The modern distribution of chironomid sub-fossils (Insecta: Diptera) in the Sierra Nevada, California: Potential for paleoclimatic reconstructions

Surface lake sediment was recovered from 57 lakes along an elevation gradient in the central, eastern Sierra Nevada of California. The surface sediment was analysed for subfossil chironomid remains in order to assess the modern distribution of chironomids in the region. The lakes sampled for the calibration dataset were between 2.0 and 40.0 m in depth, spanned an altitudinal gradient of 1360 m and a surface water temperature gradient of approximately 14 °C. Redundancy analysis (RDA) identified that five of the measured environmental variables – surface water temperature, elevation, depth, strontium, particulate organic carbon – accounted for a statistically significant amount of the variance in chironomid community composition. Quantitative transfer functions, based on weighted-averaging (WA), partial least squares (PLS) and weighted-averaging partial least squares (WA-PLS), were developed to estimate surface water temperature from the chironomid assemblages. The best model was a WA model with classical deshrinking, which had a relatively high coefficient of determination (r² = 0.73), low root mean square error of prediction (RMSEP = 1.2 °C) and a low maximum bias (0.90 °C). The results from this study suggest that robust quantitative estimates of past surface water temperature can be derived from the application of these models to fossil chironomid assemblages preserved in late-Quaternary lake sediment in this region.     

Diatom-inference models for acid neutralizing capacity and nitrate based on 41 calibration lakes in the Sierra Nevada, California, USA

We investigated relationships among modern diatom species composition and physical and chemical characteristics of high-elevation lakes of the Sierra Nevada (California), to develop transfer functions that can be used to infer historic lake conditions. Data were collected from 50 lakes in National Parks and Forests of the central and southern Sierra Nevada. Multivariate statistical methods revealed that acid neutralizing capacity (ANC) and nitrate accounted for significant variation in diatom taxa. A training set with 242 modern diatom taxa from a subset of 41 lakes was used to develop transfer functions for ANC and nitrate using weighted averaging techniques. ANC and nitrate calibration ranges were 23.0–137 μEq/L and 0.18–9.5 μM, respectively. Coefficients of determination for the models were: ANC: R² = 0.76, and R _jackknife ²  = 0.44; NO₃: R² = 0.67, and R _jackknife ²  = 0.27. The ANC model was applied to the top 50 cm of sediments in Moat Lake to reconstruct ANC from ca. AD 350 to 2005. The reconstruction suggests that ANC declined by about 40 % (101–60 μEq/L) between the 1920s and the 1960s and then recovered to pre-1920s levels during 1980–2000. The magnitude of this ANC excursion was the largest observed during the past 1,600 years. We hypothesize that temporal variations in ANC were influenced by: (1) changes in rates of acid deposition, especially nitric acid and (2) variations in the timing and magnitude of snowmelt runoff.     

Diatom assemblages from Adirondack lakes (New York,USA) and the development of inference models for retrospective environmental assessment

Detrended canonical coreespondence analysis (DCCA) was used to examine the relationships between diatom species distributions and environmental variables from 62 drainage lakes in the Adirondack region, New York (USA). The contribution of lakewater pH, Al_m (monomeric Al), NH₄, maximum depth, Mg, and DOC (dissolved organic carbon) were statistically significant in explaining the patterns of variation in the diatom species composition. Twenty-three and sixteen diatom taxa were identified as potential indicator species for pH and Al_m, respectively (i.e. a taxon with a strong statistical relationship to the environmental variable of interest, a well defined optimum, and a narrow tolerance to the variable of interest). Using weighted-averaging regression and calibration, predictive models were developed to infer lakewater pH (r ²=0.91), Al_m (r ²=0.83), DOC (dissolved organic carbon) (r ²=0.64), and ANC (acid neutralizing capacity; r ²=0.90). These variables are of key importance in understanding watershed acidification processes. These predictive models have been used in the PIRLA-II (Paleoecological Investigation of Recent Lake Acidification-II) project to answer policy-related questions concerning acidification, recovery, and fisheries loss.