Characterization of lake systems
Lake trout populations in L260, L223, L224, and L373 are sampled in autumn of each year for various parameters including abundance, growth, and condition. Total length and weight for lake trout sampled in 2017 were extracted from the IISD-ELA database. Fulton’s condition factor (K) was calculated for each individual using the formula K = 100 x weight/length3. Statistical differences in average total length, weight, and condition factor among each lake-by-lake comparison were assessed using a one-way analysis of variance (ANOVA) followed by a Tukey’s Honest Significant Difference (HSD) test using a significance threshold of adj-p ≤ 0.05.
As zooplankton are an important food source for lake trout, zooplankton abundances by species for L260, L223, L224, and L373 in late September/early October 2017 were extracted from the IISD-ELA database. For each lake, total abundance of zooplankton, species richness (no. of species), Shannon Weiner diversity indices (H’), and Shannon equitability indices (EH) were calculated. The Shannon Weiner diversity index measures biodiversity, accounting for both species richness and evenness, and was calculated using H ’ = - ∑pi ln pi , where pi is the proportion of total individuals found in species i. The Shannon equitability index measures evenness of the community and was calculated using EH =H’ /Hmax , where Hmax is ln(species richness). Equitability is a value between 0 and 1, with 1 being complete evenness.
Since 1968, data for water quality, biological variables, and atmospheric conditions have been collected at the IISD-ELA. Data from September and October 2017 were extracted from the database for each target lake, which included the following environmental parameters: temperature (°C), dissolved oxygen (mg/L), conductivity (S/m), pH, alkalinity (µEq/L), chlorophyll a (µg/L), total dissolved nitrogen (µg/L), total dissolved phosphorus (µg/L), suspended carbon (µg/L), suspended phosphorus (µg/L), suspended nitrogen (µg/L), ammoniacal nitrogen (NH3-N; µg/L), and nitrate-nitrogen (NO3-N; µg/L). For consistency, values for each parameter were extracted for the epilimnion and metalimnion in each lake and averaged to obtain representative values for each lake. Statistical differences in average water quality parameters among each lake-by-lake comparison were assessed using a one-way ANOVA followed by a Tukey’s Honest Significant Difference (HSD) test using a significance threshold of adj-p ≤ 0.05. Using the aforementioned variables for each lake, PCA was used to reduce multidimensionality of water quality data and to graphically visualize similarity in lakes based on water quality characteristics. As the water quality parameters possess different units, all values were scaled to a unitless form of zero mean and variance of one prior to analysis in order to make the variables comparable. Results of the PCA were also used to examine the relationships between variables and the magnitude of importance of each variable on the principal components. Unsupervised hierarchical clustering of water quality parameters for each lake was used to further visualize similarities among lakes.