Midges (Chironomidae,Ceratopogonidae, Chaoboridae) as a temperature proxy: a training set from Tasmania,Australia

Chironomid and ceratopogonid head capsules, along with Chaoborus mandibles, were used to model mean temperature of the warmest quarter (TWARM) in Tasmania. Our transfer function is based on midge assemblages and 21 environmental variables sampled from 47 lakes. Canonical correspondence analysis (CCA) revealed seven variables that account for a significant (P ≤ 0.05) portion of the explainable variance. In order of explanatory power, these were pH, TWARM, annual radiation, magnesium, annual precipitation, SiO₂, and depth. TWARM was modeled using weighted averaging partial least squares (WA-PLS) and generated a model with and RMSEP = 0.94. Advances in chironomid paleoecology are progressing very quickly in the Southern Hemisphere. Chironomid identification guides and autecological data are available for many regions, highlighting the potential for developing midge-based quantitative models to address hemispheric and interhemispheric climate hypotheses.     

Diatom-based water chemistry reconstructions from northern Sweden: a comparison of reconstruction techniques

The diatom composition in surface sediments from 119 northern Swedish lakes was studied to examine the relationship with lake-water pH, alkalinity, and colour. Diatom-based predictive models, using weighted-averaging (WA) regression and calibration, partial least squares (PLS) regression and calibration, and weighted-averaging partial least squares (WA-PLS) regression and calibration, were developed for inferences of water chemistry conditions. The non-linear response between the diatom assemblages and pH and alkalinity was best modelled by weighted-averaging methods. The lowest prediction error for pH was obtained using weighted averaging, with or without tolerance downweighting. For alkalinity there was still some information in the residual structure after extracting the first weighted-averaging component, which resulted in a slight improvement of predictions when using a two component WA-PLS model. The best colour predictions were obtained using a two component PLS model. Principal component analysis (PCA) of the prediction errors, with some characteristics of the training set included as passive variables, was performed to compare the results for the different alkalinity predictive models. The results indicate that calibration techniques utilizing more than one component (PLS and WA-PLS) can improve the predictions for lakes with diatom taxa that have broad tolerances. Furthermore, we show that WA-PLS performs best compared with the other techniques for those lakes that have a high relative abundance of the most dominant taxa and a corresponding low sample heterogeneity.