ICES Journal of
Marine Science
ICES Journal of Marine Science (2016), 73(2), 227– 238. doi:10.1093/icesjms/fsv126
Original Article
Is the Russell Cycle a true cycle? Multidecadal zooplankton
and climate trends in the western English Channel
M. Conor McManus 1,2*, Priscilla Licandro3, and Steve H. Coombs 4
1
Graduate School of Oceanography, University of Rhode Island, Narragansett, RI 02882, USA
RPS ASA, South Kingstown, RI 02879, USA
3
Sir Alister Hardy Foundation for Ocean Science, The Laboratory, Citadel Hill, Plymouth PL1 2PB, UK
4
The Marine Biological Association, The Laboratory, Citadel Hill, Plymouth PL1 2PB, UK
2
*Corresponding author: tel: + 44 401 789 6224; fax: +44 401 789 1932; e-mail: [email protected]
McManus, M. C., Licandro, P., and Coombs, S. H. Is the Russell Cycle a true cycle? Multidecadal zooplankton and climate trends in
the western English Channel. – ICES Journal of Marine Science, 73: 227 – 238.
Received 5 February 2015; revised 3 June 2015; accepted 26 June 2015; advance access publication 21 July 2015.
The Russell Cycle is one of the classical examples of climate influence on biological oceanography, represented as shifts in the marine plankton over
several decades with warm and cool conditions. While the time-series data associated with the phenomenon indicate cyclical patterns, the question
remains whether or not the Russell Cycle should be considered a “true cycle”. Zooplankton time-series data from 1924 to 2011 from the western
English Channel were analysed with principal component (PC), correlation and spectral analyses to determine the dominant trends, and cyclic
frequencies of the Russell Cycle indicators in relation to long-term hydroclimatic indices. PC1 accounted for 37.4% of the variability in the zooplankton data with the main contributions from non-clupeid fish larvae, southwestern zooplankton, and overall zooplankton biovolume. For
PC2 (14.6% of data variance), the dominant groups were northern fish larvae, non-sardine eggs, and southern fish larvae. Sardine eggs were the
major contributors to PC3 (representing 12.1% of data variance). No significant correlations were observed between the above three components
and climate indices: Atlantic Multidecadal Oscillation, North Atlantic Oscillation, and local seawater temperature. Significant 44- and 29-year
frequencies were observed for PC3, but the physical mechanisms driving the cycles are unclear. Harmonic analysis did not reveal any significant
frequencies in the physical variables or in PCs 1 and 2. To a large extent, this is due to the dominant cycles in all datasets generally being long
term (.50 years or so) and not readily resolved in the examined time frame of 88 years, hence restricting the ability to draw firm conclusions
on the multidecadal relationship between zooplankton community dynamics in the western English Channel and environmental indices. Thus,
the zooplankton time-series often associated and represented as the Russell Cycle cannot be concluded as being truly cyclical.
Keywords: Atlantic Multidecadal Oscillation, North Atlantic Oscillation, Russell Cycle, western English Channel, zooplankton.
Introduction
Climate variability has been linked to changes in marine populations and ecosystems through atmospheric and oceanic processes
(Cushing, 1982). Oceanographic and meteorological variables, including windstress and direction (Fromentin and Planque, 1996),
precipitation and river run-off (Lindahl et al., 1998; Kimmel et al.,
2009), oceanic inflow, and sea temperature (Drinkwater et al.,
2003; Genner et al., 2010), have been compared against the abundances of marine organisms to understand how phenological
changes have altered marine ecosystems. Since the second half of
the 20th century, studies have related these oceanographic,
# International
meteorological, and subsequent biological changes to larger scale
climate oscillations such as the North Atlantic Oscillation (NAO)
and the Atlantic Multidecadal Oscillation (AMO). The NAO is a
measure of the atmospheric pressure system over North Atlantic
between the Icelandic Low and the Azores High (Hurrell, 1995).
The NAO has been found to influence windspeed direction, air temperatures, and precipitation in the North Atlantic, with varying
effects on marine organisms. Particularly for plankton, wind and
air temperature conditions resulting from the NAO phases have
impacted water column mixing and stratification, transporting
oceanic waters to coastal regions, and causing significant changes
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228
in zooplankton community composition (Drinkwater et al., 2003;
Alheit et al., 2005; Edwards et al., 2006; Holliday et al., 2011;
Licandro et al., 2011). The AMO index represents the cyclical
changes in sea surface temperature, sea level pressure, and ocean circulation driven by Atlantic Meridional Overturning Circulation fluctuations (Nye et al., 2014). The AMO is often used as an indicator for
the detrended patterns of North Atlantic water temperature, and is
believed to interact with the NAO via the changes in North Atlantic
Deep Water formation (Nye et al., 2014; Alexander et al., 2014).
The interaction between the AMO and temperature has been
studied extensively due to the impact of temperature on marine
plankton and fish physiology (Fry, 1971; Genner et al., 2010),
species’ population movements (Perry et al., 2005; Beaugrand et al.,
2009; Alheit et al., 2012), oceanographic dynamics (Schofield
et al., 2008), and marine predator–prey interactions (Friedland
et al., 2009). Oceanographic variability has been examined with
marine taxa at all trophic levels (Greene and Pershing, 2003;
Collie et al., 2008; Borkman and Smayda, 2009); however, marine
plankton species are particularly sensitive to oceanographic
changes because they and their prey are strongly susceptible to,
and reliant on, physical oceanographic conditions (Reid et al.,
1998, 2003; Edwards, 2009; Widdecombe et al., 2010). Thus, long
time-series of oceanographic and plankton data are often used to
elucidate the relations between climate and marine ecosystems.
The long history of plankton, fisheries, and oceanographic research in the western English Channel off Plymouth, UK [summarized in Southward et al. (2005)], has allowed extensive research on
the impact of climate change for close to a century. Results have
shown a sequence of changes, which have been termed the Russell
Cycle (Cushing and Dickson, 1976). The basis for the Russell
Cycle is represented by distinct, descriptive changes in several biological oceanographic indicators between the 1920s and the 1970s
(Coombs and Halliday, 2011). Plankton and fish larvae were abundant during the relatively cool period before the 1930s, but then
became sparse during the warmer period between the 1940s and
1960s. There was then a return to the abundant plankton state in a
transition to cooler conditions in the 1970s (Cushing and
Dickson, 1976; Southward et al., 1988). Corresponding changes
were observed in relative abundance of characteristic temperate and
cold-water plankton species, such as Parasagitta elegans (formerly
Sagitta elegans), and at other trophic levels including pelagic fisheries
and rockyshore communities (Cushing, 1982). Total plankton biovolume and inorganic phosphate concentrations also followed similar
patterns. Sardine eggs had an apparent inverse relationship with the
environment, with high abundances during the warm period of the
1940s and 1970s, and low abundances before and after. Parasagitta
setosa also was out of phase with most of the plankton, and replaced
P. elegans during the warm 1940s–1970s (Cushing, 1982).
Links between the AMO and marine ecosystem changes have
been well documented in the North Atlantic and adjacent seas, including the western English Channel. For example, Mieszkowska
et al. (2014) found that the prevalence of barnacle species
Semibalanus balanoides and Chthalmus spp. off of southern UK
corresponded to the warm/cool temperature shifts represented by
the AMO index. The AMO’s influence on fish species’ latitudinal
position has also been seen in the US continental shelf system,
with centres of fish biomass shifting in response to temperature
changes (Nye et al., 2009). Specific to the western English
Channel, fluctuations in herring (Clupea harengus) and sardines
(Sardina pilchardus, also known as pilchard both locally and in
earlier publications) abundances have been related to climate
M. C. McManus et al.
change (represented as temperature and wind) over several
hundred years (Alheit and Hagen, 1997). Edwards et al. (2013)
found that temperature changes associated with the AMO have
influenced the abundance of various plankton entities over the
past several decades, including sardine eggs.
It has been proposed that the temperature (warm/cool) phases
dictate the ecosystem structure in the North Atlantic and the patterns described in Cushing and Dickson (1976), with species successful in a certain regime and replacing those less fit either via
competition or displacement (Beaugrand et al., 2009); however,
the Russell Cycle itself has been linked only qualitatively to environmental conditions. Thus, it is still unclear whether it is accurate to
define the Russell Cycle events as part of a “true cycle” and how
climate oscillations or decadal temperature trends connect directly
to the plankton community in the western English Channel. We
investigated the correlations, variability and cyclical trends in
climate oscillations, temperature, zooplankton abundance, and
community structure off Plymouth to provide firmer evidence of
interactions between climate and biology in the western English
Channel. We hypothesized that the changes in marine zooplankton
species result from species composition shifts due to temperature
changes, ultimately driven by the cyclical patterns of the AMO
and NAO. Previous studies have examined the impact of climate
on individual plankton species in the western English Channel
(Blackett et al., 2014; Mieszkowska et al., 2014). However, in contrast to earlier studies that were in large part qualitative or selected
only a few individual plankton variables, our approach was to first
determine the major trends in the historical Russell Cycle zooplankton data using a diversified set of zooplankton descriptors, then
identify the climate influence using the derived zooplankton community indices. Our study aimed to verify if large-scale atmospheric/oceanographic dynamics correlate with what have long been
considered the main descriptors of the Russell Cycle and the
western English Channel zooplankton community.
Material and methods
Zooplankton data
A comprehensive outline of the sampling off Plymouth and analysis
methods is given in Southward et al. (2005) and articles referred to
therein. Briefly, the routine plankton sampling has generally been
carried out several times a month since 1924 at stations L5 and
E1, about 10 and 20 miles off Plymouth, respectively (Harris,
2010; Figure 1), with intermittent gaps of months or years in response to changing circumstances. Several nets have been used
over the period, mostly 1 and 2 m diameter ringnets in earlier
years and then variants of a 0.9 m square inlet net thereafter. All
were fitted with relatively coarse mesh (700 mm mesh aperture),
so that only relatively larger zooplankton (e.g. Calanus spp.) and
fish larvae and post-larvae (mean length of 12 mm, and all here
referred to as larvae) are sampled efficiently.
Plankton abundances were standardized as monthly means for a
standard haul of 4000 m3. Zooplankton biovolume was calculated
as the counts of each taxa raised by an estimate of the volume of
an individual for that taxa with a further allowance to convert to wetsettled volume based on observed settled volumes and zooplankton
species counts. This allows direct comparison with the settled
volumes displayed by Russell et al. (1971) to highlight the contrasting phases of the Russell Cycle. Samples were primarily collected
from station L5, but on occasion were instead collected at station
E1 or other local sampling sites. Monthly mean abundances were
229
Is the Russell Cycle a true cycle?
Radiometer (AVHRR) Pathfinder dataset (Vazquez et al., 1995).
AMO data were obtained from NOAA’s Earth System Research
Laboratory (http://www.esrl.noaa.gov/psd/data/timeseries/AMO/,
Figure 2). Winter (December–March) NAO indices were accessed
from the University of East Anglia’s Climatic Research Unit (http://
www.cru.uea.ac.uk/cru/data/nao/, Figure 2).
Statistical analysis
Figure 1. Positions of regular plankton sampling stations L5 and E1 in
the western English Channel. Dashed lines represent bathymetric
contours. Contour data were accessed from the General Bathymetric
Chart and Oceans Database (GEBCO, http://www.gebco.net/).
summed to construct annual totals for the years 1924 – 2011 inclusive. For any year in which there were missing data for months
representing .50% of the long-term annual abundance, the
annual total for that year was considered as a missing value. The
species and groups used in the present analysis (Table 1) are a
selected subset, representing the most abundant and well-sampled
entities. Groupings were constructed based on geographical range
and temporal presence/absence, as used historically to describe
the Russell Cycle and the western English Channel community
(Russell, 1973; Southward et al., 2005; Coombs and Halliday,
2011).
Sardines in the western English Channel have two distinct phases
of spawning: spring and summer/autumn (Coombs et al., 2005,
2010). In most analyses of sardine eggs and the environment off
Plymouth, the annual total egg abundance has been considered.
However, since there has been speculation that the two phases of
spawning respond differently to long-term temperature changes
(Southward, 1974), we have also examined sardine spawning in
the two separate seasons.
Physical data
Annual temperature was calculated as the average of monthly sea
surface temperature means at station E1. Missing values were
replaced with data from either the UK Meteorological Office, or in
more recent years, using the Advanced Very High Resolution
Data analyses included years 1924–2011. Zooplankton abundances
were standardized and log-transformed (log10 n + 1). Linear
Pearson’s correlations were first calculated to help identify and eliminate variables from the zooplankton dataset that were collinear and
essentially variations of each other (Table 1). This reduced the
number of zooplankton variables from an initial 20 components
to a final 10 groups used in the principle component analysis
(PCA, see Table 2) to construct variables that captured the zooplankton community trends associated with the Russell Cycle.
Missing annual abundance for the various plankton taxa was calculated using the model of Ibanez and Conversi (2002). This technique is based on several iterations of the eigenvector filtering
(EVF) method, which corresponds to a PCA calculated on an autocovariance matrix based on the original series Xt shifted with itself
several times, from 1 year to n years, choosing n according to the
autocorrelation function of Xt (in our case, from 3 to 9 years lag).
In the first iteration, which is done while retaining the missing
data, the autocorrelation function is based on a limited number of
observations, i.e. NT ¼ Ntot 2 MD, where NT is the number of
observations for the given variable, Ntot is the 88 years covered by
the time-series, and MD is the years where no sampling was available. In our case, MD varied according to the variable considered,
up to a maximum of 10 –15 consecutive missing years. The main
principle components, PCs, obtained by the first iteration of the
EVF representing at least 80% of the total variance, are then used
in the following iterations to replace the missing values; this procedure is repeated several times until the sum of square deviations of the
predicted values at iteration i and i 2 1 is ≤0.001. Ibanez and
Conversi (2002) showed that the EVF is a robust methodology to
analyse datasets with gaps. While other methods (e.g. multiyears
running average) are based on the deviation from the global
average and thus assume the stationary of the data, which is rarely
the case for plankton time-series, the EVF estimates missing
values considering simultaneously the global and local averages,
thus taking into account the global and local structure of the temporal process (Ibanez and Conversi, 2002).
Following the PCA, zooplankton PCs 1–3 were correlated with
annual temperature, and NAO and AMO indices. Pearson’s correlation coefficients and p-values testing the hypothesis of no correlation
were calculated, and the numbers of degrees of freedom were corrected to account for temporal autocorrelation, according to Pyper
and Peterman (1998). For correlation analyses, a time-lag of 0 years
was used based previous work indicating that plankton respond
quickly to changes in climate oscillations (Edwards et al., 2013).
Spectral analysis was used to identify cyclical frequencies in the
temperature, climate indices, and PCs 1 –3 time-series data
(Colebrook, 1986; Aebischer et al., 1990; Baumgartner et al., 1992;
Zebdi and Collie, 1995). Power spectral densities at given frequencies were estimated using the periodogram method with a sampling
frequency of 1 year. Dataseries were detrended before spectral analyses to remove linear trends in the data by subtracting the mean
time-series values from their respective time-series. Harmonic analyses were also performed on these six descriptors to determine the
230
M. C. McManus et al.
Table 1. Zooplankton groups evaluated for the analyses and number of years of raw data (see text) over the 88-year (1924 – 2011) sampling
period.
Group
Total fish eggs
Other (non-sardine) fish eggs*
Sardine eggs all year
Sardine eggs spring/summer*
Sardine eggs autumn*
All fish larvae
Clupeid larvae*
Other (non-clupeid) larvae*
Dragonet larvae
Flatfish larvae
Gadoid larvae
Southern fish larvae*
Northern fish larvae*
Zooplankton biovolume*
Calanus
P. elegans
P. setosa
Decapod larvae
Northwestern (NW) zooplankton*
SW zooplankton*
Composition
Mostly S. pilchardus, Sprattus sprattus, and Limanda
limanda
Mostly S. sprattus, L. limanda, and gadoid eggs
All eggs combined from the two spawning periods
Sardine eggs caught from January –August
Sardine eggs caught from August –December
Mostly Clupeidae, Callionymus spp., L. limanda, and
gadoids
Primarily all S. pilchardus or S. sprattus
Mostly Callionymus spp., L. limanda, and gadoids
Mostly Callionymus lyra
Mostly L. limanda and Soleidae
Mostly Merlangius merlangius, Trisopterus minutus, and
Gadus luscus
Capros aper
Crenilabrus melops
Trachurus trachurus
Cepola rubescens
Labrus bergylta
Dicentrarchus labrax
Labrus mixtus
Pegusa lascaris
Arnoglosssus spp.
Trachinus vipera
Michrochirus variegatus
Blennius gattorugine
Trisopterus esmarkii
Liparis montagui
Gadus morhua
C. Harengus
Chirolophis ascanii
Lebetes scorpiodes
Phrynorhombus norvegicus
Zooplankton counts converted to total settled wet
biovolume
Most C. helgolandicus
Boreal/northwestern species
Temperate/neritic species
Wide range of species through all year
Tomopteris helgolandica
Nanomia spp.
Limacina retroversa
P. elegans
Euthemisto gracilipes
Aglantha digitalis
Calanus spp. (helgolandicus)
P. Setosa
Candacia armata
Nyctiphanes couchii
Eucalanus crassus
Muggiaea atlantica
Euchaeta hebes
Liriope tetraphylla
Centropages typicus
Correlated group
Sardine eggs spring/summer
Years
55
–
Sardine eggs spring/summer
–
–
Clupeid larvae
49
61
61
56
57
–
–
Other (non-clupeid) larvae
Other (non-clupeid) larvae
Other (non-clupeid) larvae
60
56
47
47
46
–
49
–
47
–
57
Zooplankton biovolume
Northwestern (NW) zooplankton
SW zooplankton
Zooplankton biovolume
–
52
55
54
49
62
–
62
Asterisks represent final variables used in the PCA. Collinear variables lack asterisk denotation and have the corresponding variable significantly correlated
(p , 0.01) with them used in the analyses listed.
variables’ significant peaks and variances. Cycles observed equal to
the time-series length (i.e. 88 years) were considered to indicate a
cycle longer than the time-series.
Results
Principal component analysis
PCA indicated that 64.1% of overall variation is described by the
first three components, with 37.4% being accounted for in PC1
(Table 2). Non-clupeid larvae, southwestern (SW) zooplankton,
and total zooplankton biovolume were the main contributors to
PC1 (Table 2 and Figure 3). The pattern for PC1 (explained
hereafter in its inverse form) was a switch from low abundance
of the zooplankton groups from 1924 to the mid-1960s to
higher levels from the late 1960s to 2011. The SW zooplankton
group showed a more pronounced increased abundance from
2005 on.
PC2 comprised 14.6% of the total variation, and was mainly
represented by northern fish larvae, non-sardine eggs, and southern
fish larvae (Table 2 and Figure 4). PC2 showed a variable increase
of the zooplankton from 1924 to around the 1960s/1970s, then a
more systematic decline to 2011. This was mirrored in changes of
abundance of both the northern and southern groups of fish
larvae and inversely by non-sardine eggs; for this latter group, the
correlation with PC2 is only clearly apparent in the trend post the
1970s.
Sardine eggs dominated the contribution to PC3, which
represented 12.1% of the total variation (Table 2 and
Figure 5). The most evident features in the time-series of PC3
(plotted in Figure 5 and described here as the inverse) are the
low period in the 1970/1980s with sequences of higher values
before and after, as well as a tailing decline from 2003
onwards. The numbers of sardine eggs show the period of low
abundance in the mid-1970s to the mid-1980s with the
spring/summer eggs abundant before the 1970s and, though
the data are rather sparse, autumn spawned eggs from the
mid-1990s onwards; both spawning seasons show the tail off
in abundance post 2004.
231
Is the Russell Cycle a true cycle?
Table 2. Weightings for the first three PCs with percentage
of overall variation explained for each component.
Group
Sardine eggs Jan –Aug
Sardine eggs Aug –Dec
Other (non-sardine) fish eggs
Clupeid larvae
Other (non-clupeid) larvae
Southern larvae
Northern larvae
Zooplankton biovolume
NW zooplankton
SW zooplankton
PC1
(37.4%)
0.21
20.17
20.23
20.32
20.43
20.35
20.25
20.40
20.29
20.41
PC2
(14.6%)
0.01
0.14
20.45
0.21
0.09
0.40
0.54
20.12
20.34
20.37
PC3
(12.1%)
20.64
20.56
20.31
20.13
0.08
20.01
0.11
20.20
0.33
0.01
Top weightings for each PC are in bold.
Frequency analysis
The AMO, sea temperature, PC1, and PC2 appear to have major
signals at roughly 88 years, the length of the dataset (Figures 6 and 7,
and Table 4), suggesting that the these main cyclical frequencies
are longer than 88 years and cannot be resolved with the current
length of the time-series. Additionally, the E1 temperature frequencies were found to be insignificant peaks. The winter NAO index
time-series contained a wide range of cyclical frequencies (Figure 6),
though none were significant. Considering that for zooplankton
descriptors the size of temporal windows with missing data was
close or lower than 10 – 15 years, we caution that cycles around
those lengths revealed by the spectral and harmonic analyses on
PC1–PC3 could be bias. However, the longest cycles are unlikely
to be affected by missing data; therefore, we retained the longest
cycles as valid. PC3 is dominated by relatively low frequency
cycles, with significant cycles at around 29 and 44 years (Table 4
and Figure 7).
Discussion
PCs of the western English Channel zooplankton
community
Figure 2. Time-series of the physical data.
Correlations
None of the PCs were significantly correlated with the three physical environment parameters (Table 3). The winter NAO index
correlated positively with E1 temperature, whereas the AMO
index was correlated with the E1 temperature at an alpha level
equal to the significance threshold (p-value ¼ 0.05). There were
no linear correlations between the AMO and sardine eggs or
other zooplankton groups. Zooplankton biovolume was positively
correlated with several groups of fish larvae, which in turn were significantly intercorrelated. Zooplankton biovolume was significantly correlated with both southern and northern zooplankton,
with the southern zooplankton having a stronger correlation
(Table 3).
Trends of non-clupeid larvae, the SW zooplankton group, and total
zooplankton biovolume explained most of the decadal variability in
the western English Channel zooplankton community, as PC1
explained over one-third of the overall zooplankton variance
(Table 2). PC1 exhibited similar patterns over the 20th century as
described for the Russell Cycle: low zooplankton abundances
between 1940 and 1960s, preceded and followed by high abundances
in the 1920s–1930s, and after the mid-1960s, respectively. Trends
and results for PC1 and non-clupeid larvae are applicable to the
dragonet, flatfish, and gadoid abundances also, as the non-clupeid
larvae are primarily made up of these three fish groups (Table 1).
PC1 and non-clupeid trends also correspond to the historical
changes in gadoids in the North Sea, known as the “gadoid outburst”
(Cushing, 1982). Gadoids (cod, hake, whiting, and saithe) and flatfish (plaice and sole) abundances declined in the 1930s, then
increased in the 1960s, reaching their maximum abundances in
the 1970s and 1980s (Hislop, 1996). SW zooplankton group
patterns are largely driven by the trends of the copepod Calanus
helgolandicus and the chaetognath P. setosa, the latter considered
a classical biological index of the Russell Cycle (Cushing and
Dickson, 1976). The significant influence of these southern species
on the zooplankton community is likely to reflect the recent prevalence of southern plankton species in the western English Channel
232
Figure 3. Time-series of the first PC (plotted as the inverse) and the
three highest contributor parameters plotted as raw data with
interpolated data shown by the dashed sections.
M. C. McManus et al.
Figure 4. Time-series of the second PC and the three highest
contributor parameters plotted as raw data with interpolated data
shown by the dashed sections.
Is the Russell Cycle a true cycle?
Figure 5. Time-series of the third PC (plotted as the inverse) and the
two highest contributor parameters (sardine eggs by spawning period)
plotted as raw data with interpolated data shown by the dashed
sections.
due to warming waters (Hawkins et al., 2003). While PC1 and the
AMO periodicities are similar in this dataset (Figures 6 and 7),
they are not discernible with the current time-series length, nor
was PC1 significantly correlated with the AMO, NAO, or E1 temperature. Thus, no simple direct relationships can be concluded for
the dominant element of the time-series of zooplankton at E1/L5
and the hydroclimatic indices examined.
PC2 captured the trends in non-sardine eggs and both northern
and southern groups of fish larvae; however, there is again no relationship with the environmental variables. The number of observed
cycles and the range they covered for PC2 were higher and wider
than those of PC1 or PC3 (Figure 6). The lack of long significant
Table 3. Pearson correlation coefficients between zooplankton groups, climate variables, and principle components.
Sardine eggs
Aug –Dec
Other fish eggs
Clupeid larvae
Non-clupeid
larvae
Southern larvae
Northern larvae
Zoo biovolume
NW zoo
SW zoo
E1 temperature
Winter NAO
AMO
PC1
PC2
PC3
Sardine eggs
Jan –Aug
0.07
Sardine eggs
Aug –Dec
Other fish
eggs
20.07
20.14
20.31
0.18
0.14
0.18
0.16
0.27
20.19
20.26
20.11
20.33*
20.30
0.08
0.09
0.37
–
–
–
0.27*
0.19
0.24
0.04
0.18
0.05
0.04
20.03
–
–
–
0.04
20.03
0.37*
0.19
0.49**
20.06
20.12
20.01
–
–
–
Clupeid
larvae
Non-clupeid
larvae
Southern
larvae
Northern
larvae
Zoo.
biovolume
NW
zoo
SW
zoo
E1
temperature
Winter
NAO
AMO
0.58**
0.45**
0.56**
0.37**
0.61**
20.08
20.12
20.09
–
–
–
0.47**
0.38**
0.25
0.31
20.14
20.10
20.10
–
–
–
0.26
0.04
0.08
20.19
20.16
20.32
–
–
–
0.38**
0.63**
0.10
0.02
20.09
–
–
–
0.54**
20.05
0.04
0.00
–
–
–
0.17
20.07
20.08
–
–
–
0.33**
0.33
0.03
20.15
20.11
20.08
0.11
20.08
20.04
0.16
20.15
20.21
0.41**
0.48**
0.28
0.46**
0.12
0.35
0.03
20.15
20.02
–
–
–
233
Asterisks represent significant relationships at a’s of 0.05 (*) and 0.01(**). Dashes (–) indicate where PC’s were not correlated against the biological variables used for PCA because the relationships are reflected in the PCs.
234
Figure 6. Spectral analysis of the climate data (temperature, winter NAO, and AMO).
M. C. McManus et al.
Is the Russell Cycle a true cycle?
235
Figure 7. Spectral analysis of PCs 1 – 3.
cycles in PC2 is not entirely surprising, given the low cyclical patterns visually present in PC2, non-sardine eggs, and northern/
southern fish larvae indices (Figure 4).
Fluctuations in the sardine eggs were explained in PC3 (Table 2).
The patterns of PC3 corresponded to those of all sardine egg indices,
reflecting the periods of abundance in the 1930s–1960s and from
236
M. C. McManus et al.
Table 4. Significant frequencies and variances for selected
parameters.
PC1
PC2
PC3
E1 temperature
Winter NAO
AMO
Significant harmonics
(years)
a (88)
a (88)
44, 29.3
n.s.
n.s.
a (88)
% of associated
variance
42
20
17, 33
–
–
38
Long-term cycles, which cannot be resolved, are indicated by a.
the mid-1980s (Figure 4). Similar patterns for sardines, with
scarce numbers from the 1920s to the 1980s and a sudden increase
in sardines in the late 1990s–2000s, have been witnessed in other
regions of the Northeast Atlantic, the western Mediterranean,
Aegean Sea, and off northwestern Africa (Beare et al., 2004; Alheit
et al., 2012, 2014). PC3, which is associated with sardine eggs, had
significant periodicities of roughly 44 and 29 years (Table 4);
however, these cycles are less than half of what is typically expected
for the AMO of 60 –75 years (Schlesinger and Ramankutty, 1994),
suggesting that the cyclical patterns may be more directly related
to other external factors. Solar cycles or sunspots, approximately
22- or 11-year cycles, have been theorized to impact oceanography
(Friis-Christensen and Lassen, 1991); however, the PC3 and annual
sardine egg cycle is longer than that of sunspots and the mechanisms
for sunspot influence on marine ecosystems are poorly understood
(Zebdi and Collie, 1995).
Zooplankton and climate relationships
Correlation analyses revealed connectivity within the climate
indices and between different zooplankton groups, though not
among zooplankton and climate patterns. E1 water temperature
was positively correlated with the NAO and the AMO (though the
latter at the significance threshold). Relations between NAO,
AMO, and water temperature at E1 reflect the influence large-scale
atmospheric pressure and ocean circulation impacts on temperature
in the North Atlantic (Nye et al., 2014), and specifically in the
western English Channel. Recent northern movement of southern
indigenous species (such as red mullet, Mullus surmuletus, and sardines in the 1990s) are believed to be caused by changes in the atmospheric ocean system, and the resulting increased sea temperatures
(Beare et al., 2004; Alheit et al., 2014). It is hypothesized that the
warm AMO phase and resulting increased temperatures in the
western English Channel from 1930 to 1960 allowed sardines and
northern fish larvae (such as Atlantic herring, C. harengus) to
move north and remain within their thermal habitat range. As a
result, sardines thrived in the western English Channel as northern
fish larvae moved farther north; however, this mechanism has not
been entirely explained and tested (Edwards et al., 2013). While
sardine eggs (PC3) and northern fish larvae have been found to correlate positively and negatively with the AMO and temperature, respectively (Edwards et al., 2013), these correlations were not evident
in our analyses. However, the negative correlation between sardine
egg abundance and northern zooplankton (Table 3) may reflect this
thermal habitat hypothesis. The NAO has been linked to fish production (Parsons and Lear, 2001) and temperature changes in the
western English Channel (Smyth et al., 2010) and often related to
the AMO (Nye et al., 2014). While our results indicated the
impact of the NAO on temperature at E1 (Table 3), the winter
NAO index was not correlated with, nor had cyclical patterns that
matched those of zooplankton’s PCs or the AMO.
Non-significant correlations between climate and zooplankton,
as found here, contradict previous studies evaluating sardine egg
abundances, Parasagitta spp., decapod larvae, and the AMO
(Garcia-Soto and Pingree, 2011; Edwards et al., 2013). These discrepancies may reflect the changes in the relationships when more
recent data are included. For example, the relationship between
sardine eggs and AMO in the Northeast Atlantic was significant
when using data ending around 2000 (e.g. Garcia-Soto and
Pingree, 2011), but have weakened with more recent data included
in analyses. The lack of correlation between the PCs, temperature,
and climate indices also suggests that the AMO and NAO may be inadequate proxies for more complex hydroclimactic processes over
the 88-year period. Indicators more representative of the circulation
and atmospheric processes, such as a gyre circulation or wind direction indices (Klyashtorin, 1998; Hátún et al., 2009), may provide a
better correlation and understanding the climate mechanisms influencing the plankton community. Additionally, the mixed nature of
some of the zooplankton groups, perhaps with contradictory
responses, might also restrict the ability to determine links with environmental signals.
Is the Russell Cycle a true cycle?
While our results indicate some degree of cyclical behaviour in the
western English Channel zooplankton community (Table 4), spectral and harmonic analyses could not verify that the Russell Cycle is
still following a cyclical trend or is in synchrony with climate oscillations. Thus, the phenomenon associated with the Russell Cycle may
be more a reflection of different regime shifts (Collie et al., 2004;
deYoung et al., 2004) rather than sequential changes alternating
over a consistent time frame. The apparent deviation from the cyclical trend could indicate an anthropogenic warming influence on
the natural climate cycle, masking or altering an underlying oscillation. However, given the current limitations of the dataset (i.e. timeseries length, reliance on single sample point data, and incomplete
time-series), the analyses likely require a longer, more complete
and spatially varying time-series to better elucidate the cyclical patterns of zooplankton community changes in the western English
Channel. Analysing time-series data spatially and temporally have
been a challenge in oceanography, particularly when trying to describe changes related to climate. Such datasets are highly valuable
in elucidating connections between ecology and the environment;
however, these impediments make it challenging to determine
large-scale changes (Parmesan et al., 2013). For example, Henson
et al. (2010) found that 10 years of satellite data were insufficient
to show evidence of anthropogenic climate change, and that timeseries roughly four times their dataseries length would be required
to provide evidence of change due to interannual variability in productivity measurements. Given the L5/E1 zooplankton dataset is
only 10 –20 years longer than one full cycle of the climate oscillations of interest (i.e. AMO and NAO), spectral analyses may not
provide significant results in relation to the climate cycles until multiple cycle lengths of data are available.
Conclusions
Non-clupeid larvae, SW zooplankton groups, and total zooplankton biovolume contribute the most to the primary component of
variability in the western English Channel zooplankton community.
While correlation analysis identifies connectivity between climate
oscillations and temperature in the region, there were no
Is the Russell Cycle a true cycle?
correlations between the climate indices and zooplankton groups.
Spectral and harmonic analyses were able to identify significant
cycles, but only of periodicities smaller than those for climate oscillations. Consequently, shared cycles between climate and zooplankton indices were not established and prevented, confirming that the
Russell Cycle is a “true cycle”. Given the data limitation of the L5/E1
dataset, there remains a clear need for continued long-term plankton monitoring to define the dynamics between biology and the
environment.
Acknowledgements
The authors are grateful for the MBA ships crews and scientists’
efforts over many years in taking and processing the samples.
Particular thanks are due to Sir Frederick Russell, Alan Southward,
Nicholas Halliday, and Tim Smyth, who assisted in re-constructing
the E1 temperature time-series. We thank Jüergen Alheit, Gregory
Beaugrand, Chris Bumpus, Jeremy Collie, Jon Hare, and Candace
Oviatt for comments on the manuscript throughout its development. The manuscript was improved with the comments of anonymous reviewers. Recent funding for long-term monitoring has been
provided in part by the NERC Oceans 2025 programme.
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