1. Introduction

Chlorophyll a (Chl) is a photosynthetic pigment that plays a key role in photosynthesis [1]. All phytoplankton species, regardless of their specific group and taxonomic features, contain Chl in their photosynthetic apparatus. Chl concentration (CC) is broadly used as a useful index of phytoplankton biomass in laboratory research, oceanographic studies, and environmental surveys. In vivo measurements of Chl fluorescence (CF) are highly sensitive, fast and easy to conduct in a small sample volume at natural concentrations of the photosynthesizing microorganisms, including direct in situ measurements [2–6] and LIDAR remote sensing [7–10]. CF measurements can provide information about CC, phytoplankton community structure [11–16], physiological status, photosynthetic efficiency and productivity [17–28].

While CF is broadly used as a proxy of CC and phytoplankton biomass [19, 29–32], the accuracy of quantitative CC assessments is often compromised by high, up to an order of magnitude [33–36], variability in CF/CC ratio. The relationship between CF and CC depends on phytoplankton taxonomy, cell size, organization of photosynthetic apparatus and physiological status. Even frequent instrument calibrations cannot guarantee reliable and accurate CC fluorescence retrievals. CF photo-physiological regulation by light regime and nutrient availability is known to be one of the major factors affecting CF/CC variability (e.g [19, 35]. The appropriate choice of a measurement protocol may result in the CF/CC variability reduction. Some fluorometers [12, 17, 21, 22, 37, 38] provide measurements of physiological parameters that potentially can be used to adjust CF magnitudes affected by photo-physiological variability.

In this article, we use a simplified biophysical model to illustrate the problems relevant to the CF photo-physiological regulation and provide some practical recommendations that may help to improve the accuracy of CC assessment from in vivo CF measurements. The analytical results and measurement protocols are evaluated using field measurements in the Delaware and Chesapeake Bays. The abbreviations and variables used in the article are listed in Table 1.

Table 1. Abbreviations and Variables

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2. Photo-physiological regulation of chlorophyll fluorescence (model analysis)

Chl molecules are incorporated in phytoplankton cells and, therefore, are not evenly distributed in the water. Nonetheless, CC is commonly accepted for estimating the average Chl biomass per unit of water volume containing phytoplankton. Most in vivo CF originates from the Chl molecules of the light-harvesting antenna of photosystem II (PSII) in the photosynthetic apparatus of phytoplankton [39]. The relationship between CF intensity and CC can be described as

The PSII CF and thermal dissipation represent two channels of energy losses accompanying the PSII photochemical reactions. The quantum yields of photochemistry and fluorescence for dark-adapted PSII can be described, respectively, as:

When A = 1 (all PSII RCs are open in darkness), Φ_f reaches its minimal magnitude:

In darkness (A = 1) the Φ_p maximal potential magnitude can expressed via Φ_o and Φ_m as

The Φ_o/Φ_m ratio can be calculated from Eq. (6) as

An exposure to excessive ambient light may result in the gradual development of non-photochemical quenching (NPQ) that enhances thermal dissipation of the absorbed light energy. There are several photo-protective NPQ mechanisms (e.g., [39]). Depending on the intensity of incident light and the NPQ mechanisms involved, NPQ may develop over time scales ranging from seconds to minutes [44]. Recovery from NPQ action in dark conditions may require several minutes to several hours. NPQ can be described with an additional NPQ rate constant, k_N [39]. The actual maximal and minimal CFY magnitudes, and the maximal potential quantum yield of PSII photochemistry for the NPQ-affected photosystem can be expressed, respectively, as

From Eqs. (4), (10):

Thus, NPQ development should cause the most pronounced decrease in Φ'_m. Declines in Φ'_o and Φ'_p^m induced by NPQ are smaller and dependent on the phytoplankton physiological status (described by f in the model).

From Eqs. (7), (14), and (15) it follows that

3. Recommendations on CF measurement protocol for improved CC assessments

Thus, the phytoplankton CFY generally depends on the PSII photochemical functionality and the actual intensity of the incident light (both determine the PQ), as well as on the phytoplankton light exposure prior to the measurements that determine the NPQ. A potential range of the CFY photo-physiological variability can be estimated using the above analysis. The maximum value of Φ_p^m ~0.65 measured in healthy phytoplankton [5, 20] would result in Φ_m/Φ_o ~3 (Eq. (6)). Thus, the Φ_f magnitude can vary up to 3-fold, depending on the actual PQ effect (Eq. (2)). Our field data show that up to a 5-fold NPQ-induced CFY decline can be observed in the subsurface water column around noon (e.g., Fig. 3A ), depending on the light conditions and mixing regime. The maximum range of CFY natural variability caused by both PQ and NPQ can be estimated then as 15. This estimate is markedly close to the ~12-fold CF/CC variability observed in the field (e.g., [46]), suggesting that the CFY photo-physiological regulation may be one of the major factors affecting the overall variability in the relationship between CF and CC.

 

Fig. 3 Correlation between the HPLC measurements of Chl concentration (CC) in water samples 1–20 (Figs. 1, 2) and (A) underway Chl fluorescence measurements at the sampling locations (CF_U) or (B) CF measurements in the dark-adapted water samples (CF_S). Diamonds and circles represent the data from the nighttime and morning portions of the transect, respectively (marked as 1–14 and 15–20 in Figs. 1, 2). The framed and unframed regression equations are calculated for nighttime and entire data sets, respectively.

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During the active CF measurements, the PQ and NPQ magnitudes may depend on both ambient and CF excitation light. CF efficiency can vary a great deal, depending on environmental conditions, measurement protocols and phytoplankton physiological status. This provides unique opportunities for fluorescence assessment of phytoplankton photo-physiological characteristics (e.g., [25, 38]). On the other hand, CFY variability needs to be minimized or accounted for to improve the accuracy of CC fluorescence assessments.

Phytoplankton exposure to ambient light activates a complex chain of photosynthetic reactions and photo-adaptive physiological transformations and may result in NPQ development that significantly affects the CFY magnitude [39, 44, 47]. If measurement conditions permit, keeping water samples in low-light conditions for at least one hour before the measurement may restore the dark-adapted Φ_o level of CFY, which is independent of the prior “light history” of phytoplankton. It should be noted that after phytoplankton exposure to intense irradiance, even several hours of dark adaptation may be insufficient for complete recovery from NPQ [44] (for example, see Fig. 3B and Discussion).

Optical isolation of the measured sample volume eliminates the PQ component associated with the ambient light thus minimizing the overall CFY variability. If ambient light is blocked, PQ is determined by the intensity of excitation light and depends on PSII photochemical functionality (determined by f in the model). The PQ component and CFY dependence on the PSII physiology can be further minimized by using the PSII saturating fluorescence excitation that dynamically closes the PSII RCs to reach the CFY~Φ_m at the beginning of the fluorescence measurement. This also eliminates the need for optical isolation of the measured sample volume (PQ~0 regardless of the ambient light), which may simplify the in situ CF measurements.

The physiological origin of in vivo CF results in a complex CFY time transient after initiating the excitation known as Kautsky effect (e.g [44, 47]. It includes a polyphasic rise from the initial Φ_o level to its maximum Φ_m value (Φ'_o and Φ'_m, respectively, for light-adapted phytoplankton) followed by a polyphasic decline to some stationary CFY magnitude. The induction rise begins with a fast, photochemical phase (< 1 ms) followed by several thermal phases to reach Φ_m in ~100 ms under intense PSII saturating excitation [44, 47]. CFY remains almost unchanged at the maximum level for several seconds and gradually declines after that over 0.1 – 1 minute due to the development of excitation-induced NPQ.

Thus, CFY may continuously vary during the in vivo CF measurement due to the physiological mechanisms involved in the Kautsky effect, and the integral CF value is usually determined by the average CFY magnitude over the measurement time (this is discussed below regarding the ALF CF measurements). Several instrumental factors (e.g., excitation intensity and duration, measurement time, sample exposure to the excitation, etc.) may affect the average CFY value and result in a variable, instrument and protocol dependent CF/CC relationship. For example, the CF magnitude may appear to be dependent on the sample flow rate through the measurement chamber (e.g., [48]).

If a PSII saturating excitation is used for minimizing the PQ component of the CFY variability, it may be beneficial to limit the sample exposure to the excitation to ~1 second. Then the CF measurement will be conducted for most of the measurement time at the maximum level of CFY, independent of PSII photochemical functionality and not affected by the excitation-induced NPQ that would develop at the longer sample exposure. The actual measurement time may be longer if the measurements are conducted in a fast enough sample flow to limit the exposure time of the measured sample volume.

To summarize, the effect of CFY photo-physiological variability on the CF measurements can be reduced by the following (referred below as a “four-step measurement protocol”):

It is technically difficult to provide phytoplankton dark adaptation when conducting daytime in situ or flow-through underway shipboard measurements. The CF intensity may be NPQ-affected due to phytoplankton exposure to the ambient light in the water column. The NPQ effect may depend on the unknown phytoplankton “light history” and compromise the accuracy of CC fluorescence retrievals. The above model analysis shows that the PSII photochemical yield also exhibits the NPQ down-regulation (Eqs. (14), (15)). Therefore, the concurrent PY measurements may provide a potential way to adjust the CF retrievals for the NPQ effect. Equations (19) and (20) illustrate this idea, showing that the fluorescence parameters Φ_f/Φ_p and Φ_m(1/Φ_p^m −1) should remain invariant regardless of the NPQ magnitude and equal to their values in the PSII dark-adapted state. There are various measurement protocols and instruments for PY assessments [12, 37, 38, 49], so the practical implementations of this approach needs evaluation and optimization on a case-by-case basis. Below, we demonstrate with field data that the CF NPQ-adjustment using the PY measurements may provide a significant advantage over the conventional, CF-based CC assessments when it is problematic to provide phytoplankton recovery from the NPQ (e.g., in situ and flow-through underway retrievals). On the other hand, a potential dependence of the NPQ-invariant parameters on various physiological mechanisms needs to be evaluated (see Discussion).

4. Field measurements with advanced laser fluorometer

The field measurements with the Advanced Laser Fluorometer (ALF) were used to evaluate our analytical conclusions and proposed measurement protocols for improving the accuracy of CC fluorescence assessments. ALF is a compact field instrument that provides both spectrally and temporally resolved fluorescence measurements. Its design and measurement protocols are described in detail in [12]. The ALF conducts spectral deconvolution of the laser-stimulated emission to provide measurements of Chl, phycoerythrin, and CDOM fluorescence. The fluorescence intensities are normalized to water Raman scattering to account for variability in water optical properties. The ALF measurements of variable fluorescence are spectrally corrected for the non-CF background to improve the accuracy of retrievals.

The following features of the ALF design and measurement protocols make this instrument suitable for the field test of the above analytical conclusions. The ALF measurement cell is located in the sample compartment inside the instrument case and isolated from ambient light (condition 1 in Section 3). The spot size of the 405 nm laser excitation beam used for CF and VF measurements is appropriately adjusted to saturate PSII over ~100 μs (condition 2 in Section 3), thus providing the PY retrievals at PSII single-turnover (ST) time scale (e.g., [25, 45]). The spectral integration time with 405 nm excitation is limited to 1 second to avoid development of the laser-stimulated NPQ that happens over longer time scales (condition 3 in Section 3). About one hour of dark adaptation is provided for discrete water samples before in vivo fluorescence measurements with the ALF instrument (condition 4 in Section 3).

The results reported in this article are essentially based on comparison of the CF and PY measurements in the dark- and light-adapted states of phytoplankton photosynthetic apparatus. The ALF CF field measurements compliant with conditions 1–4 of the above measurement protocol are compared below with the independent HPLC CC retrievals. The use of PSII saturating excitation for ALF measurements of both PY and CF suggests the NPQ-invariance of parameter CF(PY⁻¹-1) (see Eq. (20)). The invariance of this and another fluorescence parameter, CF/PY, which can be formally derived from Eq. (19), are evaluated using the ALF field measurements and discussed below.

Some aspects of the ALF CF measurements relevant to optimizing the sample exposure time need to be briefly discussed. The internal instrument pump for discrete sample analysis operates at the flow rate of 0.1 L/min [12]. It results in ~100 ms time of phytoplankton exposure to the PSII saturating excitation (i.e. time of residence in the laser beam). This time corresponds to the PSII multiturnover time (MT) scale, and CFY exhibits a polyphasic rise typical for the Kautsky effect [44, 47]. The initial, photochemical phase is identical to the ST fluorescence induction used for the ALF PY measurements. It lasts ~100 μs, during which the PSII RCs are gradually closed and CFY reaches its maximal ST magnitude Φ_m(ST) [45]. This initial phase is followed by several thermal phases of continued CFY rise to reach its maximum MT level Φ_m(MT) ~1.5Φ_m(ST) [45] at the end of 100 ms exposure time (Fig. 1 in [47]). Though the ALF spectrometer integrates the laser-stimulated emission over ~1 s, the CF magnitude yielded by the ALF measurements in the sample flow reflects some average over the exposure time CFY magnitude, Φ_m(ST) < CFY < Φ_m(MT).

 

Fig. 1 A map of shipboard ALF underway flow-through measurements in the Delaware and Chesapeake Bays, April 15–16 2008. Green dots display the sampling locations for laboratory measurements of Chl fluorescence and concentration.

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During the underway measurements described below in Results, the 1 L/min flow rate has resulted in 10 ms phytoplankton exposure time. Since the intense (~0.1 mol photons m⁻² s⁻¹) fluorescence excitation is used in the ALF instrument, the CFY almost reaches Φ_m(MT) during the 10 ms exposure and the resulting CF magnitude is only 10% lower than measured in the samples at 0.1 L/min flow rate (see Results and Discussion). Generally, this magnitude is determined by the fluorescence excitation intensity and the sample flow rate. The latter may explain the CF dependence on the flow rate often observed with field fluorometers (e.g., [48]).

The underway shipboard measurements with the ALF instrument were conducted in the Delaware and Chesapeake Bays courtesy of the College of Marine Science (University of Delaware) onboard R/V Hugh R. Sharp during its non-stop transit from Lewes (Delaware) to Cambridge (Maryland) (see the map in Fig. 1). The water was continuously sampled by the shipboard sampling system at ~2 m below the water surface and directed to the ALF instrument through a 15 m silicon tube at the flow rate of 1 L/min. The delay between sampling and measuring the water was ~30 seconds.

The ALF measurement cycle included two measurements of spectral emission in 400–800 nm range using 405 and 532 nm laser excitation, respectively, and the temporally resolved measurement of CF induction over 100 μs in the spectral range of 670-695 nm using the pump-during-probe measurement protocol [12, 25]. The spectral integration time was 1 s, and the laser excitation was turned off before and after the spectral measurements. The fluorescence induction waveforms were averaged over 5 to 10 flashes of laser excitation at 405 nm and 10 Hz repetition rate.

Twenty water samples were collected along the transect from the discharge of the flow-through system in the 500 mL dark-amber glass bottles and stored in a dark cooler filled with ice. The sampling locations are marked with numbers in Fig. 1. The ALF fluorescence measurements of the samples were conducted at Horn Point Laboratory (University of Maryland Center for Environmental Science) courtesy of Dr. Harding in about 1 hour on arrival to Cambridge. The samples were pumped at 0.1 L/min from the sample bottles through the ALF flow measurement cell. Ten sequent spectral measurements of the sample emission stimulated at 405 nm and 532 nm were conducted in the sample flow. In addition, 10 measurements of fluorescence induction, each averaged over 10 excitation shots, were conducted between the spectral measurements. The spectral and fluorescence induction measurements were averaged over the sequent acquisitions. The collected samples were also filtered for HPLC pigment analysis conducted later at the Pigment Analysis Facility of Horn Point Laboratory.

5. Results

Figure 2 displays the results of transect measurements shown in Fig. 1. The measurements began at 19:18 May 15, 2008, continued overnight (see the sunrise mark at 06:24) and were finished at 10:43 May 16, 2008 on arrival at Cambridge. The specific features of the distributions can be related to their locations on the map in Fig. 1 via numbers that represent the sampling points in both figures.

 

Fig. 2 HPLC measurements of Chl concentration (CC) (black squares) and ALF underway fluorescence CC retrievals along the transect displayed in Fig. 1. Dark green: CC distribution calculated from the underway Chl fluorescence measurements (CF_U) as CC = 4.40CF_U using earlier ALF calibration with dark-adapted samples. Blue: ALF underway measurements of PSII photochemical yield (PY_U). Light green: CC distribution calculated as CC = 1.88CF_U/PY_U using correlation in Fig. 5C.

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To evaluate the applicability of the earlier instrument calibration, the CF underway transect measurements, CF_U (the subscript “U” here and below denotes the underway data), were converted into CC units (dark green line in Fig. 2). The conversion equation $CC=\text{4}.\text{4}0C{F}_U$was derived from the correlation (R² = 0.93) between CC retrievals with high performance liquid chromatography (HPLC) and the ALF CF measurements of the dark-adapted water samples representing diverse coastal and estuarine waters (Fig. 7A in [12]). As evident from comparison with the HPLC CC measurements in water samples collected along the transect (black squares in Fig. 2), the ALF CC assessments based on the nighttime or low- light measurements were in good agreement with the respective HPLC measurements(samples 1–14). On the other hand, the morning ALF fluorescence assessments showed significant, up to 5-fold, CC underestimation of the HPLC CC measurements.

Accordingly, the nighttime ALF CF_U transect measurements at sampling locations 1–14 showed high correlation (R² = 0.90) with the HPLC CC retrievals for the respective samples (diamonds in Fig. 3A), and the CC/CF ratio at these locations was close to the magnitude shown by the earlier ALF calibration for the dark-adapted samples (4.77 vs. 4.40). The morning ALF transect measurements at sampling locations 15–20 showed up to 5-fold lower and variable CF_U per unit of CC (circles in Fig. 3A). That resulted in poor CF_U vs. CC correlation for the entire data set that included both the nighttime and morning measurements (R² = −0.22). Similarly, the laboratory CF measurements in samples 1–14 from the low-light or nighttime of the transect showed high correlation with the HPLC CC retrievals (diamonds in Fig. 3B). The CC/CF ratio 4.35 for these samples was very close to 4.40 from the earlier ALF calibration [12], which was based on analysis of the dark-adapted samples. The morning samples 15–20 showed 10-30% lower and variable CF_U per unit of CC (circles in Fig. 3B) that resulted in lower overall CF_U vs. CC correlation (R² = 0.80).

Figures 4A and 4B allow direct comparison of the CF_S and PY_S magnitudes (the subscript “S” here and below denotes the sample measurements) measured in the dark-adapted samples and the respective underway CF_U and PY_U measurements at the sampling locations. Consistent with the plots in Fig. 3, the nighttime portion of the data (samples 1–14) show high correlation between the underway and sample measurements (R² = 0.99 and 0.84 for CF and PY, respectively). The morning underway CF_U and PY_U magnitudes were noticeably lower than the respective CF_S and PY_S values, which resulted in the reduced overall correlations for the entire set of samples 1–20 (0.68 and 0.26 for CF and PY, respectively).

 

Fig. 4 (A): Comparison of Chl fluorescence in the dark-adapted water samples (CF_S) and underway measurements at the sampling locations (CF_U). (B): Comparison of PSII photochemical yield measured in the dark-adapted water samples (PY_S) and underway at the sampling locations (PY_U). Diamonds and circles represent the nighttime (samples 1-14 in Figs. 1 and 2) and morning (samples 15-20) parts of the transect, respectively. (C) and D: Comparison of fluorescence parameters CF(PY⁻¹-1) and CF_U/PY_U for the data sets displayed in panels (A) and (B).

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Assuming that the differences between the nighttime and morning portions of the data displayed in Figs. 2, 3, 4A and 4B were caused by the NPQ of CF and PY in the sampled sub-surface water in the morning hours, the NPQ invariance of the fluorescence parameters in Eqs. (19) and (20) can be evaluated. The CF(PY⁻¹-1) magnitudes showed excellent correlation (R² = 0.96) for the entire data set that includes both nighttime and morning samples 1–20 and the respective underway measurements at the sampling locations (Fig. 4C). Similarly high correlation (R² = 0.95) was observed for the CF/PY parameter (Fig. 4D).

As evident from Figs. 5A and 5B, the CF(PY⁻¹-1) magnitudes showed reasonably good correlation with the HPLC CC retrievals for both the underway and sample measurements (R² = 0.74 and 0.83, respectively). The CF/PY parameter showed noticeably better correlations with the HPLC CC retrievals for both the underway and sample measurements (R² = 0.93 and 0.96, respectively, Figs. 5C and 5D). For evaluation, the CC_CF/PY transect distribution (light green line in Fig. 2) was calculated using the ALF underway transect measurements of CF_U and PY_U, and the regression equation $CC=\text{1}.\text{88}C{F}_U/P{Y}_U$from Fig. 5C.

 

Fig. 5 (A): Correlations between the HPLC measurements of CC in water samples and fluorescence parameters CF(PY⁻¹-1) and CF/PY calculated from Chl fluorescence (CF) and PSII photochemical yield (PY) measured in the dark-adapted water samples (B) and (D) and the underway flow-through CF and PY measurements at the sampling locations (A) and (B) marked as 1–20 in Figs. 1 and 2. Diamonds and circles represent the nighttime (samples 1-14) and morning (samples 15-20) parts of the transect, respectively.

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6. Discussion

6.1. Practical implementation of the four-step protocol

A four-step measurement protocol is proposed in section 3 to reduce the PQ and NPQ effects on the CF retrievals. The ALF in vivo CF measurements using this protocol show high correlation with independent HPLC CC retrievals (for example, see diamonds in Fig. 3A and 3B; see also Fig. 7A in [12]). Recent ALF field deployments have confirmed that in vivo CF measurements compliant with the four-step protocol can indeed provide high-accuracy CC assessments. In coastal and estuarine waters that are typically dominated by diatoms and dinoflagellates, the relationship between CF and CC can be described by a simple regression equation (e.g., $CC=\text{4}.\text{4}0CF$for the ALF measurements [12]) that does not show significant seasonal or regional variability. A more complex, non-linear relationship may need to be used in the offshore oceanic waters, particularly in the frontal zones that exhibit strong gradients in physical and chemical properties [50].

Some aspects of the practical implementation of the four-step protocol are briefly discussed below. Many benchtop and some in situ fluorometers are equipped with a dark measurement chamber that provides optical isolation of the phytoplankton-containing water volume (condition # 1 of the protocol). Technically, the PSII saturating excitation (condition # 2) can be provided by selecting an appropriate light source for fluorescence excitation and through instrument design. For example, 405 and 532 nm lasers are used for this purpose in the ALF instrument. The small cross-section and low divergence of the laser beam simplifies optimization of the optical design. The narrow-bandwidth laser excitation minimizes the spectral bandwidth of the water Raman scattering and allows for spectral deconvolution of the overlapped fluorescence bands of seawater constituents [12]. Light emitting diodes used for fluorescence excitation in various instruments, including PSII saturating fluorometers for measuring variable fluorescence, can provide a cost-efficient alternative to lasers.

Some relevant aspects of optimizing sample exposure to the excitation light (condition # 3) can be illustrated using the field data presented in Results. While both nighttime underway CF measurements and analyses of nighttime water samples showed high correlation with CC (R² = 0.90 and 0.95, respectively in Fig. 3A, B), the slopes in the correlation equations were noticeably (~10%) different. The regression equation in Fig. 4A also suggests that the underway measurements yielded 10% lower CF magnitudes than the sample measurements. As discussed in section 4, this difference can be explained by shorter exposure of phytoplankton to the excitation due to the 10-fold faster flow rate used for the underway measurements. Much stronger CF dependence on the sampling flow rate can be observed under less intense fluorescence excitation intensity used in many instruments (e.g., [48]).

Generally, the sample exposure time needs to be optimized to reduce the CF dependence on the measurement protocol. For CF measurements at the MT PSII turnover scale using PSII saturating excitation, it can be optimized with regard to the Kautsky effect [44, 47]. Under these conditions, a 1–3 second exposure may appear to be optimal. Then, after reaching Φ_m(MT) during the initial 100–200 ms of Kautsky induction (see Section 4), CFY would remain at this level during most of the exposure time. When measuring the stationary sample, the CF measurements can begin when CFY reaches Φ_m(MT) and end before beginning manifestation of excitation-induced NPQ, which develops after ~3 seconds under such conditions (Fig. 1 in [47]). The signal integration time can be adjusted in some range (assuming CFY is still ~Φ_m(MT) during the measurement) to optimize the measurement protocol. In the case of flow-through measurements, the signal integration time can be longer than the exposure time to ensure the desirable signal-to-noise ratio.

In practice, the need for prolonged (~1 hour) phytoplankton dark adaptation before the fluorescence measurements (condition # 4) limits the four-step protocol mainly to laboratory use (including shipboard measurements). For field CF measurements in stationary settings (e.g. moorings, piers, etc.), the instrument can be equipped with a sampling chamber to provide adequate dark adaptation prior to the measurements. It should be noted that, based on our field experience, even several hours of dark adaptation is often insufficient for complete recovery from the photoinhibitory NPQ [39, 44] developed in the PSII RCs in subsurface water exposed to excessive solar irradiance. For example, the “leftover” NPQ effect was evident in CF measurements of the morning surface samples discussed in Results. After 2–3 hours of dark adaptation, these samples showed an increase in CF/CC magnitudes vs. the real-time underway measurements, which were strongly affected by NPQ (circles in Figs. 3B and 3A, respectively), but these values were still variable and lower than the night-collected samples (diamonds in Fig. 3B). Only one morning sample (# 15 in Fig. 2) that had the smallest exposure to solar irradiance after sunrise and the longest (~4 hour) duration of dark adaptation, showed complete recovery from NPQ. This is indicated by the fact that the CF/CC ratio is identical to the night-collected samples (CF_S = 4 in Fig. 3B). Note that the same underway and sample data showed no difference between the morning and night measurements when the NPQ-invariant fluorescence parameter was used to correlate with CC (Figs. 5C and 5D).

6.2. CF adjusting for NPQ using PY measurements

In the case of continuous underway or in situ measurements, it is practically impossible to provide dark adaptation long enough to eliminate NPQ caused by phytoplankton exposure to ambient light prior to measurement. This may result in significant uncertainty in CC fluorescence assessments using instrument calibration with dark-adapted phytoplankton (for example, see Fig. 2). Even with an adequate analytical model, it is difficult to estimate the NPQ magnitude that is determined by the usually unknown phytoplankton light history. The above analysis shows that it may be possible to adjust the NPQ-affected CF measurements using concurrent measurements of variable fluorescence that yield PY magnitudes. The field data in Results allows for evaluating the feasibility of CC assessment using the NPQ-invariant fluorescent parameters derived from CF and PY measurements. In particular, the data sets used for calculation of CF(PY⁻¹-1) and CF/PY data in Figs. 4C and 4D included both NPQ-free and NPQ-affected data from sampling locations 1–14 and 15–20, respectively (Fig. 2). Despite significant variability in the NPQ magnitudes (Figs. 3, 4A, 4B), both CF(PY⁻¹-1) and CF/PY variables showed strong correlation (R² = 0.96 vs. 0.95, respectively, in Figs. 4C and 4D), thus demonstrating their NPQ invariance.

It should be noted that both CF and PY were measured using PSII saturating excitation. Under this condition for the dark-adapted phytoplankton, PY = Φ_p^m [12] and CF is proportional to Φ_m (see Section 4); the same is valid for light-adapted phytoplankton with the respective change in notation, Φ'_p^m and Φ'_m. Thus, the NPQ invariance of CF(PY⁻¹-1) (Fig. 4C) is fully consistent with the above model analysis (see Eq. (20)). On the other hand, despite the similarity between CF/PY and Φ_f /Φ_p ratios, the PSII saturating excitation is not described by Eq. (19), which actually predicts NPQ-invariance of Φ_f /Φ_p. Therefore, the experimentally observed NPQ invariance of the CF/PY parameter (Fig. 4D) is not justified by the simplified biophysical model discussed in Section 2. Nonetheless, this observation is still reported here, as it may provide new insight for better understanding of phytoplankton photosynthetic regulation in natural conditions and assist in improving CC fluorescence assessments.

Though both parameters performed equally well in terms of NPQ-invariance (Figs. 4C and 4D), the CF/PY ratio for both underway and sample retrievals showed better correlation with the HPLC CC measurements (R² = 0.93 and 0.96, respectively; Fig. 5). The CF(PY⁻¹-1) parameter calculated from the underway measurements at sampling locations 1–20 also showed a dramatic improvement in correlation with the HPLC CC data as compared to the CF magnitudes for the same data set (R² = 0.74 vs. −0.22 in Figs. 5A and 3A, respectively).

Thus, the CF(PY⁻¹-1) parameter can be used for reasonably accurate CC assessments from NPQ-affected measurements of CF and PY. A potential downside is that this parameter may appear to be more sensitive than CF to potential variability in the PSII photochemical functionality (determined by f in our model). Indeed, according to the model, $CF\left(P{Y}^{-\text{1}}-\text{1}\right)~F_o/F_p{}^m=k_f/f{k}_p,$while CF does not depend on f due to the PSII-saturating excitation. In particular, the significant transect variability in PY_U (and, respectively, f (see Eq. (6))) evident in the Fig. 2 might account for CF(PY⁻¹-1) correlation with HPLC CC that was lower than for nighttime, f-independent underway CF measurements (0.74 vs. 0.90 in Figs. 5A and 3A, respectively).

This may also explain the only marginal improvement in the CF(PY⁻¹-1) correlation with HPLC CC as compared to the CF correlation for the dark-adapted samples (R² = 0.83 vs. 0.80 in Figs. 5B and 3B, respectively). If our interpretation is correct, the PQ variability in CF was minimized by using PSII saturating excitation, but the CF magnitudes in the morning samples were still moderately NPQ-affected because of their incomplete recovery from NPQ (empty dots in Fig. 3B). This “leftover” NPQ effect was not manifested in the CF(PY⁻¹-1) parameter, but the overall transect variability in the PSII photochemical functionality might have affected the $CF\left(P{Y}^{-\text{1}}-\text{1}\right)/CC$relationship.

Similar comparisons for the CF/PY parameter revealed significant improvements in correlations with HPLC CC retrievals for both the underway and sample measurements: R² = 0.93 vs. −0.22 in Figs. 5C and 3A; R² = 0.96 vs. 0.80 in Figs. 5D and 3B. Though the use of CF/PY parameter was not justified by the simplified biophysical model and needs further consideration, the CF/PY ratio may be advantageous vs. the CF(PY⁻¹-1) parameter for minimizing the effects of both NPQ and PQ variability on the accuracy of CC fluorescence assessments. For evaluation, the linear regression relationship CC = 1.88CF_U/PY_U between CC and underway fluorescence measurements at the sampling locations (Fig. 5C) was used to calculate the CC transect distribution (light green line Fig. 2). As evident from comparison with the independent CC sample measurements, the concurrent CF and PY measurements provided accurate high-resolution CC data despite the significant NPQ and PSII physiological variability in the water masses.

7. Conclusion

The biophysical analysis and field measurements show that significant (up to 15-fold) photo-physiological variability in fluorescence yield is one of the major factors contributing to the overall variability in in vivo chlorophyll fluorescence per unit of chlorophyll concentration. The fluorescence yield and PSII photochemical efficiency are controlled by PQ and NPQ mechanisms and depend on incident light intensity, phytoplankton “light history”, PSII photochemical functionality, and other physiological factors. Minimizing the PQ and NPQ magnitudes can help to reduce the variability and improve the accuracy of CC fluorescence assessments. This can be achieved via isolation of the measurement volume from ambient light, PSII saturating fluorescence excitation, optimization of phytoplankton exposure to the excitation, and phytoplankton dark adaptation before the measurements. If the measurement conditions do not allow for dark adaptation (e.g., in situ or flow-though underway measurements from a moving platform), concurrent measurements of variable fluorescence can be used to adjust fluorescence intensity for non-photochemical quenching developed due to prior exposure to ambient light. The field evaluation in estuarine waters of the Chesapeake and Delaware Bays showed significant potential of this approach for improved fluorescence assessments of chlorophyll concentration. Nonetheless, it needs evaluation in more diverse coastal and offshore oceanic waters. An improved biophysical model needs to be developed to account for the complexity of the photo-physiological mechanisms involved.