Phytoplankton taxonomy, pigment composition and photo-physiological state
were studied in Galveston Bay (GB), Texas (USA), following the extreme
flooding associated with Hurricane Harvey (25–29 August 2017) using field
and satellite ocean color observations. The percentage of chlorophyll a
(Chl a) in different phytoplankton groups was determined from a
semi-analytical IOP (inherent optical property) inversion algorithm. The IOP
inversion algorithm revealed the dominance of freshwater species (diatom,
cyanobacteria and green algae) in the bay following the hurricane passage
(29 September 2017) under low salinity conditions associated with the
discharge of floodwaters into GB. Two months after the hurricane
(29–30 October 2017), under more seasonal salinity conditions, the
phytoplankton community transitioned to an increase in small-sized groups
such as haptophytes and prochlorophytes. Sentinel-3A Ocean and Land Colour Instrument (OLCI)-derived Chl a
obtained using a red / NIR (near-infrared) band ratio algorithm for the turbid estuarine
waters was highly correlated (R2>0.90) to the (high-performance liquid
chromatography) HPLC-derived
Chl a. Long-term observations of OLCI-derived Chl a
(August 2016–December 2017) in GB revealed that hurricane-induced Chl a
declined to background mean state in late October 2017. A non-negative least
squares (NNLS) inversion model was then applied to OLCI-derived Chl a maps
of GB to investigate spatiotemporal variations of phytoplankton diagnostic
pigments pre- and post-hurricane; results appeared consistent with extracted
phytoplankton taxonomic composition derived from the IOP inversion algorithm
and microplankton pictures obtained from an Imaging FlowCytobot (IFCB).
OLCI-derived diagnostic pigment distributions also exhibited good agreement
with HPLC measurements during both surveys, with R2 ranging from 0.40
for diatoxanthin to 0.96 for Chl a. Environmental factors (e.g.,
floodwaters) combined with phytoplankton taxonomy also strongly modulated
phytoplankton physiology in the bay as indicated by measurements of
photosynthetic parameters with a fluorescence induction and relaxation (FIRe)
system. Phytoplankton in well-mixed waters (mid-bay area) exhibited maximum
PSII photochemical efficiency (Fv/Fm) and a low effective absorption cross
section (σPSII), while the areas adjacent to the shelf
(likely nutrient-limited) showed low Fv/Fm and elevated
σPSII values. Overall, the approach using field and
ocean color data combined with inversion models allowed, for the first time,
an assessment of phytoplankton response to a large hurricane-related
floodwater perturbation in a turbid estuarine environment based on its
taxonomy, pigment composition and physiological state.
Introduction
Phytoplankton, which form the basis of the aquatic food web, are crucial to
marine ecosystems and play a strong role in marine biogeochemical cycling
and climate change. Phytoplankton contributes approximately half of the
total primary production on Earth,
fixing ∼ 50 Gt of carbon
into organic matter per year through photosynthesis; however, various
phytoplankton taxa affect the carbon fixation and export differently (Sathyendranath et al., 2014). Chlorophyll a (Chl a), an essential
phytoplankton photosynthetic pigment, has been considered a reliable
indicator of phytoplankton biomass and primary productivity in aquatic
systems (Bracher et al., 2015). Phytoplankton also contain several
accessory pigments such as chlorophyll b (Chl b), chlorophyll c (Chl c),
photosynthetic carotenoids (PSCs) and photo-protective carotenoids (PPCs) that
are either involved in light harvesting, or in protecting Chl a and other
sensitive pigments from photodamage (Fishwick et al., 2006). Some of
PSCs and PPCs are taxa-specific and have been considered
biomarker pigments: e.g., fucoxanthin (PSC) for diatoms, peridinin (PPC)
for certain dinoflagellates, alloxanthin (PPC) for cryptophytes, zeaxanthin
(PPC) for prokaryotes (e.g., cyanobacteria), and the degradation products of
Chl a, namely, divinyl Chl a and divinyl Chl b for
prochlorophytes (Jeffrey and Vest, 1997). High-performance liquid
chromatography (HPLC) which can efficiently detect and quantify several
chemo-taxonomically significant chlorophylls and carotenoids, when coupled
with these taxa-specific pigment ratios, allows phytoplankton taxonomic
composition to be quantified based on a pigment concentration diagnostic
procedures such as CHEMTAX (Mackey et al., 1996). Furthermore,
phytoplankton pigments with distinct absorption characteristics strongly
influence the light absorption by phytoplankton (Bidigare et al., 1990;
Ciotti et al., 2002; Bricaud et al., 2004). As such, phytoplankton
absorption spectra have been used to infer underlying pigments including
phytoplankton taxonomy by Gaussian decomposition (Hoepffner and
Sathyendranath, 1991; Lohrenz et al., 2003; Ficek et al., 2004; Chase et
al., 2013; Moisan et al., 2013, 2017; Wang et al., 2016).
More importantly, phytoplankton optical properties (absorption and
backscattering) bearing the imprints of different pigments and cell size
are important contributors to reflectance in a waterbody (Gordon et al.,
1988). Morel and Prieur (1977) first reported the feasibility of
calculating the phytoplankton absorption coefficients and other inherent
optical properties (IOPs) from measured subsurface irradiance reflectance
based on the simplified radiative transfer equation. Improvements in
semi-analytical inversion algorithms to derive IOPs from in situ and
remotely sensed reflectance spectra have been reported (Roesler and
Perry, 1995; Hoge and Lyon, 1996; Lee et al., 1996; Garver and Siegel, 1997;
Carder et al., 1999; Maritorena et al., 2002; Roesler and Boss, 2003; Chase
et al., 2017). Roesler et al. (2003) further modified an earlier IOP
inversion algorithm used in Roesler and Perry (1995) by adding a set of
five species-specific phytoplankton absorption spectra and derived the
phytoplankton taxonomic composition from the field-measured remote-sensing
reflectance.
Phytoplankton pigment composition varies not only between taxonomic groups
but also with the photo-physiological state of cells and environmental stress
(e.g., light, nutrients, temperature, salinity, turbulence and
stratification) (Suggett et al., 2009). The photosynthetic pigment field is
an important factor influencing the magnitude of fluorescence emitted by
phytoplankton, with active fluorometry commonly used to obtain estimates of
phytoplankton biomass (D'Sa et al., 1997). Advanced active fluorometry
termed as fast repetition rate (FRR; Kolber et al., 1998) and
analogous techniques such as FIRe (Suggett et al., 2008) allows for the simultaneous measurements
of the maximum PSII photochemical efficiency (Fv/Fm; where Fm
and Fo are the maximum and minimum fluorescence yields, and Fv is
variable fluorescence obtained by subtracting Fo from Fm) and the
effective absorption cross section (σPSII) of a
phytoplankton population. These have been used as diagnostic indicators for
the rapid assessment of phytoplankton health and photo-physiological state
linked to environmental stressors. Considerable effort has been invested to
achieve a deeper understanding of the impacts of environmental factors and
phytoplankton taxonomy on photosynthetic performance of natural communities
from field and laboratory fluorescence measurements (Kolber et al., 1988;
Geider et al., 1993; Schitüter et al., 1997; Behrenfeld and Kolber,
1999; D'Sa and Lohrenz, 1999; Holmboe et al., 1999; Moore et al., 2003).
Furthermore, knowledge of photo-physiological responses of phytoplankton in
combination with information on phytoplankton taxonomic composition could
provide additional insights on regional environmental conditions.
Synoptic mapping of aquatic ecosystems using satellite remote-sensing has
revolutionized our understanding of phytoplankton dynamics at various spatial
and temporal scales in response to environmental variabilities and climate
change. It has also provided greater understanding of biological response to
large events such as hurricanes in oceanic and coastal waters (Babin et al.,
2004; Acker
et al., 2009; D'Sa, 2014; Farfan et al., 2014; Hu and Feng, 2016). Although
the primary focus of ocean color sensors has been to determine the Chl a
concentration and related estimates of phytoplankton primary production
(Behrenfeld and Falkowski, 1997), more recently several
approaches have been developed based on phytoplankton optical signatures to
derive spatial distributions of phytoplankton functional types (PFTs) (Alvain
et al., 2005; Nair et al., 2008; Hirata et al., 2011), phytoplankton size
classification (Ciotti et al., 2002; Hirata et al., 2008; Brewin et al.,
2010; Devred et al., 2011) and phytoplankton accessory pigments (Pan et al.,
2010, 2011; Moisan et al., 2013, 2017; Sun et al., 2017). The basis of these
satellite-based remote-sensing algorithms rely on distinct spectral
contributions from phytoplankton community composition (e.g., taxonomy, size
structure) to remote-sensing reflectance (Rrs, sr-1);
however, these studies have all been confined to open ocean and shelf waters.
In contrast, satellite studies of phytoplankton pigments have been limited in
the optically complex estuarine waters where the influence from wetlands,
rivers and coastal ocean make phytoplankton communities highly variable and
complex.
In this study, field bio-optical measurements and ocean color remote-sensing
data (Sentinel-3A OLCI) acquired in Galveston Bay, a shallow estuary along
the Gulf coast (Texas, USA; Fig. 1), are used to investigate the spatial
distribution of phytoplankton pigments, their taxonomic composition and
photo-physiological state following the extreme flooding of the Houston
metropolitan area and surrounding areas due to Hurricane Harvey, and the
consequent biological impact of the floodwater discharge into the bay. The
paper is organized as follows: Sect. 2 describes the field data
acquisition and laboratory processing, and Sect. 3 presents the algorithms and
methods used to distinguish phytoplankton groups, retrieve spatial
distribution of pigments and calibrate phytoplankton physiological
parameters. Results and discussions (Sects. 4 and 5) and summary (Sect. 6) address the main contributions and findings of this paper.
Sentinel-3A OLCI RGB image (29 October 2017) with locations of
sampling sites in Galveston Bay acquired on 29 September (red asterisk),
29 October (green circles) and 30 October 2017 (blue solid squares),
respectively.
Data and methodsStudy area
Galveston Bay (GB), a shallow water estuary (∼2.1 m average
depth), encompasses two major sub-estuaries: San Jacinto Estuary (also
divided as Upper GB and Lower GB) and Trinity Estuary (Trinity Bay) (Fig. 1). It is located adjacent to the heavily urbanized and industrialized
metropolitan areas of Houston, Texas (Dorado et al., 2015). The deep
(∼14 m) narrow Houston Ship Channel connects the bay to the
northern Gulf of Mexico (nGoM) through a narrow entrance, the Bolivar Roads
Pass. Tidal exchange between GB and the nGoM occurs through the entrance
channel with diurnal tides ranging from ∼0.15 to
∼0.5 m. The major freshwater sources to GB are the Trinity
River (55 %), the San Jacinto River (16 %) and Buffalo Bayou (12 %) (Guthrie et al., 2012). The San Jacinto River was frequently observed to
transport greater amounts of dissolved nutrients into GB than the Trinity
River (Quigg, 2011); however, the negative relationship between nitrate
concentrations and salinity observed in the mid-bay area (adjacent to Smith
Point) (Santschi, 1995) indicated Trinity River to be a major source of
nitrate in GB. The catastrophic flooding of Houston and surrounding areas
associated with Hurricane Harvey resulted in strong freshwater inflows into
GB from the San Jacinto River (>3300 m3 s-1; USGS
08067650) on 29 August 2017 and the Trinity River (>2500 m3 s-1; USGS 08066500 site at Romayor, Texas) on
30 August 2017,
respectively. Although the discharge from the two rivers in the upstream
returned to normal conditions (∼50–120 m3 s-1) in
about 2 weeks after the Hurricane passage, salinity remained low for over a
month following the hurricane passage (D'Sa et al., 2018).
Sampling and data collection
Surface water samples were collected at a total of 34 stations during two
surveys on 29 September and 29–30 October 2017 (Fig. 1). Samples at
stations 1 to 14 (red asterisk on top of green circle; Fig. 1) along the
Trinity River transect were collected repeatedly on 29 September and 29 October 2017, respectively. An additional nine sampling sites (blue squares; Fig. 1)
around the upper Galveston Bay and in the East Bay were sampled on 30 October 2017.
The surface water samples were stored in coolers and filtered on the same
day. The filter pads were immediately frozen and stored in liquid nitrogen
for laboratory absorption spectroscopic and HPLC measurements of the
samples. An optical package equipped with a conductivity–temperature–depth
recorder (SBE; Sea-Bird Scientific) and a fluorescence induction and relaxation system
(FIRe; Satlantic Inc.) were used to obtain profiles of salinity, temperature,
pressure and phytoplankton physiological parameters (Fv/Fm and
σPSII). Measurements of backscattering were
also made at each station using the WETLabs VSF-3 (470, 530, 670 nm)
backscattering sensor (D'Sa et al., 2006). Included in the optical package
was also a hyperspectral downwelling spectral irradiance meter (HyperOCR;
Satlantic Inc.). The irradiance data from the HyperOCR were processed using ProSoft
7.7.14 and the photosynthetically active radiation (PAR) were estimated from
the irradiance measurements. The above-water reflectance measurements were
collected using a GER 1500 512iHR spectroradiometer in the 350–1050 nm
spectral range. At each station, sky radiance, plate radiance and water
radiance were recorded (each repeated three times) and processed to obtain
the above-water remote-sensing reflectance (Joshi et al., 2017). A total
of 43 Sentinel-3A OLCI full resolution mode, cloud free level-1 images were
obtained over GB between 1 August 2016 and 1 December 2017 from the European
Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) website and
pre-processed using Sentinel-3 Toolbox Kit Module (S3TBX) version 5.0.1 in
Sentinel Application Platform (SNAP). These Sentinel-3A OLCI data were
further atmospherically corrected to obtain remote-sensing reflectance
(RrsOLCI, sr-1) using Case-2 Regional/Coast Color
(C2RCC) module version 0.15 (Doerffer and Schiller, 2007). River
discharge information during August 2016–December 2017 was downloaded from
the USGS Water Data (USGS) for the Trinity River at Romayor, Texas (USGS
08066500), and the west flank of the San Jacinto River (USGS 08067650).
Individual pictures of microplankton (10 to 150 µm) recorded
by an Imaging FlowCytobot (IFCB) located at the entrance to Galveston Bay
were downloaded (http://dq-cytobot-pc.tamug.edu/tamugifcb, last access: 8 November 2018) for pigment
validation.
Absorption spectroscopy
Surface water samples were filtered through 0.2 µm nuclepore
membrane filters, and the colored dissolved organic matter (CDOM) absorbance
(ACDOM) was obtained using a 1 cm path length quartz cuvette on a
PerkinElmer Lambda-850 UV–VIS spectrophotometer equipped with an
integrating sphere. The quantitative filter technique (QFT) with
0.7 µm GF/F filters was used to measure absorbance of
particles (Atotal) and non-algal particles (ANAP) inside an
integrating sphere at 1 nm intervals from 300 to 800 nm. The absorption
coefficients of CDOM (aCDOM), NAP (aNAP), particles (atotal)
and phytoplankton (aPHY) were calculated using the following equations:
aCDOM(λ)=2.303×ACDOM(λ)L,
where L is the path length in meters. The aCDOM were corrected for
scattering, temperature and baseline drift by subtracting an average value
of absorption between 700 and 750 nm for each spectrum (Joshi and D'Sa,
2015).
2atotal(λ)=2.303×Atotal(λ)Vfiltered/Sfilter,3aNAP(λ)=2.303×ANAP(λ)Vfiltered/Sfilter,4aPHY(λ)=atotal-aNAP,
where Vfiltered is the filtered volume of sample, Sfilter is the
area of filter pads and the path length correction for filter pad was
applied according to Stramski et al. (2015).
Pigment absorption spectra
The water samples were filtered with 0.7 µm GF/F filter. The
filter pads were stored in liquid nitrogen until transferred into 30 mL
vials containing 10 mL cold 96 % ethanol (Ritchie, 2006). The vials
were spun evenly to ensure full exposure of the filter pad to the ethanol
and then kept in the refrigerator (in the dark) overnight. The pigment
solutions at room temperature were poured off from vials into 1 cm cuvettes
and measured on a PerkinElmer Lambda-850 UV–VIS spectrophotometer to obtain
pigment absorbance spectra (Apig) while 90 % ethanol was used as a
blank (Thrane et al., 2015). The total absorption coefficients of
pigments apig(λ) were calculated as follows:
apig(λ)=2.303×ApigλL×VethanolVfiltered,
where L is the path length in meters, Vethanol is the volume of
ethanol and Vfiltered is the filtered volume of the water samples.
HPLC measurements
Water samples were filtered through 0.7 µm GF/F filters and
immediately frozen in liquid nitrogen for HPLC analysis using the methods
reported by Barlow et al. (1997). The detected pigments along with their
abbreviations are listed in Table 1. Diagnostic biomarker pigments are
marked in bold letters (Table 1).
Pigment information acquired from HPLC samples in Galveston Bay. Diagnostic biomarker
pigments are marked in bold.
Note: (1) [Chl b],
[Allo], [Fuco], [Peri], [Zea], [Buta] and [Hexa] are considered as diagnostic pigments for PFTs (Moisan et al., 2017).
FIRe measurements
An in situ FIRe system (Satlantic Inc.) was used to characterize phytoplankton photosynthetic physiology
during the two surveys in GB. The FIRe system is based on illuminating a sample
with an intense flash of light to instantaneously saturate the reaction
centers of photosystem II (PSII); under these light conditions, reaction
centers do not accept electrons and most of the absorbed light energy is
dissipated as fluorescence. The fundamental parameter measured by the FIRe system is
fluorescence yield F(t), which is the emitted fluorescence divided by the
irradiance intensity (no unit). In contrast to strong flashes, dark adaption
enables all reaction centers of PSII to be open with least fluorescence
emitted, thus resulting in minimal fluorescence yield (Fo). Maximum
fluorescence yield (Fm) can be obtained after sufficient irradiation
when all reaction centers are closed. Maximum photochemical efficiency,
which quantifies the potential of converting light to chemical energy for the
PSII reaction centers (Moore et al., 2006), was calculated as (Fm-Fo)/Fm=Fv/Fm. The functional absorption cross
section σPSII
(Å2quantum-1) measures the
capability of reaction centers to absorb light from the ambient environment.
The FIRe system was deployed at a slow descent rate, with 12 and 20 vertical
profiles obtained during the first and second surveys, respectively. All
measurements were programmed using standard protocols of single saturating
turnover (ST) flash saturation of PSII (Kolber et al., 1998). Flashes
were generated from highly uniform blue LEDs at 455 nm with
approximately 30 nm half-bandwidth. Chl a fluorescence was stimulated using a saturating
sequence of 80 1.1 µs flashes applied at 1 µs
intervals, eight sequences were averaged per acquisition, and the fluorescence
signal was detected at 668 nm. All data were processed using standard
FIReCom software (Satlantic Inc.). In addition, samples of 0.2 µm filtered
sea water at each station were used as “blank” to remove the background
fluorescence signals (Cullen and Davis, 2003); in this step, the
fluorescence from the filtered samples (without phytoplankton) was
subtracted from in situ fluorescence signals to get more accurate values of
Fv/Fm.
Retrieving phytoplankton groups from above-water
Rrs
A fundamental relationship that links subsurface remote-sensing reflectance
(rrs) and the IOPs was expressed using a quadratic
function developed by Gordon et al. (1988):
rrs=g1⋅uλ+g2⋅uλ2;uλ=bbatotal+bb,
where the parameters g1 (∼0.0788) and g2 (∼0.2379) are
values for turbid estuarine waters (Joshi and D'Sa, 2018); rrs is
the subsurface remote-sensing reflectance that was obtained from above-water
remote-sensing reflectance (Rrs) using the following equation
(Lee et al., 2002)
rrs=Rrs0.52+1.7×Rrs.
The total backscattering coefficient bb is comprised
of water (bbw) and particulates including both organic and inorganic
particles (bbp), while the total absorption coefficients (atotal)
can be further separated into four sub-constituents (Roesler and Perry,
1995) as indicated by
bb=bbw+bbp;atotal=aw+aphy+aCDOM+aNAP,
where aw, aphy, aCDOM, and aNAP represent the
absorption coefficients of pure water, phytoplankton, colored dissolved
organic matter and non-algal particles, respectively.
The IOP inversion algorithm for retrieving IOPs from Rrs require
known spectral shape (eigenvector) of each component in Eq. (8) to estimate
the magnitude (eigenvalue) of each component (Table 2). The spectral shape
can be adjusted by changing the values of slope based on characteristics of
the study area. It is worth noting that a single averaged phytoplankton
eigenvector does not provide species information, whereas a set of several
species-specific phytoplankton eigenvectors allow estimates of species
composition. The IOP inversion algorithm applied in this study makes use of
mass-specific phytoplankton absorption spectra of 10 groups,
namely dinoflagellates, diatoms, chlorophytes, cryptophytes, haptophytes, prochlorophytes,
raphidophytes, rhodophyta, red cyanobacteria and blue cyanobacteria; these
were obtained from Dierssen et al. (2006) and Dutkiewicz et al. (2015) as
eigenvectors rather than using one averaged aphy(λ)
spectrum. Subsequently, the inversion algorithm iterates repeatedly to
minimize the difference between modeled Rrs and in situ measured
Rrs (Rrs_insitu) until a best fit is achieved while
allowing for alterations of all parameters listed in Table 2 (Chase et al.,
2017). The absolute percent errors between modeled and measured values of
Rrs, aphy, aCDOM, aNAP and
bbp were calculated as
%error=Xmodeled-XmeasuredXmeasured×100.
Retrieving pigments from Sentinel-3A OLCI RrsReconstruction of pigment absorption spectrum by multiple linear
regression
Total pigment absorption spectra apig(λ) obtained during both surveys (Eq. 5) were modeled
as a third-order function of HPLC measured-Chl a (Chl a_HPLC)
concentration at each station as (Moisan et al., 2017)
apigλ=C3×Chla_HPLC3+C2×Chla_HPLC210+C1×Chla_HPLC+C0,
where vector coefficient C=[C3,C2,C1,C0], are
wavelength-dependent coefficients that range from 400 to 700 nm at 1 nm
intervals; these were further applied to Sentinel-3A OLCI Chl a to calculate
apig_OLCI at each pixel as
apig_OLCIλ=C3×Chla_OLCI3+C2×Chla_OLCI211+C1×Chla_OLCI+C0,
where Chla_OLCI is Sentinel-3A OLCI derived Chl a concentration (259×224 pixels). The obtained image
represents the value of apig_OLCI at a certain wavelength and 301 images
of apig_OLCI can be obtained in the 400–700 nm
wavelength range at 1 nm intervals.
Satellite retrieval of pigments using non-negative least squares (NNLS)
inversion model
The apig_OLCI is a mixture of
n pigments with known absorption spectra
ai(λ), i=1,2,…,n at
wavelength λ (nm); thus, apig_OLCIλ can
be considered as a weighted sum of the individual component spectra (Thrane
et al., 2015) at each image point as
apig_OLCIλ=x1×a1λ+x2×a2λ+…xn×anλ,
where Aλ=[a1λ,a2λ,…anλ] represents the mass-specific spectra of
16 pigments (Chl a, Chl b, Chl c1, Chl c2, pheophytin a,
pheophytin b, peridinin, fucoxanthin, neoxanthin, lutein, violaxanthin,
alloxanthin, diadinoxanthin, diatoxanthin, zeaxanthin and β-carotenoid) which are the in vitro pigment absorption spectra over
pigment concentrations and can be extracted from supplementary R scripts of
Thrane et al. (2015). The vector coefficient
X=[x1,x2,…xn] corresponds to
the concentrations (µg L-1) of these distinct pigments;
note that X cannot be negative, therefore non-negative least
squares (NNLS) was used to guarantee positive solutions of X
(Moisan et al., 2013; Thrane et al., 2015). Equation (12) can be further expressed
as
apig400OLCIapig401OLCI⋮apig700OLCI=13x1x2⋮xn×a1400,a2400,…an400a1401,a2401,…an401⋮a1700,a2700,…an700.
Processing approach
Sentinel-3A OLCI pigment concentration maps were generated using the processing pathway 1
(Fig. 2) that includes the following: (1) developing empirical relationships
between HPLC-measured Chl a and Rrs_insitu band ratio
for Sentinel-3A OLCI band 9 (673 nm) and band 11 (709 nm) to generate
Sentinel-3A OLCI Chl a maps and (2) converting Chl a concentration to
apig_OLCIλ maps and subsequently decomposing
apig_OLCIλ into individual pigment spectra to generate phytoplankton pigment
maps for GB. In processing pathway 2, phytoplankton taxonomic composition at
each sampling station was obtained from a 10-species IOP inversion model,
which uses Rrs_insitu as input and estimates Chl a
concentration of each phytoplankton group (Fig. 2). Finally, CDOM-corrected
FIRe measurements of Fv/Fm and σPSII
were combined with phytoplankton taxonomy to assess photosynthetic
physiology of different phytoplankton groups.
Parameters and eigenvectors used in the semi-analytical inversion
algorithm.
ParameterEquationSlopeEigenvalueaCDOM(λ)aCDOM(λ)=MCDOM×exp-SCDOM×(λ-λ0); λ0=443SCDOMMCDOMaNAP(λ)aNAP(λ)=MNAP×exp-SNAP×(λ-λ0); λ0=443SNAPMNAPaphyλaphyλ=∑Chlai×aphi*; aphi* is the spectral shapeChlaiof each phytoplankton group.bbpλbbpλ=Bbp×(λ0/λ)Sbp; λ0=443SbpBbp
Note: aphi*λ for 10 different groups of
phytoplankton used in this study were extracted from Dierssen et al. (2006) and Dutkiewicz et al. (2015).
Flowchart showing the three processing steps as follows: (1) retrieving
pigments spatial distribution maps from OLCI, (2) distinguishing
phytoplankton groups, and (3) assessing phytoplankton physiological
parameters and their linkages to taxonomic groups.
ResultsPhytoplankton taxonomy and physiological state from field
observationsMeasurements of above-water remote-sensing reflectance
Above-water remote-sensing reflectances (Rrs_insitu)
from the two surveys (Fig. 3) reflect the influence of the absorbing and
scattering features of water constituents. Low reflectance (∼675 nm) caused by Chl a red light absorption and maximum reflectance at
green wavelength (∼550 nm) were observed. Obvious dips at
∼625 nm versus reflectance peaks ∼650 nm were
observed in spectra during both surveys, which could be attributed to
cyanobacteria modulation of the spectra (Hu et al., 2010). The
reflectance peak around 690–700 nm was obvious at most sampling sites
except at stations 13 and 14 adjacent to the nGOM and were likely due to the
effect of Chl a fluorescence (Gitelson, 1992; Gilerson et al., 2010).
The peak positions at stations with lower Chl a concentration
(∼5µg L-1) were observed at 690–693 nm;
however, the peaks shifted to longer wavelengths of 705 and 710 nm for
stations 23 and 19 with extremely high Chl a concentrations of
∼31 and 50 µg L-1, respectively (Fig. 3).
Rrs_insitu spectra at stations in GB on
29 September and 29–30 October 2017; vertical bars represent Sentinel-3A
OLCI spectral bands.
Performance of the IOP inversion algorithm
The IOP inversion algorithm was applied to Rrs_insitu data
(Fig. 3) obtained during the two surveys in GB. The mean errors for modeled
aCDOM, aNAP, aphy and
bbp_470 at all wavelengths for the 34 stations were 5.86 %,
6.83 %, 12.19 % and 10.79 %, respectively (Table 3). A total of
eight phytoplankton groups (dinoflagellate, diatom, chlorophyte, cryptophyte,
haptophyte, prochlorophyte, raphidophyte and blue cyanobacteria) were
spectrally detected from IOP inversion algorithm. The sum of eight
eigenvalues of Chli (Table 2) represents the modeled total Chl a
(TChl a_mod) of the whole phytoplankton community. The TChl a_mod
is better correlated with HPLC-measured total Chl a (TChl a_HPLC) for
survey 2 (green circle; Fig. 4a) with R2∼0.92, compared to survey 1
(red color; Fig. 4a). In addition, the TChl a_mod appears to be slightly
higher than TChl a_HPLC for survey 2. The modeled aCDOM
(aCDOM_mod) are in close agreement with the
spectrophotometrically measured aCDOM at 412 nm (Fig. 4b), with
the aCDOM obtained on 29 September 2017 much higher than that
obtained on 29–30 October 2017. The modeled bbp
(bbp_mod) are well correlated with in situ bbp
(bbp_insitu) at 470 nm (Fig. 4c) with higher R2 (0.81)
observed on 29 September 2017. In addition, both modeled and field-measured
bbp showed stronger backscattering at most stations on
29 September 2017 than those on 29–30 October 2017.
(a) Validation of TChl a_mod via
HPLC-measured TChl a; individual %Chl a of each detected taxa versus
corresponding %DP shown with (d) cryptophyte, (e) chlorophyte,
(f)
cyanobacteria, (g) diatom, (h) dinoflagellate and (l) haptophyte; red and
green dots indicate the samples on 29 September and 29–30 October 2017,
respectively. Comparison between in situ measurements and modeled results
with (b)aCDOM (412) and (c)bbp (470).
Error statistics over all wavelengths and sampling stations
(N=301×34=10234; 12 and 22 stations on 29 September and 29–30 October 2017) from a semi-analytical IOP inversion algorithm.
The Chl a percentage (%Chl a), which is Chli/ TChl a, was also
compared with diagnostic pigment percentage (%DP), which is the specific DP
for each phytoplankton group over the sum of DP (∑DP). The DP for diatoms (fucoxanthin),
dinoflagellates
(peridinin), cryptophytes (alloxanthin), chlorophytes (Chl b), haptophytes
(19′-hexanoyloxyfucoxanthin) and cyanobacteria (zeaxanthin) referred in Moisan et al. (2017) were used in this study. The R2 between
%Chl a and %DP for different phytoplankton groups ranges from 0.15 to
0.81 (Fig. 4). The %Chl a of cryptophytes is between 5 % and 42 % and is well
correlated with alloxanthin /∑DP (R2∼0.62–0.72; Fig. 4d) for both
surveys. In addition, the cryptophyte %Chl a at station 19 and 23 on 30 October 2017 was highest (∼40 %) in coincidence with
the highest value of alloxanthin /∑DP (Fig. 4d).
Furthermore, the relationship between chlorophyte %Chl a and Chl b/∑DP (R2∼0.55; Fig. 4e) shows that
chlorophytes during survey 1 contributed a higher fraction to the whole
phytoplankton community compared to survey 2. The %Chl a of cyanobacteria
highly correlated with zeaxanthin /∑DP with
R2 larger than 0.7 (Fig. 4f) for both surveys. Low
%Chl a of dinoflagellates in coincidence with low peridinin /∑DP (R2∼0.78) were observed at stations
along the transect; however, an increased contribution of dinoflagellates
appeared adjacent to the entrance during both surveys (Fig. 4g).
Variations in phytoplankton community structure
Reconstruction of the phytoplankton absorption coefficients spectra revealed
variations in phytoplankton community structure (Fig. 5) even several weeks
after Hurricane Harvey. The modeled aphy spectra
(aphy_mod) at stations 6, 13, 17 and 19 (Fig. 5a–f)
yielded spatiotemporal differences of phytoplankton taxonomic composition in
GB. The strong absorption peak around 625 nm induced by cyanobacteria was
observed at most of the stations for both modeled results and in situ
measurements (Fig. 5a, c and e) except at stations adjacent to the entrance
(Fig. 5b and d). The aphy_mod at station 6 was primarily
dominated by group of cyanobacteria (blue line) and chlorophytes (green line)
on 29 September 2017 (Fig. 5a); in contrast, the spectrum of chlorophytes
contributed very little at station 6 on 29 October 2017 (green line; Fig. 5c). Furthermore, the shape of spectra for samples obtained at station 13
shows strong dinoflagellate modulation versus extremely low cyanobacteria
contribution during survey 1 (red line; Fig. 5b). However, small-size groups
like the haptophytes and prochlorophytes displayed increasing proportions at
station 13 on 29 October 2017 (Fig. 5d). Station 17 in the East Bay was
dominated by cyanobacteria (blue line; Fig. 5e) and cryptophyte (pink line;
Fig. 5e) absorption spectra, whereas, on 30 October 2017, the main spectral
features at station 19 in the upper GB was from cryptophytes (pink line) and
chlorophytes (green line; Fig. 5f).
Reconstruction of phytoplankton absorption coefficients spectra at
stations 6 (a) and 13 (b) on 29 September 2017, at stations 6 (c) and 13 (d) on 29 October 2017 and at stations 17 (e) and 19 (f) on 30 October 2017 based on
the mass-specific absorption spectra of different phytoplankton groups
including diatoms, chlorophytes, dinoflagellates, cryptophytes, cyanobacteria
(blue), haptophytes, prochlorophytes and raphidophytes presented using
different colors.
The corresponding taxa-specific %Chl a derived from IOPs inversion
algorithm for the two surveys on 29 September and 29–30 October 2017 are
shown in Fig. 6a and b, respectively. Cyanobacteria (blue bars) and
chlorophytes (green bars) constituted over 55 % of the phytoplankton
communities during survey 1 (29 September 2017; Fig. 6a). In addition,
chlorophytes, known to proliferate in freshwater environments, were present in a higher
fraction than that observed in survey 2 (Fig. 6). Further, chlorophytes
together with diatoms (Fig. 6a) accounted for ∼60 %
of TChl a_mod at many stations with a
well-mixed water column (e.g., stations 7, 8 and 9 as inferred from salinity
profiles; not shown) on 29 September 2017. Cryptophytes, haptophytes and
raphidophytes became minor components of the community and accounted in
total to ∼25 % of TChl a_mod
(Fig. 6a). Furthermore, contribution by the dinoflagellate group to
TChl a_mod was low inside the bay, but showed
increasing %Chl a (∼30 %) in higher-salinity waters
adjacent to the nGOM (red color; Fig. 6a). Cyanobacteria (blue color; Fig. 7) exhibited a slightly elevated percentage during survey 2 (∼60 d after the hurricane passage, 29–30 October 2017) and were quite
abundant in East Bay (stations 16, 17 and 18) where the water was calm and
stratified (as indicated by the salinity profiles – not shown). In addition,
cyanobacteria were not prevailing adjacent to the nGOM (stations 12, 13 and
14) and close to San Jacinto (stations 19, 20, 21, 23 and 24), where
cryptophytes and chlorophytes showed dominance (Fig. 6b). The %Chl a of
chlorophytes obtained at stations along the Trinity River transect decreased
by ∼10 % on 29–30 October 2017 compared to 29 September 2017. Small-size groups like haptophytes and prochlorophytes increased on
29–30 October 2017 and were more abundant adjacent to the nGOM, accounting
for more than 25 % of the TChl a_mod.
Phytoplankton taxonomic compositions detected from IOP inversion
algorithm on (a) 29 September and (b) 29–30 October 2017 in Galveston Bay;
phytoplankton groups are represented in different colors as shown in the
legend.
Environmental conditions and physiological state of phytoplankton
community
The surface salinity presented a pronounced seaward increasing gradient
along the transect (stations 3–14) during both surveys (Fig. 7a) with
primarily lower salinity throughout the bay during survey 1 in comparison to
survey 2, which indicated the freshening impact was still ongoing even 4
weeks after Hurricane Harvey. The salinity was ∼15 at station
16 and decreasing when going further into East Bay (∼10 at
stations 17 and 18; Fig. 7a). In upper GB, salinity at stations 19–24 did
not vary significantly (∼15), increasing along with the
distance away from the San Jacinto River mouth with highest value
(∼17.5) at station 24. During both surveys, lowest Chl a
(Fig. 7b) were observed adjacent to the nGOM, and the highest Chl a were
closest to the river mouth. The photosynthetically active radiation (PAR),
which were calculated from down-welling irradiance (not shown here),
decreased significantly with depth, but surface PAR (Fig. 7c) were similar
in magnitude at all stations. Pigment ratios including TChl a/ TP
(0.58–0.68), PSC / Chl a (0.07–0.26) and AP / TP (0.34–0.42) were obtained from
HPLC measurements and shown in Fig. 7d, e and f, respectively.
Environmental conditions (salinity, light field), pigment
composition and physiological state in GB surface waters (red bars
indicating samples from 29 September 2017 and blue bars representing
samples from 29 and 30 October 2017). (a) Salinity, (b)
Chl a
concentration, (c) PAR, (d) Chl a/ TP, (e) PSC / Chl a, (f) AP / TP,
(g)Fv/Fm and (h)σPSII.
The CDOM-calibrated and 0–0.5 m depth-averaged photosynthetic parameters
Fv/Fm varied from 0.41 to 0.64 (Fig. 7g) while σPSII was in the range of 329–668 Å2quantum-1(Fig. 7h). The
highest σPSII and lowest Fv/Fm
appeared adjacent to the nGOM (stations 12–14). Conversely, values of
Fv/Fm at stations 7–9 with a well-mixed water column were high
with low values of σPSII. Both Fv/Fm
and σPSII did not directly correlate with Chl a
(e.g., high Chl a∼51µg L-1 at station 19
corresponded to a relatively low level of Fv/Fm∼0.45 versus high σPSII∼550Å2quantum-1). However, the
stations with high Fv/Fm coincided with the high fraction of Chl a
(Chl a/ TP) and the low fraction of AP (AP / TP) (Fig. 7d and f). In contrast,
σPSII showed an overall positive relationship
with AP / TP but altered negatively with Chl a/ TP during both surveys. The
lowest (highest) value of σPSII
(Fv/Fm) were observed at station 9, corresponding to the highest
Chl a/ TP value (∼0.64) on 29 October 2017. The highest
AP / TP and PSC / Chl a were obtained from stations adjacent to the
nGOM.
Fv/Fm and σPSII taxonomic
signatures
Distinct pigments housed within phytoplankton light-harvesting antennae can
strongly influence PSII light-harvesting capability and the photosynthetic
quantum efficiency of phytoplankton (Lutz et al., 2001). In this study, we
observed an inverse relationship (R2∼0.63–0.81; Fig. 8a and d)
between the Fv/Fm and σPSII that
appeared related to taxonomic signals during surveys 1 and 2 in GB.
Stations 1–9 along the transect were considered as a well-mixed group with
no dominance by any particular group (black circles; Fig. 8a–c); stations
10–14 close to the entrance were, however, strongly dominated by
dinoflagellates and haptophytes (red symbol; Fig. 8a–c) during survey 1.
This well-mixed group displayed low values of σPSII
(∼390–439 Å2quantum-1) and
high levels of Fv/Fm (∼0.42–0.65), with
Fv/Fm approaching 0.65 at station 9 on
29 September 2017 (Fig. 8a). However, enhanced contributions of
dinoflagellates and haptophytes around the entrance corresponded to a decline
of Fv/Fm (0.3–0.4) against an increase of σPSII
(500–600Å2quantum-1)
during survey 1. Furthermore, samples obtained from survey 2 at stations
1–9, stations 10–14, stations 16–18 and stations 19–24 were considered as
well mixed (black), dinoflagellate–haptophyte-dominated (red),
cyanobacteria-dominated (blue) and cryptophyte–chlorophyte-dominated
(green), respectively. Stations 16–17 dominated by cyanobacteria (blue
triangles; Fig. 8d) showed high level of Fv/Fm
(0.5–0.6) and relatively low
values of σPSII (300–400Å2quantum-1). The
Fv/Fm and
σPSII of cryptophyte–chlorophyte-dominated stations
showed a moderate level of Fv/Fm (0.4–0.5) and σPSII
(580–680Å2quantum-1). More
importantly, tight positive relationships existed between measurements of
Fv/Fm and Chl a/ TP (R2∼0.31–0.63;
Fig. 8b and e). On the other hand, σPSII values were
positively correlated with the PSC / Chl a with R2∼0.6
(Fig. 8c and f). The PSC / Chl a of cyanobacteria-dominated group (blue
symbols) and the well-mixed group (brown symbols) were relatively low. The
highest PSC / Chl a and lowest Chl a/ TP values were observed
for the dinoflagellate–haptophyte-dominated group, corresponding to the lowest σPSII
and the highest Fv/Fm. In addition, the
cryptophyte–chlorophyte-dominated group had high levels of PSC/TChl a
(∼0.18–0.26) and a slightly higher Chl a/ TP level compared to
the dinoflagellate–haptophyte-dominated group. Overall, well-mixed groups
with high proportion of large-size phytoplankton (e.g., diatoms and
chlorophytes) showed higher Chl a/ TP levels along with larger
Fv/Fm and smaller σPSII values
than those stations with a high fraction of dinoflagellates and
picophytoplankton (e.g., haptophytes and prochlorophytes)(Fig. 8c and f).
(a, d)σPSII vs. Fv/Fm;
(b, e)Fv/Fm versus Chl a/ TP; and (c, f)σPSII versus PSC / Chl a on 29 September and
29–30 October 2017, respectively. The data points identified by dominant taxa with black, red,
green and blue symbols denoting well-mixed, dinoflagellate–haptophyte-dominated, cryptophyte–chlorophyte-dominated and cyanobacteria-dominated
groups, respectively.
(a) Relationship between the percentage of the fluorescence yield
of CDOM measured by FIRe against HPLC-measured Chl a concentration. The dashed circle corresponds to data acquired in the area adjacent to the GOM. (b) Comparisons between Rrs_insitu and
Rrs_OLCI at band 9 (673 nm) and band 11 (709 nm). The dashed circles correspond to stations 1, 3 and 4 located close to the river mouth and are considered as outliers. (c)
Exponential relationships between HPLC-measured Chl a concentrations and
Rrs_insitu band ratio (673 nm / 709 nm) in GB on September
29 (R2=0.89), 29 October (R2=0.93) and 30 October (R2=0.97). Red, green and blue lines and symbols indicate data sets
obtained on 29 September, 29 October and 30 October 2017, respectively.
Satellite observations of phytoplankton pigmentsAn OLCI Chl a algorithm and its validation
Blue to green band ratio algorithms have been widely used to study Chl a in
the open ocean and shelf waters (D'Sa et al., 2006; Blondeau-Patissier et
al., 2014); however, these bands generally fail in estuarine waters due to
strong blue absorption by the high levels of CDOM and suspended particulate
matter, especially after flooding events associated with hurricanes (D'Sa et al., 2011, 2018; Joshi and D'Sa, 2018). The
percentage contribution by CDOM fluorescence (blank) to maximum fluorescence
yield (Fm) obtained from in situ FIRe (Fig. 9a) demonstrated that Chl a
fluorescence was strongly influenced by high amounts of CDOM fluorescence in
GB, especially during the first survey (29 September 2017) when the bay
was under strong floodwater influence (red triangles; Fig. 9a). The CDOM
fluorescence signal constituted ∼25 % in the region
adjacent to the nGOM (stations 12–14), between 25 % and 50 % in the upper
GB and up to ∼65 % in Trinity Bay, which implies that blue
and even green bands are highly contaminated by CDOM and might not be the
most suitable bands for estimating Chl a in GB. However, an increase in peak
height near 700 nm and its shift towards a longer wavelength (Fig. 3) can be
used as a proxy to estimate Chl a concentration (Gitelson, 1992).
The C2RCC atmospheric-corrected Rrs_OLCI at each of
the sampling sites were further compared with Rrs_insitu
(Fig. 3) at phytoplankton red absorption (∼673 nm) and Chl a
fluorescence (∼700 nm) bands (Fig. 9b). The C2RCC performed
overall better for the second survey on 29–30 October 2017 (green and blue
symbols; Fig. 9b) than the first survey on 29 September 2017 (red
triangles; Fig. 9b) when stations 1, 3 and 4 (circled triangles; Fig. 3c)
adjacent to the Trinity River mouth were included; these stations were the
last sampling sites in the afternoon (∼ 04:30 pm) and under
somewhat cloudy conditions. The time difference between the satellite pass and
in situ measurements, differences in sky conditions and in shallow water depths also likely
introduced more errors at these locations. The R2 between
Rrs_OLCI and Rrs_insitu at red and
near infra-red (NIR) bands was 0.89 when the data from stations 3 and 4 were
excluded, suggesting good usability of these two bands for Chl a empirical
algorithms in GB. Thus, the higher the Chl a concentration, the stronger the
red light absorption, resulting in higher reflectance at 709 nm;
consequently, negative correlations were observed between red / NIR band ratio
and Chl a. The ratio of red (∼673 nm) / NIR (709 nm)
reflectance bands from in situ measurements were overall highly correlated
with HPLC-measured Chl a with R2∼0.96, 0.94 and 0.98 on
29 September, 29 and 30 October 2017, respectively (Fig. 9c). The
Sentinel-3A OLCI Chl a maps (Fig. 10a–c) were generated for all data based
on the relationship between Chl a and the red / NIR band ratio as
ChlaµgL-1=216.38×exp(-2.39914×Rrs(673)Rrs(709))[All
data],
The OLCI-derived Chl a (Fig. 10a–c) showed a good spatial agreement with
Chl a_HPLC (Fig. 10d–f). In addition, a
comparison of this algorithm with that of Gilerson et al. (2010) revealed
slightly better performance (not shown) inside of GB and especially in the
area adjacent to the shelf.
Chl a concentrations generated based on an in situ band ratio
(Rrs673 /Rrs709) algorithm with (a), (b) and (c) representing Chl a
distribution on 29 September, 29 and 30 October 2017, respectively.
Panels (d), (e) and (f) show the validation between HPLC-measured Chl a and
OLCI-derived Chl a on 29 September, 29 and 30 October 2017,
respectively.
The Chl a concentration on 29 September 2017 was overall higher than on
29–30 October 2017 throughout the bay. East Bay displayed very high Chl a
concentrations, with the highest value (>30µg L-1) observed on 29 September 2017 (Fig. 10a). The
narrow shape and shallow topography of East Bay results in relatively higher
water residence time (Rayson et al., 2016); thus, the reduced exchange with
shelf waters likely leaves the East Bay vulnerable to eutrophication. The
average Chl a concentrations on 29–30 October 2017 were ∼15µg L-1 along the transect (station 1–11) and ∼4–6 µg L-1 (station 12–14) close the entrance of GB. In
addition, the Chl a concentration adjacent to San Jacinto River mouth
(>16µg L-1) was higher than that in Trinity
Bay, which might suggest that the San Jacinto inflow had higher nutrient
concentrations than Trinity Bay as also previously reported (Quigg et al.,
2010). Furthermore, the OLCI-Chl a maps on 29 and 30 October 2017 show
extremely high Chl a concentrations in a narrow area adjacent to the San
Jacinto River mouth, with Chl a approaching ∼40µg L-1 at station 19 (Fig. 10c).
Long-term Chl a observations in comparison with the Hurricane Harvey
event
OLCI-derived Chl a maps between August 2016 and November 2017 (Fig. 11a1–a15) and time series of averaged Chl a in the areas of
Trinity Bay, East Bay and adjacent to the nGOM (Fig. 11b) revealed regionally
different responses to freshwater discharge from the San Jacinto and the
Trinity Rivers (Fig. 11b). Due to the relatively much higher discharge from
the Trinity River, the spatial distribution of Chl a in Trinity Bay (Fig. 11)
generally indicates its greater influence than the San Jacinto River. During
the winter and spring of 2017, phytoplankton Chl a peaks of ∼32µg L-1 were observed in Trinity Bay (Fig. 11b) after
high inflows from both rivers (Fig. 11a5–a8) and then decreased to
∼20µg L-1 in summer (July and August 2017). Generally, overall lower Chl a
values (∼10µg L-1) were observed between September and December 2016 compared to 2017 in the absence of
significant meteorological and hydrological events (Fig. 11a1–a4). However, with the East Bay less directly affected by
river discharge, Chl a levels remained fairly constant in the range of
∼18–24 µg L-1 before the hurricane. In
contrast, extremely high river discharge (∼3300 m3 s-1) induced by Hurricane Harvey in late August 2017, elevated
the Chl a concentrations in both the Trinity and East Bay to higher levels as observed on 14 September 2017
(∼30–35 µg L-1; Fig. 11a11) compared to the mean state of the fall season in 2016. Chl a then
continuously decreased through September and October 2017 in Trinity Bay and
East Bay, and were relatively low (≤10µg L-1) in
November 2017 under no additional pulses of river discharge. Chl a levels adjacent
to the entrance of GB, which exhibited much lower values year round than that
of the Trinity Bay and East Bay, also showed a slightly positive response to the
enhanced river discharge and the hurricane-induced flooding events. In
addition, Chl a was always observed in low concentrations along the Houston Ship Channel.
(a1-15) OLCI-derived Chl a shown for the period of 31 August 2016–25 November 2017. (b) Trinity River discharge at Romayor, Texas
(USGS 08066500; black line), and the west flank of the San Jacinto River
(USGS 08067650; blue line); the green, red and gray lines and symbols represent
the mean of Chl a at stations 1–7 in Trinity Bay, at stations 17–18 in East
Bay and at stations 12–14 close to the entrance of GB corresponding to 43
cloud-free Sentinel-3A OLCI images (colored symbols; dated symbols
correspond to images a1-15).
Reconstruction of total pigment absorption spectra from OLCI-derived Chl a
The reconstructed apig(λ) based on the third order
function of Chl a_HPLC (gray lines; Fig. 12a and
b) agreed well with the spectrophotometrically measured
apig(λ) (black lines; Fig. 12a and b) during both
surveys (R2=0.86; Fig. 12c). The R2 for modeled versus measured
apig(λ) are between 0.76 and 1.00 from 400 to 700 nm with averaged R2 of whole spectra reaching ∼0.82 on
29 September 2017 and ∼0.89 on 29–30 October 2017,
respectively. The vector coefficients
C=[C3,C2,C1,C0]
obtained from Eq. (10) were further applied to Eq. (11) to generate
apig_OLCI(λ) based on OLCI-derived Chl a images on 6 July (Fig. 11a9), 29 September (Fig. 10a), 29–30 October
(Fig. 10b–c) and 25 November 2017 (Fig. 11a15), respectively; these
contained 259×224 pixels in each image. The
apig_OLCI(λ) at each pixel was
retrieved at 1 nm intervals, and thus 301 images of apig_OLCI(λ) representing each wavelength were obtained over
GB.
Spectrophotometrically measured and multiple-regression fitted
apig(λ) spectra acquired on (a)
29 September and (b) 29–30 October 2017 in GB. Gray and black lines
represent modeled and measured results, respectively. (c) Comparison between
modeled and spectrophotometrically measured
apig(λ) for all data with color
representing wavelength.
Accuracy of phytoplankton pigment retrievals from Sentinel-3A OLCI
The reconstructed apig_OLCI(λ) was
spectrally decomposed into 16 individual pigment spectra at each pixel based
on Eq. (13). A comparison of all data between HPLC-measured pigments and
NNLS algorithm inverted pigments shows that R2 ranged from a low of
0.40 for diatoxanthin to 0.96 for Chl a, and RMSE was in the range of
0.103–0.584 (Table 4). The NNLS-modeled Chl a also correlated well with
OLCI-derived Chl a (R2=0.98; Fig. 13a), with each exhibiting similar
quantitative and spatial patterns. For the other 15 simultaneously simulated
pigments, R2 of only seven pigments were greater than 0.650 (Table 4). In
addition, the resulting RMSEs were less than 0.3 for most of pigments, except
zeaxanthin, violaxanthin, diatoxanthin and diadinoxanthin. Further, for
those pigments with relatively lower RMSE, their slopes were very close to 1
and y intercepts approached to 0. Five NNLS-derived versus HPLC-measured
diagnostic pigments including alloxanthin, Chl b, zeaxanthin, fucoxanthin
and peridinin are shown in Fig. 13. The R2 between NNLS-derived and
HPLC-measured pigments for surveys 1 and 2 was highest for alloxanthin
(0.91; Fig. 13b). For the other pigments R2 was 0.854 for Chl b (Fig. 13c), 0.689 for zeaxanthin (Fig. 13d), 0.645 for fucoxanthin (Fig. 13e) and
0.566 for peridinin (Fig. 13f).
Statistical results between HPLC-measured and NNLS-modeled
pigments.
Sentinel-3A OLCI-derived pigment concentrations against HPLC-measured
pigment concentrations
in Galveston Bay: (a) Chl a, (b) alloxanthin, (c) Chl b, (d) zeaxanthin,
(e)
fucoxanthin and (f) peridinin. Red and green symbols indicate data sets
obtained on 29 September and 29–30 October 2017, respectively. The dashed circle includes the station located in algal bloom area.
Spatiotemporal variations of diagnostic pigments
Flooding due to Hurricane Harvey not only enhanced Chl a but also affected
the phytoplankton pigments composition. NNLS-retrieved pigment maps for
July, September, October and November 2017 including those of alloxanthin,
Chl b, zeaxanthin, fucoxanthin and peridinin (Fig. 14) show different
levels of variations before and after the hurricane event. Alloxanthin,
which is unique to cryptophytes (Wright and Jeffrey, 2006), exhibited the same
spatial distribution patterns (Fig. 14a1–e1) with Chl a.
Alloxanthin was especially low (∼0.5µg L-1, Fig. 14a1) in the major basin area on 6 July 2017 before
the hurricane and slightly elevated (∼0.7µg L-1, Fig. 14b1) in September and October 2017 after the hurricane
passage. Furthermore, extremely high alloxanthin (∼3.5µg L-1, Fig. 14c1–d1) was observed adjacent to
San Jacinto River mouth on 29–30 October 2017, which coincided with the
high %Chl a of cryptophytes at stations 19 and 23 (Fig. 6b). The bloom
with high concentration of alloxanthin on 29 October 2017 (∼3.5µg L-1; Fig. 14c1) then extended to a broader
area on 30 October 2017 (Fig. 14d1).
Sentinel-3A OLCI-derived maps of
diagnostic pigment concentrations for Galveston Bay. Simulated
(a1–e1) alloxanthin, (a2–e2) Chl b,
(a3–e3) zeaxanthin, (a4–e4) fucoxanthin and
(a5–e5) peridinin concentrations. Panels (a),
(b), (c), (d) and (e) represent columns
(maps for 6 July, 29 September, 29–30 October and 25 November 2017),
respectively and panels 1–5 represent rows (pigments). Panels (f),
(g), (h) and (l) are the corresponding IFCB data
for 6 July, 29 September, 29–30 October and 25 November 2017, respectively.
Note that IFCB pictures of freshwater species including chlorophytes and
cyanobacteria that appeared on 20–30 September 2017 have been zoomed in for
better clarity.
Chl b is abundant in the group of chlorophytes (green algae) (Hirata et al.,
2011) and the spatial distributions of Chl b (Fig. 14a2–e2) also
showed strong correlations with Chl a on 6 July 2017, 29 September, 29–30 October and 25 November 2017. The NNLS-derived
Chl b
exhibited overall low values (∼0.5–2 µg L-1; Fig. 14a2) before the hurricane and showed obvious elevation
throughout the bay after the hurricane, eventually decreasing to
pre-hurricane level by 25 November 2017. Furthermore, Chl b concentrations
observed on 29 September 2017 were higher than that on 29–30 October 2017,
which corresponded to a decline of the chlorophyte percentage derived from the
IOP inversion algorithm (Fig. 6). More importantly, images obtained from
IFCB at the entrance to GB also detected a freshwater Chlorophyte species
(Pediastrum duplex; Fig. 14g) on 29 September 2017. However, this species was rarely observed
in IFCB images for the other dates (Fig. 14a1 and Fig. 14c1–e2). In addition, Chl b concentrations approached
∼2.8µg L-1 in the bloom area
and the corresponding green discoloration of water was also observed during
the field survey on 30 October 2017.
Zeaxanthin is known as taxa-specific pigment for prokaryotes (cyanobacteria)
(Moisan et al., 2017; Dorado et al., 2015). NNLS-derived zeaxanthin maps
(Fig. 14a3–e3) displays significantly different patterns to Chl a concentrations, exhibiting low concentrations in the areas where the Chl a concentrations were high. For
example, zeaxanthin was especially low in the bloom area on 29–30 October 2017, which agreed well with low %Chl a of cyanobacteria at stations 19
and 23 (Fig. 6), thus indicating that this localized algal bloom event was
not associated with cyanobacteria. In addition, the zeaxanthin concentration was high
∼3.0µg L-1 (Fig. 14a3) in both GB
and shelf waters on July 6 July 2017 before the hurricane event. Later,
zeaxanthin increased slightly on 29 September 2017 (Fig. 14b3) with
IFCB data detecting N2-fixing cyanobacteria (Anabaena spp.; Fig. 14g) and
remained elevated on 29–30 October 2017 (Fig. 14b3–c3).
Zeaxanthin eventually decreased to very low values (∼1.2µg L-1; Fig. 14e3) on 25 November 2017.
Fucoxanthin is a major carotenoid found in diatoms (Hirata et al.,
2011; Moisan et al., 2017) and the NNLS-derived fucoxanthin maps (Fig. 14a4–e4) show highly similar distribution patterns with Chl a.
Maps of fucoxanthin show low concentrations on 6 July 2017
(∼1.5µg L-1; Fig. 11a4), and
display a large increase on 29 September 2017 (∼1.6–3.0 µg L-1; Fig. 11b4). The diatoms group detected by the IFCB
were dominated by marine species before the hurricane but subsequently
shifted to freshwater species (e.g., Pleurosigma; Fig. 14g) and then back to marine
species after October 2017. Overall, fucoxanthin concentrations in GB were
relatively higher during survey 1, which corresponded to the higher %Chl a of diatoms (Fig. 6) compared to survey 2. Although, fucoxanthin decreased
to low values on 25 November 2017 (∼1.6µg L-1; Fig. 11e4), it accounted for a higher fraction of phytoplankton
diagnostic pigments compared to other dates in July, September and October 2017.
Peridinin, a primary biomarker pigment for certain dinoflagellates (Örnólfsdóttir et al., 2003), also displayed significantly
distinct patterns in comparison to Chl a (Fig. 14a5–e5). On 6 July 2017, peridinin was ∼0.24–0.36 µg L-1,
accounting for a high proportion of the diagnostic pigments; meanwhile,
diversity of marine dinoflagellate species observed by the IFCB at this time
was also high (Fig. 14f). However, peridinin decreased (∼0.001–0.05 µg L-1) after the hurricane, with freshwater
dinoflagellate species (Ceratium hirundinella; Fig. 14g) detected by the IFCB on 29 September 2017. In addition,
maps of peridinin during both surveys (Fig. 14b5–d5) present a higher concentration (∼0.3µg L-1)
in higher salinity waters adjacent to the bay
entrance, which agreed well with the increasing fraction of dinoflagellate
at stations 10–14 detected from IOP inversion model (Fig. 6). In contrast,
peridinin was found in low concentrations in both GB and shelf waters on 25 November 2017 (Fig. 14e5), with dinoflagellate species rarely observed by the IFCB (Fig. 14l).
DiscussionPerformance of the semi-analytical IOP inversion algorithm
The residuals between Rrs_insitu and Rrs_mod on
29 September and 29–30 October 2017 are negative in the blue (400–450 nm)
and red (610–630 nm) spectral range at most stations, whilst keeping
positive ∼700 nm, which could be attributed to a number of factors.
First, the underestimation near 700 nm by the IOP inversion model is
possibly induced by the absence of a fluorescence component in the IOP
inversion model; thus, Rrs_insitu containing fluorescence
signals were generally higher than Rrs_mod near 700 nm. Second,
in the range of 610–630 nm, the absorption was overestimated at most of the
stations; in this spectral range, the shape of spectra was strongly modulated
by cyanobacteria absorption. Thus, this overestimation at ∼620 nm is
likely introduced by the input absorption spectrum (eigenvector) for
cyanobacteria since all of the aphi*(λ) inputs are general absorption spectral shapes for different phytoplankton
groups. However, the spectra of
aphi*(λ) can vary in magnitude
and shape associated with package effects under different environmental
conditions (e.g., nutrient, light and temperature) even for the same species
(Bricaud et al., 2004). More detailed absorption spectra of phytoplankton
under different conditions (e.g., high or low light and replete or poor nutrients) could improve the performance of the
IOP algorithm. Furthermore, the role of scattering might be another key
factor to explain differences between Rrs_insitu and
Rrs_mod for the whole spectra. The quantity and composition of
suspended materials including phytoplankton, sediment and minerals will
collectively determine bbp(λ) in both shape and
magnitude. However, the input eigenvector of bbp(λ) in
the present study was not divided into detailed sub-constituents and was a
sum spectrum-based on a power-law function (Table 2). In reality,
bbp(λ) spectra are not smooth and regular, and thus the
bbp(λ) values of phytoplankton and sediment might
introduce errors to the whole spectrum due to their own scattering
characteristics.
Distributions of NNLS-retrieved phytoplankton pigments from Sentinel-3A
OLCI
The NNLS-inversion algorithm showed relatively higher R2 for those
pigments that better correlated with HPLC-measured Chl a (e.g., Chl b and
alloxanthin), which was reasonably consistent with Moisan et al. (2017). This
outcome could potentially be attributed to the fact that the NNLS-pigment
inversion algorithm was developed based on the relationship between
HPLC-measured Chl a and spectrophotometer-measured
apig(λ). For instance, pigments that were relatively
poorly correlated with HPLC-measured Chl a, such as fucoxanthin,
diatoxanthin and diadinoxanthin on 29–30 October 2017, the OLCI-derived
concentrations in the cryptophyte–chlorophyte algal bloom area showed higher
concentrations than those of HPLC measurements (e.g., values in gray circle;
Fig. 13e), thus resulting in lower R2. However in previous studies
(Moisan et al., 2017; Pan et al., 2010), relatively higher R2 versus lower RMSE were observed between
satellite-derived and HPLC-measured fucoxanthin compared to this study. This
was likely because in addition to being based on long-term measurements, fucoxanthin is one of the most
abundant diatom biomarker pigments in coastal waters and correlated very well
with Chl a in their study area (United States northeast coast). In contrast, the cryptophyte–chlorophyte
algal bloom area with extremely high Chl a appeared to disturb the
correlations between Chl a and fucoxanthin in this study. Also, pigments
with apparent high values in algal bloom areas, such as Chl b, Chl c,
alloxanthin, lutein, showed higher R2 with RMSE less than 0.3. Thus, in
situ measurements of Chl a and apig(λ) in waters with
stronger gradients in magnitude and greater variations in phytoplankton
community structures could potentially increase the challenge of applying
NNLS-inversion algorithms in optically complex estuarine waters. Further, the
highly dynamic estuarine environment could contribute as well to additional
uncertainties in the validation of inverted pigments due to variations such
as turbulence, turbidity or light field that are likely to occur during the
time interval between in situ and Sentinel-3 OLCI (∼4 h) observations.
HPLC measurements also cannot detect extremely low pigment concentrations;
for example, HPLC-measured peridinin concentrations were
0.001 µg L-1 at several stations, however, OLCI-derived
peridinin showed higher and variable values at these stations (data in gray
circles; Fig. 13f), thus this could increase the RMSE of the NNLS-inverted
peridinin. It was also found that the slopes of all pigments were smaller
than 1, which demonstrate that NNLS-inverted pigments were relatively smaller
than HPLC measurements, especially for those stations located in the algal
bloom area; this could most likely be attributed to the underestimation of
Chl a values by the Sentinel-3 OLCI empirical algorithms in the algal bloom
area. Algal bloom dominated by the cryptophytes group, which is also known to
cause red tides worldwide, to some degree, could increase red reflectance and
thus increase ratio values of red / NIR and decrease estimated Chl a
values. Therefore, reliable estimates of satellite-detected Chl a is
crucial for the accuracy of retrieved pigments. The goal of the empirical
Chl a algorithm for Sentinel-3A OLCI is to obtain a more accurate
estimation of surface Chl a concentration, which is better for retrieving
other accessory pigments. However, the primary limitation of Chl a
empirical algorithms in this study was that the derived relationships between
red / NIR and Chl a in GB may only be valid within a specific time
period due to temporally limited field observations versus highly dynamic
estuarine environments. Therefore, a Chl a empirical algorithm that is more
broadly applicable over a longer time period will largely improve the
accuracy of retrieved pigments over a series of remote-sensing images and can
be more useful for spatiotemporal studies of phytoplankton functional
diversity. More importantly, the highly similar absorption spectra of many
carotenoids are another key issue limiting the accuracy of spectral
decomposition techniques. Although the 16 input pigment spectra used in this
study were selected from Thrane et al., 2015), which were correctly
identified from unknown phytoplankton community structure with low error rate
reported from Monte Carlo tests, the potential effects of aliasing spectra of
some pigment pairs (e.g., fucoxanthin vs. peridinin, diadinoxanthin vs.
lutein, β-carotene vs. zeaxanthin) could still be a factor. Thus, the
reported errors or R2 for the retrieved total carotenoids in Thrane et
al. (2015) were apparently lower or higher, respectively, than those of
modeled total chlorophylls, which showed consistency with this
study. Although the predicted pigments showed a range of R2 and RMSE
with known uncertainties, all are within the acceptable range and could be
useful for studying the spatiotemporal responses of PFTs to environmental
variations, especially in such optically complex estuaries.
The derived maps of phytoplankton diagnostic pigments appeared to be
reasonably correlated with HPLC-measured diagnostic pigments and showed
overall agreement with extracted phytoplankton taxonomic compositions
detected from the IOP inversion algorithm. The retrieved diatom-specific
fucoxanthin maps, however, show high concentrations compared to other
pigments adjacent to the entrance (Fig. 13b4 and c4), which
contradicts the diatom %Chl a calculated from the IOP inversion
algorithm. As the Chl a fraction of diatoms was relatively uniform at
stations 12–14 (Fig. 6b) Nair et al.(2008) concluded that
fucoxanthin can occur in other phytoplankton types (e.g., raphidophyte and
haptophyte). Fucoxanthin and/or
fucoxanthin derivatives such as 19′-hexanoyloxyfucoxanthin can also replace
peridinin as the major carotenoid in some dinoflagellates (e.g.,
Karenia brevis; Jeffrey and Vest, 1997). The elevated contributions
from groups of dinoflagellates, haptophytes and prochlorophytes adjacent to
the entrance (stations 10–14; Fig. 6b) along with high concentrations of
fucoxanthin likely suggest the presence of elevated fractions of haptophytes
and dinoflagellates, and further implies that fucoxanthin is an ambiguous
marker pigment for diatoms. This could also explain the poor correlation
between inverted %Chl a and %DP observed for the groups of diatoms
and haptophytes (Fig. 4g and l). These results also further suggest the
inherent limitations of using a DP-type comparison between major biomarker
pigments and phytoplankton groups because the major assumption for DP-type
methods is that the diagnostic pigment of distinct phytoplankton groups are
uncorrelated to each other. This assumption is invalid in that concentrations
of major biomarker pigments are significantly correlated with each other and
also may vary in time and space under some external environmental stress
(e.g., temperature, salinity, mixing, light and nutrient) (Latasa and
Bidigare, 1998).
Response of phytoplankton taxa to environmental conditions
Previous studies showed diatoms to be the most abundant taxa in GB, and they tend
to be more dominant during winter and spring, corresponding to periods of high
freshwater discharge and nutrient-replete conditions (Dorado et al.,
2015; Örnólfsdóttir et al., 2004a). Transitions from
chain-forming diatoms such as Chaetoceros and rod-like diatoms pre-flood to small
cells, such as Thalassiosira and small pennate diatoms were generally observed during
high-river discharge periods (Anglès et al., 2015; Lee, 2017). In
contrast, cyanobacteria were the most abundant species during the warmer
months (June–August) when river discharge was relatively low
(Örnólfsdóttir et al., 2004b). Further, phytoplankton groups
in GB responded differentially both taxonomically and spatially to the
freshening events due to their contrasting nutrient requirements and
specific growth characteristics. For instance, most phytoplankton taxa
(e.g., diatom, chlorophyte and cryptophyte) can be positively stimulated by
fresh inflows due to their relatively rapid growth rate (Paerl et al.,
2003). However, Roelke et al. (2013) also documented that cyanobacteria
and haptophytes in the upper GB were not sensitive to nutrient-rich waters
from both rivers due to the extra nutrients obtained from N2-fixation
abilities and mixotrophic characteristics, respectively. In the lower part
of GB, dinoflagellates and cyanobacteria are known to be more dominant
during the low-river discharge due to their preference for higher phosphorus
(P) compared to some other groups, and to low turbulence (Lee, 2017), and
thus are generally inversely related to the fresh inflows (Lee, 2017; Roelke et al., 2013).
Perturbations following Hurricane Harvey affected the phytoplankton
taxonomic composition with alterations in phytoplankton community structure
observed as the GB system transitioned from marine to freshwater then to
marine system (Figs. 6 and 14). Higher fractions of zeaxanthin and peridinin
and the presence of large and slow-growing marine dinoflagellates detected
by the IFCB pre-hurricane (6 July 2017) indicate that both cyanobacteria and
dinoflagellates were the main groups of the phytoplankton community during
summer, and were likely associated with warmer temperature and lower river flow
(Lee, 2017). Later, massive Chl a observed in September 2017 and the
decline of Chl a to background state in October 2017 were likely
associated with the hurricane-induced high-river discharge and the resulting
variations in nutrient concentration and composition. Higher fractions of
diatoms and chlorophytes accompanied by increasing fucoxanthin and Chl b on
29 September 2017 to some extent agreed well with measurements of Steichen
et al. (2018) in the two weeks following Hurricane Harvey that freshwater species
(diatoms, green algae and cyanobacteria) appeared immediately following the
flooding event. The greater abundance of diatoms and chlorophytes during survey 1
in comparison to survey 2 were likely due to their rapid growth rates,
enhanced nutrient uptake rates, and tolerance of low salinity and high
turbulence under high nutrient loading conditions following the freshwater
inflows (Roy et al., 2013; Santschi, 1995). Therefore, it is not
surprising that Chl b concentrations showed very low values in July and
November, 2017, when river discharge was correspondingly low. Cyanobacteria,
which normally prefer low salinity conditions, also showed specific
responses to this flood event. On 29 September 2017, zeaxanthin slightly
increased compared to summer season in July 2017. The decline of diatoms
and chlorophytes versus slightly increased cyanobacteria levels observed on 29–30 October 2017 could be attributed to the relatively slow growth rates of
cyanobacteria compared to that of chlorophytes and diatoms (Paerl et al.,
2003); cyanobacteria appeared to have lagged behind these groups in terms of
responding to enhanced freshwater discharge when longer residence times were
again restored. In contrast, the presence of green algae and cyanobacteria
could as well be explained by the clarity and turbidity gradient of
water. Quigg et al. (2010) reported that when turbidity was relatively
high, chlorophytes dominated over cyanobacteria with biomass ratio of
chlorophyte/cyanobacteria greater than two, which supported our observations
that chlorophyte dropped off whilst cyanobacteria increased during survey 2
on 29–30 October 2017. In addition the highest cyanobacteria percentage in
East Bay suggests that calm and stratified waters may
accelerate cyanobacteria growth as the buoyancy regulation mechanism of cyanobacteria
is possibly restricted by the water mixing (Roy et al., 2013). The peridinin concentration,
which initially decreased in September and then increased in the lower GB on 29–30 October 2017, suggests that dinoflagellates show overall preference
for high-salinity waters. Furthermore, previous IFCB observations from
Biological and Chemical Oceanography Data Management Office (BCO-DMO) showed
that algal blooms after hurricanes in the nGOM were initially dominated by
diatoms, and subsequently transitioned to blooms of dinoflagellates, likely
associated with nutrient ratios and chemical forms of nutrients supplied by
the flood waters and rainfall (Heisler et al., 2008). In addition, high
concentrations of peridinin observed along the Houston Ship Channel might
provide evidence that the ballast water addition from shipping vessels
likely promotes harmful species of dinoflagellates (Steichen et al., 2015).
Finally, low concentrations of all pigments on 25 November 2017 with
relatively higher fractions of fucoxanthin compared to previous dates (Fig. 14), indicate the major role of marine diatoms at that time and further
confirms that diatoms can be found under a wide range of inflows in GB.
The localized cryptophyte–chlorophyte bloom that occurred ∼60 d after Hurricane Harvey on 29–30 October 2017 was captured by both
satellite and in situ measurements. This bloom might not be associated with
the flooding events of Hurricane Harvey, and could be linked to
nutrient-rich runoff flowing into GB, reflecting the sensitivity and rapid
response of the phytoplankton community to nutrient input in GB. In shallow and
turbid estuaries, human activities are altering the environment and causing
phytoplankton changes in diversity and biomass to occur more frequently.
Dugdale et al. (2012) reported that variations of the phytoplankton community in
the San Francisco estuary could be attributed to anthropogenically elevated
concentration of ammonium, which restrains the uptake of nitrate, thus
reducing the growth and reproduction of larger diatoms and shifting towards
smaller species (e.g., cryptophytes and green flagellates). Furthermore, “pink
oyster” events related to alloxanthin of cryptophytes in GB occurred more
frequently from September through October in recent years (Paerl et al.,
2003). The eastern side of the Houston Ship Channel in the mid-bay region was reported as
the area most heavily impacted by the intense “pink oyster” events. Previous
studies and present observations both suggest that this
cryptophyte–chlorophyte-dominated bloom could be promoted by the
nutrient-driven eutrophication from the Houston Ship Channel, urbanization and
industrialization along the upper San Jacinto River complex.
Photo-physiological state of natural phytoplankton community
In this study, the CDOM-corrected Fv/Fm and σPSII likely represented a composite of both phytoplankton
taxonomy and physiological stress (e.g., nutrient and mixing). Typically,
lowest N and P concentrations were measured closest to the nGOM (Quigg
et al., 2009). Phytoplankton communities living close to nGOM were usually in
poor nutrient conditions and would have been expected to maximize their light
harvesting (increase in σPSII) due to nutrient
stress. Simultaneously, phytoplankton cells might experience a decline of
functional proportion of reaction centers of PSII (RCII), which means a
decrease in Fv/Fm. The observed low levels of Fv/Fm and
Chl a/ TP versus high values of σPSII and AP / TP
adjacent to the nGOM showed agreement with previous studies that the
fraction of carotenoids is higher for nutrient-poor cultures (Schitüter et al., 1997; Holmboe et al., 1999). In contrast,
phytoplankton in well-mixed waters (stations 7–9) might experience abundant
nutrients due to the resuspension of the cyclonic gyre around Smith Point; as
such, their photosynthetic machinery were likely healthier. Aiken et al. (2004) documented that the Chl a/ TP ratio was relatively higher when plants
were in good growing conditions, which is similar to the observations in
this study that phytoplankton have a higher fraction of Chl a accompanying
higher rate of photosynthetic efficiency (Fv/Fm) under nutrient
replete conditions. Overall, the spatial pattern of Fv/Fm and
σPSII in GB could be mainly attributed to
physiological stress of nutrient and hydrodynamic conditions since the light
availability (PAR) during the sampling period did not spatially vary
significantly at the surface. Furthermore, FIRe measurements
(Fv/Fm and σPSII) also presented a
taxonomic signal superimposed upon environmental factors. Each cluster with
different dominant taxa (well-mixed group, chlorophyte–cryptophyte,
cyanobacteria and dinoflagellate–haptophyte) displayed different
physiological characteristics. The taxonomic sequence of eukaryotic groups
from high Fv/Fm, low σPSII to low
Fv/Fm, high σPSII in the present
observations showed potential effects of phytoplankton cell size
corresponding to diatoms, chlorophytes, cryptophytes, dinoflagellates and
haptophytes. The prokaryote (cyanobacteria) had relatively high values of
Fv/Fm and low values of σPSII; this
agreed with Fv/Fm for some species of nitrogen-fixing
cyanobacteria that can range from 0.6 to 0.65 (Berman-Frank et al.,
2007). Yet, it is difficult to separate the contributions from environmental
factors and taxonomic variations to the changes of FIRe fluorescence signals
since all these parameters are interrelated. Different phytoplankton
groups and sizes will display distinct physiological traits (Fv/Fm and σPSII) when experiencing considerable
environmental pressures. Thus, effects of physiological stress on
Fv/Fm and σPSII
variations for natural samples can only be determined when taxonomic
composition can be excluded as a contributor (Suggett et al., 2009).
Conclusions
Field measurements (salinity, pigments, optical properties and physiological
parameters) and ocean color observations from Sentinel-3A OLCI were used to
study the effects of extreme flooding associated with Hurricane Harvey on the
phytoplankton community structures, pigment distributions and their
physiological state in GB. Flooding effects made the entire GB transition
from saline to freshwater then back to a more marine-influenced system. The
band ratio (red / NIR) of Rrs_insitu were negatively
correlated with HPLC-measured Chl a in an exponential relationship
(R2>0.93). The satellite-retrieved Chl a maps yielded much
higher Chl a concentrations on 29 September 2017 compared to
29–30 October 2017 with lowest Chl a observed adjacent to the shelf
waters. The phytoplankton taxonomic composition was further retrieved from
Rrs_insitu using a 10-species IOP inversion algorithm. The
phytoplankton community generally dominated by estuarine marine
diatoms and dinoflagellates before flood events, was
altered to freshwater species of diatoms, green algae (chlorophytes) and
cyanobacteria during survey 1. It also showed an increase of small-size
species including cryptophytes, haptophytes, prochlorophytes and
cyanobacteria accompanied by a decline of chlorophytes and diatoms during
survey 2.
Phytoplankton diagnostic pigments retrieved using an NNLS inversion model
based on Sentinel-3A OLCI Chl a maps also confirmed spatiotemporal
variations of phytoplankton taxonomy. The NNLS-retrieved diagnostic pigment
maps showed overall spatiotemporal agreement with HPLC measurements with
R2 ranging from 0.40 (diatoxanthin) to 0.96 (Chl a)
during both surveys. Alloxanthin, Chl b and fucoxanthin, which exhibited
similar patterns with Chl a, showed different levels of increase after
Hurricane Harvey. In contrast, NNLS-derived zeaxanthin and peridinin
presented significantly low values in the area where Chl a concentrations
were high. Further, maps of zeaxanthin and peridinin display relatively
higher fractions on 6 July 2017 before the hurricane compared to other
diagnostic pigments. However, the peridinin concentration decreased post-hurricane on
29 September 2017 and then increased a bit on 29–30 October 2017.
Concentrations of Chl a and all biomarker pigments eventually decreased to
low levels in November 2017 when GB returned to its typical environmental
state.
Finally, the retrieved phytoplankton taxonomic compositions from the IOP
inversion algorithm were linked with FIRe-measured photosynthetic parameters
(Fv/Fm and σPSII) to assess the
effects of physiological stress and taxonomic contributions on phytoplankton
photosynthetic performance. An inverse relationship between the
Fv/Fm and σPSII were observed during
both surveys. The phytoplankton community in well-mixed waters (around Smith
Point) showed high Fv/Fm against low σPSII; in contrast, the area with poor nutrient conditions
(adjacent to the shelf waters) showed low Fv/Fm and elevated
σPSII. Taxonomic signatures of Fv/Fm
and σPSII revealed diverse physiological
characteristics with dinoflagellate–haptophyte group showing the lowest
Fv/Fm versus the highest σPSII,
whereas prokaryotes of the cyanobacteria-dominated group showed high values of
Fv/Fm and low values of σPSII.
Overall, this study using field and ocean color data combined with inversion
algorithms provided novel insights on phytoplankton response to an extreme
flood perturbation in a turbid estuarine environment based on taxonomy,
pigment composition and physiological state of phytoplankton.
Data availability
Data from field measurements are available upon request from the
corresponding author.
Author contributions
BL and ED conceived and designed the research; BL, ED and IJ collected and
processed the data; BL analyzed the data and all authors contributed to
writing the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank the European Space Agency (ESA) and the European
Organization for the Exploitation of Meteorological Satellites (EUMETSAT) for
providing access to the Sentinel-3 OLCI ocean color data and the Sentinel-3
Toolbox Kit Module (S3TBX) version 5.0.1 in the Sentinel Application Platform
(SNAP). We also would like to thank the Phytoplankton Dynamics Lab of Texas
A&M University at Galveston for the near-real-time microplankton pictures
recorded by an Imaging FlowCytobot, which are made available on the web. We
are grateful to Bill Gibson from the Coastal Studies
Institute, Louisiana State University, for providing logistic
support for field operations. Eurico D'Sa acknowledges NASA
support through grant no. 80NSSC18K0177.
Review statement
This paper was edited by Maria Tzortziou and reviewed by two anonymous referees.
References
Acker, J., Lyon, P., Hoge, F., Shen, S., Roffer, M., and Gawlikowski, G.:
Interaction of Hurricane Katrina with optically complex water in the Gulf of
Mexico: interpretation using satellite-derived inherent optical properties
and chlorophyll concentration, IEEE Geosci. Remote S., 6,
209–213, 2009.
Aiken, J., Fishwick, J., Moore, G., and Pemberton, K.: The annual cycle of
phytoplankton photosynthetic quantum efficiency, pigment composition and
optical properties in the western English Channel, J. Mar. Biol. Assoc. UK, 84, 301–313, 2004.
Alvain, S., Moulin, C., Dandonneau, Y., and Bréon, F.-M.: Remote sensing
of phytoplankton groups in case 1 waters from global SeaWiFS imagery, Deep-Sea Res. Pt. I, 52, 1989–2004, 2005.
Anglès, S., Jordi, A., and Campbell, L.: Responses of the coastal
phytoplankton community to tropical cyclones revealed by high-frequency
imaging flow cytometry, Limnol. Oceanogr., 60, 1562–1576, 2015.
Barlow, R., Cummings, D., and Gibb, S.: Improved resolution of mono-and
divinyl chlorophylls a and b and zeaxanthin and lutein in phytoplankton
extracts using reverse phase C-8 HPLC, Mar. Ecol. Progr. Ser., 161, 303–307,
1997.
Behrenfeld, M. J. and Falkowski, P. G.: Photosynthetic rates derived from
satellite-based chlorophyll concentration, Limnol. Oceanogr., 42, 1–20,
1997.
Behrenfeld, M. J. and Kolber, Z. S.: Widespread iron limitation of
phytoplankton in the South Pacific Ocean, Science, 283, 840–843, 1999.
Berman-Frank, I., Quigg, A., Finkel, Z. V., Irwin, A. J., and Haramaty, L.:
Nitrogen-fixation strategies and Fe requirements in cyanobacteria, Limnol.
Oceanogr., 52, 2260–2269, 2007.
Bidigare, R. R., Ondrusek, M. E., Morrow, J. H., and Kiefer, D. A.: In-vivo
absorption properties of algal pigments, Ocean Optics X, Intl. Soc. Opt.
Photonics., 1302, 290–303, 1990.
Blondeau-Patissier, D., Gower, J. F., Dekker, A. G., Phinn, S. R., and
Brando, V. E.: A review of ocean color remote sensing methods and
statistical techniques for the detection, mapping and analysis of
phytoplankton blooms in coastal and open oceans, Progr. Oceanogr., 123,
123–144, 2014.
Bracher, A., Taylor, M. H., Taylor, B., Dinter, T., Röttgers, R., and
Steinmetz, F.: Using empirical orthogonal functions derived from
remote-sensing reflectance for the prediction of phytoplankton pigment
concentrations, Ocean Sci., 11, 139–158,
https://doi.org/10.5194/os-11-139-2015, 2015.
Brewin, R. J., Sathyendranath, S., Hirata, T., Lavender, S. J., Barciela, R.
M., and Hardman-Mountford, N. J.: A three-component model of phytoplankton
size class for the Atlantic Ocean, Ecol. Modell., 221, 1472–1483, 2010.Bricaud, A., Claustre, H., Ras, J., and Oubelkheir, K.: Natural variability
of phytoplanktonic absorption in oceanic waters: Influence of the size
structure of algal populations, J. Geophys. Res.-Oceans, 109, C11010, 10.1029/2004JC002419, 2004.
Campbell, D., Hurry, V., Clarke, A. K., Gustafsson, P., and Öquist, G.:
Chlorophyll fluorescence analysis of cyanobacterial photosynthesis and
acclimation, Microbiol. Molecul. Biol. Rev., 62, 667–683, 1988.
Carder, K. L., Chen, F., Lee, Z., Hawes, S., and Kamykowski, D.:
Semianalytic Moderate-Resolution Imaging Spectrometer algorithms for
chlorophyll a and absorption with bio-optical domains based on
nitrate-depletion temperatures, J. Geophys. Res.-Oceans, 104, 5403–5421,
1999.
Chase, A., Boss, E., Zaneveld, R., Bricaud, A., Claustre, H., Ras, J.,
Dall'Olmo, G., and Westberry, T. K.: Decomposition of in situ particulate
absorption spectra, Meth. Oceanogr., 7, 110–124, 2013.
Chase, A., Boss, E., Cetinić, I., and Slade, W.: Estimation of
phytoplankton accessory pigments from hyperspectral reflectance spectra:
toward a global algorithm, J. Geophys. Res.-Oceans, 122, 9725–9743, 2017.
Ciotti, A. M., Lewis, M. R., and Cullen, J. J.: Assessment of the
relationships between dominant cell size in natural phytoplankton
communities and the spectral shape of the absorption coefficient, Limnol.
Oceanogr., 47, 404–417, 2002.
Cullen, J. J. and Davis, R. F.: The blank can make a big difference in
oceanographic measurements, Limnol. Oceanogr., 12, 29–35, 2003.
D'Sa, E. J.: Assessment of chlorophyll variability along the Louisiana coast
using multi-satellite data, GISci. Remote Sens., 51, 139–157, 2014.
D'Sa, E. J. and Lohrenz, S. E.: Theoretical treatment of fluorescence
detection by a dual-fiber-optic sensor with consideration of sampling
variability and package effects associated with particles, Appl. Optics, 38,
2524–2535, 1999.
D'Sa, E. J., Lohrenz, S. E., Asper, V. L., and Walters, R. A.: Time series
measurements of chlorophyll fluorescence in the oceanic bottom boundary
layer with a multisensor fiber-optic fluorometer, J. Atmos. Ocean. Technol.,
14, 889–896, 1997.
D'Sa, E. J., Miller, R. L., and Del Castillo, C.: Bio-optical properties and
ocean color algorithms for coastal waters influenced by the Mississippi
River during a cold front, Appl. Optics, 45, 7410–7428, 2006.
D'Sa, E. J., Korobkin, M., and Ko, D. S.: Effects of Hurricane Ike on the
Louisiana–Texas coast from satellite and model data, Remote Sens. Lett., 2,
11–19, 2011.D'Sa, E. J., Joshi, I., and Liu, B.: Galveston Bay and coastal ocean
optical-geochemical response to Hurricane Harvey from VIIRS ocean color,
Geophys. Res. Lett., 45, 10579–10589, 10.1029/2018GL079954,
2018.
Devred, E., Sathyendranath, S., Stuart, V., and Platt, T.: A three component
classification of phytoplankton absorption spectra: Application to
ocean-color data, Remote Sens. Environ., 115, 2255–2266, 2011.
Dierssen, H. M., Kudela, R. M., Ryan, J. P., and Zimmerman, R. C.: Red and
black tides: Quantitative analysis of water-leaving radiance and perceived
color for phytoplankton, colored dissolved organic matter, and suspended
sediments, Limnol. Oceanogr., 51, 2646–2659, 2006.
Doerffer, R. and Schiller, H.: The MERIS Case 2 water algorithm, Intl. J.
Remote Sens., 28, 517–535, 2007.Dorado, S., Booe, T., Steichen, J., McInnes, A. S., Windham, R., Shepard,
A., Lucchese, A. E., Preischel, H., Pinckney, J. L., and Davis, S. E.:
Towards an understanding of the interactions between freshwater inflows and
phytoplankton communities in a subtropical estuary in the Gulf of Mexico,
PLoS One, 10, e0130931, 10.1371/journal.pone.0130931, 2015.
Dugdale, R., Wilkerson, F., Parker, A. E., Marchi, A., and Taberski, K.:
River flow and ammonium discharge determine spring phytoplankton blooms in
an urbanized estuary, Estuar. Coast. Shelf Sci., 115, 187–199, 2012.Dutkiewicz, S., Hickman, A. E., Jahn, O., Gregg, W. W., Mouw, C. B., and
Follows, M. J.: Capturing optically important constituents and properties in
a marine biogeochemical and ecosystem model, Biogeosciences, 12, 4447–4481,
10.5194/bg-12-4447-2015, 2015.
Farfan, L. M., D'Sa, E. J., and Liu, K.: Tropical cyclone impacts on coastal
regions: the case of the Yucatan and the Baja California Peninsulas, Mexico,
Estuar. Coast., 37, 1388–1402, 2014.
Ficek, D., Kaczmarek, S. A., Stoñ-Egiert, J., Wozniak, B., Majchrowski,
R., and Dera, J.: Spectra of light absorption by phytoplankton pigments in
the Baltic; conclusions to be drawn from a Gaussian analysis of empirical
data, Oceanologia, 46, 533–555, 2004.
Fishwick, J. R., Aiken, J., Barlow, R., Sessions, H., Bernard, S., and Ras,
J.: Functional relationships and bio-optical properties derived from
phytoplankton pigments, optical and photosynthetic parameters; a case study
of the Benguela ecosystem, J. Mar. Biol. Assoc. UK, 86, 1267–1280, 2006.
Garver, S. A. and Siegel, D. A.: Inherent optical property inversion of
ocean color spectra and its biogeochemical interpretation: 1. Time series
from the Sargasso Sea, J. Geophys. Res.-Oceans, 102, 18607–18625, 1997.
Geider, R. J., La Roche, J., Greene, R. M., and Olaizola, M.: Response of
the photosynthetic apparatus of phaeodactylum tricornutum (bacillariophyceae
to nitrate, phosphate, or iron starvation, J. Phycol., 29, 755–766, 1993.
Gilerson, A. A., Gitelson, A. A., Zhou, J., Gurlin, D., Moses, W., Ioannou,
I., and Ahmed, S. A.: Algorithms for remote estimation of chlorophyll-a in
coastal and inland waters using red and near infrared bands, Opt. Express,
18, 24109–24125, 2010.
Gitelson, A.: The peak near 700 nm on radiance spectra of algae and water:
relationships of its magnitude and position with chlorophyll concentration,
Int. J. Remote Sens., 13, 3367–3373, 1992.
Gordon, H. R., Brown, O. B., Evans, R. H., Brown, J. W., Smith, R. C.,
Baker, K. S., and Clark, D. K.: A semianalytic radiance model of ocean
color, J. Geophys. Res.-Atmos., 93, 10909–10924, 1988.
Guthrie, C. G., Matsumoto, J., and Solis, R.: Analysis of the influence of
water plan strategies on inflows and salinity in Galveston Bay, Final report
to the United States Army Corps of Engineers, Contract #R0100010015, Texas
Water Development Board, Austin, Texas, USA, 71 pp., 2012.
Heisler, J., Glibert, P. M., Burkholder, J. M., Anderson, D. M., Cochlan,
W., Dennison, W. C., and Lewitus, A.: Eutrophication and harmful algal
blooms: a scientific consensus, Harmful Algae, 8, 3–13, 2008.
Hirata, T., Aiken, J., Hardman-Mountford, N., Smyth, T. J., and. Barlow, R.
G.: An absorption model to determine phytoplankton size classes from
satellite ocean colour, Remote Sens. Environ., 112, 3153–3159, 2008.Hirata, T., Hardman-Mountford, N. J., Brewin, R. J. W., Aiken, J., Barlow,
R., Suzuki, K., Isada, T., Howell, E., Hashioka, T., Noguchi-Aita, M., and
Yamanaka, Y.: Synoptic relationships between surface Chlorophyll-a and
diagnostic pigments specific to phytoplankton functional types,
Biogeosciences, 8, 311–327, 10.5194/bg-8-311-2011, 2011.
Hoepffner, N. and Sathyendranath, S.: Effect of pigment composition on
absorption properties of phytoplankton, Mar. Ecol. Progr. Ser., 73, l–23,
1991.
Hoge, F. E. and Lyon, P. E.: Satellite retrieval of inherent optical
properties by linear matrix inversion of oceanic radiance models: an analysis
of model and radiance measurement errors, J. Geophys. Res.-Oceans, 101,
16631–16648, 1996.
Holmboe, N., Jensen, H. S., and Andersen, F. Ø.: Nutrient addition
bioassays as indicators of nutrient limitation of phytoplankton in a
eutrophic estuary, Mar. Ecol. Progr. Ser., 186, 95–104, 1999.
Howarth, R. W., Marino, R., Lane, J., and Cole, J. J.: Nitrogen fixation in
freshwater, estuarine, and marine ecosystems, 1. Rates and importance,
Limnol. Oceanogr., 33, 669–687, 1988.Hu, C., and Feng, L.: Modified MODIS fluorescence line height data product
to improve image interpretation for red tide monitoring in the eastern Gulf
of Mexico, J. Appl. Remote Sens., 11, 012003, 10.1117/1.JRS.11.012003, 2016.
Hu, C., Cannizzaro, J., Carder, K. L., Muller-Karger, F. E., and Hardy, R.:
Remote detection of Trichodesmium blooms in optically complex coastal
waters: Examples with MODIS full-spectral data, Remote Sens. Environ., 114,
2048–2058, 2010.
Jeffrey, S. and Vest, M.: Introduction to marine phytoplankton and their
pigment signatures, in: Phytoplankton pigment in oceanography, edited by:
Jeffrey, S. W., Mantoura, R. F. C., and Wright, S. W., UNESCO Publishing,
Paris, France, 37–84, 1997.
Joshi, I. D. and D'Sa, E. J.: Seasonal variation of colored dissolved
organic matter in Barataria Bay, Louisiana, using combined Landsat and field
data, Remote Sens., 7, 12478–12502, 2015.Joshi, I. D. and D'Sa, E. J.: An estuarine-tuned quasi-analytical algorithm
(QAA-V): assessment and application to satellite estimates of SPM in
Galveston Bay following Hurricane Harvey, Biogeosciences, 15, 4065–4086,
10.5194/bg-15-4065-2018, 2018.
Joshi, I. D., D'Sa, E. J., Osburn, C. L., Bianchi, T. S., Ko, D. S.,
Oviedo-Vargas, D., Arellano, A. R., and Ward, N. D.: Assessing chromophoric
dissolved organic matter (CDOM) distribution, stocks, and fluxes in
Apalachicola Bay using combined field, VIIRS ocean color, and model
observations, Remote Sens. Environ., 191, 359–372, 2017.
Kolber, Z. S., Zehr, J., and Falkowski, P. G.: Effects of growth irradiance
and nitrogen limitation on photosynthetic energy conversion in photosystem
II, Plant Physiol., 88, 923–929, 1988.
Kolber, Z. S., Prášil, O., and Falkowski, P. G.: Measurements of
variable chlorophyll fluorescence using fast repetition rate techniques:
defining methodology and experimental protocols, BBA-Bioenergetics, 1367,
88–106, 1998.
Latasa, M. and Bidigare, R. R.: A comparison of phytoplankton populations of
the Arabian Sea during the Spring Intermonsoon and Southwest Monsoon of 1995
as described by HPLC-analyzed pigments, Deep-Sea Res. Pt. II, 45,
2133–2170, 1998.
Lee, Z., Carder, K. L., Peacock, T., Davis, C., and Mueller, J.: Method to
derive ocean absorption coefficients from remote-sensing reflectance, Appl.
Optics, 35, 453–462, 1996.
Lee, Z., Carder, K. L., and Arnone, R. A.: Deriving inherent optical
properties from water color: a multiband quasi-analytical algorithm for
optically deep waters, Appl. Optics, 41, 5755–5772, 2002.Lee, H. A.: Effects of Physical Disturbance on Phytoplankton Diversity and
Community Composition in Galveston Bay, TX, during an Extreme Flooding Event,
Master's thesis, Texas A & M University, available at
http://hdl.handle.net/1969.1/161576 (last access: 29 April 2019), 2017.
Lohrenz, S. E., Weidemann, A. D., and Tuel, M.: Phytoplankton spectral
absorption as influenced by community size structure and pigment composition,
J. Plankton Res., 25, 35–61, 2003.
Lutz, V. A., Sathyendaranath, S., Head, E. J., and Li, W. K.: Changes in the
in vivo absorption and fluorescence excitation spectra with growth
irradiance in three species of phytoplankton, J. Plankton Res., 23, 555–569,
2001.
Mackey, M., Mackey, D., Higgins, H., and Wright, S.: CHEMTAX-a program for
estimating class abundances from chemical markers: application to HPLC
measurements of phytoplankton, Mar. Ecol. Progr. Ser., 144, 265–283, 1996.
Maritorena, S., Siegel, D. A., and Peterson, A. R.: Optimization of a
semianalytical ocean color model for global-scale applications, Appl.
Optics, 41, 2705–2714, 2002.
Moisan, T. A., Moisan, J. R., Linkswiler, M. A., and Steinhardt, R. A.:
Algorithm development for predicting biodiversity based on phytoplankton
absorption, Cont. Shelf Res., 55, 17–28, 2013.Moisan, T. A., Rufty, K. M., Moisan, J. R., and Linkswiler, M. A.: Satellite
observations of phytoplankton functional type spatial distributions,
phenology, diversity, and ecotones, Front. Mar. Sci., 4, 189,
10.3389/fmars.2017.00189, 2017.
Moore, C. M., Suggett, D. J., Holligan, P. M., Sharples, J., Abraham, E. R.,
Lucas, M. I., Rippeth, T. P., Fisher, N. R., Simpson, J. H., and Hydes, D.
J.: Physical controls on phytoplankton physiology and production at a shelf
sea front: a fast repetition-rate fluorometer based field study, Mar. Ecol.
Progr. Ser., 259, 29–45, 2003.
Moore, C. M., Suggett, D. J., Hickman, A. E., Kim, Y.-N., Tweddle, J. F.,
Sharples, J., Geider, R. J., and Holligan, P. M.: Phytoplankton
photoacclimation and photoadaptation in response to environmental gradients
in a shelf sea, Limnol. Oceanogr., 51, 936–949, 2006.
Morel, A. and Prieur, L.: Analysis of variations in ocean color 1, Limnol.
Oceanogr., 22, 709–722, 1977.
Nair, A., Sathyendranath, S., Platt, T., Morales, J., Stuart, V., Forget,
M.-H., Devred, E., and Bouman, H.: Remote sensing of phytoplankton
functional types, Remote Sens. Environ., 112, 3366–3375, 2008.
Örnólfsdóttir, E. B., Pinckney, J. L., and Tester, P. A.:
Quantification of the relative abundance of the toxic dinoflagellate,
karenia brevis (dinophyta), using unique photopigments, J. Phycol., 39,
449–457, 2003.
Örnólfsdóttir, E. B., Lumsden, S. E., and Pinckney, J. L.:
Nutrient pulsing as a regulator of phytoplankton abundance and community
composition in Galveston Bay, Texas, J. Experiment. Mar. Biol. Ecol., 303,
197–220, 2004a.
Örnólfsdóttir, E. B., Lumsden, S. E., and Pinckney, J. L.:
Phytoplankton community growth-rate response to nutrient pulses in a shallow
turbid estuary, Galveston Bay, Texas, J. Plankton Res., 26, 325–339, 2004b.
Paerl, H. W., Valdes, L. M., Pinckney, J. L., Piehler, M. F., Dyble, J., and
Moisander, P. H.: Phytoplankton photopigments as indicators of estuarine and
coastal eutrophication, AIBS Bull., 53, 953–964, 2003.
Pan, X., Mannino, A., Russ, M. E., Hooker, S. B., and Harding Jr., L. W.:
Remote sensing of phytoplankton pigment distribution in the United States
northeast coast, Remote Sens. Environ., 114, 2403–2416, 2010.
Pan, X., Mannino, A., Marshall, H. G., Filippino, K. C., and Mulholland, M.
R.: Remote sensing of phytoplankton community composition along the
northeast coast of the United States, Remote Sens. Environ., 115, 3731–3747,
2011.
Quigg, A., Roelke, D., and Davis, S. E.: Freshwater inflows and the health
of Galveston Bay: influence of nutrient and sediment load on the base of the
food web, Final report of the coastal coordination council pursuant to
National Oceanic and Atmospheric Administration Award No. NA07NOS4190144,
Texas A&M University at Galveston, Texas, USA, 49 pp., 2009.
Quigg, A., Litherland, S., Phillips, J., and Kevekordes, K.: Phytoplankton
productivity across Moreton Bay, Queensland, Australia: the impact of water
quality, light and nutrients on spatial patterns, in: Proceedings of the
13th International Marine Biological Workshop, Moreton Bay, Queensland,
Australia, 7–25 February 2005, 355–372, 2010.
Quigg, A. S.: Understanding the role of nutrients in defining phytoplankton
responses in the Trinity-San Jacinto Estuary, Final report to Interagency
Cooperative Contract No. 1104831134, Texas A & M University at Galveston,
Texas, USA, 56 pp., 2011.
Rayson, M. D., Gross, E. S., Hetland, R. D., and Fringer, O. B.: Time scales
in Galveston Bay: An unsteady estuary, J. Geophys. Res.-Oceans, 121,
2268–2285, 2016.
Ritchie, R. J.: Consistent sets of spectrophotometric chlorophyll equations
for acetone, methanol and ethanol solvents, Photosynthesis Res., 89, 27–41,
2006.
Roelke, D. L., Li, H.-P., Hayden, N. J., Miller, C. J., Davis, S. E., Quigg,
A., and Buyukates, Y.: Co-occurring and opposing freshwater inflow effects
on phytoplankton biomass, productivity and community composition of
Galveston Bay, USA, Mar. Ecol. Progr. Ser., 477, 61–76, 2013.Roesler, C. S. and Boss, E.: Spectral beam attenuation coefficient retrieved
from ocean color inversion, Geophys. Res. Lett., 30, 1468,
10.1029/2002GL016185, 2003.
Roesler, C. S. and Perry, M. J.: In situ phytoplankton absorption,
fluorescence emission, and particulate backscattering spectra determined from
reflectance, J. Geophys. Res.-Oceans, 100, 13279–13294, 1995.
Roesler, C. S., Etheridge, S. M., and Pitcher, G. C.: Application of an
ocean color algal taxa detection model to red tides in the Southern
Benguela, in: Proceedings of the Xth International Conference on Harmful
Algae, Florida Fish and Wildlife Conservation Commission and
Intergovernmental Oceanographic Commission of UNESCO, St. Petersburg,
Florida, USA, October, 2002, 303–305, 2003.
Roy, E. D., White, J. R., Smith, E. A., Bargu, S., and Li, C.: Estuarine
ecosystem response to three large-scale Mississippi River flood diversion
events, Sci. Total Environ., 458, 374–387, 2013.
Santschi, P. H.: Seasonality in nutrient concentrations in Galveston Bay,
Mar. Environ. Res., 40, 337–362, 1995.
Sathyendranath, S., Aiken, J., Alvain, S., Barlow, R., Bouman, H., Bracher,
A., Brewin, R., Bricaud, A., Brown, C., and Ciotti, A.: Phytoplankton
functional types from Space, in: Reports of the International Ocean–Colour
Coordinating Group (IOCCG); 15, International Ocean-Colour Coordinating
Group, P.O. Box 1006, Dartmouth, Nova Scotia, B2Y 4A2, Canada, 1–156, ISSN
1098-6030, 2014.
Schitüter, L., Riemann, B., and Søndergaard, M.: Nutrient limitation
in relation to phytoplankton carotenoid/chiorophyll a ratios in freshwater
mesocosms, J. Plankton Res., 19, 891–906, 1997.
Steichen, J. L., Denby, A., Windham, R., Brinkmeyer, R., and Quigg, A.: A
tale of two ports: Dinoflagellate and diatom communities found in the high
ship traffic region of Galveston Bay, Texas (USA), J. Coastal Res., 31,
407–416, 2015.
Steichen, J. L., Windham, R., Hala, D., Kaiser, K., Labonte, J. M.,
Petersen, L. H., Bacosa, H., Bretherton, L., Kamalanathan, M., Setta, S.,
and Quigg, A.: Rapid physicochemical and biological assessment of Galveston
Bay in the wake of Hurricane Harvey, abstract#AI44D-3023, in: Ocean
Sciences Meeting 2018, American Geophysical Union, Portland, Oregon, USA,
February, 2018.
Stramski, D., Reynolds, R. A., Kaczmarek, S., Uitz, J., and Zheng, G.:
Correction of pathlength amplification in the filter-pad technique for
measurements of particulate absorption coefficient in the visible spectral
region, Appl. Optics, 54, 6763–6782, 2015.
Suggett, D. J., Warner, M. E., Smith, D. J., Davey, P., Hennige, S., and
Baker, N. R.: Photosynthesis and production of hydrogen peroxide by
Symbiodinium (pyrrhophyta) phylotypes with different thermal tolerances 1,
J. Phycol, 44, 948–956, 2008.
Suggett, D. J., Moore, C. M., Hickman, A. E., and Geider, R. J.:
Interpretation of fast repetition rate (FRR) fluorescence: signatures of
phytoplankton community structure versus physiological state, Mar. Ecol.
Progr. Ser., 376, 1–19, 2009.
Sun, D., Huan, Y., Qiu, Z., Hu, C., Wang, S., and He, Y.: Remote-sensing
estimation of phytoplankton size classes from GOCI satellite measurements in
Bohai Sea and Yellow Sea, J. Geophys. Res.-Oceans, 122, 8309–8325, 2017.Thrane, J.-E., Kyle, M., Striebel, M., Haande, S., Grung, M., Rohrlack, T.,
and Andersen, T.: Spectrophotometric analysis of pigments: a critical
assessment of a high-throughput method for analysis of algal pigment mixtures
by spectral deconvolution, PloS one, 10, e0137645,
10.4319/lo.1997.42.5_part_2.1137, 2015.
Wang, G., Lee, Z., Mishra, D. R., and Ma, R.: Retrieving absorption
coefficients of multiple phytoplankton pigments from hyperspectral remote
sensing reflectance measured over cyanobacteria bloom waters, Limnol.
Oceanogr. Meth., 14, 432–447, 2016.
Wright, S. W. and Jeffrey, S. W.: Pigment markers for phytoplankton
production, in: Marine organic matter: biomarkers, isotopes and DNA, The
Handbook of Environmental Chemistry, edited by: Volkman, J. K., Springer,
Berlin, Heidelberg, Germany, 71–104, 2006.