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Multivariate analyses of phytoplankton pigment fluorescence from a freshwater river network Ruchi Bhattacharya, and Christopher L. Osburn Environ. Sci. Technol., Just Accepted Manuscript • Publication Date (Web): 16 May 2017 Downloaded from http://pubs.acs.org on May 20, 2017

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Environmental Science & Technology

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Multivariate analyses of phytoplankton pigment fluorescence from a freshwater river

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network

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Ruchi Bhattacharya*, Christopher L. Osburn

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Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University,

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Raleigh, NC, USA 27606

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*Corresponding author info: [email protected]

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ACS Paragon Plus Environment


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Abstract

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Monitoring phytoplankton classes in river networks is critical to understand phytoplankton

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dynamics, and to predict the ecosystem response to changing land-use and seasons. Applicability

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of phytoplankton fluorescence as a quick and effective ecological monitoring approach is

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relatively unexplored in freshwater ecosystems. We used multivariate analyses of fluorescence

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from pigment extracted in 90% acetone to assess the variability in phytoplankton classes,

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herbivory and organic matter quality in a freshwater river network. Four models developed by

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Parallel Factor Analysis (PARAFAC) of fluorescence excitation and emission matrices identified

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6 components: Model 1 (Pheophytin-A, Chlorophyll-A), Model 2 (Chl-B, Chl-C), Model 3 (Phe-

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B) and Model 4 (Phe-C). Redundancy analyses revealed that in summer, urban and agricultural

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streams were abundant in chlorophylls, fresh organic matter and organic nitrogen; whereas in

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winter, streams were high in phaeopigments. A slow moving, light-limited wetland stream was

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an exception as high phaeopigment abundance was observed in both seasons. The PARAFAC

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components were used to develop a partial least square regression-based model (r2=0.53;

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NSE=0.5; n=147) that successfully predicted Chl-A concentrations from an external sub-set of

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river water samples (r2=0.41; p

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95% of the variation in the data. An efficient alternating least squares approach is used to

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minimize residuals between measured and modeled EEMs and usually produces robust models

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[45; 46]

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. The PARAFAC Models were validated by first dividing the data array into two halves,

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and then using Tucker’s congruence coefficient (TCC, set to 95% similarity) to test separate

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PARAFAC models fit to each half (split-half validation)[24]. Random initialization was next used

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on 10 models to find the best-fit model having the lowest sum of squared error [46]. Residuals

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were determined as the difference between the measured and the modeled EEMs [47]. In case

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distinct residual peaks with intensities of few orders of magnitude lower than the measured

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PARAFAC components were observed at a specific excitation/emission pair, then residual EEMs

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were modeled using in-house MATLAB functions built around the DOMFluor toolbox [24]. The

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modeled residuals were also split-half validated using TCC and random initialization methods

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described above.

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The principle component analysis (PCA) - a linear response ordination method [37] was

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used to explore the spatio-temporal variability in PARAFAC components. The PCA was

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conducted on the maximum fluorescence intensity (FMax) of each PARAFAC component, 9



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typically used as a qualitative measure of the strength/presence of each component [44; 45]. The

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PCA was conducted on the water quality parameters to explore their spatio-temporal variability

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(Table S2). Prior to conducting PCA the distribution of PARAFAC component Fmax and water

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quality parameters were checked by constructing q-q plots and histograms; parameters not-

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normally distributed were centered and log transformed log10 (n+10). Linear regressions were

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conducted for outlier identification (high leverage) and removal. The PCA was also used to

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explore the inter-variable relationships by calculating correlation coefficients between the

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variables [48]. Major gradients in the dataset were identified by calculating correlation

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coefficients for the variables and each of the ordination axes [48]. Next, the relation between the

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water quality parameters and the PARAFAC component FMax were determined by redundancy

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analysis (RDA). In order to identify a minimum subset of variables that significantly explain

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variation in the PARAFAC components, we first assessed the multi-collinearity in the data set by

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checking for high variance inflation factor (VIF > 10). Second, the redundant variables were

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removed through step-wise regression with Monte Carlo permutation tests [49]. Other than the

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statistical significance of each variable, their relative ecological importance in explaining

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phytoplankton abundance was also considered prior to variable removal. We further conducted

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variance partitioning to explore the percent variance uniquely explained by each variable. The

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PCA and RDA were conducted using “vegan 2.3-1” package in R statistical software [50].

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Partial least squares regression (PLS) [26] was conducted on PARAFAC component Fmax

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from using “rioja” package in R statistical software [51]. The PLS model was calibrated with the

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sample Subset 1 (n = 147), that was analyzed for pigment fluorescence, Chl-A concentrations

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and other water quality parameters. The water quality parameter that individually explained the 10


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most variance was used for the development of the calibration model. Leave-one-out cross

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validation procedure (Jack-knife) was used to validate the error predictions [52, 53]. The model

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efficiency was evaluated by r2 and Nash-Sutcliffe Efficiency value (NSE). The PLS based

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calibration model was externally validated using the PARAFAC component Fmax values from a

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second sample subset (Subset 2, n = 75). The sample subset 2 was analyzed for pigment

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fluorescence, and Chl-A, but the associated water quality parameters were not available.

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However, we ensured that both subset 1 and 2 were representative of the environmental and

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seasonal gradients.

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Results

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PARAFAC Modeling

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Figure 1 PARAFAC model component excitation and emission spectral loading (shown by the solid line). The excitation and emission loadings of a split-half validated component (shown by the dashed line) is also plotted with each PARAFAC component. An overlap between the PARAFAC and split-half validated component indicates good model performance.

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A total of four PARAFAC models (see Figures S2 and S3) were developed using two sets of

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data: 1) the chlorophyll pigment EEMs and 2) the phaeopigment EEMs. The first PARAFAC

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model (Model1) was developed from the chlorophyll pigment EEMs and resulted in two

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components that were validated by split-half analysis. The excitation (Ex) and emission (Em)

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spectral loadings of the two-modeled components resembled Phe-A (Model1-Phe-A) and Chl-A 12


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(M1-Chl-A), respectively (Figure 1A-B, Table 1, Figure S2). Examination of the residuals EEMs

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from Model 1 revealed two low intensity, distinct fluorescence peaks. The second PARAFAC

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model (Model 2) was developed using these residual EEMs and two validsated components were

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identified as Chl-B (M2-Chl-B) and Chl-C (M2-Chl-C) (Figure 1C-D, Table 1). Then, the

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phaeopigment EEMs were used to develop a two-component PARAFAC model (Model 3), and

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the validated components corresponded with Phe-A (M3-Phe-A; not shown in Figure 1) and Phe-

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B (M3-Phe-B) (Figure 1E; Table 1). The model 3 residuals also revealed a distinct low intensity

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fluorescence peak. Thus, the final, and fourth model (Model 4) was developed from the residual

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EEMs from Model 3, and the validated component was identified as Phe-C (M4-Phe-C) (Figure

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1F, Table 1, Figure S2).

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Spatial and temporal variability in PARAFAC components

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PCA conducted on the PARAFAC components Fmax (log10 (n+10)) showed that PCA

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axis 1 and 2 explained 47.7% and 32.9% of the variance, respectively (Figure 2A). The M3-Phe-

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B and M4-Phe-C vectors were significantly and positively correlated to each other (r2 = 0.4, p

Multivariate Analyses of Phytoplankton Pigment Fluorescence from

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