The relationship between absorption at 676 nm normalized to chlorophyll-a, i.e., specific absorption aph*(676), and various optical and environmental properties is examined in extensive data sets from Case I and Case II waters found globally to assess drivers of variability such as pigment packaging. A better understanding of this variability could lead to more accurate estimates of chlorophyll concentrations from in situ optical measurements that may be made autonomously. Values of aph*(676) ranged from 0.00006 to 0.0944 m2/mg Chl a across all sites studied, but converged on median and mean values (n = 563) of 0.0108 and 0.0139 m2/mg Chl a respectively, with no apparent relationship with various optical properties, latitude, coastal or open ocean environment, depth, temperature, salinity, photoadaptation, ecosystem health, or albedo. Relative consistency in aph* across such diverse water types and the full range in chlorophyll concentration suggests a single aph* may be used to estimate chlorophyll concentration from absorption measurements with better accuracy than currently thought.
© 2016 Optical Society of America
Phytoplankton are the single-celled photosynthesizers that live in the ocean and form the base of the food chain. They are crucial to global ecosystems, responsible for around 50% of global primary production . Estimates of phytoplankton biomass and primary productivity routinely employ extracted chlorophyll a concentration [2–4]. However, discretely collected samples do not capture variability at high temporal and spatial frequencies, or synoptically over large scales. Developing and refining in situ optical approaches to measure biomass using autonomous vehicles and remote sensing platforms has therefore been a research focus [5–7].
One optical technique typically used to estimate in situ chlorophyll a concentration ([Chl a]) is fluorescence, which is a highly variable proxy due to its dependence on both specific absorption and fluorescence quantum yield . Phytoplankton absorption (aph) should be a more accurate proxy because it is only dependent on specific absorption (aph*; absorption cross-section per mass unit of pigment), although expected accuracy in using a nominal aph* for all phytoplankton may still be around 50% due to variability in pigment packaging in cells. Studies have found that aph(676) and [Chl a] are significantly positively correlated [9–11]. These studies use 676 nm, the pigment absorption peak in the red waveband [9,12–14]. Due to weak influence of accessory pigments at 676 nm, aph*(676) should be a reasonable proxy for packaging of Chl a [15–17].
Pigment packaging refers to the way pigment molecules are contained within discrete packages (within chloroplasts, within cells, and within colonies), rather than being uniformly distributed within the medium, and depends on both cell size and intracellular pigment concentration. Theoretical [18–20], laboratory [21–25], and in situ [10,26–30] studies show that an increase in cell size or intracellular pigment concentration will lead to an increase in the packaging effect, lessening light absorption efficiency and causing pigment aph* values to decrease. Studies have shown cell size can be approximated using spectral particulate attenuation (i.e. light transmission), as the spectral attenuation slope (cp slope) has been found to be linearly related to the power law slope of particle size distributions [31–34]. Thus, because aph* varies primarily due to pigment packaging, and both pigment packaging and spectral attenuation slope are related to cell size distributions, there is a possibility aph* and spectral attenuation may be used to optically assess the relationship between pigment packaging and cell size. Spectral attenuation can also be readily measured autonomously with commercially available instrumentation.
Due to surface area to volume ratios, cell size in natural environments can be positively related to available nutrients in the water , which in turn can positively drive concentration of chlorophyll a within the cell . This relationship can often be seen in the transition from coastal, eutrophic waters dominated by large cells to off-shore, oligotrophic waters dominated by small cells. Furthermore, studies have shown a general inverse relationship between aph*(676) and chlorophyll a concentrations as the level of pigment packaging decreases from coastal high-chlorophyll waters to off-shore low-chlorophyll waters [12,30,36]. This illustrates that the relationships between size, chlorophyll a concentration, and aph*(676) are largely driven by environmental parameters.
Table 1 portrays the range of aph*(676) values found in other studies, demonstrating the need to examine these relationships at a global level to capture localized variability within a larger, all-encompassing universal relationship. The filter pad technique [44–46] was primarily used to determine absorption values in these studies, whereas in situ ac-9 and ac-s devices were used in our study [47,48]. Additionally, most of the studies only used baseline correction to obtain aph(676), which involves subtracting a constant aph value (typically around 750 nm) from the entire spectrum, rather than absorption line height correction as in our study (methodology explained below). Roesler and Barnard (2013) showed aph*(676) values derived using the line height method were more consistent than aph*(676) values derived from simple baseline subtraction .
Our study examines the relationship between aph*(676) and other optical and environmental properties in global data sets from a wide range of Case I and Case II waters to assess dependencies on pigment packaging and phytoplankton size dynamics. Based on previous theoretical, laboratory, and in situ studies, a negative relationship between size and aph*(676) is hypothesized. More accurate estimates of aph*(676) from optical measurements could enable more accurate retrievals of chlorophyll concentrations resolved over fine scales with sensors that may be deployed autonomously.
Data was collected from 11 global locations, varying widely across both Case I (>20 km from shore and >400 m depth, i.e., generic metrics to ensure sites are outside continental shelf) and Case II (<20 km from shore and <400 m depth) environments [Fig. 1]. Note these Case I and II designations are, as usual, somewhat arbitrary and are intended only as a rough grouping. For each cruise, in situ inherent optical properties (IOPs) and hydrographic data were collected. Absorption and attenuation were measured using either a WET Labs (Philomath, Oregon) ac-9 or ac-s , backscattering measurements with either a WET Labs BB3 or BB9 , and temperature and salinity measurements with Seabird (Bellevue, Washington) SBE49 CTD instrumentation. Chlorophyll a concentrations were assessed using High Performance Liquid Chromatography (HPLC) . Full HPLC pigment analyses were conducted for 9 of the 11 sites studied, and LIS only had HPLC from 1 of the 3 study years (we are missing full HPLC data from BIOSOPE, Lake Erie, and LIS years 2004 and 2005). Each chlorophyll a measurement was then matched to an averaged bin of IOP data concurrent with the time and depth of the chlorophyll sample. The temporal cut-offs used varied from site to site based on the stability of ocean characteristics present there, but ranged from 10 minutes at patchy coastal sites to 40 minutes at stable off-shore sites.
aph values were calculated at 676 nm for each data point using the line height absorption method, which involves calculating the height of the peak absorption above the baseline absorption between 650 and 715 nm [9,52]. As mentioned previously, 676 nm, the pigment absorption peak in the red waveband, is due almost entirely to chlorophyll a [9,12–14]. Any minor contributions to the absorption peak from accessory pigments (i.e. chlorophyll b), as well as absorption contributions from CDOM and non-algal absorption, should be removed via the line height absorption method since those spectra are monotonic in this region [9,12]. The aph values were then normalized to the extracted chlorophyll a concentration for each sample to retrieve aph* at 676 nm. Spectral beam attenuation (cp) slopes were calculated for each sample with a power law fit using an unconstrained non-linear optimization procedure over the wavelength range of 412-650 nm (MATLAB fminsearch, Nelder-Mead method) .
All data analysis was carried out with MATLAB (MATHWORKS, Natick, MA, USA). Relationships between aph*(676), cp slope, and [T Chl a] were determined using MATLAB’s curve fitting tool to find best fit curves via least-squares methods, and p-values were determined using Pearson correlations for linear relationships and by fitting nonlinear regression models (MATLAB fitnlm) for non-linear relationships. Nonlinear regression models considered within MATLAB’s curve fitting tool included power law, exponential, logarithmic, polynomial, Fourier, and Gaussian fits. Significance for linear relationships was reported when p<0.05. However, every non-linear relationship tested produced p-values that indicated extremely high significance (i.e. <10−5). This does not necessarily mean that these relationships were significant; rather, with such a large number of observations (n = 563), it is possible for the p-value to indicate high significance even if the relationship only explains a small portion of the observed variance, as a p-value is simply an indication of a non-zero effect. For this reason, p-values for the non-linear relationships were not included in this analysis. Differences between sites for aph*(676), pigments, and single scattering albedo (b/c) were tested with a non-parametric Kruskal-Wallis one-way analysis of variance, as the data at most sites failed to satisfy the normality criterion (Anderson-Darling test, p<0.05).
A principal component analysis was conducted at an attempt to understand how interacting variables may be contributing to the results. Although there were some significant relationships between the optical and environmental variables, (i.e. a positive correlation between cp slope, depth, and salinity, and a negative correlation between temperature and [Chl a]), aph*(676) was orthogonal to all variables. Since our other analyses captured this same result, the principle component analysis was deemed redundant and was not included in this manuscript.
The correlation between chlorophyll a concentration and aph(676) was not found to be as significant as the correlations found in more localized studies such as Roesler and Barnard (2013) (r2 = 0.55 vs. r2 = 0.95 in the latter example) . However, a significant linear dependence does appear to be present (r2 = 0.55, p<0.001) [Fig. 2], and aph* at 676 nm has an influence on the variation present at low chlorophyll levels. At these values, especially <2 mg/m3, the slope of the data becomes steeper, and a power curve becomes a slightly better representation of the data, albeit the improvement is not statistically significant. This nonlinearity is well known [10,12,37], and has been attributed to pigment packaging. Strong applicability of the linear relationship may be because the line height method is removing some degree of the package effect through subtraction of background absorption.
Sites predominantly accounting for high aph*(676) values were the Florida Keys and GOCI [Figs. 2 and 3, Table 2]. Mean aph*(676) value ranges for the sites studied (0.0063 to 0.0253 m2/mg Chl a) are similar to those found in other studies, especially those calculated using the line height method [Table 1]. It is clear from both our study and values from the literature that variation exists between sites and studies, however, aph*(676) values from our study generally had surprising consistency around a median of 0.0108 m2/mg Chl a [Fig. 3].
The backscattering ratio, bbp/bp, often used as a metric for particle type [53,54], ranged from 0.0009 to 0.0336, where low values are considered indicative of particles with low density and low relative refractive index (such as cytoplasm-filled cells) and high values are associated with high density, high relative refractive index particles such as minerals. Lake Erie was the only site with a median bbp/bp value (2.7%) above 2%, and was unique in having high concentrations of colony-forming cyanobacteria. Phytoplankton have been shown to have bbp/bp between 0.1% and 3% [55,56], although background detrital particles in prepared cultures may have accounted for some of the higher values in this range. Nonetheless, observed median bbp/bp between 0.005 to 0.01 for BIOSOPE, Hawaii, GOCI, OCVal, and CLIVEC are likely associated with a significant phytoplankton influence. Median bbp/bp between 0.01 to 0.02 for GASEX, Florida Keys, VIIRS, LIS, and ECOMON likely also have a significant phytoplankton component, but “harder” mineral-type particles may also influence optical properties at these sites. Even for sites with significant nonalgal particle abundance, we can expect typical monotonically decreasing absorption with increasing wavelength for this fraction [57,58], which will be removed with the line height method. Variation observed in aph*(676) is therefore most likely associated with phytoplankton light adaptation and residual pigment packaging effects.
Potential packaging effects can be examined in relationships between aph*(676) vs. [T Chl a] and cp slope [Figs. 4(a) and 4(b)], indicated by the black dotted line in each plot. Note Bricaud et al. (1995) used the baseline correction method rather than line height correction method to obtain aph*(676). In our aph*(676) vs. [T Chl a] plot [Fig. 4(a)], there appeared to be distinctly negative-trending relationships when focusing on individual sites, with slopes much steeper than that predicted by Bricaud et al. (1995) . The trend through the entire data set, however, is much more consistent than Bricaud et al. (1995), with the strong clustering at 0.0108 m2/mg Chl a. [T Chl a] and cp slope [Fig. 4(b)] showed a negative exponential relationship, and, despite the negative trend between both aph*(676) and [T Chl a] and [T Chl a] and cp slope, there was also an insignificant relationship between aph*(676) and cp slope [Fig. 4(c)]. Lack of a dependence of aph*(676) on cp slope is inconsistent with our initial hypothesis, although the relative consistency in aph*(676) throughout the broad composite data set is notable. Data subsets for Case I, Case II, and seasonal periods did not produce significantly different trends.
There was no significant relationship between aph*(676) and depth, temperature, or salinity [Fig. 5]. Although there does appear to be relationships between depth, salinity, and size (e.g., smaller particles tend to dominate deeper depths at higher salinities), aph* converged at 0.0108 m2/mg Chl a across the full dynamic range of all three parameters. Moreover, these parameters cannot account for the higher levels of variation seen in aph* values.
Congruent with the insignificant relationship between aph*(676) and depth, higher concentrations of the photoprotective carotenoid (PPC) pigments diadinoxanthin and diatoxanthin are not consistently associated with higher aph*(676) values [Fig. 6]. Hawaii showed the highest [PPC]:[Chl a] ratios (Kruskal-Wallis test, p<0.001). In addition, the Florida Keys and LIS showed significantly higher [diadinoxanthin]:[Chl a] ratios than the mean (Kruskal-Wallis test, p<0.001 and p = 0.002 respectively) and Hawaii shows significantly higher [diatoxanthin]:[Chl a] ratios than the mean (Kruskal-Wallis, p<0.001). Thus, adaptation to high light conditions does not appear to be a consistent contributor to aph*(676) variation.
Phytoplankton “health,” as evidenced by relative phaeophytin concentrations, could contribute to variations in aph*(676), as it would be included in the absorption measurement but not the chlorophyll measurement. However, the relationship between [phaeophytin]:[chlorophyll a] and aph*(676) was also insignificant, with GASEX showing significantly higher concentrations than the other sites aside from the Florida Keys and GOCI (Kruskal-Wallis test, p<0.001) [Fig. 7].
Another source of high variation within aph*(676) may come from albedo, the ratio of scattering to attenuation at 532 nm [Fig. 8]. Phytoplankton species with high albedo from “hard” shells (e.g., frustules and theca plates) may have physiological adaptations to compensate for scattering losses, such as increasing chlorophyll to increase absorption capacity, thereby decreasing aph*(676) values . Although the relationship between albedo and aph*(676) was not found to be significant, Hawaii and LIS showed significantly higher albedo values than the mean of all sites (Kruskal-Wallis test, p<0.001 and p = 0.002 respectively) [Fig. 8].
Phytoplankton functional groups may be able to account for some of the observed variability. Although full HPLC pigment analyses were not run for every study site, from the HPLC data available, the sites with highest aph*(676) values, i.e., Florida Keys and GOCI, exhibited similar species compositions, with cyanobacteria (indicated by [zeaxanthin]:[T Chl a]) dominating, followed by similar levels of diatoms and coccolithophores (indicated by [fucoxanthin]:[T Chl a] and [19’-hexanoyloxyfucoxanthin]:[T Chl a] respectively), and with lower values of dinoflagellates (indicated by [peridinin]:[T Chl a]) and cryptophytes (indicated by [alloxanthin]:[T Chl a]) [Fig. 9] [59–62].
Based on this study and others [Fig. 3, Tables 1 and 2], clear site-by-site variation in aph*(676) values is apparent. However, considering the entire global data set, aph*(676) values tended to consistently group around the median value 0.0108 m2/mg Chl a with no clear relationship with latitude, coastal or open ocean environment, depth, temperature, salinity, photoadaptation, ecosystem health, or albedo. This value is very close to the aph*(676) value of 0.010 found by Roesler and Barnard (2013) in their study using similar methods for observations in the Gulf of Maine and the Equatorial Pacific . Slight negative trends exist between aph*(676) and [T Chl a] and [T Chl a] and cp slope, however these trends were very weak, showing the convergence on 0.0108 m2/mg Chl a over the full dynamic range [Figs. 4(a) and 4(c)]. Clear deviation above the 0.0108 m2/mg Chl a median is observed, however, for the Florida Keys and GOCI [Figs. 1 and 2, Table 2]. For the entire data set, the 0.0108 m2/mg Chl a mean value had 25th and 75th quartile bounds of 0.0073 and 0.0149, respectively, so that one would have a 50% probability of falling in this range when applying this value to line height absorption at 676 nm to derive [Chl a].
To try and account for some of the higher aph*(676) values found in these two sites, we looked at other potential factors that could influence pigment packaging within phytoplankton, including photoadaptation, ecosystem health, albedo, and phytoplankton species composition. For photoadaptation, a convenient proxy is depth, because decreased irradiance typically causes chlorophyll a per cell to increase, thereby increasing pigment packaging with depth [23,27,63]. In our study, aph*(676) was not correlated to depth, however this could be skewed by our lack of fully representative data at depth. BIOSOPE accounted for 90% of measurements >100m, 86% of measurements between 50 and 100m, and 23% of measurements between 20 and 50m. Under high-light conditions at the surface, phytoplankton will often increase concentrations of photoprotective carotenoid (PPC) pigments, typically associated with reduced pigment packaging [64,65]. However, higher concentrations of the PPC pigments diadinoxanthin and diatoxanthin did not correspond to sites with higher aph*(676) [Fig. 6].
To study the influence of phytoplankton “health”, we analyzed phaeophytin concentrations. Phaeophytins are the degradation products of chlorophyll molecules, thus their presence represents algal senescence. As chlorophyll degrades to phaeophytin, chlorophyll a concentrations will decrease, causing increased aph*(676) values. Concentrations of phaeophytin were found to be highest at GASEX, which is not a site of high aph*(676) values, and next highest at the Florida Keys and GOCI [Fig. 7]. The fact that GASEX has the highest value does not agree with other studies, which found that high concentrations of phaeophytin corresponded to higher aph*(676) values [29,43,66].
The one factor that did seem to be consistent in both the Florida Keys and GOCI was phytoplankton species composition [Fig. 9]. The most notable difference between these two sites and the others (aside from Hawaii) is that there is a dominance of cyanobacteria, whereas diatoms dominate the species composition at the other sites. This cyanobacteria is likely Synechococcus or Prochlorococcus, both of which can have cell sizes similar to or smaller than the wavelength of visible light (~1 µm and ~0.5 µm respectively) [67,68]. The dominance of small cells could be driving aph*(676) values up at these two sites, and might not necessarily be captured in the cp slope measurement, as cp slopes are dominated by particles in the ~0.5-40 µm size range for open ocean distributions [33,69,70]. Although Hawaii has a similar species composition as well, the significantly higher ratio of [zeaxanthin]:[T Chl a] is likely representative of the xanthophyll cycle conversion of violaxanthin to zeaxanthin in high light conditions rather than a significantly greater dominance of cyanobacteria, as also suggested by the other high PPCs seen at the site [Fig. 6].
Some studies have found that pigment packaging might be higher in coccolithophores relative to non-coccolithophore species due to albedo [23,71]. Coccolithophores may increase [Chl a] within their cells to cope with the disadvantage of high albedo from their calcite shells, which would decrease aph*(676). In our study, albedo (scattering to attenuation ratio at 532 nm) was found to be highest at Hawaii and LIS, and GASEX, Hawaii, and VIIRS showed the greatest values of [19’-hex]:[T Chl a] [Figs. 8 and 9]. It is possible that the dominance of coccolithophores at these sites may drive down aph*(676) values, causing these values to be lower than one would expect for Case I waters typically dominated by small particles.
Error and bias must also account for some of the variations observed within and among the data sets. Data were compiled from a range of different cruises with diverse missions, therefore the data were not collected in a uniform manner to best fit the needs of this study. For example, potential time-space discrepancies could exist, as the times of IOP measurement collection and chlorophyll sampling often differed up to 40 minutes in Case I waters and 10 minutes in Case II waters, allowing some time for oceanic conditions to change between measurements.
In addition, there is a lack of instrument uniformity between the cruises. Absorption measurements were taken with both ac-9 and ac-s devices, with bandwidths at 675 nm of 10 nm and 15.5 nm, respectively. Conversely, most historical comparison studies [Table 1] used spectrophotometers with bandwidths typically <5 nm . The wider bandwidths associated with the ac devices will lower aph*(676) values via spectral smearing, presumably accounting for both the lower median value found in this study compared to the mean values from other studies [Table 1], and also possibly differing values between sites that used the ac-s versus the ac-9 . Moreover, the median and mean values found here may be strictly considered only applicable to WET Labs ac devices. A final possible cause for the seemingly lower aph*(676) values in our study compared to those in Table 1 is our use of line height correction rather than baseline correction to remove scattering offsets. Baseline corrected values will be skewed higher. This difference can be seen in Roesler and Barnard’s study (2013) in Table 1, with values calculated by baseline correction methods almost two times higher than values calculated with line height absorption methods .
Another source of error could be cp slope values. As mentioned above, cp slopes are dominated by particles in the ~0.5-40 µm size range in open ocean environments, which contains the lower, dominant portion of the phytoplankton size domain, causing a direct dependence of cp on phytoplankton biomass . However, cp also captures signals of inorganic, detrital, and heterotrophic particles, which may skew its reliability as a predictor of exclusively phytoplankton size dynamics in environments where these contributions are significant (e.g. high suspended sediments, senescent phytoplankton detrital material), or in the situation where the phytoplankton may be too small to be detected, as with the cyanobacteria at the Florida Keys and GOCI .
Even with the broad geographic and Case I and II representation in the data set considered here, the inclusion of more data to the study, especially at depth, higher latitudes, and Case I waters would help make our analysis more robust and reduce bias. As mentioned above, the majority of depth data comes from BIOSOPE, causing depth observations to be biased towards the very clear south Pacific gyre. There is also some location bias, with half of the sites on the eastern United States coast, and a north/south spatial divide between Case II and Case I sites. In addition, there are less Case I than Case II sites (5 vs. 6).
None of the variables tested in this study can consistently account for the majority of the variation above the 0.0108 m2/mg Chl a median aph*(676) value. Based on previous studies [29,74], variation was expected to be higher given the diverse environmental conditions and species encountered. However, other studies have found a uniform aph*(676) value as well. Babin et al. (1993) found that at all size scales in both estuarine and gulf waters of the St. Lawrence River outflow, aph*(676) (baseline subtracted) stayed fairly constant around 0.014 m2/mg Chl a . Additionally, Perkins et al. (2014) also found that the annual average of their aph*(676) value (baseline subtracted) stayed constant at around 0.017 m2/mg Chl a over the course of their study in Lake Onondaga, NY from 2005 to 2009 . Constancy of aph*(676) over varying environments and time is consistent with our study.
The fact that the data as a whole grouped around an aph*(676) value of 0.0108 m2/mg Chl a may be indicative of broad physiological constraints on cells with a relatively narrow optimal window for the level of pigment packaging. Phytoplankton may be striking a balance between the energy it takes to make and package chlorophyll while also maximizing the cross-section for absorbing photons for photosynthesis. As more and more energy is expended in manufacturing chlorophyll, less and less benefit in terms of absorption cross-section will be realized. The floor in terms of that balance may be around an aph*(676) value of 0.0108 m2/mg Chl a.
Although theoretical studies have shown relationships between pigment packaging, size, and intracellular pigment concentration, theory assumes that the particles are spherical and that the absorbing material is uniformly distributed within the cells [75,76]. In reality, the shape of the cell and the chloroplasts within, as well as the distribution of pigment within the cells can vary based on species and the physiological state of the cells, and can cause significant variations in aph*. Phytoplankton are able to change the position of light-absorbing structures in relation to the light source, particularly in response to variable light conditions, and some can even change the shape of these structures, all without changing the relative size or pigment concentration of the cell. Studies have shown that upon exposure to high light, chloroplasts will migrate to the valvar ends of the cell and aggregate, causing a decline in aph* [29,77,78]. Additionally, it has been found that changing the shape of chloroplasts or the cell from spherical to cylindrical will reduce pigment packaging [77,79]. Colonial phytoplankton dynamics, such as previously described for cyanobacteria, can also cause changes in aph* dynamics. By changing packaging by varying the proximity between cells, cyanobacteria can change aph* values completely independently from the individual cell size and chlorophyll concentrations .
Future studies into deviations from theoretically-derived relationships between aph*(676), size, and chlorophyll a concentrations are clearly needed to better understand some of the differences seen in natural phytoplankton populations. This includes analysis of changing aph* from a physiological perspective, with both size and chlorophyll concentration held constant. It also includes better understanding how environmental factors, phytoplankton functional groups, light history, and ecosystem health drive differences in these relationships both independently and conjunctively.
The goal of this study was to better quantify the relationship between pigment packaging and phytoplankton size over a diverse range of water types to help refine in situ optical estimations of chlorophyll a made using the line-height method. aph*(676) values were analyzed in relation to various optical properties, latitude, coastal versus open ocean environments, depth, temperature, salinity, photoadaptation, ecosystem health, albedo, and phytoplankton species composition. Overall, aph*(676) values remained constant across all variables, grouping around a median value 0.0108 m2/mg Chl a. Two sites, Florida Keys and GOCI, exhibited deviation above this median value. This may be caused by a dominance of either Synechococcus or Prochlorococcus cyanobacteria at these sites as evidenced by high [zeaxanthin]:[T Chl a] levels. Their small size (~1 µm and ~0.5 µm respectively) could be driving up the aph*(676) values at these sites, but might be too small to be accurately captured in the cp slope measurement. The consistency of the aph*(676) median value could represent an optimal window for pigment packaging due to physiological constraints, where phytoplankton might have to balance the energy it takes to make and package chlorophyll while also maximizing the cross-section for absorbing photons for photosynthesis. Although further studies will help confirm the accuracy of this median value, based on the convergence of aph*(676) on 0.0108 m2/mg Chl a in this study, this value can be used to convert absorption line height at 676 nm to [Chl a].
Presented data came from field research supported by the NASA Ocean Biology and Biogeochemistry, Terrestrial Hydrology, and Calibration and Validation programs, the Office of Naval Research Environmental Optics, Environmental Optics and Biology, Coastal Geophysics and Optics, and MURI programs, and the joint NSF-NIH National Institute of Environmental Health Sciences Oceans and Human Health program. Analysis was supported by NASA Ocean Biology and Biogeochemistry, ONR Coastal Geophysics and Optics, and the Harbor Branch Oceanographic Institute Foundation. A special thank you to Scott Freeman, Heather Groundwater, and Nicole Stockley for their assistance with data collection and processing, to Horn Point Laboratory at the University of Maryland for the majority of the HPLC pigment analyses, and to Malcolm McFarland for many very helpful discussions.
References and links
1. Z. V. Finkel, J. Beardall, K. J. Flynn, A. Quigg, T. A. V. Rees, and J. A. Raven, “Phytoplankton in a changing world: cell size and elemental stoichiometry,” J. Plankton Res. 32(1), 119–137 (2010). [CrossRef]
2. J. H. Ryther and C. S. Yentsch, “The estimation of phytoplankton production in the ocean from chlorophyll and light data,” Limnol. Oceanogr. 2(3), 281–286 (1957). [CrossRef]
3. C. J. Lorenzen, “Surface chlorophyll as an index of the depth, chlorophyll content and primary productivity of the euphotic layer,” Limnol. Oceanogr. 15(3), 479–480 (1970). [CrossRef]
4. T. L. Hayward and E. L. Venrick, “Relation between surface chlorophyll, integrated chlorophyll and integrated primary production,” Mar. Biol. 69(3), 247–252 (1982). [CrossRef]
5. H. R. Gordon and A. Morel, “Remote assessment of ocean color for interpretation of satellite visible imagery. A review,” in Lecture Notes on Coastal and Estuarine Studies, R. T. Barber, N. K. Mooers, M. J. Bowman, and B. Zeitzschel, eds. (Springer-Verlag, 1983).
6. S. Sathyendranath and A. Morel, “Light emerging from the sea—Interpretation and uses in remote sensing,” in Remote Sensing Applications in Marine Science and Technology, A. P. Cracknell, ed. (D. Reidel Publishing Company, 1983).
7. S. Sathyendranath and T. C. Platt, “Computation of aquatic primary production: Extended formalism to include effect of angular and spectral distribution of light,” Limnol. Oceanogr. 34(1), 188–198 (1989). [CrossRef]
8. A. Bricaud, C. S. Roesler, J. S. Parslow, and J. Ishizaka, “Bio-optical studies during the JGOFS-equatorial Pacific program: a contribution to the knowledge of the equatorial system,” Deep Sea Res. Part II Top. Stud. Oceanogr. 49(13-14), 2583–2599 (2002). [CrossRef]
9. C. S. Roesler and A. H. Barnard, “Optical proxy for phytoplankton biomass in the absence of photophysiology: Rethinking the absorption line height,” Method Oceanogr. 7, 79–94 (2013). [CrossRef]
10. C. S. Yentsch and D. A. Phinney, “A bridge between ocean optics and microbial ecology,” Limnol. Oceanogr. 34(8), 1694–1705 (1989). [CrossRef]
11. A. Bricaud, H. Claustre, J. Ras, and K. Oubelkheir, “Natural variability of phytoplanktonic absorption in oceanic waters: Influence of the size structure of algal populations,” J. Geophys. Res. 109(C11), C11010 (2004). [CrossRef]
12. A. Bricaud, M. Babin, A. Morel, and H. Claustre, “Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: Analysis and parameterization,” J. Geophys. Res. 100(C7), 13321–13332 (1995). [CrossRef]
13. N. B. Nelson, B. B. Prézelin, and R. R. Bidigare, “Phytoplankton light absorption and the package effect in California coastal waters,” Mar. Ecol. Prog. Ser. 94, 217–227 (1993). [CrossRef]
14. T. Fujiki and S. Taguchi, “Variability in chlorophyll a specific absorption coefficient in marine phytoplankton as a function of cell size and irradiance,” J. Plankton Res. 24(9), 859–874 (2002). [CrossRef]
15. H. M. Sosik and B. G. Mitchell, “Effects of temperature on growth, light absorption, and quantum yield in Dunaliella tertiolecta (Chlorophyceae),” J. Phycol. 30(5), 833–840 (1994). [CrossRef]
16. A. Morel and A. Bricaud, “Inherent optical properties of algal cells including picoplankton: theoretical and experimental results,” Can. Bull. Fish. Aquat. Sci. 214, 521–559 (1986).
17. E. Millán-Núñez, J. R. Lara-Lara, and J. S. Cleveland, “Variations in specific absorption coefficients and total phytoplankton in the Gulf of California,” CCOFI Rep. 39, 159–168 (1998).
19. A. Morel and A. Bricaud, “Theoretical results concerning light absorption in a discrete medium, and application to specific absorption of phytoplankton,” Deep-Sea Res. 28(11), 1375–1393 (1981). [CrossRef]
20. J. T. O. Kirk, “A theoretical analysis of the contribution of algal cells to the attenuation of light within natural waters,” New Phytol. 77(2), 341–358 (1976). [CrossRef]
21. Z. Dubinsky, P. G. Falkowski, and K. Wyman, “Light harvesting and utilization by phytoplankton,” Plant Cell Physiol. 27(7), 1335–1349 (1986).
22. A. Bricaud, A. L. Bedhomme, and A. Morel, “Optical properties of diverse phytoplankton species: experimental results and theoretical interpretation,” J. Plankton Res. 10(5), 851–873 (1988). [CrossRef]
23. B. G. Mitchell and D. A. Kiefer, “Chlorophyll a specific absorption and fluorescence excitation spectra for light-limited phytoplankton,” Deep-Sea Res. 35(5), 639–663 (1988). [CrossRef]
24. T. Berner, Z. Dubinsky, K. Wyman, and P. G. Falkowski, “Photoadaptation and the ‘package’ effect in Dunaliella tertiolecta (Chlorophyceae),” J. Phycol. 25(1), 70–78 (1989). [CrossRef]
25. H. M. Sosik and B. G. Mitchell, “Absorption, fluorescence, and quantum yield for growth in nitrogen-limited Dunaliella tertiolecta,” Limnol. Oceanogr. 36(5), 910–921 (1991). [CrossRef]
26. B. G. Mitchell and D. A. Kiefer, “Variability in pigment specific particulate fluorescence and absorption spectra in the northeastern Pacific Ocean,” Deep-Sea Res. 35(5), 665–689 (1988). [CrossRef]
27. A. Bricaud and D. Stramski, “Spectral absorption coefficients of living phytoplankton and nonalgal biogenous matter: A comparison between the Peru upwelling area and the Sargasso Sea,” Limnol. Oceanogr. 35(3), 562–582 (1990). [CrossRef]
28. N. Hoepffner and S. Sathyendranath, “Bio-optical characteristics of coastal waters: absorption spectra of phytoplankton and pigment distribution in the western North Atlantic,” Limnol. Oceanogr. 37(8), 1660–1679 (1992). [CrossRef]
29. M. Babin, J.-C. Therriault, L. Legendre, and A. Condal, “Variations in the specific absorption coefficient for natural phytoplankton assemblages: Impact on estimates of primary production,” Limnol. Oceanogr. 38(1), 154–177 (1993). [CrossRef]
30. J. S. Cleveland, “Regional models for phytoplankton absorption as a function of chlorophyll a concentration,” J. Geophys. Res. 100(C7), 13333–13344 (1995). [CrossRef]
31. P. Diehl and H. Haardt, “Measurement of the spectral attenuation to support biological research in a ‘plankton tube’ experiment,” Oceanol. Acta 3(1), 89–96 (1980).
32. A. M. Ciotti, M. R. Lewis, and J. J. Cullen, “Assessment of the relationships between dominant cell size in natural phytoplankton communities and the spectral shape of the absorption coefficient,” Limnol. Oceanogr. 47(2), 404–417 (2002). [CrossRef]
33. E. Boss, M. S. Twardowski, and S. Herring, “Shape of the particulate beam attenuation spectrum and its inversion to obtain the shape of the particulate size distribution,” Appl. Opt. 40(27), 4885–4893 (2001). [CrossRef] [PubMed]
35. R. J. Geider, H. L. MacIntyre, and T. M. Kana, “A dynamic regulatory model of phytoplanktonic acclimation to light, nutrients, and temperature,” Limnol. Oceanogr. 43(4), 679–694 (1998). [CrossRef]
36. V. Stuart, S. Sathyendranath, T. Platt, H. Maass, and B. D. Irwin, “Pigments and species composition of natural phytoplankton populations: effect on the absorption spectra,” J. Plankton Res. 20(2), 187–217 (1998). [CrossRef]
37. N. Hoepffner and S. Sathyendranath, “Effect of pigment composition on absorption properties of phytoplankton,” Mar. Ecol. Prog. Ser. 73, 11–23 (1991). [CrossRef]
38. Y. Zhang, Y. Yin, M. Wang, and X. Liu, “Effect of phytoplankton community composition and cell size on absorption properties in eutrophic shallow lakes: field and experimental evidence,” Opt. Express 20(11), 11882–11898 (2012). [CrossRef] [PubMed]
39. M. Perkins, S. W. Effler, and C. M. Strait, “Phytoplankton absorption and the chlorophyll a-specific absorption coefficient in dynamic Onondaga Lake,” Inland Waters 4(2), 133–146 (2014). [CrossRef]
40. A. J. P. Effler, F. Peng, S. W. Effler, C. M. Strait, M. Perkins, and K. L. Schulz, “Light absorption by phytoplankton and minerogenic particles in Cayuga Lake, New York,” Inland Waters 5(4), 433–450 (2015). [CrossRef]
41. S. E. Lohrenz, A. D. Weidemann, and M. Tuel, “Phytoplankton spectral absorption as influenced by community size structure and pigment composition,” J. Plankton Res. 25(1), 35–61 (2003). [CrossRef]
42. P. A. Stæhr and S. Markager, “Parameterization of the chlorophyll a-specific in vivo light absorption coefficient covering estuarine, coastal and oceanic waters,” Int. J. Remote Sens. 25(22), 5117–5130 (2004). [CrossRef]
43. C. S. Roesler, M. J. Perry, and K. L. Carder, “Modeling in situ phytoplankton absorption from total absorption spectra in productive inland marine waters,” Limnol. Oceanogr. 34(8), 1510–1523 (1989). [CrossRef]
44. B. G. Mitchell, “Algorithms for determining the absorption coefficients of aquatic particulates using the quantitative filter technique (QFT),” SPIE Ocean Opt. X 1302, 137–148 (1990). [CrossRef]
45. J. S. Cleveland and A. D. Weidemann, “Quantifying absorption by aquatic particles: a multiple scattering correction for glass-fiber filters,” Limnol. Oceanogr. 38(6), 1321–1327 (1993). [CrossRef]
46. C. S. Roesler, “Theoretical and experimental approaches to improve the accuracy of particulate absorption coefficients derived from the quantitative filter technique,” Limnol. Oceanogr. 43(7), 1649–1660 (1998). [CrossRef]
47. J. R. V. Zaneveld, R. Bartz, and J. C. Kitchen, “A reflective-tube absorption meter,” SPIE Ocean Opt. X 1302, 124–136 (1990). [CrossRef]
48. M. E. Ondrusek, R. R. Bidigare, K. Waters, and D. M. Karl, “A predictive model for estimating rates of primary production in the subtropical North Pacific Ocean,” Deep Sea Res. Part II Top. Stud. Oceanogr. 48(8-9), 1837–1863 (2001). [CrossRef]
49. M. S. Twardowski, J. M. Sullivan, P. L. Donaghay, and J. R. V. Zaneveld, “Microscale quantification of the absorption by dissolved and particulate material in coastal waters with an ac-9,” J. Atmos. Ocean. Technol. 16(6), 691–707 (1999). [CrossRef]
50. J. M. Sullivan, M. S. Twardowski, J. R. V. Zaneveld, and C. Moore, “Measuring optical backscattering in water,” in Light Scattering Reviews 7: Radiative Transfer and Optical Properties of Atmosphere and Underlying Surface, A. Kokhanovski, ed. (Springer Praxis Books, 2013).
51. Horn Point Laboratory, University of Maryland’s Center for Environmental Science, 2020 Horns Point Road, Cambridge, MD 21613.
52. H. M. Sosik, R. E. Green, W. S. Pegau, and C. S. Roesler, “Temporal and vertical variability in optical properties of New England shelf waters during late summer and spring,” J. Geophys. Res. 106(C5), 9455–9472 (2001). [CrossRef]
53. M. S. Twardowski, E. Boss, J. B. Macdonald, W. S. Pegau, A. H. Barnard, and J. R. V. Zaneveld, “A model for estimating bulk refractive index from the optical backscattering ratio and the implications for understanding particle composition in Case I and Case II waters,” J. Geophys. Res. 106(C7), 14129–14142 (2001). [CrossRef]
54. D. A. Jay, S. A. Talke, A. Hudson, and M. Twardowski, “Estuary turbidity maxima revisited: instrumental approaches, remote sensing, modeling studies, and new directions,” in Fluvial-Tidal Sedimentology, Developments in Sedimentology, Vol. 68, P. Ashworth, J. Best, and D. Parsons, eds. (Elsevier, 2015), pp. 49–109.
55. R. D. Vaillancourt, C. W. Brown, R. L. Guillard, and W. M. Balch, “Light backscattering properties of marine phytoplankton: Relationships to cell size, chemical composition and taxonomy,” J. Plankton Res. 26(2), 191–212 (2004). [CrossRef]
56. A. L. Whitmire, W. S. Pegau, L. Karp-Boss, E. Boss, and T. J. Cowles, “Spectral backscattering properties of marine phytoplankton cultures,” Opt. Express 18(14), 15073–15093 (2010). [CrossRef] [PubMed]
57. S. Tassan and G. M. Ferrari, “A sensitivity analysis of the ‘Transmittance–Reflectance’ method for measuring light absorption by aquatic particles,” J. Plankton Res. 24(8), 757–774 (1998). [CrossRef]
58. D. Stramski, M. Babin, and S. B. Woźniak, “Variations in the optical properties of terrigenous mineral-rich particulate matter suspended in seawater,” Limnol. Oceanogr. 52(6), 2418–2433 (2007). [CrossRef]
59. M. Tamm, R. Freiberg, I. Tõnno, P. Nõges, and T. Nõges, “Pigment-based chemotaxonomy--a quick alternative to determine algal assemblages in large shallow eutrophic lake?” PLoS One 10(3), e0122526 (2015). [CrossRef] [PubMed]
60. S. W. Jeffrey, R. F. C. Mantoura, and S. W. Wright, Phytoplankton Pigments in Oceanography: Guidelines to Modern Methods (UNESCO Publishing, 1997), Chap. 2.
61. W. A. Kozlowski, D. Deutschman, I. Garibotti, C. Trees, and M. Vernet, “An evaluation of the application of CHEMTAX to Antarctic coastal pigment data,” Deep Sea Res. Part I Oceanogr. Res. Pap. 58(4), 350–364 (2011). [CrossRef]
62. M. D. Mackey, D. J. Mackey, H. W. Higgins, and S. W. Wright, “CHEMTAX—a program for estimating class abundances from chemical markers: application to HPLC measurements of phytoplankton,” Mar. Ecol. Prog. Ser. 144, 265–283 (1996). [CrossRef]
63. P. G. Falkowski, Z. Dubinsky, and K. Wyman, “Growth-irradiance relationships in phytoplankton,” Limnol. Oceanogr. 30(2), 311–321 (1985). [CrossRef]
64. K. L. Carder, F. R. Chen, Z. P. Lee, S. K. Hawes, and D. Kamykowski, “Semianalytic Moderate-Resolution Imaging Spectrometer algorithms for chlorophyll a and absorption with bio-optical domains based on nitrate-depletion temperatures,” J. Geophys. Res. 104(C3), 5403–5421 (1999). [CrossRef]
65. L. B. Eisner, M. S. Twardowski, T. J. Cowles, and M. J. Perry, “Resolving phytoplankton photoprotective: photosynthetic carotenoid ratios on fine scales using in situ spectral absorption measurements,” Limnol. Oceanogr. 48(2), 632–646 (2003). [CrossRef]
66. M. Babin, D. Stramski, G. M. Ferrari, H. Claustre, A. Bricaud, G. Obolensky, and N. Hoepffner, “Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe,” J. Geophys. Res. 108(C7), 3211 (2003). [CrossRef]
67. F. Partensky, W. R. Hess, and D. Vaulot, “Prochlorococcus, a marine photosynthetic prokaryote of global significance,” Microbiol. Mol. Biol. Rev. 63(1), 106–127 (1999). [PubMed]
68. R. J. Olson, S. W. Chisholm, E. R. Zettler, and E. V. Armbrust, “Pigments, size, and distribution of Synechococcus in the North Atlantic and Pacific Oceans,” Limnol. Oceanogr. 35(1), 45–59 (1990). [CrossRef]
69. A. Morel, “Diffusion de la lumière par les eaux de mer. Résultats expérimentaux et approche théorique,” in Optics of the Sea, AGARD Lecture Ser. 61 (NATO, 1973), pp. 3.1.1–3.1.76.
70. D. Stramski and D. Kiefer, “Light scattering by microorganisms in the open ocean,” Prog. Oceanogr. 28(4), 343–383 (1991). [CrossRef]
71. W. M. Balch, K. A. Kilpatrick, and C. C. Trees, “The 1991 coccolithophore bloom in the central North Atlantic. 1. Optical properties and factors affecting their distribution,” Limnol. Oceanogr. 41(8), 1669–1683 (1996). [CrossRef]
72. J. T. O. Kirk, Light and Photosynthesis in Aquatic Ecosystems (CSIRO Publishing, 2007), Chap. 4.2.
73. M. J. Behrenfeld and E. Boss, “Beam attenuation and chlorophyll concentration as alternative optical indices of phytoplankton biomass,” J. Mar. Res. 64(3), 431–451 (2006). [CrossRef]
74. H. M. Sosik and B. G. Mitchell, “Light absorption by phytoplankton, photosynthetic pigments, and detritus in the California Current System,” Deep Sea Res. Part I Oceanogr. Res. Pap. 42(10), 1717–1748 (1995). [CrossRef]
75. S. Sathyendranath, L. Lazzara, and L. Prieur, “Variations in the spectral values of specific absorption of phytoplankton,” Limnol. Oceanogr. 32(2), 403–415 (1987). [CrossRef]
77. D. A. Kiefer, “Chlorophyll a fluorescence in marine centric diatoms: responses of chloroplasts to light and nutrient stress,” Mar. Biol. 23(1), 39–46 (1973). [CrossRef]
78. W. Nultsch, “Movements,” in Algal Physiology and Biochemistry, W.D.P. Stewart, ed. (Blackwell, 1974).
79. Z. V. Finkel, A. J. Irwin, and O. Schofield, “Resource limitation alters the 3/4 size scaling of metabolic rates in phytoplankton,” Mar. Ecol. Prog. Ser. 273, 269–279 (2004). [CrossRef]
80. S. Agusti and E. J. Phlips, “Light absorption by cyanobacteria: Implications of the colonial growth form,” Limnol. Oceanogr. 37(2), 434–441 (1992). [CrossRef]