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.