A chironomid-based mean July temperature inference model from the south-east margin of the Tibetan Plateau, China

A chironomid-based calibration training set comprised of 100 lakes from south-western China was established. Multivariate ordination analyses were used to investigate the relationship between the distribution and abundance of chironomid species and 18 environmental variables from these lakes. Canonical correspondence analyses (CCAs) and partial CCAs showed that mean July temperature is one of the independent and significant variables explaining the second-largest amount of variance after potassium ions (KC) in 100 south-western Chinese lakes. Quantitative transfer functions were created using the chironomid assemblages for this calibration data set. The second component of the weighted-average partial least squares (WA-PLS) model produced a coefficient of determination ( r(2) (bootstrap)) of 0.63, maximum bias (bootstrap) of 5.16 and root-mean-square error of prediction (RMSEP) of 2.31 degrees C. We applied the transfer functions to a 150-year chironomid record from Tiancai Lake (26 degrees 38'3.8 N, 99 degrees 43' E; 3898m.a.s.l.), Yunnan, China, to obtain mean July temperature inferences. We validated these results by applying several reconstruction diagnostics and comparing them to a 50-year instrumental record from the nearest weather station (26 degrees 51'29.22'' N, 100 degrees 14'2.34'' E; 2390m.a.s.l.). The transfer function performs well in this comparison. We argue that this 100-lake large training set is suitable for reconstruction work despite the low explanatory power of mean July temperature because it contains a complete range of modern temperature and environmental data for the chironomid taxa observed and is therefore robust.