Canonical correspondence analysis (CCA) was used to explore the relationship between measured environmental variables and surficial diatom (Bacillariophyceae) assemblages in alkaline lakes from southeastern Ontario. Total nitrogen (TN), watershed area, alkalinity, and maximum depth each explain significant (P ≤ 0.05) directions of variance in the distribution of diatom taxa.
TN was highly correlated to total phosphorus (TP) (r = 0.92), chlorophyll a (r = 0.86), and Secchi depth (r =0.77). When a series of CCAs were run with the first axis constrained to each of these variables in turn, the ratio of the eigenvalue of the first axis to that of the second axis (λ1/λ2) was highest for TN, indicating that TN best explained the distribution of the diatom assemblages in this set of lakes. Furthermore, results of Monte Carlo permutation tests indicated that these four variables did not act independently on the diatom assemblages. Therefore, TN was selected to represent these four closely related variables to infer lake trophic status.
Weighted‐averaging regression and calibration (with classical deshrinking) were used to develop transfer functions to infer TN from the relative abundances of 83 diatom taxa recovered from the surficial sediments of 51 lakes. There was a good correlation between diatom‐inferred TN concentrations and measured TN concentrations (r2= 0.75, n = 51).
The weighted‐averaging regression and calibration model was used to infer lake trophic status (represented by TN) from diatom assemblages presented in the sediments from Little Round Lake, Ontario. These data were used in conjunction with historical land‐use data in order to quantify the sequence and extent of nutrient enrichment related to human activity in the watershed area.