Statistical analysis
Principal components analysis (PCA) was used to group the years
(2008-2020) based on physico-chemical variables and water level
fluctuations. Linear regression (y = ax + c) and waveform sine
regression (y = a*sin (2*p*x/b + c) analyses were performed to determine
the best-fitting model to explain the patterns of lake level
fluctuations (WLFs) over the years. Where both models were not
significant, a Locally Weighted Scatter Plot Smoother (LOWESS,
Cleveland, 1979) was used to describe the pattern of lake level
fluctuations. LOWESS is based on a weighted least squares algorithm that
gives local weights the most influence while minimizing the effects of
outliers. A smoothness parameter (f) of 0.2 was found to adequately
smooth the data without distorting the temporal patterns.
Pearson’s correlation coefficient was used to determine the concordance
between WLF indicators (DLTM and Amplitude, WLamp) and fisheries
variables and with water quality parameters (conductivity, turbidity,
chlorophyll-a, depth, DO, temperature, TP,
PO43–,
NO3–, TN,
SiO44–,
NH4+ and WQI). Both Pearson’s and
linear regression analyses were conducted on log (x + 1) transformed
data to meet the required assumption of normality of the dataset (Zar,
2010). The frequency distribution displayed as a histogram of pixel
depth was used to group the sampling period in years from 1956 to 2021
based on the increased or decreased rate of the depth while, the lake
water level – lake surface area relationship was determined using a
linear regression model.
Linear regression and non-linear Gaussian distribution (Zar, 2010) were
used to determine the influence of WLFs on the lake’s fishery yields and
condition factor as a measure of growth. The Gaussian distribution
follows a unimodal pattern and tested the hypothesis that the lake
fisheries production and fish condition will correspond to optimum WLFs
levels below and above which a decline is realized. All the graphical
plots were implemented in the Sigma Plot software package.
RESULTS