JOURNAL OF PLANKTON RESEARCH
j
VOLUME
32
j
NUMBER
12
j
PAGES
1649 – 1660
j
2010
Monitoring changes in phytoplankton
abundance and composition in the
Northwest Atlantic: a comparison of
results obtained by continuous plankton
recorder sampling and colour
satellite imagery
ERICA J. H. HEAD1* AND PIERRE PEPIN2
1
1006, DARTMOUTH, NS,
5667, ST JOHN’S, NL, CANADA A1C 5X1
FISHERIES AND OCEANS CANADA, BEDFORD INSTITUTE OF OCEANOGRAPHY, PO BOX
CANADA, NORTHWEST ATLANTIC FISHERIES CENTRE, PO BOX
CANADA B2Y
4A2
2
AND FISHERIES AND OCEANS
*CORRESPONDING AUTHOR: [email protected]
Received May 14, 2010; accepted in principle July 13, 2010; accepted for publication August 14, 2010
Corresponding editor: Roger Harris
Phytoplankton abundance in the NW Atlantic was measured by continuous plankton recorder (CPR) sampling along tracks between Iceland and the western
Scotian Shelf from 1998 to 2006, when sea-surface chlorophyll (SSChl) measurements were also being made by ocean colour satellite imagery using the SeaWiFS
sensor. Seasonal and inter-annual changes in phytoplankton abundance were
examined using data collected by both techniques, averaged over each of four shelf
regions and four deep ocean regions. CPR sampling had gaps (missing months) in
all regions and in the four deep ocean regions satellite observations were too
sparse between November and February to be of use. Average seasonal cycles of
SSChl were similar to those of total diatom abundance in seven regions, to those
of the phytoplankton colour index in six regions, but were not similar to those of
total dinoflagellate abundance anywhere. Large inter-annual changes in spring
bloom dynamics were captured by both samplers in shelf regions. Changes in
annual (or 8 months) averages of SSChl did not generally follow those of the CPR
indices within regions and multi-year averages of SSChl, and the three CPR
indices were generally higher in shelf than in deep ocean regions. Remote sensing
and CPR sampling provide complementary ways of monitoring phytoplankton in
the ocean: the former has superior temporal and spatial coverage and temporal
resolution, and the latter provides better taxonomic information.
KEYWORDS: CPR phytoplankton indices; sea-surface chlorophyll; seasonal
cycles; inter-annual trends
doi:10.1093/plankt/fbq120, available online at www.plankt.oxfordjournals.org. Advance Access publication September 24, 2010
Published by Oxford University Press 2010
JOURNAL OF PLANKTON RESEARCH
j
VOLUME
I N T RO D U C T I O N
The earth is warming as a result of the accumulation of
carbon dioxide in the atmosphere, and this is causing
temperatures to rise in many regions of the world’s oceans
(IPCC, 2007). As well, as the oceans absorb more carbon
dioxide, they are becoming more acidic (Orr et al., 2005).
Carbon dioxide is converted into organic carbon by
plants, both on land and in the ocean. Approximately
45% of the earth’s primary production has been attributed to oceanic phytoplankton (Falkowski et al., 2004), and
some of this photosynthetically fixed carbon ultimately
falls to the ocean floor, where it can remain sequestered
for centuries. Changes in temperature and acidity and
other indirect effects of global warming have the potential
to affect phytoplankton composition, productivity and
abundance in the ocean. These changes can in turn influence the fate of phytoplankton carbon: how much is
transferred up the food chain, how much is respired
(re-cycled) and how much sinks out. In our changing
climate, it is important to monitor and document changes
that are occurring in the phytoplankton and to increase
our understanding of the interactions between climate
and phytoplankton by investigating events occurring on
inter-annual and inter-decadal time scales, in order to
help in forecasting how marine ecosystems will respond to
predicted climate change scenarios in the future.
Two methods are currently being used to monitor
phytoplankton at ocean basin scales: satellite remote
sensing and in situ sampling by means of continuous
plankton recorders (CPRs). Both methods have their
strengths and weaknesses. The strengths of remote
sensing are the ability to obtain nearly synoptic measurements over broad spatial scales with relatively frequent
sampling, and the capacity for near real-time data retrieval. Weaknesses include a dependence on fair weather
(i.e. clear skies), a limited capacity to retrieve taxonomic
information (Sathyendranath et al., 2004; Raitsos et al.,
2008), a reliance on the general applicability of algorithms for CASE I oceanic waters and a lack of algorithms for CASE II coastal waters, and the relatively
short life-times of individual satellites. In contrast, CPR
sampling is not weather dependent, has used a consistent sampling methodology since 1948 (Reid et al., 2003),
and because organisms are collected, and identified and
enumerated under the microscope, it can give taxonomic information to the genus and species level, with
results that are equally valid in both oceanic and coastal
waters. On the other hand, sampling is done only once
a month, and is restricted to the trade routes of the commercial vessels that tow the CPRs, with date and route
variations determined by the shipping companies. In
addition, the data are semi-quantitative and only
32
j
NUMBER
12
j
PAGES
1649 – 1660
j
2010
available after sample analysis and data archiving, which
is generally at least 1 year after sampling.
Since 1998, Canadian scientists have been operating
the Atlantic Zone Monitoring Programme (AZMP) to
observe, document and investigate climatic and ecosystem
changes in the Northwest Atlantic (Therriault et al., 1998).
Satellite imagery and CPR sampling are components of
the programme, and both include making observations in
shelf and deep oceans regions: ocean colour data are routinely compiled from satellites surveying the entire
Northwest Atlantic and CPR sampling is along the Z and
E lines between Reykjavik, Iceland and St John’s
Newfoundland, and between St John’s and the New
England coast, respectively. Both types of sampling used
consistent methodologies during the first 9 years (1998–
2006) of the AZMP, with the satellite mounted SeaWiFS
sensor on the one hand, and the standard CPR sampling
on the other. CPR sampling was deficient in 2007 and
2008, however, and from 2008 onwards there are missing
months in the SeaWiFS data, as the satellite nears the end
of its lifetime. Thus, sampling between 1998 and 2006
provides a 9-year period over which to compare results
obtained in the Northwest Atlantic using the two
methods. There have been previous studies comparing
the results of satellite imagery and CPR sampling elsewhere, but these were calibration exercises aimed at
making direct quantitative comparisons of results of CPR
sampling over small portions of their tracks with results
from satellite passes that were made along those tracks
within a few days (Batten et al., 2003; Raitsos et al., 2005).
Here, the question is not whether the CPR and ocean
colour satellite imagery provide quantitatively comparable
data for small areas of the ocean over short periods of
time, but rather whether the two techniques, applied over
relatively broad time and space scales, give the same patterns of change, either temporally in the same region (e.g.
monthly and from year-to-year) or spatially over the longterm (e.g. shelf versus open ocean). The temporal resolution for the comparison is that set by the CPR sampling,
which is monthly. The spatial (regional) divisions that
were used were made on the basis of the distribution of
the CPR sampling tracks, hydrography and bathymetry.
The overall objectives of this study are to see whether
CPR sampling and ocean colour satellite observations
give similar changes in phytoplankton abundance for a
series of regions in the NW Atlantic (1) for seasonal
cycles averaged over the entire sampling period; (2) for
seasonal cycles within individual years, and hence
whether both methods capture inter-annual changes in
spring bloom dynamics; (3) for inter-annual changes
over the 9-year sampling period, and hence whether
sea-surface chlorophyll (SSChl) concentrations can be
estimated for decades prior to the 1990s and 2000s,
1650
E. HEAD AND P. PEPIN
j
COMPARISON OF CPR AND SATELLITE COLOUR IMAGERY
when CPR data were available, but satellite data were
not. The final objective is to see whether both sampling
methodologies give the same spatial differences in longterm average values across regions, with consistent
relationships between SSChl and the CPR phytoplankton indices in all regions.
METHOD
CPR plankton data
The CPR is a mechanical sampler that is towed behind
commercial vessels at a depth of 7 m. A complete
description of CPR, its origin, history and operating
procedures are given elsewhere (Reid et al., 2003). The
essential elements that follow are as reported by Batten
et al. (Batten et al., 2003). A square 1.2 cm aperture at
the front allows water to enter and plankton are filtered
onto a continuously moving band of silk gauze of mesh
size 270 mm. Another roll of gauze is continuously
drawn over the filtering gauze, once it has passed the
entrance to the tunnel through which the water exits.
The resulting sandwich is rolled on to a spool in a
storage tank that contains formalin as a preservative.
Once the tow and the ship’s voyage are complete, the
roll is returned to the laboratory of the Sir Alister
Hardy Foundation for Ocean Science (SAHFOS), the
organization that operates the CPR survey in the North
Atlantic, where the gauzes are laid out and cut into sections that are equivalent to 18 km of towing.
Every second sample was analysed for this study. Data
on the phytoplankton colour index (PCI) and the abundance of diatoms and dinoflagellates were provided by
SAHFOS under a Joint Project Agreement with Fisheries
and Oceans Canada. The PCI gives a semi-quantitative
estimate of total phytoplankton biomass. It is determined
by laying the CPR silk against a white background and
by assigning each 18 km sample a colour category, by
reference to a standard colour chart (Colebrook, 1960),
with four categories having nominal intensities of 0, 1, 2
and 6.5 (Hays and Lindley, 1994). Although the nominal
size of the mesh is 270 mm, the gauze is made of silk
which therefore retains particles that are much smaller so
that PCI is considered to be an index of overall phytoplankton biomass. Diatoms and dinoflagellates are identified to as high a taxonomic level as is practicable, on 20
fields of view of diameter 295 mm, at 450 magnification, but a “binning” procedure is used so that not all
cells are examined individually and abundance estimates
are, such as the PCI, semi-quantitative. In this study, the
abundance of all species of diatom or dinoflagellate taxa
were summed for each sample, which represents 3 m3.
Total diatom and total dinoflagellate abundances per
sample were log (N þ 1) transformed, and values within
each sampling region (Fig. 1) were averaged for each
month of each year. Values for the PCI were averaged in
the same way but without the log (N þ 1) transformation.
CPR sampling did not occur in all months in every
region and in order to calculate annual averages for the
phytoplankton indices, we needed to fill in for the
missing months. To do this, when there were data in
the preceding and subsequent months, the average of
these was used to fill in the missing value. In some
instances, there were two consecutive missing months and
in these cases, linear interpolation between the preceding
and subsequent months’ values was used to estimate
missing observations. The two procedures are essentially
the same, with the degree of interpolation depending on
the length of the gap. This linear interpolation procedure
would have smoothed out extreme events and would have
led to conservative values for annual averages: this was
judged to being preferable to using a fixed seasonal cycle
(e.g. Richardson et al., 2006). In three cases where three
consecutive months were missing, we used the monthly
average from all sampled years between 1998 and 2006
to fill in the values for the missing months.
SeaWiFS-derived chlorophyll concentrations
SeaWiFS data were acquired from the NASA Goddard
Space Flight Center’s Ocean Colour Data Processing
System (OCDPS) and are the result of a reprocessing that
took place in 2004. Data used were Level 3, monthly
average products (99 km resolution) of near sea-surface
chlorophyll a concentration (SSChl, mg m23), estimated
using the Ocean Chlorophyll 4—version 4 (OC4-v4)
algorithm (O’Reilly et al., 1998), with corrections for
atmospheric influences, such as cloud cover, sun-glint and
water vapour. SSChl concentrations were computed as
monthly averages over each region, which were defined
by rectangles that included all sampling positions along
the CPR tracks between 1957 and 2006, but omitting
those to the north of 508N between 45 and 538W
(Table I). In some regions for some months, satellite data
were scarce because of cloud cover. In our comparison,
we used data only for months when there were observations covering more than 10% of the pixels in a given
region in every year.
The latitudinal limits of the regions were based primarily on the areas covered by the CPR tracks in all
sampling years since 1957, but the ranges in the deep
ocean regions were quite large, and we recognized that
phytoplankton dynamics might vary with latitude
within these regions. We therefore tested the effect of
using SSChl concentrations calculated for the entire
1651
JOURNAL OF PLANKTON RESEARCH
j
VOLUME
32
j
NUMBER
12
j
PAGES
1649 – 1660
j
2010
Fig. 1. Positions where individual CPR samples were collected between 1957 and 2006 and boxes over which satellite measurements of
sea-surface chlorophyll were averaged. Black symbols are the sample positions between 1998 and 2006; grey symbols are from earlier sampling
years (1957–1997). The bathymetry is represented by the 200, 1000, 2000 and 3000 m contours. The shelf regions are the western Scotian
Shelf (WSS), the eastern Scotian Shelf (ESS), the South Newfoundland Shelf (SNL), the Newfoundland Shelf (NLS). The regions east of the shelf
are defined by their longitudinal limits (e.g. 40–458W is bounded by 408W and 458W).
Table I: Longitudinal and latitudinal limits Analysis
of the regions over which satellite derived SSChl concentrations were treated as the dependent varivalues of sea-surface chlorophyll were averaged able and contrasted with CPR indices of abundance
Region
Southern
limit (88 N)
Northern
limit (88 N)
Western
limit (88 W)
Eastern
limit (88 W)
WSS
ESS
SNL
NLS
40 –458W
35 –408W
30 –358W
25 –308W
41.70
43.34
44.58
44.28
45.55
48.53
53.32
56.45
44.82
46.43
47.30
50.00
60.47
61.58
62.76
64.39
66.00
62.00
57.00
53.00
45.00
40.00
35.00
30.00
62.00
57.00
53.06
45.00
40.00
35.00
30.00
25.00
WSS, western Scotian Shelf; ESS, eastern Scotian Shelf; SNL, South
Newfoundland Shelf; NLS, Newfoundland Shelf.
regions versus those calculated using values for a series
of sub-regions, which had the same longitudinal limits,
but with latitudinal limits reduced such that they still
covered most of the CPR tracks run in 1998 – 2006.
These sub-regions corresponded to the middle thirds of
the 40– 458W and 35– 408W regions, having latitudinal
limits of 50.52 – 55.508N and 57.23 – 61.588N, respectively, and to the northern halves of the 30– 358W and
25– 308W regions, having latitudinal limits of 58.04 –
62.768N and 60.42 –64.398N, respectively.
using univariate regression analysis for each region separately to compare temporal patterns of change (comparing
regional monthly and annual averages) and over all
regions to compare spatial patterns (comparing multi-year
averages). Multivariate analyses were also performed, but
found to give no better results than univariate analysis.
Thus, the results are not presented here.
R E S U LT S
Comparisons for overall data sets
Satellite data were inadequate or unavailable in
December for the Newfoundland Shelf (NLS) region, and
in January, February, November and December for all
regions east of 458W. Gaps in the CPR data occurred at
random in all regions and there was no year in which
every region was sampled in every month. In general,
however, CPR samples were collected in at least 8 months
of the year in every year in all regions. Exceptions were in
2002 in the South Newfoundland Shelf (SNL) region
1652
E. HEAD AND P. PEPIN
j
COMPARISON OF CPR AND SATELLITE COLOUR IMAGERY
(7 months sampled) and the 25–308W region (6 months
sampled). There were two consecutive missing months on
the eastern Scotian Shelf (ESS) in 2000 and 2002, on the
SNL in 2000 and on the NLS in 2005. There were three
consecutive months missing on the SNL and in the 40–
458W and 25–308W regions in 2002.
When all of the monthly average values from all
regions were included (excluding months and regions
for which there was either no satellite data or no CPR
data) CPR estimates of PCI, diatoms and dinoflagellates
increased with increasing SSChl (Fig. 2) and all three
CPR indices were significantly positively correlated with
SSChl (P , 0.001), although there was considerable
scatter in the data.
Seasonal cycles of SSChl concentration and
CPR indices of phytoplankton abundance
Average seasonal cycles for the 9 years of data were calculated from the monthly averages for all four indices,
using only the months for which there were data, i.e.
omitting missing CPR sampling months and the
months for which satellite data were unreliable (Fig. 3).
For the shelf regions, peaks in monthly averages for
diatom abundance, PCI and SSChl were more-or-less
co-incident, with maximal values in April. All shelf
regions also showed low values for these three variables
in summer, and increases in fall. For dinoflagellates,
abundances on the Scotian Shelf were low in spring
and higher in summer and fall, whereas those in the
Newfoundland Shelf regions (SNL and NLS) were high
in spring, but with no obvious spring peak, low in
summer and high in fall. In the 40– 458W region, all
variables had a late spring peak (May or June) of lower
intensity than for shelf regions, and a prolonged period
of intermediate abundance during summer and fall.
East of 408W, all of the CPR indices had low values in
winter (December– March) and SSChl values were low
in March. Increases in phytoplankton abundance in
spring occurred later in deep water regions than in shelf
regions and spring peaks were of lower maximum
value. In the deep ocean regions, phytoplankton levels
generally did not decrease much during summer,
remaining relatively high throughout summer and fall.
For the monthly averages shown in Fig. 3, there were
significant positive correlations (P , 0.05) between
diatom abundance and SSChl in all regions except the
30– 358W region and between PCI and SSChl in the
six regions west of 358W (Table II). There were no significant relationships between dinoflagellates and SSChl
anywhere. It should be noted that these regressions
included 12 months in each of the three most westerly
regions, but because of the lack of satellite data during
Fig. 2. Relationships between individual monthly regional averages
of sea-surface chlorophyll and the phytoplankton colour index (top
panel), diatom abundance (middle panel) or dinoflagellate abundance
(bottom panel).
winter months at high latitudes, the regressions included
only 11 months in the NLS region (January –
November) and only 8 months in the four most easterly
regions (March –October).
The average seasonal cycle of SSChl over the entire
40–458W region was similar to that over the sub-region
covering the middle third of the latitudinal range (Fig. 4),
which was the area through which most of the CPR
tracks ran in 1998–2006 (Fig. 1). For the 35–408W
region, the sub-region covering the middle third of the
latitudinal range, which also covered most of the CPR
tracks, had higher monthly values than were found for
the entire region. Average seasonal cycles in the northern
sub-regions of the 30–358W and 25–308W regions were
very similar to those for the entire regions, and again
1653
JOURNAL OF PLANKTON RESEARCH
j
VOLUME
32
j
NUMBER
12
j
PAGES
1649 – 1660
j
2010
Fig. 3. Average (1998–2006) seasonal cycles of diatom abundance (DIATS), dinoflagellate abundance (DINOS), the phytoplankton colour
index (PCI) and satellite derived chlorophyll (SSCHL) concentration for regions in the Northwest Atlantic.
Table II: Results of regression analyses
between monthly average (1998– 2006)
values for the phytoplankton colour index
(PCI) and satellite-based measurements of
sea-surface chlorophyll (SSChl) concentration,
and between diatom or dinoflagellate
abundance and SSChl concentration in eight
regions in the Northwest Atlantic
Region
PCI, r 2
Diatom
abundance, r 2
Dinoflagellate
abundance, r 2
WSS
ESS
SNL
NLS
40 –458W
35 –408W
30 –358W
25 –308W
0.69**
0.73**
0.83**
0.89**
0.59*
0.64*
NS
NS
0.88**
0.49*
0.39*
0.55**
0.75**
0.68*
NS
0.51*
NS
NS
NS
NS
NS
NS
NS
NS
Note that the comparison is made over 12 months for the WSS, ESS and
SNL, for 11 months for the NLS and for 8 months for regions between
45 and 258W (*denotes relationships significant at the P , 0.05 level; **,
at the P , 0.01) and that there was no interpolation for missing months
in the CPR data.
these sub-regions covered most of the CPR tracks
between 1998 and 2006. For all four deep ocean regions,
the same significant correlations were found for the
average seasonal cycles between PCI and sub-regional
SSChl concentrations as were found for the entire
regions (Table II). In contrast, diatom abundance was significantly correlated with sub-regional SSChl concentration in the 30–358W region (r 2 ¼ 0.70, P , 0.01) and
not in the 25–308W region; the opposite pattern to that
seen for the entire regions. In addition, dinoflagellate
abundance was significantly correlated with sub-regional
SSChl concentration in the 35–408W region (r 2 ¼ 0.55,
P , 0.05) and the 30–358W region (r 2 ¼ 0.65, P , 0.05).
For individual years, monthly values for SSChl were
significantly correlated (P , 0.05) with PCI in 7 years
out of 9 in the NLS region, in 5 years on the ESS, in 4
years in the SNL region, in 3 years on the WSS and in
0 – 2 years elsewhere. Monthly SSChl values were correlated with diatom abundance values in 7 years in the
NLS region, in 5 years on the WSS, in 3 years in the
30– 358W region and in 0 – 2 years elsewhere. Monthly
values for SSChl were correlated with dinoflagellate
abundance values in 0 – 1 years everywhere. Using the
sub-regional values of SSChl for the deep ocean regions,
instead of the regional values, gave similar results.
Inter-annual trends in SSChl concentration
and CPR indices of phytoplankton
abundance
Annual or “year-round” averages of the CPR phytoplankton indices were estimated by including interpolated values for missing months (Fig. 5). Annual (12
months) averages were calculated for the Scotian (WSS,
ESS) and South Newfoundland (SNL) shelves, while
“year-round” (using only the months for which reliable
satellite data were available) averages were calculated
for the other regions (January – November, in the NLS
region and March– October, in regions east of 458W).
These were compared with annual or year-round
averages for SSChl. There were only two significant
correlations between any of the CPR phytoplankton
indices and SSChl in any of the regions over the 9 years
of sampling. One was between PCI and SSChl on the
1654
E. HEAD AND P. PEPIN
j
COMPARISON OF CPR AND SATELLITE COLOUR IMAGERY
Fig. 4. Comparison of average monthly (upper row) and year-round (lower row) estimates of sea-surface chlorophyll for regions between 45 and
258W in the NW Atlantic over the entire latitudinal ranges shown in Fig. 1 (regional SSChl) or in the middle one-third (40– 45 and 35–408W)
or northern half (30– 35 and 25–308N) (sub-regional SSChl).
Fig. 5. Annual or year-round averages for diatom abundance (DIATS), dinoflagellate abundance (DINOS), the phytoplankton colour index
(PCI) and satellite derived chlorophyll (SSCHL) concentration for regions in the Northwest Atlantic.
NLS (r 2 ¼ 0.45, P , 0.05) and was negative; the other
was diatom abundance and SSChl in the 35– 408W
region (r 2 ¼ 0.51, P , 0.05) and was positive. Using the
sub-regional SSChl values for the deep ocean regions,
rather than the regional values, gave the same results.
Regional differences in multi-year average
SSChl concentrations and CPR indices of
phytoplankton abundance
Multi-year (1998 – 2006) average PCI values and diatom
and dinoflagellate abundances were calculated from the
annual or year-round averages as calculated above, i.e.
including interpolated values for missing months
(Fig. 6). For the CPR data, these were higher for shelf
regions than for open ocean regions when all months of
the year were included, while for the SSChl data
annual averages were higher on the Scotian Shelf than
was the annual average on the SNL or the January –
November average on the NLS. When the March–
October averages were used to compare across all
regions, the regional differences in diatom abundance
more-or-less disappeared, while the patterns did not
change much for the other two CPR indices or for
1655
JOURNAL OF PLANKTON RESEARCH
j
VOLUME
32
j
NUMBER
12
j
PAGES
1649 – 1660
j
2010
Fig. 6. Regional multi-year (1998– 2006) annual average abundances for diatoms, dinoflagellates and the phytoplankton colour index, and
regional annual or year-round averages for sea-surface chlorophyll concentration, including 12 months of data for the CPR indices in all regions
and 12 (WSS, ESS, SNL), 11 (NLS) or 8 (45– 258W) of data per year for sea-surface chlorophyll concentration (left-hand column). Regional
1998– 2006 average March– October abundances for all regions for diatoms, dinoflagellates, the PCI and satellite derived chlorophyll
concentrations (right-hand column).
SSChl. The multi-year year-round (March– October)
averages for the PCI and dinoflagellate abundance were
significantly correlated with SSChl across regions (r 2 ¼
0.52, P , 0.05 and r 2 ¼ 0.58, P , 0.05, respectively,
data as in right hand column of Fig. 5). When all
months that had satellite data were used to calculate
regional annual or year-round averages, diatom and
dinoflagellate abundance were significantly correlated
with SSChl across regions (r 2 ¼ 0.61, P , 0.05 and
r 2 ¼ 0.52, P , 0.05, respectively, data as in left hand
column of Fig. 5).
DISCUSSION
Regional hydrography and remarks
concerning inter- and intra-regional and
temporal variability
In this study, we have compared the results of two
methods of measuring phytoplankton abundance at
ocean basin scales, either of which can be used to
monitor ecosystem change. Because CPR sampling is
monthly, this is the highest temporal resolution that
1656
E. HEAD AND P. PEPIN
j
COMPARISON OF CPR AND SATELLITE COLOUR IMAGERY
could be used in this comparison. In terms of spatial
resolution, when CPR data are being used there is
always a trade-off between how much segmentation can
be applied before losing too much data and the statistical power of the results. In this study, we have used the
same divisions as were used elsewhere in a study of the
inter-decadal variability of CPR phytoplankton and
zooplankton abundance in the NW Atlantic since 1957
(Head and Pepin, this volume). The choice as to how to
divide the study area here and there was based mainly
on where the CPR tracks have been run since 1957,
and on the regional hydrography and bathymetry,
bearing in mind the problems associated with excessive
segmentation. The Scotian Shelf is strongly influenced
by the outflow from the Gulf of St Lawrence (Loder
et al., 1998), through Cabot Strait, although the influx
of slope water into central regions, and subsequent westward advection, means that the WSS is generally
warmer than the ESS and that distributions of some
plankton species are somewhat different (e.g. Calanus
species, Head et al., 1999). For this reason, the Scotian
Shelf was divided into western and eastern regions. On
the Newfoundland Shelf, the inshore branch of the
Labrador Current has a strong influence in both the
NLS and SNL regions, but while the NLS region is also
influenced by the offshore branch of the Labrador
Current beyond the shelf-break, the SNL is not, which
is why these two regions were separated. Farther east,
the deep ocean regions were divided at 58 longitudinal
intervals, although all four regions were more or less
within the sub-polar gyre. The 40– 458W region covers
the central gyre, but includes a small area over Flemish
Cap in the west and the Greenland Shelf in the north.
The 35– 408W region covers the western Irminger Sea,
with water depths of .2000 m, the 30– 358W region
covers the central Irminger Sea and the portion of the
Reykjanes Ridge .1000 m in depth, and the 25– 308W
region covers the eastern Irminger Sea and the portion
of the Reykjanes Ridge ,1000 m in depth. The three
regions between 30 and 458W have had very similar
average temperatures over the decades since the 1960s
(Head and Pepin, this volume), whereas the 25 –308W
region has been a little warmer than the others, since it
covers an area where a portion of the North Atlantic
Current turns north to form the Irminger Current
(Bersch, 1995). East of 458W, the latitudinal ranges
covered by the CPR tracks were large, especially during
the sampling years prior to the 1990s. Here, however,
we have shown that between 1998 and 2006, satellite
determinations of SSChl over the entire latitudinal
range of each deep ocean region were generally very
similar to those in sub-regions that were more restricted,
but that still covered most of the CPR tracks between
1998 and 2006 (Fig. 4), and we have also shown that
the use of the regional (rather than sub-regional) SSChl
values did not affect our findings. Nevertheless, it is
probable that the spatial and temporal differences in
the methods of data collection did influence the results
of our methodological comparison.
Variability is characteristic of the distributions of phytoplankton in the ocean and it occurs over wide ranges
of spatial and temporal scales (Mann and Lazier, 1996).
The strongest and most widespread signal in temperate
regions, such as those included in this study, is the seasonal cycle, with low phytoplankton levels occurring in
winter and high levels in blooms in spring and/or fall.
In addition, however, phytoplankton blooms can occur
locally at any time during the growth season in areas
where surface nutrient levels are enhanced, such as at
fronts or associated with meso-scale eddies in the open
ocean, or as the result of tidal mixing or coastal upwelling in shelf regions. All of these processes occur within
one or more of our study regions, and sub-regional
spatial (,100 km) and temporal (, monthly) variability
is evident in satellite images of SSChl and temperature
(e.g. see http://www.mar.dfo-mpo.gc.ca/science/ocean/
ias/seawifs/). Satellite observations made over relatively
large areas, such as those used in this study, will tend to
“average out” spatially localized blooms. In addition, as
blooms are often ephemeral, lasting only 1 or 2 weeks,
monthly averaging will further reduce values obtained
for satellite derived SSChl concentrations for a given
region, compared with the maximum value that might
actually have occurred somewhere within that region
over a few days. In contrast, CPR sampling is spatially
restricted along the ships’ tracks and occurs over the
course of a day or less for each of our regions. Hence,
CPR sampling could sometimes capture local, shortlived bloom events within a region, or could miss them
altogether. Thus, close relationships between SSChl and
CPR phytoplankton indices were not to be expected,
although with enough data we had hoped to be able to
see large signals reflected in the data sets collected by
either method, which, to a large extent, we did.
When we compared CPR and satellite data for all
regions and all months (Fig. 2) there was a considerable
amount of scatter, but overall, and for the PCI and
diatom abundance especially, the CPR indices did
increase with increasing SSChl. The distributions of the
CPR data, however, also highlighted another factor that
could further confound methodological comparisons.
The CPR indices, and especially those for diatom abundance and PCI, tended towards upper limits, which correspond to the maximum values that can be recorded
according to the CPR analytical protocols used at
SAHFOS. No individual phytoplankton species can
1657
JOURNAL OF PLANKTON RESEARCH
j
VOLUME
exceed a count of 750 000 cells per sample, and no
sample can have a value greater than 6.5 for the PCI
(Batten et al., 2003). Thus, if phytoplankton levels are
very high, CPR methods will underestimate phytoplankton abundance, relatively to values that could be
observed by satellite. Inspection of the data suggested
that in this study, this occurred only in shelf regions and
always in ,12% and generally ,5% of the original
samples, and so this was probably not a problem here,
but elsewhere it could be more important.
Average regional seasonal cycles and
observations in years with unusual spring
bloom dynamics
Average seasonal cycles of diatom abundance and the
PCI were similar to those of SSChl in all of the study
regions (Fig. 3), despite the differences that could have
been generated by the different sampling methods. In
other work comparing SeaWiFS-derived chlorophyll
and CPR data, the PCI was used as the main CPR
index of phytoplankton biomass (Batten et al., 2003;
Raitsos et al., 2005). In this study, however, average
monthly values of the PCI were correlated with SSChl
in six out of eight regions, while diatom abundance was
correlated with SSChl in these six plus one more
(Table II). This suggests that diatoms dominate the phytoplankton biomass (chlorophyll) over most of the NW
Atlantic study area. Regression parameters (slopes and
intercepts) for the SSChl versus diatom abundance and
SSChl versus PCI varied among regions (data not
shown), however, suggesting that the relationship
between the CPR indices and total phytoplankton
biomass is not uniform over all study regions.
CPR indices were not correlated with SSChl in most
regions in most individual years, even when they were
over the average seasonal cycle, and this could be
related to several factors. One is that sometimes critical
(i.e. bloom) months were missing from the CPR data
sets, which would have greatly diminished the magnitude of the seasonal signal. As well, however, the differences in spatial and temporal resolution of the data
collection methods would have been accentuated when
data from single months and years were used. These
same factors, together with an imperfect method for
interpolating missing CPR data, could also have contributed to the lack of correlation between the CPR
indices and SSChl on an inter-annual basis (see below).
The occurrence of missing months in the CPR data
set sometimes made it difficult to distinguish when the
spring bloom started or when it peaked in individual
years. This is an important question because changes in
the timing of the spring bloom influence the timing of
32
j
NUMBER
12
j
PAGES
1649 – 1660
j
2010
zooplankton reproduction and the recruitment success
of several commercially important finfish and invertebrates (Ellertsen et al., 1989; Head et al., 2005; Koeller
et al., 2009). Between 1998 and 2006, there were only a
few years when we could look for these changes
because of missing months in the CPR data and even
when the data were adequate, in most years seasonal
cycles were similar. Nevertheless, for one sampling year
(1999), on the Scotian Shelf (WSS and ESS) diatom
abundance and the PCI were much higher in January –
March than they were on average (cf. Figures 3 and 7)
and the SSChl peak was a month earlier than usual.
Also, in 2003 in the SNL and NLS regions, the same
three indices suggested the bloom was later, or lasted
longer, than average. The details of the dynamics of the
CPR and SSChl signals were not entirely consistent in
either year, however. For the deep water regions, neither
the SSChl nor the CPR indices showed any obvious
year-to-year differences in seasonal cycles. Thus, on the
basis of our limited observations, our assessment is that
in regions where spring bloom peaks are well-defined
and large, major changes in timing (by 1 month or
more) are probably captured by both sampling
methods. Inter-decadal changes in spring bloom
dynamics have been observed by means of CPR
sampling on the Scotian Shelf between the 1970s and
1990s (Head and Sameoto, 2007), but overall we
suggest that satellite measurements are preferable for
defining the timing of spring blooms, firstly because the
superior temporal resolution of the data and secondly
because the time series are usually complete.
Inter-annual variations in phytoplankton
biomass
Inter-annual changes in CPR indices rarely matched
those in SSChl in any region, despite the relatively
large year-to-year changes in diatom abundance in
WSS, ESS and NLS regions and the relatively large
and consistent positive trends in diatom and dinoflagellate abundance between 2002 or 2003 and 2006 in
regions east of 458W. As suggested above, gaps in the
CPR data sets, and spatial and temporal variability
within regions may have had some influence on these
results, but this could perhaps be reduced including
more “pseudo-replicates”, for example, by comparing
multi-year periods. This is how Head and Sameoto
(Head and Sameoto, 2007) and Head and Pepin (this
volume) investigated time series trends in CPR-derived
plankton abundances in the NW Atlantic; by comparing
annual averages calculated over years within decadal, or
near decadal, periods. Head and Pepin (this volume)
found significant increases in the decadal annual
1658
E. HEAD AND P. PEPIN
j
COMPARISON OF CPR AND SATELLITE COLOUR IMAGERY
Fig. 7. Seasonal cycles of diatom abundance (DIATS), dinoflagellate abundance (DINOS), the phytoplankton colour index (PCI) and satellite
derived chlorophyll (SSCHL) concentration for shelf regions, for years where the bloom was early (WSS, ESS, 1999, upper row) or late (SNL,
NLS, 2003, bottom row).
averages for the PCI in all shelf regions used here
between the 1970s and the 1990s, for diatom abundance in all shelf regions except the NLS and for dinoflagellates in all shelf regions except the WSS. In all
cases, however, there were no significant differences in
decadal averages between the 1990s (1992 – 1999) and
the 2000s (2000 – 2006). Thus, for shelf regions, the
changes within the sampling period used here were not
as large as they have been in the past, suggesting that if
satellites with ocean colour sensors flying had been
flying in the decades prior to the 1990s, then they
perhaps would have recorded lower decadal average
annual phytoplankton levels.
Head and Pepin (this volume) found small increases
in diatom and dinoflagellate abundances in the 2000s
over the 1990s in all deep ocean regions, and very slight
increases in the PCI in the 1990s compared with the
1980s. The variability associated with these decadal
averages was such, however, that these changes were not
significant. The fact that the pronounced increases in
diatom abundance in the early 2000s did not show up
in the SSChl data is disappointing (Fig. 5), although in
one region there was a positive correlation (35 – 408W).
SSChl levels on the SNL and NLS were lower than
might have been expected from the observations of PCI
and diatom abundance, which is another manifestation
of the fact that the relationships between these CPR
indices and total phytoplankton biomass varied among
regions. This variability would also have influenced
relationships across regions between the multi-year
annual or year-round averages of CPR indices and
those of SSChl, which were, nevertheless, significant in
four out of six cases. A variety of factors might contribute to the inter-regional differences in relationships
between individual CPR indices and SSChl, including,
for example, differences in the composition of the phytoplankton community, in terms of size structure and
optical properties, which probably vary both regionally
and seasonally. Whatever the explanation, however, our
observations provide a cautionary note for those using
data of different types from different sources over different time scales. We suggest that more comparative
studies, such as this one, need to be carried out for
more different regions, so that we can have a better
understanding of how to inter-weave the different data
types in long-term basin scale studies.
Inter-regional differences in phytoplankton
biomass
CONCLUDING REMARKS
Multi-year (1998 – 2006) annual averages of phytoplankton levels derived from CPR measurements were higher
in shelf regions than in the open ocean (Fig. 6),
although when the winter months were omitted, diatom
abundance was more or less the same everywhere.
Remote sensing and CPR sampling provide useful and
complementary ways of monitoring phytoplankton in
the open ocean. Both contribute something different:
remote sensing has superior temporal and spatial coverage and temporal resolution (except in winter at high
1659
JOURNAL OF PLANKTON RESEARCH
j
VOLUME
latitudes), while CPR sampling allows species identification, so that it can be used to examine questions
related to phytoplankton composition and diversity. In
addition, CPR sampling has been carried out in the
NW Atlantic since the late 1950s, so that it allows us a
view of the past, when there was no monitoring by satellite. In future, both techniques will be essential if we
are to understand the effects of environmental conditions on phytoplankton abundance, distribution and
production. We will need to do this in order to anticipate how marine ecosystems will respond to predicted
future climate change. More comparative studies are
needed so that we can interpret the changes we see.
AC K N OW L E D G E M E N T S
The authors would like to thank Heidi Maass for processing the SeaWiFS satellite data and the Continuous
Plankton Recorder Team for their continued commitment to the CPR survey in the NW Atlantic. Glen
Harrison and Bill Li provided helpful suggestions
during the preparation of the manuscript.
32
j
NUMBER
12
j
PAGES
1649 – 1660
j
2010
Head, E. J. H. and Sameoto, D. D. (2007) Interdecadal variability in
zooplankton and phytoplankton abundance on the Newfoundland
and Scotian shelves. Deep-Sea Res. II, 54, 2686– 2701.
Head, E. J. H., Harris, L. R. and Petrie, B. (1999) Distribution of
Calanus spp. on and around the Nova Scotia Shelf in April:
evidence for an offshore source of Calanus finmarchicus to the
central and western regions. Can. J. Fish. Aquat. Sci., 56,
2463–2476.
Head, E. J. H., Brickman, D. and Harris, L. R. (2005) An exceptional
haddock year-class and unusual environmental conditions on the
Scotian Shelf in 1999. J. Plankton Res., 27, 597–602.
Falkowski, P. G., Katz, M. E., Knoll, A. H. et al. (2004) The
evolution of modern eukaryotic phytoplankton. Science, 305,
354 –360.
IPCC. (2007) Intergovernmental Panel on Climate Change. Fourth
Assessment Report (AR4). http://www.ipcc.ch.
Koeller, P., Fuentes-Yaco, C., Platt, T. et al. (2009) Basin-scale coherence in phenology of shrimps and phytoplankton in the North
Atlantic ocean. Science, 324, 791–793.
Loder, J. W., Petrie, B. and Gawarkiewicz, G. (1998) The coastal
ocean off northeastern North America: a large-scale view. In
Robinson, A. R. and Brink, K. H. (eds), The Sea. Vol. 11. Wiley and
Sons, New York, NY, pp. 105–133.
Mann, K. H. and Lazier, J. R. N. (1996) Dynamics of Marine Ecosystems:
Biological–Physical Interactions in the Oceans. 2nd edn. Blackwell
Science, Ltd, Abingdon, UK.
O’Reilly, J. E., Maritorena, S., Mitchell, B. G. et al. (1998) Ocean
colour algorithms for SeaWiFS. J. Geophys. Res., 103, 13279– 13294.
FUNDING
This work is funded as part of the activities of the
Atlantic Zone Monitoring Programme of Fisheries and
Oceans Canada.
REFERENCES
Batten, S. D., Walne, A. W., Edwards, M. et al. (2003) Phytoplankton
biomass from continuous plankton recorder data: an assessment of
the phytoplankton colour index. J. Plankton Res., 7, 697 –702.
Bersch, M. (1995) On the circulation of the northeastern North
Atlantic. Deep-Sea Res. I, 42, 1583– 1607.
Colebrook, J. M. (1960) Continuous plankton records: methods of
analysis, 1950–59. Bull. Mar. Biol., 5, 51–64.
Orr, J. C., Fabry, V. J., Aumont, O. et al. (2005) Anthropogenic ocean
acidification over the twenty-first century and its impact on calcifying organisms. Nature, 437, 681–686.
Raitsos, D. E., Reid, P. C., Lavender, S. J. et al. (2005) Extending the
SeaWiFS chlorophyll data set back 50 years in the northeast
Atlantic. Geophys. Res. Lett., 32, L06603.
Raitsos, D. E., Lavender, S. J., Maravelias, C. D. et al. (2008)
Identifying four phytoplankton functional types from space: an ecological approach. Limnol. Oceanogr., 53, 605–613.
Reid, P. C., Colbrook, J. M., Matthews, J. B. L. et al. and the
Continuous Plankton Recorder Team. (2003) The Continuous
Plankton Recorder: concepts and history, from Plankton Indicator
to undulating recorders. Prog. Oceanogr., 58, 115–173.
Richardson, A. J., Walne, A. W., John, A. W. G. et al. (2006)
Using continuous plankton recorder data. Prog. Oceanogr., 68,
27– 74.
Ellertsen, B., Fossum, P., Solemdal, P. et al. (1989) Relation between
temperature and survival of eggs and first-feeding larvae of northeast Arctic cod (Gadus morhua L.). Rapp. P.-V. Réun. Cons. Int. Explor.
Mer., 191, 209–219.
Sathyendranath, S., Watts, L., Devred, E. et al. (2004) Discrimination
of diatoms from other phytoplankton using ocean-colour data. Mar.
Ecol. Prog. Ser., 272, 59– 68.
Hays, G. C. and Lindley, J. A. (1994) Estimating chlorophyll a abundance
from the ‘PCI’ recorded by the continuous plankton recorder survey:
validation with simultaneous fluorometry. J. Plankton Res., 16, 23–34.
Therriault, J.-C., Petrie, B., Pepin, P. et al. (1998) Proposal for a
Northwest Atlantic Zonal Monitoring Program. Can. Tech. Rep.
Hydrogr. Ocean Sci., 194, viiþ57 pp.
1660