Progress in Oceanography 58 (2003) 217–233
www.elsevier.com/locate/pocean
Summarising spatial and temporal information in CPR data
D.J. Beare a,∗, S.D. Batten b, M. Edwards b, E. McKenzie c, P.C. Reid b,
D.G. Reid a
b
a
FRS Marine Laboratory, Aberdeen, AB11 9DB, UK
Sir Alister Hardy Foundation for Ocean Science, The Laboratory, Citadel Hill, Plymouth PL1 2PB, UK
c
Department of Statistics and Modelling Science, Strathclyde University, Glasgow, GI 1XH, UK
Abstract
The Continuous Plankton Recorder survey provides pan-oceanic data on geographic distribution, species composition,
seasonal cycles of abundance, and long-term change during the last 70 years. In this paper we compare and contrast
some of the historic data-analytic protocols of the survey, focusing primarily on the various means by which spatiotemporal information in CPR data has been exposed. Relative strengths and limitations are assessed, followed by
suggestions for future approaches to the visualisation and summarising of CPR data.
 2003 Elsevier Ltd. All rights reserved.
Keywords: Continuous Plankton Recorder; Spatial; Long-term and seasonal summary; North Atlantic
Contents
1.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
2.
Spatial and temporal scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
3. Summarising spatial and temporal variability in CPR data
3.1. Interactions between spatial and temporal effects . . . .
3.1.1. Subsetting to expose interaction . . . . . . . . . . . .
3.1.2. Statistical modelling . . . . . . . . . . . . . . . . . . .
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Multivariate techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224
5. Problems explaining long-term,
5.1. General . . . . . . . . . . . .
5.2. CPR data are categorised .
5.3. Data voids . . . . . . . . . .
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seasonal,
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spatial and
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compositional
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Corresponding author. Tel.: +44-1224-295314; fax: +44-1224-295511.
E-mail address: d.beare@marlab.ac.uk (D.J. Beare).
0079-6611/$ - see front matter  2003 Elsevier Ltd. All rights reserved.
doi:10.1016/j.pocean.2003.08.005
changes in
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plankton populations.
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D.J. Beare et al. / Progress in Oceanography 58 (2003) 217–233
5.4.
6.
CPR data are observational . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
1. Introduction
Continuous Plankton Recorder (CPR) data continue to be collected along multiple tow routes in the
North Atlantic using ships of opportunity. The survey has been running since the 1930s and has led to the
accumulation of a large and complex database reflecting the rich pageant of North Atlantic planktonic
abundance and taxonomic composition (Hardy, 1939). The recent (1980 to present) development of statistical theories and an explosion in computer processing capability, has led to increasingly detailed synthesis
and visualisation presentations of the spatial and temporal variability of plankton at pan-oceanic (North
Atlantic), seasonal, and decadal scales (e.g. Colebrook & Robinson, 1961, 1965; Colebrook, 1969; Matthews, 1969; Colebrook, 1978a, 1978b, 1979a, 1979b; Colebrook, Robinson, Hunt, Roskell, John, Bottrell
et al., 1984; Colebrook & Taylor, 1984; Colebrook, 1985; Colebrook, 1986; Colebrook, 1991; Aebischer,
Coulson, & Colebrook, 1990; Planque & Fromentin, 1996; Beare & McKenzie, 1999a, 1999b, 1999c;
Planque & Taylor, 1998; Edwards, John, Hunt, & Lindley, 1999; Madden, Beare, Heath, Fraser, & Gallego,
1999; Planque & Batten, 2000; Beaugrand, Reid, Ibañez, & Planque, 2000b). In this paper we tell the
story of the gradual advancement of summary techniques used for CPR data, focusing in particular on the
difficulties caused by the general non-linearity of the data (e.g. seasonal dependence), the interaction
between spatial and temporal effects (e.g. seasonal patterns that vary with respect to location), and nonrandom sampling (e.g. only collecting night samples on a particular ferry route).
2. Spatial and temporal scales
Plankton abundance varies in spatial scale from millimetres, to metres, to entire ocean basins; while
temporal variation may be measured and analysed in terms of minutes, years, decades or millennia. The
amount of information realistically available for spatio/temporal analysis however, is constrained by the
design of the sampling survey itself. For the CPR survey, the finest spatial information available is at a
10 km scale—the distance separating each sample. In some areas where tow routes overlap a more detailed
spatial resolution may be obtained.
In terms of temporal scales, CPR tow routes are only really sampled on a monthly basis, which in our
opinion is the smallest temporal unit, at which CPR data can realistically be summarised. The time of day
is noted for each CPR sample, suggesting that CPR data can also be analysed at hourly resolutions. Extraction of an hourly signal has been attempted (Hays, 1994; Hays, 1995; Hays, Warner, & Proctor, 1995;
Hays, Warner, & Lefevre, 1996; Hirst & Batten, 1998), but considerable problems were encountered in
interpretation, because of interactions between the main temporal and spatial effects (Beare & McKenzie,
1999b). The CPR survey also provides data at decadal scales since it has been running since the 1930s.
To summarise, a close scrutiny of exactly how relevant information is collected in space and time (seasonal
and long-term) is an essential pre-requisite for CPR data summary and visualisation in order to assess
whether or not the data can be used to answer particular questions (Table 1).
D.J. Beare et al. / Progress in Oceanography 58 (2003) 217–233
219
Table 1
Details of the category counting system employed by the CPR survey
Number of individuals
Recorded value
Accepted value
counted
1
2
3
4–11
12–25
26–50
51–125
126–250
251–500
501–1000
1001–2000
2001–4000
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
6
17
35
75
160
310
640
1300
2690
3. Summarising spatial and temporal variability in CPR data
3.1. Interactions between spatial and temporal effects
Plankton abundance depends on numerous factors such as sea temperature, degree of water column
stratification, bottom depth, food availability and predator density etc. Proxies for these factors are usually
obtained by using variables of location and time (e.g. longitude, latitude, year and month), which are useful
for visualising dependence within the data. The functional forms (shapes) of such dependence are most
likely to be non-linear and further complications arise because the variables of location, long-term trend
and seasonality often depend on each other as well. Such inter-relationships between variables are described
in statistical terminology as ‘dependence’, ‘interaction’ or ‘covariation’, and ought, somehow, to be reflected
in CPR data-analytic protocols. At first this may seem to be a rather obvious point, but such interactions
have often been ignored in the past, or at least only partly accommodated.
Descriptions of seasonal cycles of abundance were first described in CPR data in the early 1940’s (e.g.
Lucas, 1940; Rae & Fraser, 1941) and have now extended to almost all of the common planktonic taxa,
e.g. fish larvae (Bainbridge, Cooper, & Hart, 1974; Coombs, 1980; Coombs & Mitchell, 1981), Thaliacea
(Hunt, 1968), decapod larvae (Lindley, 1987; Lindley, Williams, & Hunt, 1993), pteropods (Cooper &
Forsyth, 1963), and gastropods (Vane & Colebrook, 1962). Since the early studies, analysts have noted
that seasonal cycles of plankton abundance can have different timings and shapes each year (e.g. Fig. 1;
Colebrook & Robinson, 1961, 1965; Reid, Surey-Gent, Hunt, & Durrant, 1992; Beare, McKenzie, & Speirs,
1998; Reid, Edwards, Hunt, & Warner, 1998a; Edwards, Reid, & Planque, 2001), while simultaneously
varying with location (e.g. Robinson, 1970; Robinson, Aiken, & Hunt, 1986).
Summarising spatial patterns of abundance is similarly problematic because they also vary for different
month/year combinations (e.g. Colebrook, 1961; Robinson, 1965; Oceanographic Laboratory, Edinburgh,
1973; Planque & Fromentin, 1996; Planque, Hays, Ibañez, & Gamble, 1997; Planque & Ibañez, 1997;
Madden, Beare, Heath, Fraser & Gallego, 1999; Beare & McKenzie, 1999a). The occurrence of interactions
between spatial and seasonal effects may also mask long-term trends which are also influenced by location
(e.g. Planque & Ibañez, 1997; Planque & Batten, 2000) and time of year (Beare & McKenzie, 1999a,
1999b, 1999c). Temporally and spatially constant patterns of abundance for organisms that live permanently
in a dynamic environment are thus extremely unlikely to be observed. Complex hypervariate data such as
those from the CPR therefore, require dynamic data summary and data visualisation tools (Cleveland, 1993).
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Fig. 1. Contour plots of mean monthly Phytoplankton Colour during 1948–95 for thecentral North Sea, the central north-east Atlantic
and the northern north-eastAtlantic. (redrawn from Reid, Edwards, Hunt & Warner, 1998a).
3.1.1. Subsetting to expose interaction
Since the inception of the survey CPR scientists have wrestled continuously with the basic quandary of
how best to summarise plankton abundance data that depend simultaneously on a number of variables. A
common approach has been to divide the data into separate compartments and analyse the time dependence
within each. Reid, Edwards, Hunt & Warner, 1998a) divided their data into three areas (CNE Atlantic,
NNE Atlantic, and North Sea) and ‘modelled’ dependence on predictors of season (month) and long-term
trend (year) using a contouring algorithm (Fig. 1). This approach allows ‘interaction’ between the two
temporal predictors (month and year) to be visualised. However, the shape and level of the seasonal cycle
can be different each year so that potentially important spatial variability or dependence within each of
the three compartments is oversimplified. The framework basically assumes that the same long-term and
seasonal patterns occur over very large areas of ocean. To a limited extent the differences in the three
D.J. Beare et al. / Progress in Oceanography 58 (2003) 217–233
221
time series estimated within each compartment by Reid, Edwards, Hunt & Warner, 1998a) are in fact a
measure—albeit crude—of interaction between spatial and temporal factors, because each compartment is
treated separately. Therefore, by subsetting the data and dealing with the subdivisions separately, such
interactions can be further evaluated.
An analogous protocol was used by Robinson et al. (1986) to describe spatial differences in the seasonal
abundance of an assortment of planktonic taxa along a transect in the English Channel. Instead of considering how seasonality changed with year in a sub-region (e.g. Reid, Edwards, Hunt & Warner, 1998a), the
authors instead wanted to reveal how seasonality changed according to location; or in their case the ‘distance
along a transect’. To do this they contoured the plankton data in two dimensions, using covariates of month
and distance along the transect to show how the shape of the seasonal cycle varied with, or interacted
with, location. Unfortunately, Robinson et al. (1986) used a long-term aggregation of data (1974–1981) in
their analysis that may have caused an avoidable bias, which occurred because the shape of the seasonal
cycle may have been completely different in each of the seven years (1974–1981). Suppose, for the sake
of argument that single pronounced spring peaks in abundance occurred in 1974 and 1975, while single
autumn peaks occurred in 1980 and 1981. For such data, adopting the protocol of Robinson, Aiken and
Hunt (1986) would lead to the assumption of a bimodal seasonal cycle that did not happen in any of the
years. If instead, the authors had chosen to divide the data into seven subsets, one for each year, and
repeated their contouring process they would have been able to go some way towards gauging the effect
of year (long-term trend) on their interpretation of how seasonality interacts with the locational dimension.
Workers have often assumed that their estimates of seasonal dynamics were more reliable because they
were using an aggregation of data collected over many years, and there were, as a consequence, more data
points. This assumption is spurious because inter-annually constant seasonal cycles are seldom observed
in CPR data, and aggregating them over multiple years may lead to erroneous estimates of seasonal (or
spatial) structure. Planque and Batten (2000) examined how the annual cycle of abundance of Calanus
finmarchicus varied across the entire North Atlantic (Fig. 2). They did this by constructing mean seasonal
cycles at various points in the North Atlantic for the period 1958–1996. The conclusion from their protocol
was that peaks in annual cycles of abundance could differ by as much as four months across the North
Atlantic. This conclusion may be correct, but the ultimate interpretation of the analysis, however, is ambiguous because of the presence in the data of a long-term trend, and the almost certain knowledge that it is
not independent of the seasonal or spatial effects.
Thus, to deal with ‘interaction’, data-analysts can adopt one of two basic protocols. They can either
divide their data into various spatio-temporal subsets (years, months, ICES squares etc.) and carefully
examine the data within each, or they can try to ‘model’ the interactions directly using statistical fitting
algorithms.
3.1.2. Statistical modelling
Statistical modelling techniques take ‘response variables’ (e.g. copepod abundance) and attempt to link
them, via mathematical functions (plus random components), to one or more ‘predictor variables’ (e.g.
temperature & food availability). In practice, statistical modelling also often involves prior subsetting, in
order to reduce certain aspects of the variability. In an investigation into long-term changes in diel vertical
migration (DVM) behaviour of the copepod, C. finmarchicus, Beare & McKenzie (1999b) divided CPR
data into five sub-regions to lessen the impact of spatial variability. Within each of the sub-regions, 12
(January to December) stochastic models (Generalized Linear Models (GLMs)) were then built to describe
long-term trend (1958–1998) for each month individually. This was done to control the effect of seasonal
variation on interpretation, because long-term trends in plankton taxa vary between months. Only by first
accounting for the spatial, seasonal and long-term variations can the signal resulting from DVM be exposed.
The combined subsetting and statistical modelling procedure, adopted by Beare & McKenzie (1999b),
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Fig. 2. Spatial variations in the seasonal abundance of C. finmarchicus. (A) Mean time-series of monthly abundance, (B) cumulated
time-series of monthly abundance and (C) spatial distribution of the seasonal index showing geographical variability in seasonality
(redrawn from Planque & Batten, 2000). Grey intensity is proportional to the local value of the seasonal index. The seasonal index
is the month at which 50% of the total annual abundance of C. finmarchicus has been collected by the CPR survey. Increasing
seasonal indices correspond to later seasonality in the abundance of C. finmarchicus.
enabled determination of the DVM effect, but the spatial dimension was poorly controlled because of the
large size of their sub-regions.
Statistical methodology appropriate to CPR data has traditionally been divided into three separate disciplines: (1) time-series (e.g. Diggle, 1990); (2) spatial statistics (e.g. Cressie, 1991) and (3) multivariate
statistics (e.g. Krzanowski, 1988). Time series analysis, concerned with decomposing serially dependent
data into separate long-term, seasonal, cyclical and random components, has only rarely been applied to
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223
CPR data (e.g. Broekhuizen & McKenzie, 1995; Beare & McKenzie, 1999c). Spatial statistics attempt to
model 2-dimensional spatial dependence using variography (Rossi, Mulla, Journel, & Franz, 1992), while
multivariate techniques are concerned with datasets with more than one response variable for each observational or experimental unit (e.g. Krzanowski, 1988).
Most researchers, however, are generally most interested in describing combined time series, spatial and
multivariate data. CPR data are neither bona fide time series because of the spatial dimension, nor bona fide
spatial data because of the temporal dimension. Most past summaries of CPR data have been restricted to:
1. temporal analyses of spatial compartments (e.g. Taylor & Stephens, 1980; Taylor, Colebrook, Stephens, & Baker, 1992; Broekhuizen & McKenzie, 1995; Hirst & Batten, 1998; Reid, Edwards, Hunt &
Warner, 1998a; Reid, Planque, & Edwards, 1998b);
2. spatial analyses of temporal compartments (e.g. Oceanographic Laboratory, Edinburgh, 1973; Planque &
Fromentin, 1996; Planque & Batten, 2000); or
3. multivariate analyses of wide-ranging data aggregations in space and time (e.g. Colebrook, 1972, 1978a,
1978b, 1979a, 1979b, 1982; Ali, 1996; Beaugrand et al., 2000a) which may tend to oversimplify aspects
of spatial and temporal variation.
Recently, statistical methods have been developed that attempt to model temporal and spatial dependence
in ecological data simultaneously. Spatio-temporal patterns in mackerel and horse mackerel egg abundance
were successfully described using Generalized Additive Models (GAMs) by Borchers, Buckland, Priede, &
Ahmedi (1997a) and by Borchers, Richardson, & Motos (1997b). Non-linear dependence in the data was
handled within the GAMs using spline functions, while interactions were modelled using smoothed products
of the predictor variables. Similar methods have been applied to CPR abundance data for Calanus finmarchicus and C. helgolandicus (Beare & McKenzie, 1999a, 1999b, 1999c, 1999d). The outputs from such
models allow long-term changes in both seasonality and spatial distribution to be assessed over the long
term (Figs. 3 and 4). In Fig. 3, C. finmarchicus abundances for the northwestern North Sea are plotted as
a time series (Fig. 3(A)), and as a 2D surface (Fig. 3(B)). [Note: both datasets are identical and have been
derived from the same stochastic model.] The plots show how C. finmarchicus abundance collapsed in
conjunction with changes in its seasonal structure. It is similarly possible to map changes in spatial distribution over the long term. In Fig. 4, output from 4D models are plotted to show how the geographic
distributions of three zooplankton ‘indices’ (Boreal Atlantic, Temperate Atlantic and Neritic) have changed
each May between 1958 and 1998. The output also enables seasonal change in spatial distribution to be
examined for individual years (not shown). In future, similar modelling procedures might be used to summarise the spatio-temporal patterns in many other CPR taxa.
The important caveat is that, as the dimensionality of our data modelling and exploration increases, so
problems caused by missing data mount. It is straightforward to fit plankton abundance to the covariates
of time and location (see also Borchers, Buckland, Priede & Ahmedi, 1997a, Borchers, Richardson and
Motos, 1997b) and use the model parameters to predict over an evenly spaced, temporally resolved grid
as was done here (e.g. Fig. 4). The fact that the data have been modelled in such a manner does not mean,
however, that problems relating to non-randomness of the sampling (confounding) and the data voids have
all been conveniently solved. The data used to create Fig. 4 have gaps in space and time, through which
the model interpolates using propinquitous (in space and time) available information. Standard fit diagnostics, residuals, R-squared etc. can be used to gauge the quality of the fit, which is acceptable in this case
(Fig. 4), but where data are missing over large areas it is impossible to know what would have been recorded
had those areas actually been sampled. Simple statistical functions (polynomials, smoothers) probably do
reasonably well where sampling has been representative, but one cannot know for certain. Statistical models
are only as good as the input data.
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Fig. 3. (A) Time series and (B) surface plot of average accepted numbers per month of C. finmarchicus in the north western North
Sea between 1958 and 1998. (Note: The average accepted numbers were calculated using a multinomial logit model and the plots
show how seasonality can change as a function of long-term trend. The datasets used in both A and B are identical).
4. Multivariate techniques
A review of the application of multivariate methods used to construct ‘summary’ long-term trends for
CPR data is provided by Ali (1996), who focused on Principle Component Analysis (PCA),
Minimum/Maximum Autocorrelation Analysis (MAFA; Solow, 1994) and Cluster Analysis. Ali commented
that the long-term trends extracted using PCA and MAFA are often difficult to interpret because only
statistical criteria (variance; lag-1 autocorrelation) are used in their construction. The fact that data must
also be aggregated prior to constructing the necessary 2-dimensional matrices may also result in a loss of
information: yearly averages for particular sub-regions, for example, will obscure how seasonality affects
the interpretation of long-term trend.
For these reasons Ali (1996) suggested that alternative index numbers could usefully be constructed
from CPR data using ad-hoc scientific criteria for the ‘weights’ instead. In other disciplines index numbers
are essential tools for summarising large, multivariate systems (e.g. FTSE-100 Share Index, Retail Price
Index, House Price Index). Analyses by Beaugrand, Ibañez & Reid (2000), using a range of diversity
indices, have suggested that the North Atlantic can be divided into different regions based on assemblages
of planktonic species. These ideas have been used by us to experiment with three possible index numbers
(e.g. Boreal Atlantic Index; Temperate Atlantic Index; Neritic Index) for the North Sea, based on aggregations of CPR zooplankton taxa with similar long-term, seasonal, and spatial behaviours. These indices
have then been modelled in space and time using GAMs, and have revealed long-term ecosystem and
water mass changes (see also Beare, Gislason, Astthorsson, & McKenzie, 2000). The ‘Temperate Atlantic
Index’ for example, is plotted for June 1958 and 1998 (Fig. 4). This plot shows there were pronounced
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Fig. 4. Change in the spatial distribution of Boreal Atlantic (BA), Temperate Atlantic, (TA) and Neritic (N) indices in the North
Sea between June 1958 and June 1998. Indices determined from probability of recording in a CPR sample: (BA) C. finmarchicus,
(TA) C. helgolandicus, Candacia armata or Centropages typicus, (N) Centropages hamatus or Temora longicornis. Grey scale
corresponds to the probability or presence estimated using a Generalised Additive Model from the Binomial family.
long-term spatial changes in the abundances of temperate Atlantic species during that period, which are
probably related to increasing sea temperatures and changing patterns of Atlantic inflow via the English
Channel and Fair Isle Current. The extension of such an approach to cover the entire North Atlantic using
aggregations like those suggested by Beaugrand is an exciting future prospect.
5. Problems explaining long-term, seasonal, spatial and compositional changes in plankton
populations.
5.1. General
The CPR survey has supplied most of our long-term, seasonal and spatial information on zooplankton
populations in the North Atlantic, but the abiotic and biotic mechanisms that cause these observations
remain poorly understood. Successful scientific interpretation of CPR data is compromised by a number
of considerations. CPR plankton data are typically examined using predictor variables of time (long-term
trend, month) and location (latitude, longitude), which are obviously extremely useful for purposes of data
summary, but cannot directly reflect causative mechanisms. Phytoplankton abundance does not soar in
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spring because it is April, but because ambient temperatures, light levels and water column stability begin
to become suitable for growth. Long-term trend, month and location etc. are certainly useful for descriptive
purposes, but the incorporation of scientifically more meaningful covariates (e.g. salinity, temperature, light
levels) directly into CPR data analyses and stochastic models might lead to more satisfying outcomes.
5.2. CPR data are categorised
The CPR survey records plankton abundance data in the form of ordered categories (Colebrook, 1960;
Oceanographic Laboratory, Edinburgh, 1973; Warner & Hays, 1994) known as ‘recorded values’ (Table
1). For such data, traditional statistical techniques based on the Normal Distribution (e.g. linear regression,
analysis of variance) are inappropriate and any statistical conclusions based on them are almost certainly
unreliable (Lindsey, 1995). For example, the mean and standard deviation of a sample of accepted numbers
with at least one number greater than three cannot be interpreted in the usual way because such accepted
numbers represent ‘bins’.
During the last 20 years, advances in statistical methods mean that it is now possible to model categorised
data, such as those from the CPR, directly (McCullagh & Nelder, 1983). One published report involved
modelling the probability of getting any one of the recorded values in a CPR sample using a multinomial
logit transformation (e.g. Beare & McKenzie, 1999a). An example of its output for data on C. finmarchicus
is plotted in Fig. 5. The red bars, for example, correspond to the likelihood of recording a zero in May
between 1958 and 1998 (top), or a zero between January and December in 1965 (bottom). Similarly the
darker green, dark blue, and turquoise bands reflect the probabilities of recording ones, twos, and threes
respectively. The non-parallelism, a result of the separate model fitted to each recorded value, is emphasised
in the pictures, viz. the seasonal (February to December) narrowing of the zero recorded value (red) probability band, versus the widening of the band (deep pink) representing a recorded value of four. The abrupt
widening of the deep pink band in both graphs (Fig. 5 top and bottom) happens because it is the first band
to represent an aggregation of numbers on the recorded value scale (Warner & Hays, 1994). This fifth
category could represent any number of animals between 4 and 11, and so the probability band widens
because there is more chance of recording any of eight numbers (4–11) than of just one, which is the case
for recorded values 0, 1, 2 and 3. Such types of model have an appeal for applications to analyses of CPR
data, because they allow more confident interpretation of output statistics than the standard Gaussian-based
techniques of the past.
5.3. Data voids
Data voids cause serious problems for analysis and summarising of CPR data because they reduce the
ability to separate the seasonal, spatial and long-term components; especially where interactions occur
(Hays, Carr, & Taylor, 1993). The fixed depth horizon (all samples are taken at ca 10m; Hays & Warner,
1993) of the CPR causes difficulties, since seasonal (Heath, Backhaus, Richardson, Slagstad, Beare, Dunn
et al., 1999a; Heath & Jonasdottir, 1999), and diel (Hays, Warner & Proctor, 1995) vertical patterns of
migration are crucial factors in the life cycles of many North Atlantic zooplankton species. These behaviours
may bias what we actually interpret as seasonal or spatial pattern. Consider the copepod Metridia lucens,
which is only recorded in CPR samples during darkness (Hays, Warner & Proctor, 1995). Examination of
M. lucens data from the CPR for the northern North Sea shows that the animal is only recorded in that
area during wintertime, but the pronounced diel vertical migration behaviour of the species means that we
cannot know if this wintertime peak in M. lucens abundance is real, or a result of the longer hours of
darkness in winter. It is crucially important that the potential influence of non-random sampling in CPR
data is considered when trying to assess spatio-temporal variability.
Consider CPR observations between 1969 and 1980 in an arbitrary sub-region (56.5–57°N; 0–3°W) of
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Fig. 5. Stages 5 and 6 Calanus finmarchicus in the northwest North Sea: bar-plots showing the proportion of the overall probability
estimated for each recorded value in May between 1958 and 1998 (top) and between January and December during 1965 (bottom).
Red reflects the probability of recording a zero, green a one, dark blue a two, and so on up the recorded value scale (Warner &
Hays, 1994).
the northern North Sea, split by year and month (Table 2). In 1969, sampling was evenly spread throughout
the year, but later data voids begin to emerge and in 1979 there were no January, March, or May to
November samples, so, strictly speaking, separating inter-annual and seasonal effects using this information
is impossible. As a result even straightforward questions regarding the shape of the long-term trend, seasonal pattern, and how they interact with each are sometimes unanswerable, because the necessary information is not available in the data. In this case, the areal extent of the arbitrary sub-region could be increased
228
D.J. Beare et al. / Progress in Oceanography 58 (2003) 217–233
Table 2
Numbers of CPR samples recorded per month between 1969 and 1980 in an arbitrary sub-region (56.5–57°N;0–3°W) in the northeast North Sea
Year
Jan
Feb
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
5
0
5
5
5
3
5
5
0
0
0
0
5
5
5
5
2
0
5
0
4
5
4
3
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
5
4
5
5
0
4
4
1
1
5
0
0
5
5
5
5
4
0
5
5
1
5
5
3
5
4
5
5
2
5
4
4
10
10
0
3
10
5
6
5
5
4
0
5
6
6
0
3
6
7
5
5
4
0
5
0
3
5
0
0
5
5
5
4
4
4
0
2
1
5
0
4
5
5
3
4
3
5
6
2
5
5
0
0
4
5
5
0
0
3
5
2
5
5
0
3
Nov
Dec
4
5
3
4
0
0
5
5
4
5
0
4
5
5
5
4
0
5
5
4
0
0
3
1
until observations were spread evenly throughout the year, although this might introduce additional biases
as a result of spatial variation.
Data aggregation has been used extensively in the literature to overcome problems of sparsity and nonrandomness. Achieving the right balance is, in truth, very difficult, and there are no entirely satisfactory
solutions. We make this point here only to keep future CPR data analysts alert to the potential difficulties
caused by sparse data, data voids and non-randomness, and to note that data aggregation is not necessarily
a solution.
5.4. CPR data are observational
Continuous Plankton Recorder data are ‘observational’ and not derived from ‘designed experiments’
indicating that confirmatory statistics of the type promoted by Fisher (see Fisher, 1990) in the early decades
of the last century have limited usefulness for deducing scientific mechanism. Traditional univariate, correlative approaches to linking CPR and environmental time series are useful first steps, but may lead to
oversimplification and probable ultimate failure. Single predictor variables such as the Gulf Stream Index
(Taylor & Stephens, 1980, 1998), the NAO Index (Fromentin & Planque, 1996; Planque & Fromentin,
1996), or sea temperature are unlikely to produce satisfactory predictive models when the effect of each
is examined individually. This situation arises because the actual underlying scientific mechanisms that
force the observed changes are not incorporated directly into the overall data-analytic and conceptual frameworks. Temperature, salinity, stratification, food availability, advection and overwintering location might
all simultaneously affect the abundance of a particular copepod, and the level of one might influence that
of another and so on. Scientifically interpretable models with multiple predictors that can interact with
each other are thus essential.
To illustrate the point further, consider the CPR data displayed in Fig. 4. Correlation coefficients were
calculated between the Boreal Atlantic abundance index (almost totally dominated by C. finmarchicus)
displayed in Fig. 4 and sea temperature (1958 to 1998), and then plotted on maps (Fig. 6). The blue areas
represent correlations with large negative signs where C. finmarchicus abundance fell (1958 to 1998) and
temperature increased (1958 to 1998), the red areas large positive correlations where both sea temperature
(1958 to 1998) and C. finmarchicus abundance declined (1958 to 1998). Clearly, the long-term connection
between the two variables (sea temperature and C. finmarchicus) varies with month and geographic position,
and interactions between temperature and other factors (e.g. stratification, salinity, Atlantic inflow) may
D.J. Beare et al. / Progress in Oceanography 58 (2003) 217–233
229
Fig. 6. Spatial and seasonal patterns in correlation (1958 and 1998) between sea temperature (at 15 m) and the abundance of C.
finmarchicus. Only correlation coefficients ⬎ +0.6 and ⬍ ⫺0.6 are plotted. The blue areas represent negative correlations (C. finmarchicus abundance falls while sea temperatures rise), the red areas positive correlations (C. finmarchicus abundance also falls but
sea temperatures rise). The blanks represent areas where no linear relationships between the two variables were identified.
need to be considered if a satisfactory explanatory model for C. finmarchicus is to be found. Interestingly,
the relationship is most pronounced when the abundances of C. finmarchicus in the North Sea are at
seasonal minima (winter). Comparing data averaged over large areas (e.g. North Sea, English Channel,
North East Atlantic) might miss such observations because long-term trends in environmental variables
can, and do, vary from month to month, and from place to place.
6. Conclusion
It is worth remembering that all temperate ecological and meteorological time series data are highly
seasonal, and usually have an important spatial dimension. These seasonal and spatial signals are usually
far larger than those resulting from long-term trends. This means that environmental time series are usually
highly correlated with each other. Unfortunately, auto-correlation between successive (time series) and
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D.J. Beare et al. / Progress in Oceanography 58 (2003) 217–233
Fig. 7. Long-term and seasonal changes in (A) C. finmarchicus abundance in the northwest North Sea and (B) the North Atlantic
Oscillation index.
nearby (spatial) data points means that the relationships are not ‘statistically significant’, and isolating those
that are actually forcing the long-term changes in the plankton, is difficult.
One prospect is that coincidental changes in seasonal (or spatial) patterns may provide evidence for links
between environmental time series data. It has been reported, for example, that the seasonal structure of
the North Atlantic Oscillation Index and the C. finmarchicus time series both changed at around the same
time in the late 1960’s (Fig. 7; Beare & McKenzie, 1999b). Whether such relationships ultimately prove
to be scientifically useful remains to be seen, but such links are certainly worth seeking in the analysis of
ecological time series data. The complexity of the long-term links between plankton and their environment
is not in doubt, but the success of the next 70 years of CPR data collection will depend on our ability to
build models capable of assessing multi-dimensional, dynamic interaction among the factors that initiate
change in plankton populations.
Acknowledgements
We would like to thank all those CPR scientists and technicians, past and present, who have contributed
to this priceless dataset. The Natural Environmental Research Council are also owed a debt of thanks for
providing funding support for this work as part of the thematic programme: Marine Productivity.
D.J. Beare et al. / Progress in Oceanography 58 (2003) 217–233
231
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