibration-in-space (transfer function) to quantitatively infer July air temperature using biological indicators (chironomids) preserved in lake sediments

Calibration-in-space (i.e. modern taxonomic assemblages of biota from many lakes located along a wide temperature gradient calibrated against meteorological data) is generally used to derive species-specific optima and tolerances. This results in transfer functions which then are applied to subfossi...

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Bibliographic Details
Published in:Palaeogeography, palaeoclimatology, palaeoecology palaeoclimatology, palaeoecology, 2011-01, Vol.299 (1-2), p.281-288
Main Authors: Larocque-Tobler, Isabelle, Grosjean, Martin, Kamenik, Christian
Format: Article
Language:English
Subjects:
Chironomidae
Climate change
Freshwater
Late Holocene
Paleoclimatology
Paleolimnology
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Summary:Calibration-in-space (i.e. modern taxonomic assemblages of biota from many lakes located along a wide temperature gradient calibrated against meteorological data) is generally used to derive species-specific optima and tolerances. This results in transfer functions which then are applied to subfossil assemblages to quantitatively reconstruct environmental variables such as air/water temperature. Developing such transfer functions is time- and money-consuming, thus many biota-inferred temperature records are either based on transfer functions from other regions which might not take into account local characteristics or are only used qualitatively. In varved Lake Silvaplana (Engadine, Switzerland), another way of obtaining quantitative climate reconstructions from taxonomical assemblages preserved in lake sediments was assessed for the past 1000years. A calibration-in-time (i.e. taxonomic-assemblage-of-biota time series calibrated against meteorological data covering the same time period) was developed for chironomids (non-biting midges) using a weighted-average-partial-least-square (WAPLS) model and compared with a calibration-in-space model. The calibration-in-time had a weaker correlation coefficient (r2=0.71) than the calibration-in-space (r2=0.86), but the error of prediction (RMSEP=0.58°C) and the maximum bias (Max Bias=0.73°C) outperformed the statistics of the calibration-in-space (RMSEP=1.5°C; Max Bias=1.72). This result is probably due to the smaller temperature gradient of the calibration-in-time (6.5°C) than the calibration-in-space (11.5°C). For the last 150years, the Pearson correlation coefficient was significant between the two reconstructions (rPearson=0.52; p
ISSN:0031-0182
1872-616X
DOI:10.1016/j.palaeo.2010.11.008