Chironomid-based inference models for estimating mean July air temperature and water depth from lakes in Yakutia, northeastern Russia

Abstract

We investigated the subfossil chironomid fauna of 150 lakes situated in Yakutia, northeastern Russia. The objective of this study was to assess the relationship between chironomid assemblage composition and the environment and to develop chironomid inference models for quantifying past regional climate and environmental changes in this poorly investigated area of northern Russia. The environmental data and sediment samples for chironomid analysis were collected in 5 consecutive years, 2003–2007, from several regions of Yakutia. The lakes spanned wide latitudinal and longitudinal ranges and were distributed through several environmental zones (arctic tundra, typical tundra, steppe-tundra, boreal coniferous forest), but all were situated within the zone of continuous permafrost. Mean July temperature (TJuly) varied from 3.4°C in the Laptev Sea region to 18.8°C in central Yakutia near Yakutsk. Water depth (WD) varied from 0.1 to 17.1 m. TJuly and WD were identified as the strongest predictor variables explaining the chironomid communitiy composition and distribution of the taxa in our data set. Quantitative transfer functions were developed using two unimodal regression calibration techniques: simple weighted averaging (WA) and weighted averaging partial least squares (WA-PLS). The two-component TJuly WA-PLS model had the best performance. It produced a strong coefficient of determination (r2boot = 0.87), root mean square error of prediction (RMSEP = 1.93), and max bias (max biasboot = 2.17). For WD, the one-component WA-PLS model had the best performance (r2boot = 0.62, RMSEP = 0.35, max biasboot = 0.47).