A weighted-averaging regression and calibration model for inferring lakewater salinity using chrysophycean stomatocysts from lakes in western Canada

Chrysophycean stomatocysts were identified and enumerated from the surface sediments of 60 lakes located on the Interior Plateau of British Columbia. The lakes span a salinity gradient from freshwater to hypersaline (0.0–92.4 g L^–1), with the majority being fresh to hyposaline. One hundred and ten stomatocyst morphotypes, almost all of which were previously described, were identified from the lake sediments. The first axis of direct gradient analysis, which was highly significant, was essentially a salinity axis (i.e. [Ca], [Mg], [K], [Na], [SO₄], [DIC], and [Cl]). Most cysts were found to have fairly broad tolerances, with the narrowest tolerances occurring among morphotypes with the lowest salinity optima. Weighted-averaging regression and calibration techniques were used to develop an inference model to measure the relationship between measured average lakewater salinity and stomatocyst inferred salinity (apparent r²=0.80). Simple weighted-averaging produced a model with a lower bootstrapped RMSE of prediction than weighted-averaging with tolerance downweighting. These data indicate that chrysophyte stomatocysts are useful quantitative indicators of past lakewater salinity (in the freshwater to hyposaline range) in B.C. lakes, and can be used to strengthen the interpretations from diatom-inference models already developed from the same region.     

Assessment of freshwater diatoms as quantitative indicators of past climatic change in the Yukon and Northwest Territories,Canada

We identified, enumerated, and interpreted the diatom assemblages preserved in the surface sediments of 59 lakes located between Whitehorse in the Yukon and Tuktoyaktuk in the Northwest Territories (Canada). The lakes are distributed along a latitudinal gradient that includes several ecoclimatic zones. It also spans large gradients in limnological variables. Thus, the study lakes are ideal for environmental calibration of modern diatom assemblages. Canonical correspondence analysis, with forward selection and Monte Carlo permutation tests, showed that maximum lake depth and summer surface-water temperature were the two environmental variables that accounted for most of the variance in the diatom data. The concentrations of sodium and calcium were also important explanatory variables. Using weighted-averaging regression and calibration techniques, we developed a predictive statistical model to infer lake surface-water temperature, and we evaluated the feasibility of using diatoms as paleoclimate proxies. This model may be used to derive paleotemperature inferences from fossil diatom assemblages at appropriate sites in the western Canadian Arctic.     

Scaled chrysophytes and pH inference models: the effects of converting scale counts to cell counts and other species data transformations

Predictive pH models developed using scaled chrysophytes (Synurophyceae, Chrysophyceae) have thus far been based on the relative abundance of scales and not whole cells. This paper examines the effects of transforming scale to cell numbers on the predictive abilities of pH inference models, and the effects of logarithmic and square-root transformations of the species data on the predictive abilities of pH inference models.Very similar pH inference models were developed based on either the relative abundance of scales or cells. Thus, in this data-set, there appears to be no statistical advantage in transforming raw scale counts to cell counts prior to calculating the relative abundances. However, if one wishes to compare paleochrysophyte populations to actual long-term limnological chrysophyte collections, a scale-to-cell transformation would be desirable. Logarithmic and square-root transformations of the species data improve the pH inference models. These transformations increase the effective number of occurrences of chrysophyte taxa when compared to the untransformed scale and cell pH models. The logarithmic and square-root transformations improve the pH inference models because the dominant taxa, which are often pH generalists, are down-weighted in comparison to the more pH specialist, sub-dominant taxa. We suggest researchers use either a logarithmic or square-root transformation on chrysophyte scale data to improve quantitative reconstructions of lakewater pH and possibly other variables.     

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.