Journal of Plankton Research Vol.19 no.9 pp.1265-1274, 1997
Phytoplankton patchiness: quantifying the biological contribution
using Fast Repetition Rate Fluorometry
Peter G.Strutton 14 , James G.Mitchell1, John S.Parslow2 and
Richard M.Greene 3
'School of Biological Sciences, Flinders University of South Australia, Adelaide,
South Australia,
2
CSIRO Division of Fisheries, Hobart, Tasmania, Australia and 3Department of
Oceanography, Texas A&M University, TX, USA
4
Present address for correspondence: Monterey Bay Aquarium Research Institute,
PO Box 628, Moss Landing, CA 95039-0628, USA. E-mail:[email protected]
Abstract The coupling between physical and biological processes is important in the marine environment because phytoplankton growth and turbulent miring operate on similar time scales. Since the
1970s, the relative contribution of these two parameters to phytoplankton patchiness has been studied
using analytical tools such as spectral analysis. Here, for the first time, we combine spectral analysis
with Fast Repetition Rate Fluorometry as a method of quantifying the importance of photosynthetic
efficiency in the biological-physical interactions that lead to oceanic chlorophyll distributions. The
results indicate that photosynthetic efficiency is correlated with the sum of the corresponding chlorophyll power spectrum; a measure of the total spatial variability of chlorophyll. In addition, high photosynthetic rates are associated with regions where the spatial distributions of chlorophyll and salinity
differ, as quantified by the slopes of their respective power spectra. The results are particularly evident
at large spatial scales, representing an empirical verification of previous theoretical work regarding
phytoplankton spatial structure.
Introduction
Since the pioneering work of Platt, Denman and Okubo (Platt, 1972; Denman
and Platt, 1976; Denman et al, 1977), the interaction between phytoplankton
community dynamics and turbulence has been the subject of increasingly detailed
studies, both theoretically and empirically [see Steele (1978) and Powell and
Okubo (1994) for reviews on the topic]. Platt (1972) used newly available flowthrough fluorescence technology to collect time series of chlorophyll a concentration at a station in the Gulf of St Lawrence. Making use of Taylor's Frozen
Field Hypothesis (Tennekes and Lumley, 1973), the extension from temporal
variation at a point to spatial variation associated with ocean currents was made.
It was concluded that the power spectrum of chlorophyll a closely followed the
krm rule derived by Kolmogorov (1941) for the inertial subrange. This is the
region of the turbulence spectrum where energy is transferred from larger eddies
(which derive their energy from sources such as winds and tidal patterns) to the
smallest eddies (which dissipate their energy into heat; viscous dissipation).
Platt's discovery implied that the spatial distribution of phytoplankton was
governed primarily by the turbulent environment, and not by the net growth rate
(which includes division and predation) of the cells themselves.
Denman and Platt (1976) refined this initial work, using dimensional analysis
to define two distinct regions in the chlorophyll a power spectrum. If T (S) represents the time taken for a turbulent eddy to transfer its energy to an eddy half its
© Oxford University Press
1265
P.tiStnitton et oL
size, and a (s~]) is the doubling time of the phytoplankton, then for a"1 > T, the
growth rate of the phytoplankton is insufficient to produce a spatial distribution
that is different from that of quantities such as temperature or salinity. The phytoplankton behave as passive tracers. However, for a"1 < T, the phytoplankton are
doubling sufficiently quickly for their spatial distribution to be no longer nullified
by turbulence. The spatial structure of the community, as represented by the
chlorophyll a distribution, cannot be destroyed by the diffusive action of the
eddies.
The relationship between a and T was quantified by Denman et al (1977), who
proposed a typical patch size for phytoplankton in the open ocean of 5-10 km,
using average values for net phytoplankton growth rate and turbulent diffusion.
Subsequent widespread application of spectral analysis to time series and transects of physical and biological data permitted a general understanding of phytoplankton patchiness on kilometre scales. Attention then turned to understanding
the top-down (zooplankton grazing) and bottom-up (light, micro- and macronutrient) contributions to the net growth rate, a (Parsons et al, 1984).
More recently, the iron hypothesis (Martin et al, 1994; Coale et al, 1996) has
dominated research into the relative contribution of nutrients and grazing to
phytoplankton growth, in conjunction with the development of a more effective
technique of measuring phytoplankton primary productivity: Fast Repetition
Rate (FRR) Fluorometry (Kolber and Falkowski, 1993; Kolber et al, 1994;
Behrenfeld et al, 1996). By using a sequence of 64 flashes, each of -0.5 us duration, separated by ~3 us, the FRR fluorometer measures the change in the fluorescence signal at 685 nm, from a minimum value of Fo to a maximum of Fm. From
these parameters, one can derive the maximum change in the quantum yield of
fluorescence, A<J>m = (Fm - F0)/Fm, which is a measure of the photochemical
efficiency of photosystem II (PSH). Under optimum conditions, A<t>m reaches a
maximum value of 0.65, but under the influence of nutrient limitation or photoinhibition, A(J)m decreases, and typical values for the iron-deficient (but macronutrient-replete) equatorial Pacific are -0.3 (Kolber et al, 1994). Thus, by
normalizing A<J>m to 0.65, one can obtain a measure of the fraction of operational
PSH reaction centres: / - A^n/O^. By incorporating / into a semi-empirical
model, Kolber and Falkowski (1993) obtained a correlation of 0.86 between fluorescence- and 14C-derived values of phytoplankton productivity.
FRR Fluorometry has several advantages over traditional methods that rely on
isotope incorporation or oxygen evolution. Most importantly, the spatial or temporal resolution over which measurements can be made is greatly increased, as
each assay takes of the order of minutes, compared to hours for the abovementioned techniques. As a result of this, the instrument can be used in flowthrough mode at sea, to produce transect data in the same manner as one collects
temperature, salinity or fluorescence data. In addition, artefacts associated with
the isolation of natural assemblages of phytoplankton, such as trace metal toxicity and light shock, are minimized due to the lack of incubation required.
In this study, we have combined FRR Fluorometry with spectral analysis of
transects of salinity and chlorophyll a from Antarctic waters to examine coupling
between the biological and physical processes that contribute to phytoplankton
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FRR Fluorometry and biological-physical coupling
heterogeneity. The results indicate that high values of A<}>m are associated with
enhanced spatial heterogeneity of chlorophyll, which differs from the spatial
structure of a passive tracer such as salinity.
Method
Data collection
Data were collected during Voyage 4 of the RSV 'Aurora Australis',
January-March 1996. The cruise track, as shown in Figure 1, was from Hobart to
Davis Station (29 January), along the Antarctic coast to Casey Station (19-22
February) and Durmont D'urville (25 March), returning to Hobart via MacQuarie Island (28 March). Time (UTC), position, ship's speed, water temperature, salinity, in vivofluorescence,incident photosynthetically available radiation
(PAR, 400-700 nm) and incident total solar radiation (300-3000 nm) were logged
every 10 s for the duration of the cruise, using the ship's on-board computer
system. The FRR fluorometer was used in flow-through mode and positioned
such that its water flow was in parallel with the thermosalinograph and fluorometer. Defining a flash sequence as one set of 64 flashes (-0.5 us each), as
described in the Introduction, the FRR fluorometer was configured to use a cycle
of 4 X 16flashsequences (where each flash sequence is separated by ~2 s), hence
producing a data point approximately every 128 s.
Post-cruise, combined data files of all above-mentioned parameters were compiled. From these, transects were selected, defined as regions where the ship's
speed was >5 knots (-2.5 m s"1). This is a necessary condition for the employment
80
100
120
140
160
Longitude [E]
Fig. L Cruise track of Voyage 4 of the 'Aurora Australis'; 19 January-31 March 1996.
1267
P.GStnrtton el at
of Taylor's Frozen Field Hypothesis (aka the Frozen Turbulence Approximation)
which states that if the speed of a probe [U (m s~')] traversing a turbulence field
is large compared to the velocity field [u (m sr1)], then the media can be regarded
as essentially stationary. This is made possible by the substitution t = x/U, where
t (s) is time and x (m) is position (Tennekes and Lumley, 1973). For the transect
data used here, u = 0.1 m s~l and (/2:2.5 m s-1, which satisfies the condition u«U
and permits the substitution above, which in the case of spatial data is rearranged
to x = tU. Put simply, this means that the position along the transect can be
approximated as a function of the position of the ship, and is not complicated by
the turbulence field through which the ship is moving.
As with all data collection of this type, there is the possibility that small-scale
structures in the parameters recorded may be modified by smearing in the ship's
underway seawater system. For the 'Aurora Australis', the presence of a 60 1
debubbling tank and 60 m of tubing was found to induce a 500 ± 5 s delay between
the seawater intake and the laboratory where the thermosalinograph, Turner
fluorometer and FRR fluorometer were located. In addition to the delay, the
smearing that occurred in the pipes was found to act in the same way as an 8 min
moving-average filter on the data recorded in the laboratory, equivalent to 1-2
km in the spatial domain, depending on the ship's speed. There is a seawater temperature sensor at the inlet, and in the laboratory; however, neither of these have
been used in the analysis presented here: the former because it will undoubtedly
be of different (higher) resolution than the salinity andfluorescencedata, and the
latter because it will not only be smeared in the ship's plumbing, but also modified in a non-conservative way due to heat exchange across the walls of the pipe
(our investigations reveal a 1-2°C difference between the two temperature
sensors). Consequently, we have used salinity as the physical parameter with
which to compare the biological data, as it will be subject to essentially identical
delay and smearing effects, by virtue of the thermosalinograph's proximity to the
fluorometers. Given the magnitude of the smearing experienced in the underway
system of the 'Aurora Australis', any aliasing effects induced by the ship's vertical movement should be minimized, as the smearing time is well in excess of the
period of the ship's pitch and roll.
After extracting transects on the basis of the ship's speed, further refinement
took place, rejecting data where PAR > 100 umol nr 2 s"1, on the basis that light
levels above this value would cause photoinhibition (Kirk, 1994), and hence give
artificially low values of A$m, not indicative of the true health of the phytoplankton. The shorter transects (total distance < 100 km) were then discarded,
leaving 23 transects of 100 km each for analysis (Figure 2). By comparing the in
vivoflow-throughfluoresencedata with extracted chlorophyll samples taken from
the instrument's outlet, a calibration curve was constructed in order to convert
fluorescence to chlorophyll a. The correlation between the two parameters was r
= 0.341 {n = 77, P < 0.005), hence justifying the use offluorescenceas an indicator
of phytoplankton abundance. Furthermore, the rejection of data where PAR >
100 fimol m~2 s"1 means that, by default, mostly night-time data were used. This
would presumably minimize the effect of diel variation in the fluorescence data,
which could affect the relationship between fluorescence and chlorophyll a.
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FRR Fhiorometry and biological-physical coupling
1.3
0
20
40
60
33
100
80
Distance [km]
Fig. 2, Example of a 100 km transect of salinity (psu, solid line), chlorophyll (|jg H, dotted line) and
N
N
Chlorophyll
3 3.6
XA
O
E
/ \ ^\
t
3
8 2B
l/li
n
n
i
%
2
?
1
1
1.6
1
-4.6
1
1
-3.6
Fig. 3. Example of a log-iog power spectrum plot for salinity (solid line) and chlorophyll (dotted line)
corresponding to the transect shown in Figure 2.
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P.GStnrtton el at
180
Fig. 4. Graphical representation of the correlation between the sum of the chlorophyll power spectrum and the corresponding mean value of A<}>m. Higher values of A<j>m are associated with greater
spatial variance of chlorophyll (P < 0.05). The horizontal error bars are the 95% confidence intervals
of the mean value of A<J>m for each transect.
Data analysis
Power spectra of the salinity and chlorophyll a transects were constructed. The
power spectrum shows the contribution to the total variance from wave number
bands spanning three orders of magnitude, and Figure 3 shows an example of a
log-log plot of one such spectrum. The abscissa is the log of the wave number [\,
(nr 1 )] which is the inverse of the spatial scale, hence the variance decreases with
increasing X, indicating that measurements of a given parameter taken close to
each other are more similar in magnitude than measurements separated by tens
of kilometres. The mean value of A4>m and its 95% confidence interval were calculated for each transect, and compared to the sum and slope of the salinity and
chlorophyll power spectra.
Results
A<f>m and chlorophyll variance
In addition to calculating the variance of chlorophyll via spectral analysis, initial
calculations indicated that the mean value of A<|>m for each transect was correlated (P < 0.01) with the variance and the mean of the corresponding chlorophyll data. Since the variance is influenced by the mean value, the coefficient of
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FRR Ftnorometry and biological-physical coupling
Fig. 5. Graphical representation of the relationship between A<)>m and the (-statistic, used to quantify
the difference between the slopes of the chlorophyll and salinity power spectra for a given transect.
The magnitude of the (-statistic is significant (P < 0.05) for A<t>m > -0.3, i.e. the slopes of the chlorophyll and salinity power spectra are significantly different at the 95% confidence level for A<j>m > -0.3.
The horizontal error bars are the 95% confidence intervals of the mean value of A<J>m for each transect.
variance, which is the variance normalized to the mean, was also calculated, but
no significant correlations were observed. However, the variance, as quantified
by the sum of the chlorophyll power spectrum (Bendat and Piersol, 1986), was
correlated with the mean value of A<)>m for each transect (Figure 4). The advantage of the power spectrum is that it permits partitioning of the variance into
ranges of spatial scale. For spatial scales in excess of 10 km, the correlation
between A4>m and the sum of the chlorophyll power spectrum was significant for
P < 0.01. Taking into account the whole transect, i.e. spatial scales > 1 km, the
significance level was P < 0.05. There was no observed relationship between the
sum of the salinity power spectrum and Afym or the sum of the chlorophyll power
spectrum.
A<f>m and biological-physical
decoupling
For each transect, a comparison of the slopes of the power spectra for salinity and
chlorophyll was made using a Mest (Zar, 1984). It was found that A4>m was significantly positively correlated (P < 0.05) with the magnitude of the f-statistic,
which is used here as a measure of the difference between the slope of the salinity and chlorophyll power spectra for a given transect. Figure 5 shows the relationship between A<J>m and the r-statistic, for the slopes of the entire power spectra,
i.e. spatial scales 1-100 km. Again, when taking into account only spatial scales
>10 km, the significance of this relationship was enhanced, with P < 0.002.
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P.GStnrtton el al
Discussion
Our data show that the sum of the chlorophyll power spectrum is positively correlated with the corresponding value of A<t>m (Figure 4). This indicates that high
spatial variation of chlorophyll is associated with regions of high PSII efficiency.
Kolber and Falkowski (1993) showed that A<J>m can be used to derive conventional
growth rates that correlate significantly with measurements made by isotope
incorporation methods. Thus, making the assumption that A$m is not only an indicator of PSII efficiency, but also a major contributor to phytoplankton growth
rates, our data indicate that high phytoplankton growth correlates with high
spatial variation of chlorophyll. However, the fact that A4>m is not positively correlated with the sum of the corresponding salinity power spectrum is evidence
that the increase in chlorophyll spatial variability is not associated with an
increase in the physical heterogeneity of the environment. This is confirmed by a
simple comparison of the sums of the salinity and chlorophyll power spectra, but
more conclusively by the relationship between their slopes.
The comparison of the slopes of the salinity and chlorophyll power spectra via
a /-test was performed as a way of determining when the spatial structure of the
two parameters differed. It was found that as A<J>m increased, the difference in the
slope, as quantified by the /-statistic, also increased, and the correlation between
these parameters was significant for P < 0.05. Depending on the level of significance that one chooses for the /-statistic, this relationship could be used to define
a critical value of A4>m above which the scale of variation in chlorophyll spatial
structure becomes significantly distinct from that of physical parameters; in this
case, salinity. For instance, at the 95% level of significance, the slopes of the salinity and chlorophyll power spectra are significantly different for A<t>m > -0.3.
However, here it is perhaps more objective to state simply that a significant
relationship exists between biological activity and the biological-physical decoupling. This represents a form of empirical verification of Denman and Platt's
(1976) theoretical work on the relationship between phytoplankton growth rates,
chlorophyll distribution and turbulence. The relationship becomes even more
evident when one considers only data in the spatial range 10-100 km. Here, the
correlation between Atym and the /-statistic is significant for P < 0.002, and the
correlation between A<j>m and the sum of the chlorophyll power spectrum is significant for P < 0.01.
It is suggested that in the 10-100 km spatial range, the relationship between
phytoplankton growth rate, a, and the magnitude of turbulence, T, is less dynamic,
such that in the absence of any data regarding the magnitude of turbulent forces,
it is still possible to obtain a clear and significant relationship between A<j>m and
chlorophyll spatial structure. Furthermore, at low values of A(}>m (-0.3 or less),
phytoplankton dynamics are more likely to be dominated by grazing, and net
growth rates could conceivably be close to zero, thus leading to phytoplankton
spatial structure that is similar in scale to that of physical parameters, such as
salinity. Below 10 km spatial scale, one might expect that the dominance of physical or biological forces would depend on the relative magnitudes of a and T,
but in the absence of any turbulence data, it is not possible to describe this
1272
FRR Fluorometry and biological-physical coupling
relationship in detail. At smaller spatial scales (<l-2 km), the data used here have
been affected by smearing in the ship's plumbing; the relationship between biological and physical variance at these spatial scales would perhaps be best elucidated using data collected from smaller vessels, where smearing could be
minimized.
Conclusion
The results presented here have shown, for the first time, an empirically derived
link between the magnitude of phytoplankton spatial heterogeneity and photosynthetic parameters. Furthermore, high photosynthetic rates are associated with
chlorophyll spatial distributions that are divergent from the spatial variance of
physical parameters. In agreement with previous work (e.g. Powell et al, 1975;
Denman et al, 1977; Abbott et al, 1982), the decoupling of biological and physical processes becomes more obvious at larger spatial scales, particularly above
10 km. The employment of FRR Fluorometry in this way has enabled greater
understanding of the coupling between physical and biological processes in the
data sets presented here, and validated our approach to the analysis. By using the
mean value of A<f>m for each of the transects, we have in no way fully utilized the
potential of the FRR fiuorometer to produce high-resolution transects of photosynthetic parameters. Future work should focus on methods of analysis that are
able to exploit this spatiotemporal resolution fully, for instance, by investigating
local correlations between biomass and photosynthetic rates, rather than using
values averaged over 100 km.
Acknowledgements
We wish to thank Drs Zbigniew Kolber and Paul Falkowski of Brookhaven
National Laboratories for the loan of the FRR fiuorometer, and advice regarding its operation. Their enthusiasm for the sharing of ideas and expertise is very
much appreciated. We are also indebted to Pamela Brodie, Tim Ryan, Gordon
Keith, Chris Boucher, and the captain and crew of the RSV 'Aurora Australis' for
their assistance in collecting the data for this manuscript. This work was supported in part by an Australian Postgraduate Award and a CSIRO Division of
Fisheries Supplementary PhD Award (PGS), funding from the Australian
Antarctic Division and the Board of Research, Flinders University.
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Received on February 24, 1997; accepted on May 6, 1997
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