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

Chironomid based calibration training sets comprised of 100 lakes from southwestern China and a subset of 47 lakes from Yunnan Province were established. Multivariate ordination analyses were used to investigate the relationship between the distribution of chironomid species and 15 environmental variables from these lakes. Canonical correspondence analyses (CCAs) and partial CCAs showed that mean July temperature is the sole independent and significant (p < 0.05) variable that explains 16 % of the variance in the chironomid data from the 47 Yunnan lakes. Mean July temperature remains one of the independent and significant variables explaining the second largest amount of variance after potassium ions (K⁺) in the 100 south-western Chinese lakes. Quantitative transfer functions were created using the chironomid assemblages for both calibration data sets. The first component of the weighted average partial least square (WA-PLS) model based on the 47 lakes training set produced a coefficient of determination (r²_jack) of 0.83, maximum bias (jackknifed) of 3.15 and root mean squared error of prediction (RMSEP) of 1.72 °C. The two-component WA-PLS model for the 100 lakes training set produced an r²_bootstrap of 0.63, maximum bias (bootstrapped) of 5.16 and RMSEP of 2.31 °C. We applied both transfer functions to a 150-year chironomid record from Tiancai Lake (26°38'3.8 N, 99°43'E, 3898 m a.s.l), Yunnan, China to obtain mean July temperature inferences. The reconstructed results based on both models showed remarkable similarity to each other in terms of pattern. We validated these results by applying several reconstruction diagnostics and comparing them to a 50-year instrumental record from the nearest weather station (26°51'29.22"N, 100°14'2.34"E, 2390 m a.s.l). Both transfer functions perform well in this comparison. We argue that the large training set is also suitable for reconstruction work despite the low explanatory power of MJT because it contains a more complete range of modern temperature and environmental data for the chironomid taxa observed and is therefore more robust. [ABSTRACT FROM AUTHOR]