Introduction
Phytoplankton plays a major role in marine ecosystems as the most important
primary producer in the ocean (Field et al., 1998). Phytoplankton is involved in the
long-term trapping of atmospheric carbon and its role in carbon transfer
from the upper ocean layers to deep waters highlight its influence on
climate (Boyce et al., 2010; Marinov et al., 2010). Beyond its role in the carbon cycle,
phytoplankton also plays a major role in modifying the biogeochemical
properties of water masses by converting most of the inorganic matter into
available organic matter (nitrogen, phosphate, silicate, sulfur, iron) and
determining the structure of the trophic status of marine environments.
Given this importance, it is insufficient to use a single proxy, such as
chlorophyll a measurements, for quantifying and qualifying phytoplankton over
large scales when attempting to understand its role in biogeochemical
processes (Colin et al., 2004). Such a proxy does not reflect changes in community
structure (Hirata et al., 2011) and does not yield robust biomass estimations
(Kruskopf and Flynn, 2006). Yet this classical proxy is frequently used to
study the spatial and temporal variability of phytoplankton from both
remotely sensed and in situ measurements. Le Quéré (Le Quéré et al., 2005)
pointed out the importance of taking into account the functionality of
phytoplankton species when considering the influence of phytoplankton
community structure on biogeochemical processes. This functionality concept
(i.e. phytoplankton functional types, PFTs) is described as set of species
sharing similar properties or responses in relation to the main
biogeochemical processes such as the N, P, Si, C and S cycles (diazotrophs
for the N cycle such as cyanobacteria, dimethylsulfoniopropionate producers for the S cycle such as Phaeocystis, silicifiers for the Si cycle such as diatoms,
calcifiers for the C cycle such as coccolithophorids, size classes mainly used
for the C cycle).
Representative data sets of phytoplankton functional types, size classes and
specific chlorophyll a concentrations are the subject of active research
using high-frequency, in situ dedicated analysis from automated devices such as
spectral fluorometers, particle scattering and absorption spectra recording
instruments, or automated and remotely controlled scanning flow cytometers
(SFCs). Among the high-frequency in situ techniques used to quantify phytoplankton
abundance, community structure and dynamics, SFC is the most advanced
instrument, counting and recording cell optical properties at the single-cell level. This technology has recently been adapted for the analysis of
almost all the phytoplankton size classes and focuses on the resolution of
phytoplankton community structure dynamics (Dubelaar et al., 1999; Olson et al., 2003;
Sosik et al., 2003; Thyssen et al., 2008a, b). In parallel, algorithms applied
to remote sensing data have been developed which are dedicated to
characterizing phytoplankton groups, PFTs or size classes (Sathyendranath et al.,
2004; Ciotti et al., 2006; Nair et al., 2008; Aiken et al., 2008; Kostadinov et al., 2009; Uitz et al., 2010;
Moisan et al., 2012). One of these algorithms, PHYSAT, has provided a description of
the dominant phytoplankton functional types (Le Quéré et al., 2005) for
open waters on a global scale, leading to various studies concerning the PFT
variability (Alvain et al., 2005, 2013; Masotti et al., 2011; Demarcq et al., 2011; Navarro
et al., 2014). PHYSAT relies on the identification of water-leaving radiance
spectra anomalies, empirically associated with the presence of specific
phytoplankton groups in the surface water. The anomalies were labelled thanks
to the comparison with high-pressure liquid chromatography (HPLC) biomarker
pigment matchups. To date, six dominant phytoplankton functional groups in
open waters (diatoms, nanoeukaryotes, Prochlorococcus, Synechococcus, Phaeocystis-like cells, coccolithophorids) have
been found to be significantly related to specific water-leaving radiance
anomalies from SeaWiFS (Sea-viewing Wide Field-of-view Sensor) sensor
measurements at a resolution of 9 km (Alvain et al., 2008). These relationships have
been verified by theoretical optical models (Alvain et al., 2012). This theoretical
study also showed that additional groups or assemblages could be added in
the future, once accurate in situ observations are available.
Describing the community structure on a regional scale will give better
quantification and understanding of the phytoplankton responses to
environmental change and, consequently, support the modification of
theoretical considerations regarding energy fluxes across trophic levels. It
is critical for understanding community structure interactions and
particularly when it is necessary to take into account the mesoscale
structure in a specific area (D'Ovidio et al., 2010), which is the case in areas
under the influence of regional physical forcing such as the English Channel
and the North Sea. Long-term changes detected in these regions have been
shown to impact local ecosystem functioning by inducing, for instance, a
shift in the timing of the spring bloom (Wiltshire and Manly, 2004; Sharples et al.,
2009; Vargas et al., 2009; Racault et al., 2013) or specific migrations of regional (Gomez
and Souissi, 2007) or dominant phytoplankton groups (Widdicombe et al., 2010). In
addition, hydrodynamic conditions have been shown to play a strong role in
the phytoplankton distribution on a regional scale (Gailhard et al., 2002; Leterme et al.,
2008). It is therefore crucial to develop specific approaches to
characterize the phytoplankton community structure (beyond global-scale
dominance) and its high-frequency variation in time and space. In order to
achieve this, large data sets of in situ analyses resolving PFTs are essential for
specific calibration and validation of regional remote sensing algorithms
such as PHYSAT. Flow-through surface water properties analysis for remote
sensing calibration optimizes the amount of matchups (Werdell et al., 2013;
Chase et al., 2013). For the purpose of collecting high-resolution in situ data describing
phytoplankton community structure, automated SFC technology allows samples
to be collected at high frequency, resolving hourly and kilometre scales with a
completely automated system. The instrument enables single-cell analysis of
phytoplankton from 1 to 800 µm and several millimetres in length for
chain-forming cells and automated sampling allows large space and time domains to
be covered at a high resolution (Sosik et al., 2003; Thyssen et al., 2008b, 2009;
Ribalet et al., 2010).
Based on this approach, a high-frequency study of the phytoplankton
community structure in the North Sea was conducted. The in situ observations from
SFC have been used for the first time and as a first trial to label PHYSAT
anomalies detected during the sampling period. Thus, the available data set
makes it possible to distinguish between different water-leaving radiance
anomaly signatures in which significantly distinct phytoplankton community
structures can be described, rather than just the dominant communities, as is the case in previous studies. Our results are an improvement over
conventional approaches as they allow the distribution of phytoplankton
community structure to be characterized at a high resolution, from both in situ and
day-to-day water-leaving radiance anomaly maps specific to the study area.
Materials and methods
Samples were collected during the PROTOOL/DYMAPHY-project cruise on board the
RV Cefas Endeavour from the 08 to 12 May 2011 in the south-west region of the
North Sea (Fig. 1). Automated coupled sampling using a PocketFerryBox
(PFB) and a Cytosense scanning flow cytometer (SFC, Cytobuoy b.v.) started
on the 08 May at 09:00 UTC and ended on the 12 May at 04:00 UTC. Water was
continuously collected from a depth of 6 m and entered the PFB at a pressure
of 1 bar maximum. Subsurface discrete samples were collected using Niskin
bottles on a rosette and analysed using a second Cytosense SFC (stations 4,
6 and 13 were used in this paper, Fig. 1).
Flow cytometry sampling points superimposed on the mixed layer
depth (m) calculated with modelled temperature of the water column from the
FOAM AMM7 (average values from 08 to 12 May 2011). Chosen stations
for phytoplankton pictures collection with the flow cytometer are labelled
(ST: station; ST4, ST6, ST13). Yellow squares correspond to MODIS matching
points for non-turbid waters selected between 06:00 and 18:00.
Phytoplankton community structure from automated SFC
Phytoplankton abundance and group description were determined by using two
Cytosense SFCs (Cytobuoy b.v.), one was fixed close to the PFB and sampling
the continuous flow of pumped sea water, the second one was used for
pictures collection from discrete samples. These instruments are dedicated
to phytoplankton single-cell recording, enabling cells from 1 to
800 µm and several millimetres in length to be analysed routinely in 1–10 cm3 of sea water. Each single cell or particle in suspension in the
solution passes through the laser beam thanks to the principle of
hydrodynamic focusing. The instrument then records the resulting optical
pulse shapes and counts each single particle.
Automation of the continuous flow sampling
Automated measurements were run from the continuous flow of sea water
passing through the PFB. Samples for SFC were automatically collected from a
450 cm3 sampling unit where water from the continuous flow was
periodically stabilized. This sampling unit was designed to collect bypass
water from the 1 bar PFB inlet. The sampling unit water was replaced within
a minute. One of the Cytosense instruments was directly connected to the sampling unit
and two successive analyses with two distinct protocols were scheduled
automatically every 10 min.
Flow cytometry analysis
A calibrated peristaltic pump was used to estimate the analysed volumes and
send the sample to the SFC optical unit. Suspended particles were then
separated using a laminar flow and subsequently crossed a laser beam
(Coherent Inc.; 488 nm, 20 mV). The instrument recorded the pulse shapes of
forward scatter (FWS) and sideward scatter (SWS) signals as well as red,
orange and yellow fluorescence (FLR, FLO, FLY respectively) signals for each
chain or single cell. The Cytosense instrument was equipped with two sets of
photomultiplier tubes (PMTs) (high- and low-sensitivity modes),
resolving a wider range of optical signals from small (∼ < 10 µm) to large particles
(∼ < 800 µm). Two trigger levels were applied on the high-sensitivity PMT to
discriminate highly concentrated eukaryotic picophytoplankton and
cyanobacteria (trigger level: FLR 10 mV; acquisition time: 180 s; sample
flow rate: 4.5 mm3 s-1), from less concentrated nano- and
microphytoplankton (trigger level: FLR 25 mV, acquisition time: 400 s;
sample flow rate: 9 mm3 s-1). Setting the trigger on red
fluorescence was preferred to the commonly FWS or SWS triggering as a
tradeoff between representative phytoplankton data sets and non-fluorescing
particles/noise recording, but this procedure affected the SWS and FWS pulse
shapes to some extent. To ensure good control and calibration of the
instrument settings, a set of spherical beads with different diameters was
analysed daily. This allowed the definition of estimated-size
calibration curves between total FWS (in arbitrary units, a.u.) and actual bead
size. This set of beads included 1, 6, 20, 45 and 90 µm yellow-green
fluorescence from Polyscience Fluoresbrite microspheres; 10 µm orange
fluorescence Invitrogen polystyrene FluoroSpheres; and 3 µm 488 nm
Cyto-cal™ alignment standards. To correct for the high refraction index
of polystyrene beads that generates an underestimation of cell size, we
defined a correcting factor by using 1.5 µm silica beads
(Polyscience, silica microspheres; Foladori et al., 2008). The phytoplankton
community was described using several two-dimensional cytograms built with
the Cytoclus® software. For each autofluorescing phytoplankton
cell analysed, the integrated value of FLR pulse shape (total red
fluorescence (TFLR), in a.u.) was calculated. For each phytoplankton cluster, the
amount of TFLR is reported per unit volume (TFLR cm-3, a.u. cm-3).
The TFLR cm-3 of each resolved phytoplankton cluster was summed total
TFLR cm-3) and was used as a proxy for chlorophyll a concentration. The
TFLR signal was corrected from high-sensitivity PMT saturation signal in the
case of highly fluorescing cells (> 4000 mV) thanks to the low-sensitivity PMTs that behaved linearly with the high-sensitivity PMT,
allowing the reconstruction of the high-sensitivity signal.
Discrete samples were collected during the cruise and analysed using a
second Cytosense SFC equipped with the image-in-flow system. The samples
were analysed using settings similar to those of the Cytosense coupled to
the PFB. The amount of pictures was determined before each sample
acquisition and pictures were randomly collected within the largest
particles until the predetermined number of pictures was reached.
Temperature and salinity
The PFB (4H-JENA©) was fixed on the wet laboratory bench, close to
the Cytosense, in order to share the same water inlet. This instrument
recorded temperature and conductivity (from which salinity was computed)
from the clean water supplied by the ship's seawater pumping system at a
frequency of one sample every minute.
Within the PFB data set, only data related to automated SFC analyses were
selected for plotting temperature–salinity diagrams.
Chlorophyll a
Samples for HPLC analyses and bench-top fluorometry (Turner® fluorometer) were collected randomly
within 6 h periods before or after the supposed on-board Aqua MODIS
(Moderate Resolution Imaging Spectroradiometer) sensor passage (12:30 UTC) to fulfil classical requirements in terms of in situ and remotely sensed
matchup criteria. Samples were collected from the outlet of the PFB,
filtered onto GF/F filters and stored directly in a -80 ∘C
freezer. The HPLC analyses were run on an Agilent Technologies, 1200 series.
Pigments were extracted using 3 cm3 ethanol containing vitamin E
acetate as described by Claustre et al. (2004) and adapted by Van Heukelem and
Thomas (2001). For bench-top fluorometry, the filters were subsequently
extracted in 90 % acetone. Chlorophyll a (Chl a) concentration was evaluated
by fluorometry using a Turner Designs model 10AU fluorometer (Yentsch and
Menzel, 1963). The fluorescence was measured before and after acidification
with HCl (Lorenzen, 1966). The fluorometer was calibrated using known
concentrations of commercially purified Chl a (Sigma-Aldrich®).
The PFB was equipped with a multiple fixed-wavelength spectral fluorometer
(AOA fluorometer, bbe©) sampling once every minute to obtain Chl a
values.
MODIS Chl a values corresponded to level-3 binned data consisting of the
accumulated daily level-2 data with a 4.6 km resolution.
Mixed layer depth
Daily water column temperature mapping was obtained from the Forecasting
Ocean Assimilation Model 7 km Atlantic Margin model (FOAM AMM7), available
from the MyOcean database (http://www.myocean.eu.org/). Model output temperature
depths were as follows: 0, 3, 10, 15, 20, 30, 50, 75, 100, 125 and 150 m.
Average mixed layer depth (MLD) on the five sampling days was calculated from
daily temperature data sets. MLD was defined as the depth associated with an
observed temperature difference of more than 0.2 ∘C with respect
to the surface (defined at 10 m; de Boyer Montégut et al., 2004).
Matching method between in situ and remotely sensed observations for
phytoplankton community structure
The PHYSAT approach is based on the identification of specific signatures in
the water-leaving radiance (nLw) spectra measured by an ocean colour sensor.
It is described in detail by Alvain et al. (2005, 2008). Briefly, this empirical
method has been first established by using two kinds of simultaneous and
coincident measurements: nLw measurements and in situ measurements of diagnostic
phytoplankton pigments. The presence of a specific phytoplankton group was
established based on pigment analysis. In a first step, this approach has
allowed for detection of four dominant phytoplankton groups identified within the
available in situ data set, based on the pigment inventories. Four groups were
detected first (diatoms, nanoeukaryotes, Synechococcus and Prochlorococcus) only in cases where they were dominant. Note
that “dominant” here is used following the definition by Alvain et al. (2005) as situations in
which a given phytoplankton group is a major contributor to the total
diagnostic pigments. This represented a limitation in using other potential
phytoplankton in situ analysis. In a second step, coincident remotely sensed radiance
anomalies (RAs) spectra between 412 and 555 nm were transformed into specific
normalized water-leaving radiance or RA spectra in order to evidence the
second-order variability of the satellite signal. This was done by dividing
the actual nLw by a mean nLw model (nLwref), which depends only on the
standard Chl a.
Then, coincident nLw spectra and in situ analysis were used to show that every
dominant phytoplankton group sampled during in situ sampling is associated with a
specific RA spectrum in terms of shape and amplitude. Based on this, a set
of criteria has been defined in order to characterize each group in function
of its RA spectrum, first by minimum and maximum values approach and more
recently using neuronal network classification tools (Ben Mustapha et al., 2014).
These criteria can be applied to global daily archives to get global maps of
the most frequent group of dominant phytoplankton. When no group prevails
over the month, the pixels are associated with an “unidentified”
phytoplankton group.
In this study, remotely sensed observations were selected on the basis of
quality criteria that ensured a high degree of confidence in PHYSAT as
described in Alvain et al. (2005). Thus, pixels were only considered when
clear-sky conditions were found and when the aerosol optical thickness, a proxy of
the atmospheric correction steps quality, was lower than 0.15. The effects
of sediments and/or coloured dissolved organic matter (CDOM) were minimized by focusing on phytoplankton
dominated waters as defined from the optical typology described in
Vantrepotte et al. (2012). Waters classified as turbid were therefore
excluded from the empirical relationship since the PHYSAT method is
currently not available for such areas. Waters classified as non-turbid
using the same criteria were selected and the PHYSAT algorithm applied to them. To
link coincident in situ and remotely sensed observations, a matchup exercise was
carried out. Matching points between in situ SFC samples (considered as in situ data) and
4.6 km resolution MODIS pixels (highest level-3 binned resolution) were selected
by comparing their concomitant position day after day. When more than one
in situ SFC sample was found in a MODIS pixel the averaged value of TFLR
(a.u. cm-3) for each phytoplankton group was calculated.
Statistics
Statistics were run in R software (CRAN, http://cran.r-project.org/). Before running correlation and comparison
tests on the different in situ sensors (for Chl a and total TFLR), the Shapiro
normality test was run. When data did not follow a normal distribution, a
Wilcoxon signed rank test was applied. Correlations between data were
defined using Spearman's rank correlation coefficient.
As the PHYSAT approach is based on the link between specific RA spectra (in
terms of shapes and amplitudes) and specific phytoplankton composition, the
set of remotely sensed data was separated into distinct groups with similar
RAs. The PHYSAT RA found over the studied area and matching the in situ SFC samples
was differentiated by applying a k-means clustering partitioning method
(tested either around means (Everitt and Hothorn, 2006) or around menoids (Kaufman and Rousseeuw, 1990)). The appropriate number of clusters (distinct
PHYSAT RA) was decided with a plot of the within groups sum of squares by
number of clusters extracted. A hierarchical clustering was computed to
illustrate the k-means clustering method. Within each k-means cluster,
SFC-defined phytoplankton community was described and differences between
TFLR cm-3 per phytoplankton group were compared within the different
PHYSAT spectra clusters using the Wilcoxon signed rank test.
Results
Temperature, salinity and mixed layer depth
The sampling track crossed four North Sea marine zones: western Humber,
Tyne, Dogger, eastern Humber and Thames (Fig. 1). The PFB measured
temperature associated with the SFC samples ranged between 8.83 and 12.39 ∘C with an average of 10.67 ± 0.72 ∘C.
Minimal temperatures were found in the western Humber area
(53–55∘ N and -1–1∘ E) and maximal temperatures were
found in the Thames area (54–52∘ N, 2–4∘ E; Fig. 2a).
Salinity from the PFB ranged between 34.02 and 35.07 with an average value
of 34.6 ± 0.26. Highest salinity values were found in the Dogger area
above 55∘ N and in the limit between the Humber and the Thames
areas, 53∘ N. Lowest salinity values were found in the Tyne area
around 55∘ N, -1∘ E and in the Thames area (by the
Thames plume; Fig. 2b).
(a) Temperature and (b) salinity measured with the PocketFerryBox.
Presented data are selected to match the scanning flow cytometry collected
samples. Grey bars delimit the traversed marine areas: H, Humber;
T, Tyne; D, Dogger; Th, Thames.
The mixed layer depth calculated from the FOAM AMM7 was used to illustrate
the physical environment of the traversed water masses. Different mixed
layer depth characterized the sampled area, with deeper MLD in the northern
part (15 to 30 m) and a shallower MLD in the southern area (∼ 10 m, Fig. 1). A tongue of shallow MLD (∼ 10 m) surrounded by
deeper MLD (∼ 20 m) crossed the sampling area at
∼ 55∘ N and ∼ 3∘ E.
Phytoplankton community from SFC analysis
A total of 247 SFC validated analysed samples were collected during this
experiment. Average distance between samples collected with the automated
SFC was of 2.2 ± 1.8 km when the system ran continuously. The sampling
rate was 25 ± 45 min. Up to 10 phytoplankton clusters were resolved
(Fig. 3) based on their optical fingerprints from SFC analysis. The 10
discriminated clusters were labelled as follows: PicoORG (Fig. 3a), PicoRED
(Fig. 3a), NanoSWS (Fig. 3b), NanoRED1 (Fig. 3c), NanoRED2 (Fig. 3b and c);
Micro1 (Fig. 3c and d), MicroLowORG (Fig. 3a), NanoORG and MicroORG (Fig. 3d), and Micro2 (Fig. 3d). Pictures were randomly collected (between 20 and 60
pictures per sample within Micro2) and were used to illustrate the most
frequently encountered class (Fig. 4). Station 4 (Fig. 4a), sampled at 12 m,
showed mostly a mixture of dinoflagellate-like cells (25 pictures collected
within 47 of the Micro2 cluster's counted cells). Station 6 (Fig. 4b) sampled at 7 m, showed pictures composed mainly of diatoms (Thalassiosira and Chaetoceros, 11 images collected
among 28 of the Micro2 cluster's counted cells). Station 13 (Fig. 4c) sampled at 7 m, gave a mixture of diatoms and dinoflagellates (58 pictures shot among the
99 of the Micro2 cluster's counted cells: 5 Chaetoceros, 30 Rhizosolenia, 10 dinoflagellates, 1 flagellate
and several unidentified cells).
(a) TFLO vs. TFLR (a.u.) cytogram with an FLR trigger level at 10 mV
showing the PicoORG, PicoRED and MicroLowORG clusters.
(b) Maximum SWS (a.u.) vs. TFLR (a.u.) cytogram with an FLR trigger level at 10 mV
showing the NanoSWS and NanoRED2 clusters and 3 µm beads.
(c) TFLR (a.u.) vs. TFWS (a.u.) cytogram with an FLR trigger level at 10 mV
showing the NanoRED1, NanoRED2 and Micro1 clusters.
(d) TFLO vs. TLFR (a.u.) cytogram with an FLR trigger level of 25 mV showing
the NanoORG1, MicroORG, Micro1 and Micro2 clusters and 10 µm
beads. Cluster colours are consistent across different panels.
Pictures of cells from the scanning flow cytometer image in flow
device collected within the Micro2 cluster. Surface closest stations where
Micro2 abundance was the highest (station 4, 6, and 13) are illustrated.
Cell abundance, average cell size and TLFR cm-3 for each cluster are
illustrated in Figs. 5, 6 and 7 respectively. Average abundance and sizes
of each cluster are addressed in Table 1. PicoRED cells were, on average, the
most abundant in the studied area (Fig. 5b and Table 1), followed by
NanoRED2, PicoORG, NanoRED1 and Micro1 (Fig. 5f, a, c and g respectively,
Table 1). The other clusters' abundances were below 1.102 cells cm-3 on average (Fig. 5d, e, h, i, j; Table 1). PicoORG cells were
the smallest estimated (Fig. 6a, Table 1), while the largest estimated were
MicroORG, MicroLowORG and Micro2 cells (Fig. 6h, i and j respectively,
Table 1).
Abundance (103 cells cm-3) of each phytoplankton cluster
resolved with the scanning flow cytometer. In order to show evidence of distribution,
scales have not been homogenized. Grey bars separate the traversed
marine areas: H, Humber; T, Tyne; D, Dogger; Th, Thames.
Average estimated size for each phytoplankton cluster resolved
with the scanning flow cytometer. In order to show evidence of distribution,
scales have not been homogenized. Grey bars separate the traversed marine areas:
H, Humber; T, Tyne; D, Dogger; Th, Thames.
The western Humber zone (Fig. 1) was marked by the highest abundances of
PicoRED, PicoORG, MicroORG, MicroLowORG and Micro1 (Fig. 5b, a, h, i and g). The eastern part of the Humber zone (Fig. 1) was marked by the highest
abundances of NanoRED1 and Micro1 (as for the western part; Fig. 5c, g).
High values of PicoRED were also observed in this part of the Humber zone.
The Tyne zone (Fig. 1) had the highest abundance of NanoORG and Micro2
clusters (Fig. 5d, j), and the lowest abundance of PicoRED and NanoSWS.
High abundance values of MicroORG were also observed (Fig. 5h). The size of
the NanoSWS and the NanoRED2 were the greatest in this zone (Fig. 6e, f).
The Dogger zone (Fig.1) was dominated in terms of abundance by the PicoRED
and the PicoORG, where the sizes were the smallest (Fig. 6b and a) but did
not show the highest abundance values. The cell sizes of Micro1 were the
greatest in this zone (Fig. 6g). Observations in the Thames zone (Fig. 1)
produced the maximal abundance of NanoSWS and NanoRED2 (Fig. 6e, f). Sizes
were the greatest for PicoORG, NanoRED1 and NanoSWS (together with the Tyne
zone; Fig. 6a, c, e). TFLR follows similar trends to abundance (Fig. 7).
Minimal, maximal, average and standard deviation of abundance
(cells cm-3) for each defined phytoplankton cluster followed by the
size-estimated (µm) average ± standard deviation values.
Cluster's name
Abundance
Average abundance ± SD
Average size ± SD
min–max (cells cm-3)
(cells cm-3)
(µm)
PicoORG
25–18 710
1559 ± 2821
1.09 ± 0.17
PicoRED
275–26 960
5674 ± 4647
1.83 ± 0.32
NanoRED1
97–7172
888 ± 942
2.33 ± 0.33
NanoORG
< 10–759
87 ± 150
5.8 ± 2.1
NanoSWS
< 10–376
99 ± 93
10 ± 2.56
NanoRED2
200–54 880
4187 ± 7878
6.4 ± 1.4
Micro1
< 10–4392
420 ± 769
16.9 ± 5.6
MicroORG
< 10–306
48 ± 60
23.5 ± 10
MicroLowORG
< 10–687
69 ± 111
23.75 ± 8.6
Micro2
< 10–420
37 ± 59
65.5 ± 21.0
Scanning flow cytometer total red fluorescence per unit volume
(SFC TFLR cm-3) for each phytoplankton cluster. Superimposed large
white squares are the matching points with MODIS pixels in non-turbid waters
between 06:00 and 18:00. Diamonds correspond to the night SFC samples matching
MODIS passage but are not taken into account because of the possible differences
between day and night community structures. In order to show evidence of distribution,
scales have not been homogenized. Grey bars separate the traversed
marine areas: H, Humber; T, Tyne; D, Dogger; Th, Thames.
Comparison between scanning flow cytometry, total red fluorescence and
chlorophyll a analysis
Several bench-top and in situ instruments, i.e. HPLC, Turner fluorometer and the PFB
AOA fluorometer, were used to give exact and/or proxy values of Chl a.
Similarly to temperature and salinity, the PFB AOA fluorometer samples were
selected to match SFC samples. Overall values of Chl a originating from these
instruments were superimposed to the total TFLR cm-3 (by summing up the
TFLR cm-3 values of the observed cluster) and the MODIS Chl a values
matching the points in Fig. 8. HPLC values varied between 0.21 and 7.58 µg dm-3 with an average of
1.57 ± 2.01 µg dm-3. Turner fluorometer values varied between 0.41 and 2.31 with an
average of 1.24 ± 0.7 µg dm-3. AOA fluorometer values
varied between 0.73 and 28.53 µg dm-3with an average of 4.44 ± 5.54 µg dm-3. The total TFLR cm-3 from SFC,
normalized with 3 µm bead red fluorescence varied between 5011 and
399 200 a.u. cm-3 with an average value of 64 394.5 ± 67 488.4 a.u. cm-3. The Shapiro normality test showed non-normality for each of
the variables, so a Wilcoxon test was run between techniques involving similar
units. HPLC and Turner Chl a concentrations were not significantly different
(n= 9, p= 0.65) and the correlation was significant (Spearman, r= 0.98,
Table 2). The absolute values from both techniques were significantly
different from the AOA fluorometer values (n= 9, p < 0.001 for both)
but were significantly correlated (Spearman, r= 0.86 and r= 0.82 for the HPLC
and Turner fluorometer respectively, Table 2). The SFC total TFLR
(a.u. cm-3) from summing up the TFLR of all the phytoplankton groups was
used for comparison with other Chl a determinations. Correlations with the
AOA fluorometer, HPLC and Turner fluorometer results were all
significant as shown in Table 2.
Spearman's rank correlation coefficient between the different
methods used for chlorophyll a estimates and with the total TFLR from the
scanning flow cytometer per unit volume.
Spearman's correlation
SFC TFLR cm-3
AOA fluorometer
HPLC Chl a
Turner Chl a
coefficient
(a.u.) n= 247
(µg dm-3) n= 254
(µg dm-3)n= 12
(µg dm-3) n= 9
SFC
1
0.93***
0.82***
0.82***
Total TFLR cm-3 (a.u.)
AOA fluorometer
1
0.86***
0.82***
(µg dm-3)
HPLC Chl a
1
0.98***
(µg dm-3)
Turner Chl a
1
(µg dm-3)
***p < 0.001; **p < 0.01.
SFC total TFLR per cm-3 compared to Chl a analyses using
different instruments. Refer to “Material and methods” for a detailed
description of each method. Blue triangles: AOA fluorometer PFB (Chl a µg dm-3). Black diamonds: SFC total TFLR cm-3
(a.u. cm-3). Green triangles: Turner fluorometer (Chl a µg dm-3). Grey triangles: HPLC (Chl a µg dm-3). Red squares:
MODIS Chl a values corresponding to non-turbid waters (after Vantrepotte et
al., 2012) and selected between 06:00 and 18:00 (Chl a µg dm-3).
PHYSAT anomalies and SFC phytoplankton community composition, extrapolation
to the non-turbid classified waters in the North Sea
Considering our database of coincident SFC in situ and MODIS remotely sensed
observations, a total of 56 matching points were identified, from which only
38 points corresponded to non-turbid classified waters. Matching points
between in situ sampling and remote sensing pixels for the purpose of the PHYSAT
empirical calibration were selected in the daytime period 06:00–18:00.
Additional samples collected out of this period results in the loss of
correlation significance between MODIS Chl a and the AOA fluorometer Chl a
within the SFC data set (r= 0.49, p= 0.06, n= 15, Spearman rank test),
leaving 15 SFC matching points (Figs. 1 and 8). The Chl a values found in
the matching points were lower than 0.5 µg dm-3 (Fig. 8).
PHYSAT RAs were calculated based on the method of Alvain et al. (2005) and the average signal was recalculated to fit the sampling
area. The RAs were separated into two distinct anomalies using the within
sum-of-squares minimization (Fig. 9a) and illustrated on a dendrogram (Fig. 9b).
These two distinct types of anomalies in terms of shape and amplitude are
illustrated in Fig. 9c and d and the anomaly characteristics are
summarized on Table 3. The first anomaly set (N1, Table 3) was composed of 5
spectra that had overall higher values than the second anomaly set (N2,
Table 3), composed of the other 10 spectra. The corresponding SFC cluster
proportion of TFLR cm-3 to the overall total TFLR cm-3 found within
the two anomalies are illustrated in Fig. 10a and b. Similarly, the
relative difference of each phytoplankton cluster's TFLR cm-3 within
the two anomalies to its overall TFLR cm-3 median value are illustrated
in Fig. 10c and d. Considering our previous analyses, N1 and N2
community structures were dominated by NanoRED2 TFLR cm-3 (Fig. 10a
and b). Regarding each distinct cluster relative difference to its overall
median value, samples corresponding to N1 anomalies had significantly higher
NanoRED1 TFLR cm-3, higher NanoORG TFLR cm-3 and higher MicroORG
TFLR cm-3; while the samples corresponding to N2 anomalies had only
higher PicoRED TFLR cm-3 (Wilcoxon rank test, N1, n= 5; N2, n= 10, Fig. 10c and d). Temperature, salinity, MODIS Chl a and SFC total TFLR cm-3
found in each in situ sample corresponding to both sets of anomalies are illustrated
in Fig. 11. Samples in the N1 pixels were found to be significantly warmer (11.3 ± 0.32 ∘C in N1 and 10.94 ± 0.23 ∘C in N2,
p < 0.1, Wilcoxon rank test, Fig. 11a), not significantly different in
terms of salinity, although N1 waters were less salty (Fig. 11b),
significantly richer in Chl a (0.87 ± 0.19 µg dm-3 in N1
and 0.43 ± 0.07 µg dm-3 in N2, p < 0.01,
Wilcoxon
rank test, Fig. 11c), but not significantly different in total
TFLR cm-3 values (Fig. 11d).
(a) Within sum of squares for the optimal number of
k nodes selected corresponding to PHYSAT anomalies. (b) Cluster dendrogram defining
the two main nodes grouping similar PHYSAT anomalies matchups (N1 and N2). (c, d) Corresponding RA spectra for N1 and N2. Red dashed
lines correspond to the minima and maxima values of the spectra as described
in Table 3.
Minimal and maximal radiance anomaly (RA) values for each collected
MODIS wavelength (nm) that characterizes the edges for the two PHYSAT
RA spectra (N1 and N2) observed in this study.
Node
RA (412) nm
RA (412) nm
RA (443) nm
RA (443) nm
RA (488) nm
RA (488) nm
RA (531) nm
RA (531) nm
Min
Max
Min
Max
Min
Max
Min
Max
N1 (n= 5)
1.06
1.30
0.96
1.24
0.91
1.10
0.91
1.09
N2 (n= 10)
0.74
0.97
0.75
0.93
0.70
0.89
0.72
0.93
Considering the specificity of each set of RAs in terms of phytoplankton and
environmental conditions, it is interesting to map their frequency of
detection in our area of interest. A pixel is associated with an anomaly
when the RA values at each wavelength fulfilled the criteria of Table 3. The
frequencies of occurrence over the sampling period based on a composite
overlapping the sampling period are illustrated in Fig. 12a and b. Pixels
corresponding to N1 anomaly were mostly found in the 54–56∘ N area
(Dogger and German, Fig. 1), following the edge between the shallow MLD
tongue and the deepest MLD zones (Fig. 1), but also near the Northern
Scottish coast (Forth, Forties and Cromarty, Fig. 12a), where MLD was
shallow (Fig. 1). The N2 anomaly pixels were mostly found in the Forties,
Fisher and German area, on much smaller surfaces (Fig. 12b).
(a, b) The clusters' proportional contribution to the total
TFLR cm-3 within each PHYSAT anomaly (N1 and N2). (c, d) Within each anomaly, the clusters' TFLR cm-3 proportional difference to
its median value calculated on the entire matching points data set. Wilcoxon
rank test was run for each cluster between the two anomalies. ***p < 0.001; **p < 0.01; *p < 0.1.
Box plots within each PHYSAT anomaly (N1, N2) of (a) temperature
(∘C), (b) salinity, (c) chlorophyll a (as estimated from MODIS
level 3 binned) and (d) total TFLR (a.u. cm-3). Wilcoxon rank test was run for
each parameter between the two anomalies. ***p < 0.001; **p < 0.01; *p < 0.1.
(a, b) Frequency of occurrence of the two distinct anomalies
(N1 and N2) over the North Sea during the sampling period (08 to 12 May 2011). Yellow squares correspond to MODIS matching points for
non-turbid waters selected between 06:00 and 18:00 and used to distinguish N1
and N2 PHYSAT anomalies.
Discussion
Water mass dynamics generates patchiness which modifies phytoplankton
community structure and makes it difficult to follow a population over time
and at a basin scale. In this context, the hourly observation of
phytoplankton at the single-cell and community level and its daily
spatial structure resolution from extrapolation using PFT remote sensing
mapping can help to follow spatial distribution of phytoplankton
communities. The improvement of PFT mapping, i.e. from dominant groups to
the community structure resolution, is one of the ideas generated in this
paper. This paper shows for the first time that SFC data sets can be used for
labelling PHYSAT anomalies at the daily scale. The SFC is a powerful
automated system aimed to be implemented in several vessels of opportunity
and monitoring programs for future PHYSAT anomalies identification at the
daily scale and at the community structure level. A recent publication that
enables the classification of a large range of anomaly spectra (Ben Mustapha
et al., 2014) should help to make this easier. Thus, the knowledge and the tools
are available, which augurs well for understanding phytoplankton
heterogeneity and variability over high-resolution spatio-temporal scales.
Indeed, resolving phytoplankton community structure over the sub-mesoscale
and hourly scale is a good way to understand the influence of environmental
short-scale events (Thyssen et al., 2008a; Lomas et al., 2009), seasonal (or not)
succession schemes, resilience capacities of the community after
environmental changes and impacts on the specific growth rates (Sosik et al., 2003,
Dugenne et al., 2014). Resolving the community structure and the causes of
variations at several temporal and spatial scales has great importance in
further understanding the phytoplankton functional role in biogeochemical
processes. This scale information is currently lacking for the global
integration of phytoplankton in biogeochemical models, mainly due to the
lack of adequate technology needed to integrate the different
levels of complexity linked to phytoplankton community structure.
Phytoplankton community description
Phytoplankton community structure from automated SFC is described through
clusters of analysed particles sharing similar optical properties. Thus
cluster identification at the species level is speculative and, as with any
cytometric optical signature, it needs sorting and genetic or microscopic
analysis to be resolved at the taxonomic level. This deep level of
phytoplankton diversity resolution requirement is not needed in
biogeochemical processes studies in which functionality is preferred to
taxonomy (Le Quéré et al., 2005). In this context, most of the
optical clusters could be described at the plankton functional type level
because of some singular similarities combining abundance, size, pigments
and structure proxies obtained from optical SFC variables (Chisholm et al., 1988;
Veldhuis and Kraay, 2000; Rutten et al., 2005; Zubkov and Burkill, 2006). The Cytobuoy
instrument used in this study was developed to identify phytoplankton cells
from picophytoplankton up to large microphytoplankton with complex shapes,
even those forming chains. Indeed, the volume analysed was close to 3 cm3, giving accurate counts of clusters
with abundances as low as 30 cells cm-3 (100 cells counted), under which the coefficient of variation
exceeds 10 % (Thyssen et al., 2008a). Such low abundances were found for
some of the clusters identified in this study (NanoORG, MicroORG and Micro2
clusters for which the median abundance value was close to 30 cells cm-3), in agreement with concentrations observed in previous
studies for the possibly related phytoplankton genus, as discussed below,
i.e. cryptophytes (Buma et al., 1992), diatoms and dinoflagellates (Leterme et al., 2006).
Previous comparisons between bench-top flow cytometry and remote sensing
(Zubkov and Quartly, 2003) could technically not include the entire size
range of nano- to microphytoplankton. The Cytobuoy SFC resolves cells up to 800 µm in theory, but this depends on the counted cells in the volume
sampled (which is approximately 10 times more than classical flow
cytometry). However, the largest part of phytoplankton production in the
North Sea is driven by cells < 20 µm (Nielsen et al., 1993), and we
can consider this size class to be correctly counted with the SFC.
Furthermore, significance between the sum of each cluster's TFLR (total
TFLR cm-3) and bulk chlorophyll measurements (Table 2 and Fig. 7)
confirms the power of SFC for phytoplankton community resolution.
PicoORG cells could be labelled Synechococcus (Waterbury et al., 1979; Li, 1994) based on their
phycoerythrin pigment fluorescence (Fig. 3a), and their size could be estimated between
0.8 and 1.2 µm (Fig. 6a) and their abundances around 102–104 cells cm-3 (Fig. 5a). PicoRED cells could be autotrophic
eukaryotic picoplankton, as their cell size varied between 1 and 3 µm
(Fig. 6b) and contained Chl a as their main pigment. Thus, PicoORG and
PicoRED clusters contained the smallest cells found above the so called
non-fluorescing/electronic noise background of this instrument (Fig. 3a and b). As Prochlorococcus is expected to be absent in these waters, we can conclude that
the cytometer observed most of the phytoplankton size classes when
sufficiently concentrated in the analysed volume. NanoRED1 cells exhibited
abundance and sizes close to those of Phaeocystis haploid flagellate cells (3–6 µm, Fig. 6c; Rousseau et al., 2007, and references therein). Their presence, mostly in the Humber area (Fig. 5c), suggests that this area corresponded to a
period between the inter-bloom (haploid stage, life stage persisting between
two blooms of diploid colonial cells) and the start of the Phaeocystis bloom (Rousseau
et al., 2007). Similarly, NanoRED2 could be referred to as Phaeocystis diploid
flagellates or free colonial cells, based on their size and abundance (4–8 µm and 0–50 × 103 cells cm3 (Figs. 6f and 5f respectively),
Rousseau et al., 2007). Their maximal abundance was found in the southern
North Sea Thames area. Their presence suggested an area of Phaeocystis colonial
blooming stage (Guiselin, 2010).
MicroORG cells, whose abundance and size are close to those of some large
cryptophytes cells, were found in the same areas as NanoORG cells (Fig. 5h
and d respectively), which are related to smaller Cryptophyceae cells. MicroLowORG cells
with sizes close to that of MicroORG cells, and although low in
concentration, emitted orange fluorescence and could represent cells with
little phycoerythrin content. NanoSWS cluster was composed of high-SWS
scattering cells that are consistent with the signature of
Coccolithophorideae cells (van Bleijswijk et al., 1994; Burkill et al., 2002). The observed abundances
did fit with the low Coccolithophorideae concentrations observed in the southern North Sea
(Houghton, 1991). The Micro1 cluster could correspond to small
nanoplanktonic diatom cells (∼ 10–30 µm, Fig. 6g).
Regarding the size range, this cluster could represent several species. They
were mainly found within the Humber area. The Micro2 cluster was mostly
composed of large diatoms (Rhizosolenia, Chaetoceros) and dinoflagellates (Fig. 4) within the size
range of 40–100 µm (Fig. 6j) as observed in the pictures (Fig. 4).
The presence of these groups illustrates the boundary between the end of the
diatom bloom and the development of a dinoflagellate bloom, from which it
could be possible to make a link with the Dinophysis norvegica and Alexandrium early summer bloom, observed
in the Tyne region by Dodge (1977). This is in agreement with the
stratification observed within the Thames zone (Fig. 1).
Phytoplankton community structure at the North Sea basin scale
The data sets from the spatial (km) and the temporal (hourly) scales for
phytoplankton community structure based on single-cell optical properties
are important for validating the methods describing phytoplankton community
structure from space. Ocean algorithms need specific information on water
properties and phytoplankton structure and are dependent on validation from
in situ observations, which is always complex to collect and limited by sky condition
criteria. The PHYSAT method was built on an empirical relationship between
dominant phytoplankton functional types from in situ HPLC analysis and RA. The
method was thus limited to dominance cases only as HPLC analysis cannot give
us more information. The remote sensing synoptic extrapolation
concerning phytoplankton community structure remains to be established and,
in spite of a theoretical validation (Alvain et al., 2012), still depends on
important in situ data point collection in order to build robust empirical
relationships. In this study, the combination of phytoplankton high-frequency analysis from an automated SFC with the PHYSAT method proved to be
an excellent calibration by giving an unprecedented amount of matching
points for only two significant sampling days (number of analysed samples
for non-turbid waters matching MODIS pixels: 38; number of samples used
between 06:00 and 18:00: 15, corresponding to 39.5 % profitability), compared
to the 14 % matching points from the GeP&CO data set (Alvain et al., 2005).
The combination of SFC and PHYSAT has shown that a first set of specific
anomalies (N1) can be associated with NanoRED1, NanoORG and MicroORG, which
contributed more to the total TFLR cm-3 (a proxy of Chl a, Fig. 7, Table 2) than in the second set of anomalies (N2), in which PicoRED cells
contributed significantly more to the total TFLR cm-3, as well as where
Micro1 contribution to total TFLR cm-3 was above its overall median
value observed along the matching points (Fig. 10d). Spatial successions
between diatoms (as could be found in the NanoRED1 and Micro1 clusters) and
cryptophytes (corresponding to the NanoORG and MicroORG specific signatures)
revealed differences in stratification, lower salinity and shallower MLD
(Moline et al., 2014; Mendes et al., 2013). Indeed, the N1 anomaly corresponds to areas of
low MLD (Fig. 1) following the main North Sea current from the south-west to
the north-east (Holligan et al., 1989), surrounding the Dogger bank. This anomaly
was also found on the north-western part of the northern North Sea,
following the Scottish coastal water current with a shallow MLD (Figs. 1,
11a). The N2 anomaly was observed with the deeper MLD of the Forties, Fisher
and German areas (Figs. 1 and 11b). These N2 areas corresponded to a
phytoplankton community still blooming, while the N1 anomaly areas might be
at a stage of late blooming, in which conditions fit Cryptophyceae
development and grazing (cells of Myrionecta rubra were observed when using the image-in-flow system, not shown). These organisms were found to be dominant in the areas
surrounding the Dogger bank from observations and counts carried out by
Nielsen et al. (1993) during the same period.
In conclusion, our study of phytoplankton community structure distribution
resolved at the sub-mesoscale evidenced the importance of the North Sea
hydrological context. Significant differences between the two sets of
anomalies observed during the sampling period are mainly due to cryptophyte-like cells and pico- to nanophytoplankton size class cells. This daily-scale
resolution, thanks to high-resolution techniques combined with single-cell and
remote technologies, will help in understanding the role of circulation and
hydrological properties of the water masses on the phytoplankton
composition, succession schema, spreading, and bloom triggering and
collapsing.