Development of paleolimnological inference models for pH, total nitrogen and specific conductivity based on planktonic diatoms

Canonical correspondence analysis (CCA was used to explore and identify statistically significant relationships between the distributions of planktonic diatoms and the physical and chemical properties of 50 Connecticut lakes. Six variables (pH, total nitrogen, calcium, sulfate, potassium and chlorophyll- a concentrations) were found to be significantly correlated with either or both of the first two extracted axes. The pH and calcium concentration, and to a lesser extent total nitrogen concentrations, were the most important variables controlling the distributions of planktonic diatoms in this suite of lakes. Paleolimnological inference models were developed for pH, total nitrogen (TN) and specific conductivity. Weighted averaging with (WAtol) and without (WA) tolerance downweighting, with and without bootstrap resampling techniques, and using either classical or inverse deshrinking methods were used to develop inference models for each variable. The pH and TN yielded sufficiently high λ1/λ2 ratios and a highly significant first (constrained) axis when entered as single variables in both constrained and partially constrained CCA analyses, supporting the idea that reliable inference models could be developed for these variables. The r2 and RMSE of prediction values ranged from 0.73 to 0.86 and 0.37 to 0.6, respectively for pH, and from 0.4 to 0.64 and 59 μg/l to 95 μg/l, respectively for TN. Inference models for specific conductivity also yielded significant goodness-of-fit statistics. However, because specific conductivity was removed from the CCA analysis due to its high variance inflation factor and did not yield a significant relationship when entered as the sole variable in a partial constrained CCA, inference models for this variable will probably not yield any additional environmental information. The use of only planktonic diatoms in construction of inference models is discussed.