Estimation of chromophoric dissolved organic matter (CDOM) and photosynthetic activity of estuarine phytoplankton using a multiple-fixed-wavelength spectral fluorometer

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 1 6 e1 6 3 0

Available online at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/watres

Estimation of chromophoric dissolved organic matter (CDOM) and photosynthetic activity of estuarine phytoplankton using a multiple-fixedwavelength spectral fluorometer Emily A. Goldman a, Erik M. Smith a,c, Tammi L. Richardson a,b,* a

Marine Science Program, University of South Carolina, 715 Sumter St., Columbia, SC 29208, USA Department of Biological Sciences, University of South Carolina, 715 Sumter St., Columbia, SC 29208, USA c Belle W. Baruch Institute for Marine and Coastal Sciences, University of South Carolina, P.O. Box 1630, Georgetown, SC 29442, USA b

article info

abstract

Article history:

The utility of a multiple-fixed-wavelength spectral fluorometer, the Algae Online Analyser

Received 11 October 2012

(AOA), as a means of quantifying chromophoric dissolved organic matter (CDOM) and

Received in revised form

phytoplankton photosynthetic activity was tested using algal cultures and natural com-

13 December 2012

munities from North Inlet estuary, South Carolina. Comparisons of AOA measurements of

Accepted 14 December 2012

CDOM to those by spectrophotometry showed a significant linear relationship, but

Available online 5 January 2013

increasing amounts of background CDOM resulted in progressively higher over-estimates of chromophyte contributions to a simulated mixed algal community. Estimates of pho-

Keywords:

tosynthetic activity by the AOA at low irradiance (w80 mmol quanta m
2 s
1) agreed well

AOA

with analogous values from the literature for the chlorophyte, Dunaliella tertiolecta, but were

Continuous monitoring

substantially lower than previous measurements of the maximum quantum efficiency of

Colored dissolved organic matter

photosystem II (Fv/Fm) in Thalassiosira weissflogii (a diatom) and Rhodomonas salina (a cryp-

Genty parameter

tophyte). When cells were exposed to high irradiance (1500 mmol quanta m
2 s
1), declines

Fv/Fm

in photosynthetic activity with time measured by the AOA mirrored estimates of cellular

Variable fluorescence

fluorescence capacity using the herbicide 30 -(3, 4-dichlorophenyl)-10 ,10 -dimethyl urea (DCMU). The AOA shows promise as a tool for the continuous monitoring of phytoplankton community composition, CDOM, and the group-specific photosynthetic activity of aquatic ecosystems. ª 2012 Elsevier Ltd. All rights reserved.

1.

Introduction

Phytoplankton are important contributors to estuarine primary production (Sellner et al., 1976; Miller et al., 1996; Underwood and Kromkamp, 1999). Phytoplankton community composition in estuaries can be highly variable in space and time (e.g. Lewitus et al., 1998; Lawrenz et al., 2010; Haese and

Pronk, 2011; Kimmerer et al., 2012); characterizing this variability is necessary if we are to understand the role that phytoplankton play in ecosystem structure and function. There is a general lack of information on how phytoplankton community composition varies on short time scales (hours to days) which stems, in part, from the fact that continuous monitoring of phytoplankton is laborious, time-consuming, and expensive

* Corresponding author. Marine Science Program, University of South Carolina, 715 Sumter St., Columbia, SC 29208, USA. Tel.: þ1 803 777 2269; fax: þ1 803 777 3922. E-mail addresses: [email protected], [email protected] (T.L. Richardson). 0043-1354/$ e see front matter ª 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.watres.2012.12.023

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(Millie et al., 2002; Istva´novics et al., 2005). Many of the common techniques used to characterize community composition require long periods for sample analysis and thus are not “realtime” in nature (Millie et al., 2002). Microscopy, in particular, is laborious, time-consuming, and requires substantial training in taxonomy. Characterization of the phytoplankton community by pigment-based approaches like high performance liquid chromatography (HPLC) and associated data analysis tools (e.g. ChemTax; Mackey et al., 1996; Wright et al., 1991), while generally faster than microscopy in sample turn-around, also requires specialized training of the operator and can be costly on a per sample basis. Thus, aquatic scientists continue to search for an effective monitoring tool for phytoplankton community composition that is not only accurate, but gives data in real-time and requires relatively little interpretation and analysis. Instruments that operate on the principle of spectral fluorescence appear to be good candidates for this task. The potential use of spectral fluorescence for characterizing phytoplankton community composition has been recognized for more than 30 years (Yentsch and Yentsch, 1979; Yentsch and Phinney, 1985). Fluorescence is a rapid, nondestructive, and non-invasive approach to characterizing phytoplankton community composition in real-time. The idea of fluorometric differentiation of algal populations based on excitation/emission spectra has been the basis of several instruments (e.g. Desiderio et al., 1997; Millie et al., 2002; Beutler et al., 2002; Seppa¨la¨ and Balode, 1998; Schreiber, 1998; Gregor and Mar sa´lek, 2004; Chekalyuk and Hafez, 2008). Spectral fluorescence approaches are based on the idea that all photosynthetic organisms contain chl a which absorbs blue light, but also contain additional accessory pigments, such as carotenoids, phycobilins, or accessory chlorophylls (chl b or chl c), that absorb other wavelengths. Depending on the pigment composition, different phytoplankton taxa absorb different wavelengths of light, resulting in a distinct taxonspecific fluorescence signature (in AOA parlance this is known as a “fingerprint”; Beutler et al., 2002). In 2001, the German company bbe Moldaenke produced the first of its instruments designed for the continuous monitoring of phytoplankton community composition. The Algae Online Analyser (AOA) is a fixed-wavelength spectral fluorometer equipped with 5 light-emitting diodes (LEDs) for excitation at w450, 525, 570, 590, and 610 nm (Beutler et al., 2002). The resulting (emitted) fluorescence of chlorophyll a (chl a) is measured at w680 nm. The shape of the spectral fluorescence signature is compared against a library of spectral signatures and is used to distinguish between taxa, while the fluorescence intensity and the group-specific fluorescence/chl a ratios are used to estimate total phytoplankton biomass (as chl a) (Beutler et al., 2002; Richardson et al., 2010; MacIntyre et al., 2011). Analysis of phytoplankton community composition by spectral fluorometry may be complicated, however, by interference from chromophoric dissolved organic matter (CDOM). CDOM absorbs both ultraviolet light (280e400 nm) and visible blue light effectively reducing the amount of damaging UV radiation for phytoplankton, but also interfering with the absorption of available blue light favored by phytoplankton (Kirk, 1994; Blough and Del Vecchio, 2002). The AOA estimates CDOM

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concentration from CDOM fluorescence using a UV-LED (370 nm) and uses the data to correct the classification of phytoplankton type and concentration (MacIntyre et al., 2011). The measurement of CDOM, especially in estuarine environments, is fundamental to the study of water-related optics, remote sensing, and aquatic ecology (Blough and Del Vecchio, 2002). Therefore, it is advantageous to have an instrument like the AOA that is capable of predicting both phytoplankton community composition and CDOM concentrations. An additional function of the AOA is the measurement of short-term variations in chlorophyll fluorescence as a means of estimating phytoplankton group-specific photosynthetic activity (Beutler et al., 2002). The AOA uses brief flashes of bright actinic light to stimulate maximal fluorescence (Fm) and calculates variable fluorescence (Fv) by comparing Fm to the minimal fluorescence measured after a brief period of dark acclimation (Fo; Kromkamp and Forster, 2003), resulting in a parameter called “algae activity”. This is returned as a variation on the Genty parameter (Fv/Fm; defined as the efficiency of excitation capture by open photosystem II reaction centers by Genty et al., 1989) and is given for each algal group in the sample. This approach, ideally suited for continuous monitoring of field samples, is also used by instruments like PulseAmplitude-Modulated (PAM) fluorometers (Heinz Walz GmbH, Germany; Schreiber, 1988), the Fast Repetition Rate (FRR) fluorometers (Kolber and Falkowski, 1993), and others (see Laney, 2011) on bulk phytoplankton populations. More recently, instruments that measure phytoplankton groupspecific variable fluorescence have been developed by Walz (e.g., the Phyto-PAM) and others. The AOA, therefore, has great potential for use in the continuous monitoring of phytoplankton community composition and photosynthetic activity, assuming that its output is accurate. Our previous work examined the utility of the AOA as a means of quantifying phytoplankton biomass and community composition in natural estuarine communities (Richardson et al., 2010). The performance of the AOA in measuring CDOM and photosynthetic activity has not been examined extensively. Accordingly, the objectives of this study were to: 1) compare CDOM estimates by the AOA to direct determinations of CDOM by absorption, to assess whether there are predictable relationships between these two approaches, 2) compare AOA-based estimates of Fv/Fm to those of an alternate method that employs the photosynthetic inhibitor, 30 -(3,4-dichlorophenyl)-10 ,10 -dimethyl urea (DCMU; Vincent, 1980) and 3) determine how increasing amounts of CDOM in the water influence the AOA’s prediction of total and group-specific chl a and photosynthetic activity. We also present data on the AOA’s ability to predict the absolute and relative abundance of phytoplankton taxonomic groups collected during the course of these experiments.

2.

Methods

Our general approach was two-fold: 1) We deployed the AOA on a dock at Oyster Landing, North Inlet Estuary, for continuous monitoring and comparative discrete sample collection; 2) We used algal cultures of species representing three major algal groups: chlorophytes, chromophytes, and

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cryptophytes, for laboratory-based AOA experiments as detailed below.

2.1. Inlet

Deployment and sampling at Oyster Landing, North

Oyster Landing is located in a shallow, tidally-dominated, salt marsh estuary within the North Inlet e Winyah Bay National Estuarine Research Reserve (NERR) on the northeastern coast of South Carolina, USA (Fig. 1). The North Inlet system consists of 32 km2 of salt marsh drained by numerous tidal creeks. The mean semidiurnal tidal range is 2.5 m on the spring tide, and 1 m on the neap tide (Kjerfve et al., 1981; Kjerfve, 1986). Average creek depth is w3 m, thus the tidal range constitutes a substantial fraction of the total water depth and the estuary is classified as “well-mixed” (Hansen and Rattray (1966) type 1a; Kjerfve et al., 1981). Before deployment, the AOA was calibrated using nutrientreplete cultures of representative species for each group: Dunaliella tertiolecta (CCMP 1320) for the chlorophytes, Synechococcus sp. (CCMP 833) for the cyanobacteria, Thalassiosira weissflogii (CCMP 1051) for the chromophytes and Rhodomonas salina (CCMP 1319) for the cryptophytes. All calibration

cultures were grown in f/2 medium (with or without silicate as appropriate; Guillard, 1995; Berges et al., 2001) at an irradiance of w50 mmol quanta m
2 s
1 of photosynthetically available radiation (PAR) on a 12:12 light:dark cycle at 22  C. The AOA was deployed twice a month for one tidal cycle (spring or neap) during the months of July, October and December 2010. One deployment was done in May 2010. The instrument sampled in continuous mode beginning at dawn and ending at dusk. The AOA baseline was set to “filtrated water” to account for the effects of CDOM on the predicted phytoplankton community composition. Sample water was pulled through opaque tubing from a fixed depth of 0.5 m below the surface. Triplicate discrete samples were collected from the surface at dawn, noon, and dusk, with intermittent sampling during the tidal day. Samples were placed into brown, acid-washed, polypropylene bottles for analysis of CDOM and phytoplankton community composition by HPLCChemTax. Voucher samples for verification of community composition were collected and analyzed by inverted microscopy following the Utermo¨hl (1958) method after fixation with Lugol’s iodine (1% final concentration). Temperature and salinity were measured at Oyster Landing as part of the NERR water quality monitoring program and data were provided by

Fig. 1 e Study site location: Oyster Landing, North Inlet, South Carolina.

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the NERR Centralized Data Management Office (cdmo.baruch. sc.edu). Chemotaxonomic photosynthetic pigments measured by HPLC were used to determine phytoplankton biomass (as total chl a) and the relative and absolute contributions of major phytoplankton taxonomic groups (Jeffery et al., 1997; Pinckney et al., 1996, 1998; Lewitus et al., 2005). Aliquots (75e200 mL) were filtered through 25-mm GF/F filters. Vacuum pump pressure was maintained below 200 mm Hg (27 kPa) and was monitored carefully during filtrations to prevent damage to cells. All filters were frozen immediately and stored at
80  C. For analysis, filters were lyophilized for 12 h at
50  C, placed in 0.75 mL of 90% acetone, and extracted at
20  C in the dark for 18e20 h. Extracts were filtered through a 0.45-mm Teflon filter (Gelman Acrodisc), dispensed into amber glass vials and placed in a refrigerated autosampler (4  C) for analysis. Filtered extracts (200 mL) were injected into a Shimadzu HPLC equipped with a monomeric (Rainin Microsorb-MV, 0.46  10 cm, 3 mm) and a polymeric (Vydac 201TP54, 0.46  25 cm, 5 mm) reverse-phase C18 column in series. A nonlinear binary gradient consisting of the solvents 80% methanol: 20% 0.50 M ammonium acetate and 80% methanol:20% acetone was used for pigment separations (Pinckney et al., 1996). Absorption spectra and chromatograms (440  4 nm) were acquired using a Shimadzu SPD-M10av photodiode array detector. Pigment peaks were identified by comparison of retention times and absorption spectra with pure standards (DHI, Denmark). The synthetic carotenoid bapo-80 -carotenal was used as an internal standard. The contribution of each algal group to overall community composition was determined using ChemTax, a matrix factorization program (Mackey et al., 1996; Wright et al., 1991). The program uses steepest descent algorithms to determine the best fit based on an initial estimate of pigment ratios for algal classes. The absolute contribution of any algal group is the concentration of total chl a (in mg L
1) contributed by that group. Relative contributions are calculated as the proportion of total chl a accounted for by the group so that the sum of contributions from all groups equals one. Initial pigment ratio files, developed for phytoplankton in North Inlet estuary, were taken from Lewitus et al. (2005). Measurements of total chl a and phytoplankton community composition were also done using the AOA. Excitation spectra were compared to a library of signature spectra (“fingerprints”) for four algal groups, described in AOA parlance as cyanobacteria, cryptophytes, “diatoms þ dinoflagellates” and “greens”. For comparison of AOA output to HPLC-ChemTax results, we defined the “greens” based on similarity of pigmentation such they included the major groups of chl b-containing microalgae (chlorophytes, euglenophytes, and prasinophytes) (Richardson et al., 2010). Estimates of CDOM were done using the AOA (which reports in relative units); this output was compared to discrete measurements of CDOM done on the filtrate from the pigment samples, i.e. anything that passed through a GF/F filter (nominal particle retention w0.7 mm) was considered dissolved. CDOM absorbance at 355, 370 and 400 nm wavelengths were measured on a dual beam UV-VIS Shimadzu 2450 spectrophotometer, using high-purity deionized water as the blank. Absorption coefficients (m
1) were calculated as

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al ¼ (Al  Ln10)/r, where l denotes the specific measurement wavelength, Al is the absorbance at this wavelength and r is the path length (m) of the cuvette (Blough and Del Vecchio, 2002).

2.2.

Laboratory experiments

Cultures of three species, T. weissflogii, D. tertiolecta, and R. salina, grown as detailed above, were used to compare the AOA’s estimates of Genty parameter to estimates of photosynthetic activity using DCMU and to examine the effects of varying CDOM concentration on the AOA’s estimate of phytoplankton community composition. For photosynthetic activity investigations, three replicate cultures were diluted in 2 L of f/2 medium to a final concentration w7 mg chl L
1. The volumes were then split into two equal parts for use as a control and a treatment sample. Control cultures were incubated at room temperature under low light (w80 mmol quanta m
2 s
1) and interrogated by the AOA every 16 min for a 2 h period. The AOA baseline was set to “filtrated water”, to correct for CDOM contributions to fluorescence. Before each interrogation, the sample went through a dark acclimation period of 240 s (the longest possible dark acclimation time with the AOA). A sub-sample of 5 mL was taken every 16 min for interrogation using DCMU-based fluorescence, as follows: 1) sub-samples were dark-acclimated for 240 s, 2) Fo was determined using a Turner Model 10 AU Fluorometer (Turner Designs Inc., Sunnyvale, CA), 3) DCMU was then added to the cuvette to a final concentration of <1%, and the resulting Fm values recorded (Johnsen and Sakshaug, 1996). Variable fluorescence (Fv) was found by taking the difference between Fo and Fm. Values for Fv/Fm were expressed as the Genty parameter (Genty et al., 1989): Fv/Fm¼(Fm
Fo)/Fm. Treatment samples were exposed to high light (w1500 mmol quanta m
2 s
1) for the 2 h investigation period using a halogen slide projector lamp. Care was taken to ensure that treatment cultures remained at room temperature and did not heat up from the slide projector lamp. The same sub-sampling protocol was used as described for the control (“low light”) measurements. Experiments were performed with triplicate cultures of each species, as well as mixtures (n ¼ 3) containing w equal concentrations (as chl a) of each algal species. All output from the AOA, which in raw form is expressed as a percentage, was divided by 100 to facilitate comparison with DCMU results. The effects of varying CDOM concentration on the AOA’s estimate of phytoplankton community composition were assessed using GF/F-filtered water from Winyah Bay, a blackwater estuary located near our North Inlet field site (see Fig. 1). A dilution series of CDOM with f/2 culture medium was made in proportions of 0, 25, 50, 75, and 100%, where the culture medium (no Winyah Bay water added; Oyster Landing water only) was designated as 0% CDOM (“baseline”) and Winyah Bay water (no culture medium) was designated as the 100% CDOM endpoint. Because Winyah Bay is a freshwaterinfluenced system, artificial seawater (Berges et al., 2001) was used to adjust the salinity to 35. Each dilution was split into two bottles for a set of control (“without algae”) and treatment (“with algae”) containers. Cultures of our three representative species (T. weissflogii, D. tertiolecta, and R. salina) grown as described above were also combined into mixtures

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of equal proportions (as chl a). Treatment dilutions were inoculated with 5 mL of the phytoplankton mixture while controls received 5 mL of culture medium. Each bottle was inoculated directly before interrogation by the AOA. Control and treatment containers were run back-to-back, starting with the “baseline” through to the 100% CDOM treatment. The “filtrated water” baseline on the AOA was used. Three interrogations with the AOA were done for each sample and average values were calculated. An aliquot from each treatment container was filtered in triplicate for HPLC-ChemTaxbased assessment for quantification of algal group biomass (as described above).

2.3.

Statistical analyses

Statistical analyses were performed using PASW Statistics 18 software, (formerly SPSS; IBM Corporation, New York, NY) with p < 0.05 defined as the measure of significance. Differences between AOA- and HPLC-derived total phytoplankton biomass (chl a) were analyzed using a single factor ANOVA, where method (AOA vs HPLC) was the fixed factor and total chl

a was the variable, for the months of May, July, October, and December. Differences between AOA and HPLC-ChemTaxderived estimations of absolute and relative abundance for group-specific chl a were analyzed using a single factor multivariate ANOVA, where method (AOA vs HPLC) was the fixed factor and algal group (“greens”, “cyanobacteria”, “chromophytes”, and “cryptophytes”) the variables. A least squares linear regression was used to determine the relationship between AOA and absorption-based predictions of CDOM concentration. For the photosynthetic efficiency experiments, a least-squares linear regression was also used to find the slope of the low and high-light treatments over time and determine whether the slopes were significantly different than zero. A two factor ANOVA was used to determine differences between AOA and DCMU-based measurements, where method (AOA vs DCMU) and light intensity (low vs high) were the fixed factor and slope was the variable. To determine the effect of increased amounts of background CDOM on AOA-based estimations of group-specific chl a, differences between AOA- and HPLC-ChemTax-derived phytoplankton biomass (chl a) and algal group composition were

Fig. 2 e Total chlorophyll a (chl a) as measured by the Algae Online Analyser and HPLC/ChemTax for two tidal cycles (spring and neap) from water collected at North Inlet, SC, in May, July, October and December 2010. The black line denotes the separation between sampling events within the month. In May, sampling was done over consecutive 3 days that did not coincide with a spring or neap tide. Note differences in y-axis scale.

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analyzed using a multivariate ANOVA, where dilution and method (AOA vs HPLC) were fixed factors and algal group abundances were the variables with an R-E-G-W-F Post hoc to test any differences between dilutions.

3.

Results

Table 1 e Multivariate ANOVAs comparing Algae Online Analyser vs. HPLC-ChemTax derived values for total chlorophyll a (chl a) and community composition in terms of absolute and relative concentrations of “greens” (prasinophytes, chlorophytes and euglenophytes), “cyanobacteria”, “chromophytes” (dinoflagellates and diatoms) and “cryptophytes”. Differences were considered significant when p < 0.05.

Water temperatures in North Inlet varied seasonally, increasing from 25  C in May to 33  C in July, declining thereafter to 23  C and 4  C in October and December 2010, respectively. Salinity remained relatively constant throughout the year ranging from 34 in the fall to 36 during the summer. Details on the physical and chemical conditions in North Inlet during the time of our study may be found through the NERR Centralized Data Management Office (cdmo.baruch.sc.edu).

Date

3.1. Phytoplankton community composition at Oyster Landing, North Inlet

July 2010

Total chl a concentrations as measured by HPLC ranged from the highest values in May (w20 mg L
1) to the lowest in December (w3 mg L
1) (Fig. 2). Measurements of total chl a by the AOA were always significantly higher than those by HPLC (Table 1, Fig. 2). On average, AOA predictions of chl a were 2.2  0.6 (s.d.) times higher than the HPLC measurements (n ¼ 37). AOA estimates of total chl a showed a significant linear relationship with the HPLC measurements (n ¼ 37, r2 ¼ 0.745, p < 0.01) (Fig. S1). Phytoplankton community composition at Oyster Landing was dominated by greens and chromophytes in May, July, and October (Fig. 3). These two groups accounted for up to 69 and 86% of total chl a, respectively, as measured by HPLC-ChemTax (Fig. 4). Cyanobacteria and cryptophytes were relatively more abundant in December, when they contributed 6.4 and 19.2% of total chl a, respectively (Fig. 4). AOA-based group-specific values for chl a concentrations were significantly different from HPLC-ChemTax-based values, with the exception of the absolute abundance of cyanobacteria in May and the greens and cryptophytes in December (Table 1). In terms of relative abundance, there was a significant difference between AOA and HPLC-ChemTax predictions of green algae during July and October, but not during May and December (Table 1). Cyanobacteria made relatively small contributions to overall phytoplankton biomass both in relative and absolute terms; predicted values between the AOA and HPLC-ChemTax were not significantly different in October and December (Table 1). Predicted cryptophyte contributions to total chl a (absolute contributions) were significantly different between the two methods for all months except December (Fig. 3, Table 1). Chromophyte chl a concentrations were significantly different between the two methods for all months, but the predicted relative contributions of the chromophytes to total chl a did not differ between methods (Table 1, Fig. 4). Note that in Fig. 4 the HPLC contributions do not equate to 100% because HPLC/ ChemTax predicts algal groups that are not detected by the AOA. In May, these “other” groups were the haptophytes and the raphidophytes. In July, the “others” were raphidophytes only, others were haptophytes only in October and haptophytes and raphidophytes in December (Fig. S2).

May 2010

Measure

Group

p-value

Absolute abundance

Greens Cyanobacteria Chromophytes Cryptophytes Chl a Greens Cyanobacteria Chromophytes Cryptophytes Greens Cyanobacteria Chromophytes Cryptophytes Chl a Greens Cyanobacteria Chromophytes Cryptophytes Greens Cyanobacteria Chromophytes Cryptophytes Chl a Greens Cyanobacteria Chromophytes Cryptophytes Greens Cyanobacteria Chromophytes Cryptophytes Chl a Greens Cyanobacteria Chromophytes Cryptophytes

0.000 0.367 0.000 0.000 0.000 0.202 0.009 0.134 0.000 0.003 0.000 0.000 0.000 0.000 0.022 0.102 0.416 0.000 0.000 0.015 0.000 0.000 0.000 0.003 0.020 0.054 0.000 0.314 0.000 0.000 0.187 0.000 0.060 0.000 0.527 0.001

Relative abundance

Absolute abundance

Relative abundance

October 2010

Absolute abundance

Relative abundance

December 2010

Absolute abundance

Relative abundance

Significant differences are in bold.

3.2. Inlet

CDOM measurements at Oyster Landing, North

Measured CDOM absorption coefficients, whether expressed as a355, a370, or a400, were typically lowest in May (mean a370 ¼ 1.2 m
1, range ¼ 0.0e2.56 m
1) and highest in October (mean a370 ¼ 2.7 m
1, range ¼ 0.8e7.7 m
1) (Table S1). Within all deployment periods, absorption coefficients showed a consistent pattern of highest values at low tide and lowest values at high tide (data not shown). Least-squares linear regression analysis revealed highly significant linear relationships between AOA- and spectrophotometric-based CDOM estimates when comparisons were made on an individual deployment basis (all r2 values were 0.58, p < 0.05; see Fig. 5). With the exception of the December/January sampling, however, spring and neap tide deployments showed differences in their

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individual regression parameters. This was especially pronounced for the October sampling, when the spring tide deployment (8 Oct) exhibited substantially greater CDOM absorption coefficients than those measured on any other deployment, despite also exhibiting the least amount of variability in AOA measures of CDOM fluorescence (Fig. 5c). As a result, the relationship between AOA and CDOM absorption coefficients deteriorated when data were pooled across sampling events, although the relationship was still statistically significant (Fig. 5d).

3.3. AOA- vs. DCMU-based estimates of photosynthetic activity In absolute terms, AOA-based estimates of Fv/Fm were lower than DCMU-derived values (Fig. 6, Table 2) but the time-course progression of values was similar: Fv/Fm stayed relatively constant under low light, as indicated by the slope of the regression lines not being significantly different from zero (Fig. 6; Table S2). The exception was the initial slope of D. tertiolecta which showed a slight (non-significant) increase with time for

measurements done by DCMU (Fig. 6a). When cultures were subjected to high irradiance, all values for Fv/Fm declined with time. There was no significant difference between methods for the rate of change for Fv/Fm with time under high light, except for the cryptophyte (Fig. 6c), which showed a more rapid decline in Fv/Fm as measured by DCMU as compared to the AOA prediction (F1,4 ¼ 56.367, p ¼ 0.002). This was the result of a slight rise in Fo with time at high irradiance that was coincident with a decline in Fm (Fig. S3). There was not a strong linear relationship between DCMU- and AOA-measured values for Fv/ Fm (Fig. 7). The highest r2 value (0.64) was obtained for the mixture of the three species, while the regression for D. tertiolecta was the weakest (r2 ¼ 0.32) (Fig. 7a, d).

3.4. Effects of background CDOM on predictions of phytoplankton biomass and community composition by the AOA The general tendency of the AOA to overestimate total chl a biomass (as described in Section 3.1) increased as the concentration of background CDOM increased (Fig. 8). Under the

Fig. 3 e Total chl a of chlorophytes (“greens”), cyanobacteria, chromophytes (diatoms D dinoflagellates) and cryptophytes as measured by the Algae Online Analyser and HPLC/ChemTax measurements for two tidal cycles (spring and neap) from water collected at North Inlet, SC, in May, July, October, and December 2010. The black line denotes the separation between sampling events within the month. In May, sampling was done over consecutive 3 days that did not coincide with a spring or neap tide. Note differences in y-axis scale.

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Fig. 4 e Relative abundance of chlorophytes (“greens”), cyanobacteria, chromophytes (diatoms D dinoflagellates) and cryptophytes as a percentage of the total chlorophyll a comparing the predictions from the Algae Online Analyser and HPLC/ChemTax for two tidal cycles (spring and neap) from water collected at Oyster Landing, North Inlet, SC, during A) May, B) July, C) October, and D) December 2010. The black line denotes the separation between sampling events within the month. In May, sampling was done over consecutive 3 days that did not coincide with a spring or neap tide. Note differences in y-axis scale. The HPLC graphs show only the algal groups estimated by the Algae Online Analyser.

baseline conditions (culture medium made with North Inlet water only; no added Winyah Bay CDOM water), the AOA overestimated total chl a biomass by a factor of 2.5  0.2 (s.d.). The 25%, 50%, 75%, and 100% CDOM treatments showed overestimates of 2.5  0.5, 2.9  0.4, 3.4  0.5, and 3.3  0.2 times, respectively (Fig. 8). HPLC-ChemTax measurements of chl a contributions by D. tertiolecta, T. weissflogii, and R. salina showed that there was no significant difference in community composition between the treatments (F4,15 ¼ 1.782, p > 0.05); that is, the chl a ebased community composition was the same in all treatments (Fig. 8). AOA-based estimations of community composition, however, showed a significant increase in the contribution of the representative chromophyte, T. weissflogii (F4,15 ¼ 201.4, p < 0.01), as the background CDOM increased (Fig. 8). Post hoc analysis showed that the chromophyte contributions within each dilution were significantly different from each other and fell into separate sub-groups (baseline 25% 50% 75% 100%). Therefore, the increase in overestimation

of total biomass by the AOA appears to be due to an overestimation of chromophyte chl a. A closer look at the AOAestimated CDOM (Fig. 9) shows that in the presence of phytoplankton (a mixture of T. weissflogii, R. salina and D. tertiolecta), the AOA underestimates CDOM concentrations as compared to controls (no phytoplankton added) and that the difference between the “with” and “without” phytoplankton treatments increases as the percent CDOM contribution increases (2-factor ANOVA; F1,30 ¼ 594.4; p ¼ 0.000). Thus, the AOA appears to be attributing progressively more CDOM signal to the phytoplankton, specifically to the chromophytes, as shown in Fig. 8.

4.

Discussion

The AOA proved useful in predicting overall trends in phytoplankton biomass and community composition, but chl a concentrations were consistently over-estimated. This was

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Fig. 5 e A comparison of CDOM absorption coefficients measured by the UV/VIS spectrophotometer at 370 nm and predicted CDOM concentration by the Algae Online Analyser at a fixed wavelength of 370 nm for water collected at Oyster Landing, North Inlet, SC, during July, October, and December 2010. Total (panel D) includes a combination for all months sampled (including May, for which specific data are not shown). Note the decreased concentration in December (panel C). The monthly data for May had a low sample size (n [ 8) and variability in the data was not enough to show a trend however values were included into the total CDOM data.

the case for both our field measurements at Oyster Landing (overestimated by a factor of 2.2  0.6 s.d.) and laboratory culture studies (2.5  0.2 s.d.). These over-estimates agree well with those of previous work at nearby Clam Bank Creek (factors of 2.2  0.8 and 2.0  0.6 for two different sampling periods; Richardson et al., 2010), and Mobile Bay (1.8  0.7 s.d.; MacIntyre et al., 2011) but are higher than reported for the Neuse River Estuary, North Carolina (1.1  0.2 s.d.; Richardson et al., 2010). As in previous studies, the significant linear relationship between AOA-estimated and HPLC-measured chl a, however, means that AOA data could be adjusted, if necessary, using a correction curve. We recommend construction of site-specific correction curves using discrete samples taken at time scales appropriate to the scale of variability (e.g., seasonally or monthly) and/or to the timescale of interest. This is especially important if the AOA is used in water quality monitoring programs (i.e. for management). Incorrect calibration and subsequent use of AOA estimates would result in erroneous management decisions. Measurements of CDOM absorption coefficients, a370, ranged from w0.01 m
1 in May to greater than 5 m
1 in

October. Similarly, for a400, values ranged from 0.00 m
1 in May to 4.67 m
1 in October. These values span the range of CDOM absorption coefficients estimated for other east coast estuaries (a400 ¼ 0.34e0.80 m
1 for Narragansett Bay; Keith et al., 2002; a350 ¼ 0.14e5.8 m
1 for the Cape Fear River plume; Kowalczuk et al., 2003; as compared to our a350 range of 0.0e9.89 m
1) but are substantially lower than values for the blackwater-dominated Lower St. Johns River, Florida (up to 32 m
1; Gallegos, 2005), which likely represent the upper extreme (Blough and Del Vecchio, 2002). The inverse relationship between a370 and water depth observed during each deployment series is consistent with tidally-driven export and dilution of terrestrial DOM, primarily from salt marsh soils (Wolaver et al., 1986). Overall, the AOA predicted CDOM absorption coefficients more reliably (as shown by higher r2 values) at the scale of individual deployments than when data were pooled across tides and seasons. This was most strongly driven by the markedly different relationship between AOA and CDOM absorption coefficients observed for the 8 October deployment. This deployment was preceded by a significant rain event (total precipitation of 230 mm from 28 to 30

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 1 6 e1 6 3 0

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Fig. 6 e Time-course measurements of Fv/Fm, by the Algae Online Analyser and DCMU, measured under low light (80 mmol photons mL2 sL1) and high light (1500 mmol photons mL2 sL1) for A) the chlorophyte Dunaliella tertiolecta, B) the diatom Thalassiosira weissflogii, C) the cryptophyte Rhodomonas salina, and D) a mixture of each species (equal parts, based on chlorophyll a). Error bars are standard deviations of the mean (n [ 3).

September). In contrast to much of the year, autumn rainfall causes the upland forested wetlands adjacent to North Inlet to drain and export CDOM that has much higher absorbance per unit organic carbon than the CDOM exported from salt marsh soils (Smith, unpublished data). Thus the contrasting sources of DOM (forested wetland vs. salt marsh) and variations in optical properties of the constituent organic compounds likely account for different relationships between fluorescence- and absorbance-based measures of DOM (Del Castillo et al., 1999). In the laboratory, the general tendency of the AOA to overestimate total chl a biomass increased by approximately 20% with each incremental increase in background CDOM, coincident with incremental under-estimates of CDOM concentration by w22%. Therefore, as CDOM increased, the AOA increasingly attributed the CDOM signal to the chromophyte channel, resulting in an overall increase in predicted total chl a. The problematic effects of CDOM on estimation of total chl a, and on chromophyte chl a specifically, have at least two (not mutually-exclusive) bases. First, absorption of blue light by CDOM reduces the intensity of the excitation beam on the target phytoplankton, which will alter the ratio of fluorescence to chl a (Markager and Vincent, 2000; MacIntyre et al.,

2011). Second, the blue light emitted by CDOM upon excitation with the UV-LED (as in the AOA) can be absorbed by the blue absorption band of chl a in all phytoplankton, and by accessory carotenoids like fucoxanthin in diatoms, which will cause a rise in fluorescence emission (assuming all other factors are constant) and hence a rise in the ratio of fluorescence to chl a (Falkowski and Raven, 2007; MacIntyre et al., 2011). In ecosystems with both high CDOM and large contributions by chromophytes like diatoms, as is the case for North Inlet, the interference effects (and hence the errors) are potentially additive. Previous work using samples from Mobile Bay showed that increasing CDOM concentrations resulted in an over-estimation of chromophyte contributions to the phytoplankton community, which was attributed to an undercorrection for CDOM interference (MacIntyre et al., 2011). It is likely that our situation is similar: while the instrument settings “correct” for CDOM interference, the instrument was not calibrated (or trained) with the CDOM we used for the laboratory experiments (Winyah Bay water). While the absolute concentrations of chromophytes were significantly different between AOA and HPLC-ChemTax estimates at Oyster Landing, the AOA reliably predicted the

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Table 2 e Average and standard deviations of Fv/Fm for three independent interrogations of the chlorophyte Dunaliella tertiolecta, the diatom Thalassiosira weissflogii, the cryptophyte Rhodomonas salina, and a mixture of the three (equal parts, based on chl a) using the Algae Online Analyser (AOA) and the herbicide dichlorophenyledimethylurea (DCMU). These statistics were calculated using at least 8 values for each interrogation during the “low” light, (ambient irradiance of w80 mmol photons mL2 sL1) phase of the experiment shown in Fig. 6. Also noted are Fv/Fm values for target (and similar) species from other studies. Abbreviations: ST [ single turnover fluorometer (brand unknown), PeP [ pump and probe fluorometer, PAM [ Pulse Amplitude Modulated fluorometer, FRRf [ Fast Repetition Rate fluorometer, Eg [ growth irradiance, Eint [ interrogation irradiance, both in units of mmol photons mL2 sL1, CV [ coefficient of variation, sd [ standard deviation, se [ standard error. Species Dunaliella tertiolecta

This study DCMU Fv/Fm

This study AOA Fv/Fm

Previous work Fv/Fm

Method

Experimental conditions

Reference

0.89  0.06

0.57  0.03

ST ST PeP PeP PAM DCMU PAM

0.72  0.04

0.30  0.03

w0.6 w0.5 0.67 (CV ¼ 1.7%) 0.40 (CV ¼ 1.5%) 0.67  0.017 sd 0.62 0.68  0.011 se 0.52  0.003 se 0.71  0.005 se 0.71 (CV ¼ 1.9%) 0.38 (CV ¼ 2.3%) 0.65 (CV ¼ 13%) 0.55 w0.6 0.69  0.014 sd 0.64  0.003 se 0.59  0.007 se 0.63  0.002 se 0.65  0.006 se 0.56  0.005 se 0.67  0.004 se 0.63 w0.65 w0.5 0.6 0.69 to 0.75 0.69 to 0.74

ST? DCMU DCMU PAM PAM PAM PAM PAM PAM PAM DCMU DCMU DCMU PAM DCMU PAM

Eg ¼ 200 Eint ¼ 1000 N-replete, Eg ¼ 200 N-depleted N-replete, Eg ¼ 130 N-replete, varying UV N-replete N-depleted N-resupplied N-replete, Eg ¼ 200 N-depleted N-replete Eg ¼ 7, N-replete Eg ¼ 170, varying Fe Eg ¼ 130, N-replete P-replete P-depleted P-resupplied Si-replete Si-depleted Si-resupplied N-replete, varying UV 1000 nM Fe 25 nM Fe N-replete N-replete, Eg ¼ 50 N-replete, Eg ¼ 100

0.54 to 0.57

FRRf

N-replete, Eg ¼ 30e80

Havelkova´-Dousova´ et al., 2004 Havelkova´-Dousova´ et al., 2004 Berges et al., 1996 Berges et al., 1996 Juneau and Harrison, 2005 Hannach and Sigleo, 1998 Lippemeier et al., 2001 Lippemeier et al., 2001 Lippemeier et al., 2001 Berges et al., 1996 Berges et al., 1996 Voronova et al., 2009 Radchenko and Il’yash, 2006 Price, 2005 Juneau and Harrison, 2005 Lippemeier et al., 2001 Lippemeier et al., 2001 Lippemeier et al., 2001 Lippemeier et al., 2001 Lippemeier et al., 2001 Lippemeier et al., 2001 Hannach and Sigleo, 1998 McKay et al., 1997 McKay et al., 1997 Parkhill et al., 2001 MacIntyre, unpubl. data MacIntyre and Richardson, unpubl. data Suggett et al., 2009

Dunaliella salina

Thalassiosira weissflogii

T. pseudonana Rhodomonas salina Storeatula major

0.84  0.02

0.33  0.04

3 species mixture

0.71  0.02

0.44  0.03

relative contributions of chromophytes to total chl a in all months tested. The group predicted most poorly by the AOA, both in terms of absolute and relative abundances, was the cryptophytes. There was no detectable pattern to the errors: in May, HPLC-ChemTax predicted cryptophytes but there was no signal in the AOA cryptophyte channel, whereas in July on the spring tide, the AOA predicted cryptophytes but there were undetectable concentrations of alloxanthin, the diagnostic photopigment for the cryptophyte group. The case when alloxanthin is present but cryptophytes are not predicted by the AOA could be explained by the fact that the AOA is using a diagnostic fingerprint that is heavily influenced by the presence of phycoerythrin (not alloxanthin), and the phycoerythrin to alloxanthin ratio is highly variable in cryptophytes like Rhodomonas sp. (MacIntyre et al., 2011; their Fig. 8k). The AOA did a good job of predicting the absolute and relative concentrations of cyanobacteria, which is consistent with previous studies using spectral fluorometry-based instruments, including the AOA (Izydorczyk et al., 2009) and the Fluoroprobe (which is also manufactured by bbe Moldaenke) (Gregor and Mar sa´lek, 2004; Gregor et al., 2005; Catherine et al.,

PeP

2012). Interestingly, Catherine et al. (2012) found that Fluoroprobe-based estimates of total phytoplankton chl a were more closely related to measurements of phytoplankton biovolume than to extracted chl a concentrations measured by spectrophotometry. Predictions of filamentous cyanobacteria like Microcystis spp., Anabaena, and Aphanizomenon using instruments based on spectral fluorometry, however, have been shown to be problematic (Gregor and Marsa´lek, 2004; Catherine et al., 2012). The large size of filamentous cyanobacteria and their tendency to aggregate can result in incomplete excitation of antenna pigments and/or shading of emitted fluorescence (Beutler et al., 2002). This was not an issue in our study as our cyanobacteria were picoplanktonic in size, however this should be kept in mind if the AOA is to be used for water quality monitoring of freshwater systems that are prone to filamentous (and potentially toxic) cyanobacterial blooms. Izydorczyk et al. (2009) found a significant correlation between cyanobacterial biovolume (instead of extracted chl a) and AOA-derived estimates of cyanobacterial chlorophyll on samples from a drinking water reservoir in Poland. They used these data to develop an “Alert

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 1 6 e1 6 3 0

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Fig. 7 e DCMU- vs. Algae Online Analyser-based estimates of Fv/Fm for A) the chlorophyte Dunaliella tertiolecta, B) the diatom Thalassiosira weissflogii, C) the cryptophyte Rhodomonas salina, and D) a mixture of the 3 species (equal parts based on chlorophyll a).

Chlorophyll a (µg L-1)

40

Thalassiosira weissflogii

35

Rhodomonas salina

30

Dunaliella tertiolecta

25 20 15 10 5 0 Baseline

25%

50%

75%

100%

Percent CDOM

Fig. 8 e The total chl a of chlorophytes (“greens”), cyanobacteria, chromophytes (diatoms D dinoflagellates) and cryptophytes comparing the predictions from the Algae Online Analyser (right column of each pair) and HPLC-derived measurements (left column of each pair) for a series of CDOM dilutions. increase in error is due to an overestimation of chromophyte chl a. Note the “baseline” has no addition of CDOM but may contain background CDOM from natural seawater.

Fig. 9 e A comparison of the predictions of the Algae Online Analyser of a series of CDOM concentration with phytoplankton (treatment) and without phytoplankton (control). Note the “baseline” has no additional CDOM.

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Level Framework” for cyanobacteria in this reservoir. Their study is an excellent example of the potential usefulness of this type of continuous monitoring instrument in water quality management. In general, values obtained for Fv/Fm by the AOA were lower than those obtained using the DCMU approach. However, there were species-specific differences in the method that gave ratios closest to those found by previous studies. For the chlorophyte D. tertiolecta, the AOA-based Fv/Fm ratios (0.57  0.03 s.d. under low irradiance; Table 2) were closest to those found in the literature. Havelkova´-Dousova´ et al. (2004), for example, examined the fluorescence dynamics of D. tertiolecta under fluctuating irradiance and found Fv/Fm values of approximately 0.6 at an irradiance of 200 mmol photons m
2 s
1 that declined to near 0.5 when cells were given intermittent exposures to w1000 mmol photons m
2 s
1 Berges et al. (1996) used a pump-and-probe approach to examine the photosynthetic capacity of D. tertiolecta when cells were nitrogen (N) replete and N starved. They found Fv/Fm ratios of 0.67 (C.V. ¼ 1.7%) for N-replete cells that declined to 0.40 (C.V. ¼ 1.5%) under N-starvation. Results of similar studies may be found, for comparison, in Table 2. There is considerable (and somewhat unpredictable) variability in Fv/Fm depending on the method used for interrogation (DCMU vs single- or multiple-turnover fluorometer), growth conditions (especially nutrient status), and dark pre-acclimation time. Our general conclusion is that, for this chlorophyte, the AOA gives a reliable (if somewhat low) approximation of photosynthetic potential. A different picture emerges for the diatom, T. weissflogii. This organism has been the subject of numerous studies (Table 2), and it appears that, in this case, values for Fv/Fm from the AOA are erroneously low. As with the chlorophyte, there is variability in Fv/Fm with growth conditions and instrumentation, but the AOA measurements are consistently low and are closer to those for N-depleted T. weissflogii in the literature (0.38; measured by pump-and-probe fluorometry by Berges et al., 1996). We believe these low values are, in part, the result of having the CDOM correction enabled while the measurements were being made. An earlier study with a different AOA instrument and the CDOM correction disabled resulted in Fv/Fm values for N-replete Thalassiosira pseudonana that were between 0.4 and 0.5, much higher than we measured in this study but still lower than concurrent measurements using a PAM fluorometer (Fv/Fm w 0.6) (MacIntyre and Richardson, unpublished data). Anomalously low Fv/Fm ratios could result from 1) quenching of the excitation irradiance, which could occur in the presence of CDOM and would result in an under-estimate of Fm but would likely have no effect on Fo, and/or 2) if the CDOM correction algorithm mistakenly attributed some portion of the diatom’s Fm to CDOM fluorescence. Data with which to compare the AOA-based Fv/Fm values for the cryptophyte are relatively rare. One study with nutrient-replete Storeatula major found that FRRf-based Fv/Fm ranged from 0.54 to 0.57 (Suggett et al., 2009, Table 2). Earlier experiments in the MacIntyre laboratory with Rhodomonas salina found DCMU-based Fv/Fm ratios from 0.69 to 0.75 (MacIntyre, unpublished) and PAM fluorometer-based ratios of 0.69e0.74 for S. major (MacIntyre and Richardson, unpublished). AOA-based Fv/Fm ratios for Rhodomonas salina from

this study are lower than all of these values (w0.33), and are lower than AOA-based values for S. major from our unpublished work (w0.51). In general, our DCMU-based Fv/Fm ratios appear to be erroneously high. We are unsure as to why this is the case, but this could be the result of improper blanking or anomalously low Fo readings. This uncertainty has led us to limit our comparison of the AOA Fv/Fm values to those available from the literature. However, the similarity in time-course measurements of Fv/Fm by the DCMU and AOA methods from low to sustained high irradiance gives us confidence in the results in a relative sense, if not in their absolute values.

5.

Conclusions

The AOA shows promise as a tool for the continuous monitoring of phytoplankton community composition, CDOM, and the group-specific photosynthetic activity of aquatic ecosystems. The AOA consistently over-estimated total chl a concentrations in this study, the strong linear relationship between HPLC and AOA-derived values would allow correction for this overestimate by scientists and water quality managers. CDOM was reliably quantified by the AOA, but increasing amounts of background CDOM resulted in progressively higher over-estimates of chromophyte contributions to a simulated mixed algal community. Estimates of photosynthetic activity were robust for the chlorophyte, Dunaliella tertiolecta, but were substantially lower than literature values for Thalassiosira weissflogii and Rhodomonas salina (a cryptophyte). This underestimate may be the result of our use of the CDOM correction baseline.

Acknowledgments We thank Dr. Hugh MacIntyre (Dalhousie University, Halifax, N.S., Canada) for access to unpublished data and for helpful comments on the manuscript. This research was funded, in part, by the Slocum Lunz Foundation (grant to EAG).

Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.watres.2012.12.023.

references

Berges, J.A., Charlebois, D.O., Mauzerall, D.C., Falkowski, P.J., 1996. Differential effects of nitrogen limitation on photosynthetic efficiency of photosystems I and II in microalgae. Plant Physiology 110, 689e696. Berges, J.A., Franklin, D.J., Harrison, P.J., 2001. Evolution of an artificial seawater medium: improvements in enriched seawater, artificial water over the last two decades. Journal of Phycology 37, 1138e1145. Beutler, M., Wiltshire, K.H., Meyer, B., Moldaenke, C., Lu¨ring, C.M., Meyerho¨fer, M., Hansen, U.P., Dau, H., 2002. A fluorometric

w a t e r r e s e a r c h 4 7 ( 2 0 1 3 ) 1 6 1 6 e1 6 3 0

method for the differentiation of algal populations in vivo and in situ. Photosynthesis Research 72, 39e53. Blough, N.V., Del Vecchio, R., 2002. Chromophoric DOM in the coastal environment. In: Hansell, D.A., Carlson, C.A. (Eds.), Biochemistry of Marine Dissolved Organic Matter. Elsevier Science, pp. 509e546. Del Castillo, C.E., Coble, P.G., Morell, J.M., Lo´pez, J.M., Corredor, J.E., 1999. Analysis of the optical properties of the Orinoco River plume by absorption and fluorescence spectroscopy. Marine Chemistry 66, 35e51. Catherine, A., Escoffier, N., Belhocine, A., Nasri, A.B., Hamlaoui, S., Ye´pre´mian, C., Bernard, C., Troussellier, M., 2012. On the use of the Fluoroprobe, a phytoplankton quantification method based on fluorescence excitation spectra for large-scale surveys of lakes and reservoirs. Water Research 46, 1771e1784. Chekalyuk, A., Hafez, M., 2008. Advanced laser fluorometry of natural aquatic environments. Limnology and Oceanography Methods 6, 591e609. Desiderio, R.A., Moore, C.C., Lantz, C., Cowles, T.J., 1997. Multiple excitation fluorometer for in situ oceanographic applications. Applied Optics 36, 1289e1296. Falkowski, P.G., Raven, J.A., 2007. Aquatic Photosynthesis. Princeton University Press, Princeton. Gallegos, C.L., 2005. Optical water quality of a blackwater river estuary: the Lower St. Johns River, Florida, USA. Estuarine Coastal and Shelf Science 63 (1e2), 57e72. Genty, B., Briantais, J.M., Baker, N.R., 1989. The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence. Biochimica Biophysica Acta 990, 87e92. Gregor, J., Mar sa´lek, B., 2004. Freshwater phytoplankton quantification by chlorophyll a: a comparative study of in vitro, in vivo and in situ methods. Water Research 38, 517e522. Gregor, J., Geri s, R., Marsa´lek, B., Hetesa, J., Marvan, P., 2005. In situ quantification of phytoplankton in reservoirs using a submersible spectrofluorometer. Hydrobiologia 548, 141e151. Guillard, R.R.L., 1995. Culture methods. In: Hallegraeff, G.M., Anderson, D.M., Cembella, A.D. (Eds.), Manual on Harmful Marine Algae. UNESCO, Paris, France, p. 550. Haese, R.R., Pronk, G.J., 2011. Intra-annual variability in primary producer groups and nitrogen dynamics in an intermittently closed estuary exposed to Mediterranean climate. Estuaries and Coasts 34 (3), 557e568. Hannach, G., Sigleo, A.C., 1998. Photoinduction of UV-absorbing compounds in six species of marine phytoplankton. Marine Ecology Progress Series 174, 207e222. Hansen, D.V., Rattray, M., 1966. New dimensions in estuary classification. Limnology and Oceanography 11, 319e326. Havelkova´-Dou sova´, H., Pra´sil, O., Behrenfeld, M.J., 2004. Photoacclimation of Dunaliella tertiolecta (Chlorophyceae) under fluctuating irradiance. Photosynthetica 42 (2), 273e281. Istvanovics, V., Honti, M., Osztoics, A., Shafik, H.M., Padisak, J., Yacobi, Y., Eckert, W., 2005. Continuous monitoring of phytoplankton dynamics in Lake Balaton (Hungary) using online delayed fluorescence excitation microscopy. Freshwater Biology 50, 1950e1970.  ski, J., Wagenvoort, A., Izydorczyk, K., Carpentier, C., Mro´wczyn  ska, M., 2009. Establishment of an Alert Jurczak, T., Tarczyn Level Framework for cyanobacteria in drinking water resources by using the Algae Online Analyser for monitoring cyanobacterial chlorophyll a. Water Research 43, 989e996. Jeffery, S., Mantoura, R.S., Wright, S. (Eds.), 1997. Phytoplankton Pigments in Oceanography: Guidelines to Modern Methods. United Nations Educational. Scientific and Cultural Organization Publishing, Paris.

1629

Johnsen, G., Sakshaug, E., 1996. Light harvesting in bloomforming marine phytoplankton: species-specificity and photoacclimation. Scientia Marina 60 (Suppl. 1), 47e56. Juneau, P., Harrison, P.J., 2005. Comparison by PAM fluorometry of photosynthetic activity of nine marine phytoplankton grown under identical conditions. Photochemistry and Photobiology 81, 649e653. Keith, D.J., Yoder, J.A., Freeman, S.A., 2002. Spatial and temporal distribution of coloured dissolved organic matter (CDOM) in Narragansett Bay, Rhode Island: Implications for phytoplankton in coastal waters. Estuarine, Coastal and Shelf Science 55, 705e717. Kimmerer, W.J., Parker, A.E., Lidstrom, U.E., Carpenter, E.J., 2012. Short-term and interannual variability in primary production in the low-salinity zone of the San Francisco Estuary. Estuaries and Coasts 35 (4), 913e929. Kirk, J.T.O., 1994. Light and Photosynthesis in Aquatic Ecosystems. Cambridge University Press, Cambridge, UK. Kjerfve, B., 1986. Circulation and salt flux in a well mixed estuary. In: Van de Kreeke, J. (Ed.), Physics of Shallow Estuaries and Bays. Springer Verlag, Berlin, pp. 22e29. Kjerfve, B., Stevenson, L.H., Proehl, J.A., Chrzanowski, T.H., Kitchens, W.M., 1981. Estimation of material fluxes in an estuarine cross section: a critical analysis of spatial measurement density and errors. Limnology and Oceanography 26, 325e335. Kolber, Z., Falkowski, P.G., 1993. Use of active fluorescence to estimate phytoplankton fluorescence in situ. Limnology and Oceanography 38, 1646e1665. Kowalczuk, P., Cooper, W.J., Whitehead, R.F., Durako, M.J., Sheldon, W., 2003. Characterization of CDOM in an organicrich river and surrounding coastal ocean in the South Atlantic Bight. Aquatic Science 65, 384e401. Kromkamp, J.C., Forster, R.M., 2003. The use of variable fluorescence measurements in aquatic ecosystems: differences between multiple and single turnover measuring protocols and suggested terminology. European Journal of Phycology 38, 103e112. Laney, S.R., 2011. In situ measurement of variable fluorescence transients. Chapter 2. In: Suggett, D.J., Prasil, O., Borowitzka, M.A. (Eds.), Chlorophyll a Fluorescence in Aquatic Sciences: Methods and Applications. Springer, pp. 129e169. Lawrenz, E., Smith, E.M., Richardson, T.L., 2010. Spectral irradiance and phytoplankton community composition in a blackwater-dominated estuary, Winyah Bay, South Carolina, USA. Estuaries and Coasts 33 (5), 1186e1201. Lewitus, A.J., Koepfler, E.T., Morris, J.T., 1998. Seasonal variation in the regulation of phytoplankton by nitrogen and grazing in a salt-marsh estuary. Limnology and Oceanography 43, 636e646. Lewitus, A.J., White, D.L., Tymowski, R.G., Geesey, M.E., Hymel, S.N., Noble, P.A., 2005. Adapting the CHEMTAX method for assessing phytoplankton taxonomic composition in Southeastern US. Estuaries 28, 160e172. Lippemeier, S., Hintze, R., Vanselow, K.H., Hartig, P., Colijn, F., 2001. In-line recording of PAM fluorescence of phytoplankton cultures as a new tool for studying effects of fluctuating nutrient supply on photosynthesis. European Journal of Phycology 36, 89e100. MacIntyre, H.L., Lawrenz, E., Richardson, T.L., 2011. Taxonomic discrimination of phytoplankton by spectral fluorescence. Chapter 7. In: Suggett, D.J., Prasil, O., Borowitzka, M.A. (Eds.), Chlorophyll a Fluorescence in Aquatic Sciences: Methods and Applications. Springer, pp. 129e169. Mackey, M.D., Mackey, D.J., Higgins, H.W., Wright, S.W., 1996. CHEMTAX e a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton. Marine Ecology Progress Series 144, 265e283.

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Markager, S., Vincent, W.F., 2000. Spectral light attenuation and the absorption of UV and blue light in natural waters. Limnology and Oceanography 45 (3), 642e650. McKay, R.M., Geider, R.J., LaRoche, J., 1997. Physiological and biochemical response of the photosynthetic apparatus of two marine diatoms to Fe stress. Plant Physiology 114, 615e622. Miller, D.C., Geider, R.J., MacIntyre, H.L., 1996. Microphytobenthos: the ecological role of the “secret garden” of unvegetated, shallow-water marine habitats. II. Role in sediment stability and shallow-water food webs. Estuaries 19, 202e212. Millie, D.F., Schofield, O.M.E., Kirkpatrick, G.J., Johnsen, G., Evens, T.J., 2002. Using absorbance and fluorescence spectra to discriminate microalgae. European Journal of Phycology 37, 313e322. Parkhill, J.P., Maillet, G., Cullen, J.J., 2001. Fluorescence-based maximal quantum yield for PSII as a diagnostic of nutrient stress. Journal of Phycology 37, 517e529. Pinckney, J.L., Millie, D.F., Howe, K.E., Paerl, H.W., Hurley, J.P., 1996. Flow scintillation counting of 14C-labeled microalgal photosynthetic pigments. Journal of Plankton Research 18, 1867e1880. Pinckney, J.L., Paerl, H.W., Harrington, M.B., Howe, K.E., 1998. Annual cycles of phytoplankton community-structure and bloom dynamics in the Neuse River Estuary, North Carolina. Marine Biology 131, 371e381. Price, N.M., 2005. The elemental stoichiometry and composition of an iron-limited diatom. Limology and Oceanography 50 (4), 1159e1171. Radchenko, I.G., Il’yash, L.V., 2006. Growth and photosynthetic activity of diatom Thalassiosira weissflogii at decreasing salinity. Biology Bulletin 33, 242e247. Richardson, T.L., Lawrenz, E., Pinckney, J.L., Guajardo, R.C., Walker, E.A., Paerl, H.W., MacIntyre, H.L., 2010. Spectral fluorometric characterization of phytoplankton community composition using the Algae Online Analyser. Water Research 39, 107e118. Schreiber, U., 1998. Chlorophyll fluorescence: new instruments for special applications. In: Garab, G. (Ed.), Photosynthesis: Mechanisms and Effects, vol. V. Kluwer Academic Publishers, Dordrecht, pp. 4253e4258. Sellner, K.G., Zingmark, R.G., Miller, T., 1976. Interpretations of the 14C method of measuring the total annual production of

phytoplankton in a South Carolina estuary. Botanica Marina 19, 119e125. Seppa¨la¨, J., Balode, M., 1998. The use of spectral fluorescence methods to detect changes in the phytoplankton community. Hydrobiologia 363, 207e217. Suggett, D.J., MacIntyre, H.L., Kana, T.M., Geider, R.J., 2009. Comparing electron transport with gas exchange: parameterizing exchange rates between alternative photosynthetic currencies for eukaryotic phytoplankton. Aquatic Microbial Ecology 56, 147e162. Underwood, G.J.C., Kromkamp, J., 1999. Primary production by phytoplankton and microphytobenthos in estuaries. Advances in Ecological Research 29, 93e153. Utermo¨hl, H., 1958. Zur Vervollkommnung der quantitativen Phytoplankton-Methodik, vol. 9. Mitteilungen der Internationalen Vereinigung fu¨r theoretische and angewandte Limnologie, p. 38. Vincent, W.F., 1980. Mechanisms of rapid photosynthetic adaptation in natural phytoplankton communities. 2. Changes in photochemical capacity as measured by DCMUinduced chlorophyll fluorescence. Journal of Phycology 16 (4), 568e577. Voronova, E.N., Il’yash, L.V., Pogosyan, S.I., Ulanova, A.Yu., Matorin, D.N., Cho, M., Rubin, A.B., 2009. Intrapopulation heterogeneity of the fluorescence parameters of the marine plankton alga Thalassiosira weissflogii at various nitrogen levels. Microbiology 78 (4), 419e427. Wolaver, T.G., Hutchinson, S., Marozas, M., 1986. Dissolved and particulate organic carbon in the North Inlet estuary, South Carolina: what controls their concentrations? Estuaries 9, 31e38. Wright, S.W., Jeffrey, S.W., Mantoura, R.F.C., Llewellyn, C.A., Bjørnland, T., Repeta, D.J., Welschmeyer, N.A., 1991. Improved HPLC method for the analysis of chlorophylls and carotenoids from marine phytoplankton. Marine Ecology Progress Series 77, 183e196. Yentsch, C.S., Phinney, D.A., 1985. Spectral fluorescence e an ataxonomic tool for studying the structure of phytoplankton populations. Journal of Plankton Research 7, 617e632. Yentsch, C.S., Yentsch, C.M., 1979. Fluorescence spectral signatures e characterization of phytoplankton populations by the use of excitation and emission spectra. Journal of Marine Research 37, 471e483.