Progress in Oceanography 58 (2003) 327–336
www.elsevier.com/locate/pocean
The CPR survey (1948–1997): a gridded database browser
of plankton abundance in the North Sea
L. Vezzulli a,∗, P.C. Reid b
a
b
Dipartimento per lo Studio del Territorio e delle sue Risorse (DIP.TE.RIS), Università di Genova, Corso Europa, 26,
16132 Genoa, Italy
The Sir Alister Hardy Foundation for Ocean Science (SAHFOS), The Laboratory, Citadel Hill, The Hoe, Plymouth, PL1 2PB,
UK
Abstract
The Continuous Plankton Recorder (CPR) survey provides a unique multi-decadal dataset on the abundance of
plankton in the North Sea and North Atlantic and is one of only a few monitoring programmes operating at a large
spatio-temporal scale. The results of all samples analysed from the survey since 1946 are stored on an Access Database
at the Sir Alister Hardy Foundation for Ocean Science (SAHFOS) in Plymouth. The database is large, containing more
than two million records (~80 million data points, if zero results are added) for more than 450 taxonomic entities. An
open data policy is operated by SAHFOS. However, the data are not on-line and so access by scientists and others
wishing to use the results is not interactive. Requests for data are dealt with by the Database Manager. To facilitate
access to the data from the North Sea, which is an area of high research interest, a selected set of data for key
phytoplankton and zooplankton species has been processed in a form that makes them readily available on CD for
research and other applications.
A set of MATLAB tools has been developed to provide an interpolated spatio-temporal description of plankton
sampled by the CPR in the North Sea, as well as easy and fast access to users in the form of a browser. Using
geostatistical techniques, plankton abundance values have been interpolated on a regular grid covering the North Sea.
The grid is established on centres of 1° longitude x 0.5° latitude (~32 × 30 nautical miles). Based on a monthly temporal
resolution over a fifty-year period (1948–1997), 600 distribution maps have been produced for 54 zooplankton species,
and 480 distribution maps for 57 phytoplankton species over the shorter period 1958–1997.
The gridded database has been developed in a user-friendly form and incorporates, as a package on a CD, a set of
options for visualisation and interpretation, including the facility to plot maps for selected species by month, year, groups
of months or years, long-term means or as time series and contour plots. This study constitutes the first application of
an easily accessed and interactive gridded database of plankton abundance in the North Sea. As a further development
the MATLAB browser is being converted to a user-friendly Windows-compatible format (WinCPR) for release on CD
and via the Web in 2003.
 2003 Published by Elsevier Ltd.
∗
Corresponding author. Tel.: +39-010-353-8069; fax: +39-010-352-169.
E-mail address: vezzulli@dipteris.unige.it (L. Vezzulli).
0079-6611/$ - see front matter  2003 Published by Elsevier Ltd.
doi:10.1016/j.pocean.2003.08.011
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L. Vezzulli, P.C. Reid / Progress in Oceanography 58 (2003) 327–336
Contents
1.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328
2. Methods . . . . . . . . . . .
2.1. Geostatistics . . . . . . .
2.2. Cartography . . . . . . .
2.3. Time series analysis . .
2.4. Computer programming
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329
329
330
330
330
3.
Functionality of the database browser . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
4.
Primary validation of interpolated data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333
5.
Discussion and future development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333
1. Introduction
The Continuous Plankton Recorder (CPR) is a high-speed plankton sampler designed to be towed from
commercially operated ships of opportunity (SOOP) over long distances (see Warner & Hays, 1994). At
the present time approximately 22 routes are operated on a synoptic basis every month of the year. Sampling
takes place in the surface layer (~7 metres). The plankton are filtered on a band of silk (with a mesh-size
of 270 µm) that moves across the sampling aperture at a rate that is proportional to the speed of the towing
ship (Hays, 1994). The sampling methodology has changed little over the last 50 years, thus ensuring the
continuity of the time series.
The Survey has compiled a unique multi-decadal dataset on the abundance of plankton for the North
Sea and North Atlantic. Data from every CPR sample analysed since 1946 are stored in an Access database.
This now amounts to ~200,000 samples, with data from around 200 new samples being added per month.
Every planktonic entity identified on each sample is recorded, and there are almost 2 million plankton
records in total. Over 450 taxa are routinely identified and counted on CPR samples. Some are identified
to the level of species, some to genus, and some to a higher taxonomic group. The database also contains
metadata such as tow locations, times and dates, ship details, a catalogue of taxa and analysis details.
All data in the CPR database are freely available as SAHFOS operates an open-data policy that is
compliant with the requirements of the Global Ocean Observing System (GOOS). Data can be extracted
from the database for selected entities, over specified periods and for areas defined by the user. For example,
all samples taken from the Dogger Bank area in the North Sea during March, April and May since 1946
could be extracted from the database, and the ‘abundance’ of selected diatom species on each sample could
be listed. Alternatively, an average value, number of samples, and standard deviation per year per month,
may be retrieved. As yet the database is neither on-line nor is it interactive and so specific requests for
data need to be made to the SAHFOS Database Manager. Access to raw data is only available to researchers
visiting Plymouth so that they can be made aware of the limitations of the archived information. Details
of the SAHFOS data policy and licence agreement form are available on the Foundation’s web site
(http://www.sahfos.org).
Because the survey uses ships of opportunity, which at times change their routes, the samples collected
are distributed irregularly in space and some areas are consistently poorly sampled. Gaps also occur in the
data either because a ship changed its route or was unable to tow because of unfavourable conditions.
Both the spatial irregularity and breaks in sampling in some areas make it difficult to apply classical
L. Vezzulli, P.C. Reid / Progress in Oceanography 58 (2003) 327–336
329
statistics in the analysis of the data. Furthermore, spatially constrained datasets, such as the 2° long. × 1°
lat. boxes used to summarise data for the CPR atlas (Edinburgh Oceanographic Laboratory, 1973) or the
larger ‘Standard Areas’ used in the past by the survey to provide monthly and annual means of plankton
abundance (Colebrook, 1975, 1978), cannot be used to take into account spatial structure and autocorrelation
between variables.
There is an increasing need in oceanography for improved access to large gridded environmental datasets
for research and modelling, and for this access to be made as simple and as close to real-time as possible.
Visualisation options and primary statistical manipulations of the gridded datasets are also of value to
researchers as they allow rapid interpretation of the data. To achieve this for a large part of the CPR
database, geostatistical interpolation techniques such as the Inverse Square Distance method, have been
used to interpolate plankton abundance data on a regular grid in the North Sea between 51°N and 62°N
and between 4°W and 8°E (i.e. the ICES fishing areas IVa, IVb and IVc). A selected set of data for key
phytoplankton and zooplankton taxa has been processed for the grid covering the North Sea, and made
available on CD as a browser for research and other applications. Using MATLAB software, plotting
options have also been developed for graphs, maps and contour diagrams. A similar suite of plotting
routines for visual interpretation is included in the Windows version of the database. The North Sea was
selected for the browser because of its importance as a regional sea and as a consequence of the high
sampling intensity in this core area of the CPR survey.
2. Methods
2.1. Geostatistics
Geostatistical interpolation methods are based on local estimates of a variable from known values at
nearby locations. Estimations from linear weighted interpolation models can be written in the general form:
ẑx,y ⫽
冘
wizi
(1)
nx,y
where ẑx,y is the estimated value of variable z at location (x,y). The term nx,y is the number of neighbours
around the location (x,y), the term zi the values of the variable z for neighbouring samples, and the weights
wi attributed to the neighbouring samples. A number of statistical methods exist that can be used to interpolate spatial patterns in ecological systems (see, for example, Ord, 1979; Legendre & Fortin, 1989) and
differ by the calculation of the matrix W of weights.
To produce the gridded database spatial interpolation of regular grid points in the North Sea have been
performed using the Inverse Square Distance method (ISD) (Crain & Bhattacharyya, 1967). In this method
the weights are inversely proportional to the squared distance between samples and location (x,y), and the
equation is as follows:
冘
ẑx,y ⫽ D2
nx,y
1
z
d2i i
(2)
where di is the distance between ẑx,y and zi and D2 is the sum of each of the squared distances d2i . The
weight for each neighbouring sample is inversely proportional to the squared distance from the point being
estimated. In this method more weight is given to the closest samples and less to those that are farther
away. A number of trial runs were carried out on the North Sea database to determine the most appropriate
interpolation radius; the maximum distance was selected at 162 nautical miles (300 km). This radius was
chosen as a compromise between the number of samples per month and the need to keep the number of
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missing pixels in the browser to a minimum. Less than 5% of the entire set of maps needed further
interpolation using temporal methods (see below). In addition, the minimum number of neighbours used
in the estimation procedure was set to five and the maximum to 15.
2.2. Cartography
Spatial representation and cartography of plankton using a geostatistical approach is not new to CPR
data (Planque, 1996; Beaugrand, Reid, Ibañez, & Planque, 2000). In the work of Planque (1996) on spatiotemporal fluctuations of Calanus in the North Atlantic, CPR variables were interpolated on a regular grid
for a large oceanic area. The grids were constituted by a set of transects in which each point was 50
nautical miles apart (92.6 km). Ordinary Kriging was used for interpolation, and spatial autocorrelation
was investigated by means of median monthly omnidirectional variograms.
A higher spatial resolution has been adopted in the browser, as sampling in the North Sea is more
frequent than in the North Atlantic. The central points of each pixel are 1° long. × 0.5° lat. apart, i.e. 32
nautical miles (59.2 km) × 30 nautical miles (55.5 km). With a temporal resolution of one month, this has
enabled the calculation of a total of 600 monthly maps for zooplankton taxa (1948–1997) and 480 monthly
maps for phytoplankton taxa (1958–1997) in the North Sea. As the North Sea is not large enough to distort
seriously the geographical representation of interpolated points on a lat./long. grid a geometric projection
has not been used.
2.3. Time series analysis
Using a common grid for every map produced, the abundance of a species in one grid cell (pixel) and
at one time (month) can easily be compared with its abundance at the same location at a different time.
It is possible to extend this to compare either two sets of pixels (maps) or an entire set of maps, so that
the spatial and temporal changes in abundance can be studied in a single analysis. This task would be easy
to achieve if all the grid cells had data for each of the 600 months (480 months for phytoplankton) and
the maps could be plotted exactly for the same number of pixels. Unfortunately, because of the fluctuations
in sampling intensity over the 50-year period (1948–1997) not all the maps have the same amount of
information (the number of samples per month ranges between 30 and 120 in the North Sea) and estimation
of abundance is not always possible for some pixels, i.e. the minimum of five samples was not available
within the ISD interpolation radius).
To avoid this problem and to enable time series to be produced, the Ratio Estimator Method (REM)
has been used to estimate abundance for missing pixels. This REM temporal interpolation method has
been described by Colebrook (1975). The technique consists of multiplying the long-term monthly mean
corresponding to a missing month in a given year, by the ratio of the sum of the non-missing values in
that year to the sum of the corresponding long-term monthly means. For each missed pixel REM has been
performed prior to the calculation of the month-by-year matrix. Unfortunately, this method lacks spatial
accuracy as a temporal interpolator, and for monthly maps in particular its precision is doubtful. However,
this technique has only been applied to ⬍5% of the interpolated pixels. Once a complete set of monthly
maps is available, time series analysis for each pixel can be performed to provide information on both
long-term trends and the seasonality of the species.
2.4. Computer programming
The sequence of six operations used to extract and process data are summarised in Fig. 1. A set of tools
has been written in MATLAB (Release 12) using a normal text editor and incorporating as a package a
total of 42 operative M-files, plus a number of reference M- /Mat- / and Text files. An M-file consists of
L. Vezzulli, P.C. Reid / Progress in Oceanography 58 (2003) 327–336
331
Fig. 1. Stepwise sequence of operations employed in the browser to extract and process plankton data from the CPR database: (A)
Raw data are extracted from the CPR plankton database. (B) Raw data are loaded into MATLAB as mat-files. (C) Data are spatially
interpolated on a 1° longitude × 1/2° latitude grid in the North Sea using the Inverse Square Distance (ISD) method. (D) Missing
points are temporally interpolated using the Ratio Estimator Method (REM). (E) A user-friendly presentation tool is used to provide
visual access to the data. (F) A function file enables the data to be exported to non-MATLAB users.
a sequence of normal MATLAB statements (function or scripts) including references to other M-files. All
geostatistics, cartography and time series analysis used in the tools have been saved on a CD disk as
function M-files. The plankton variables loaded into the database have been saved as Mat-files, which are
MATLAB specific binary files. Finally, a number of text files are included that mostly contain reference
data and the datasets for the digitised coastlines.
Once the calculations for all species in the MATLAB version have been completed, 2D output matrices
of interpolated data (600 monthly plankton values × 199 pixels) are saved for each species in an Access
format that can be fully interrogated. A Windows wizard interface (WinCPR) has been built to allow for
navigation, graphic display and data access in a user-friendly perspective.
3. Functionality of the database browser
Eight options for visual presentation of the data are provided in the form of a user-friendly browser as
part of the Windows wizard interface. The options include a facility to display, almost instantaneously,
maps for selected species by month, year, groups of months or years, graphs of long-term means and time
series and contour plots of data in month/year format. With the exception of Phytoplankton Colour, where
the colour intensity is based on a scale of 0, 1, 2, 6.5, the data are presented in the form Log(x + 1).
1. Sampling Intensity (Fig. 2(A).
This function displays the number and locations of analysed CPR samples included in the gridded database.
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L. Vezzulli, P.C. Reid / Progress in Oceanography 58 (2003) 327–336
Fig. 2. Examples of the spatial and temporal representation of plankton abundance provided by the database browser: (A) map of
sampling intensity showing routes towed and analysed samples as dots (e.g. January, 1963); (B) monthly maps of plankton abundance
(e.g. Acartia spp. in 1963); (C) annual maps of plankton abundance (e.g. Acartia spp. in 1963); (D) yearly mean time series (e.g.
Oithona spp. in pixel 187); (E) month by year contour plot (e.g. Oithona spp. in pixel 187); (F) seasonal cycle (e.g. Oithona spp.
in pixel 187 in 1952).
The plots show the tracks taken by ships of opportunity in the North Sea over the period 1948–1997.
Normally, alternate 10-mile samples along each tow are analysed for plankton. The resolution is monthly,
and the output can be either 12 maps for each month of a given year or a single map for one month.
2. Monthly Plankton Abundance (Fig. 2(B).
For a selected species, 12 maps of plankton abundance are produced by this function for each month in
a selected year. Only one year may be selected at a time.
L. Vezzulli, P.C. Reid / Progress in Oceanography 58 (2003) 327–336
333
3. Annual Plankton Abundance (Fig. 2(C).
For this option the function produces maps of annual plankton abundance for a selected species. The output
is generated from the mean of the results for each of the 12 months in a given year. It is possible to display
up to 12 maps for different years on the graphic window at any one time.
4. Monthly Long-term Plankton Abundance (same graphic output as Fig. 2(B).
This function produces 12 maps for each month of the year, representing the long-term mean of plankton
abundance per pixel in each month for the complete time series.
5. Plankton Time Series (Fig. 2(D).
This function produces a plot of annual means of abundance of a selected species as a time series with a
superimposed 5-year running mean. The plots are calculated from the 12 monthly results for each selected
pixel. Graphs can be plotted for a maximum of six pixels or as an average of a selected group of pixels
on the graphic window.
6. Month-by-Year Contour Plots (Fig. 2(E).
This sixth option produces a month-by-year contour plot of plankton abundance for a selected species for
each selected pixel. The diagrams can be displayed for a maximum of six pixels or as an average of a
selected group of pixels on the graphic window.
7. Seasonal Plankton Abundance (Fig. 2(F).
The average month-by-month abundance of a single selected species is presented as a bar chart for a
selected year or multiple of years. Graphs for up to six pixel cells or a single graph averaged for a selected
group of pixels are presented in the graphic window.
8. Average long-term plankton abundance (same graphic output as Fig. 2(C).
For a single selected species this function produces a map of the average plankton abundance for each
pixel cell over the complete time series.
Spreadsheets in MS Access format can be viewed using the drop-down menu. They contain the sampling
data, lists of phytoplankton and zooplankton taxa and the processed data for each of the taxa and pixels.
In the MATLAB CD version the numerical data used to produce each of the plots are also available as a
data matrix and can be retrieved in most popular statistical packages such as EXCEL, STATISTICA or
SYSTAT for Windows. A list of the species and taxa (Warner & Hays, 1994) included in the browser is
given in Table 1.
4. Primary validation of interpolated data
The simplest way to validate the interpolated data in a global perspective is through a comparison with
previous analyses of CPR data. We compared our results with a yearly mean time series of Phytoplankton
Colour (PC) as calculated from the work of Edwards, Reid and Planque (2001). These authors averaged
the monthly mean values for three different North Sea regions (northern, central and southern) between
1960 and 1995. First, the yearly mean time series of PC were calculated by averaging the data from 20
selected pixels homogeneously distributed in each region. Then Spearman-rank correlation coefficients were
calculated between the resulting processed series and the data of Edwards et al. (2001). The probability
values resulted in a highly significant correlation for all the areas considered (Northern North Sea rs =
0.83, p ⬍ 0.01; Central North Sea rs = 0.93, p ⬍ 0.01; Southern North Sea rs = 0.84, p ⬍ 0.01) (Fig. 3).
5. Discussion and future development
Understanding interannual, seasonal and latitudinal changes in ecosystem structure on a long-term basis
is fundamental to assessing the role of natural variability and the effects of climate forcing. Currently the
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Table 1
Plankton taxa available in the North Sea CPR browser
Phytoplankton
Phytoplankton Colour
Diatoms
Actinoptychus spp.
Asterionellopsis glacialis
Bacillaria paxillifera
Bacteriastrum spp.
Bellerochea malleus
Biddulphia alternans
Cerataulina pelagica
Chaetoceros (Hyalochaete) spp.
Chaetoceros (Phaeoceros) spp.
Corethron criophilum
Coscinodiscus concinnus
Coscinodiscus wailesii
Cylindrotheca closterium
Dactyliosolen antarcticus
Dactyliosolen fragillissimus
Ditylum brightwellii
Eucampia zodiacus
Fragilaria spp.
Guinardia flaccida
Guinardia delicatula
Guinardia striata
Gyrosigma spp.
Lauderia annulata
Leptocylindrus danicus
Leptocylindrus mediterraneus
Navicula spp.
Odontella aurita
Odontella granulata
Odontella regia
Odontella rhombus
Odontella sinensis
Paralia sulcata
Proboscia alata alata
Proboscia alata indica
Proboscia alata inermis
Pseudo-nitzschia delicatissima
Pseudo-nitzschia seriata
Rhizosolenia hebetata semispina
Rhizosolenia imbricata
Rhizosolenia setigera
Rhizosolenia styliformis
Skeletonema costatum
Stephanopyxis
Thalassiosira spp.
Thalassiothrix longissima
Dinoflagellates
Phytoplankton synonyms
Asterionella glacialis
Bacillaria paxillifer
Nitzschia closterium
Rhizosolenia fragillissima
Rhizosolenia delicatula
Rhizosolenia stolterfothii
Lauderia borealis
Dactyliosolen mediterraneus
Biddulphia
Biddulphia
Biddulphia
Biddulphia
Biddulphia
aurita
granulata
regia
rhombus
sinensis
Rhizosolenia alata alata
Rhizosolenia alata indica
Rhizosolenia alata inermis
Nitzschia delicatissima
Nitzschia seriata
Rhizosolenia imbricata shrubsolei
Zooplankton
Copepods
Acartia spp.
Anomalocera patersoni
Calanus copepodites I–IV
Calanus finmarchicus
Calanus helgolandicus
Calanus hyperboreus
Candacia armata
Centropages hamatus
Centropages typicus
Copepod nauplii
Corycaeus spp.
Euchaeta hebes
Euchaeta norvegica
Harpacticoida total
Isias clavipes
Labidocera wollastoni
Metridia lucens
Oithona spp.
Oncaea spp.
Parapontella brevicornis
Para-Pseudocalanus spp.
Pseudocalanus elongatus
Rhincalanus nasutus
Temora longicornis
Total Calanus
Total copepods
Amphipods
Caprellidea
Gammaridea
Hyperiidea
Cladocera
Evadne spp.
Podon spp.
Euphausiids
Euphausiacea total
Euphausiacea calyptopis
Other crustaceans
Caligoida
Cirripede larvae
Cumacea
Decapoda total
Isopoda
Mysidacea
Cephalochordates
Branchiostoma lanceolatum
Chaetognaths
Chaetognatha eyecount
(continued on next page)
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335
Table 1 (continued)
Phytoplankton
Ceratium bucephalum
Ceratium furca
Ceratium fusus
Ceratium horridum
Ceratium lineatum
Ceratium longipes
Ceratium macroceros
Ceratium minutum
Ceratium tripos
Dinophysis spp.
Prorocentrum spp.
Phytoplankton synonyms
Zooplankton
Chaetognatha traverse
Echinoderms
Echinoderm larvae
Echinoderm post-larvae
Larvaceans
Larvacea
Molluscs
Lamellibranch larvae
Clione limacina
Limacina retroversa
Fish
Fish eggs
Young fish
Polychaetes
Polychaeta larvae
Tomopteris spp.
Polyzoans
Cyphonautes larvae
Thaliaceans
Salps
Fig. 3. Correlations between annual mean time series of Phytoplankton Colour from the work of Edwards et al. (2001) (solid line)
and a corresponding interpolated time series from an average of twenty pixels in this study (dotted line) for the central North Sea
(rs = Spearman correlation coefficient; p = probability).
study of the main climatic variables involved in global ecosystem change is based on gridded databases
and is mainly generated from spatial interpolation techniques such as the geostatistics algorithms used by
NASA in the CLIMATE database for the terrestrial surface of the globe and for the NASA Baseline
Climatological Datasets.
In addition to providing gridded data, the database browser developed in our study also provides a
unique, simple and fast access to the dataset, primary statistical processing and a series of graphical facilities. Ultimately, the processed data can be accessed for large ecological studies and for other research of
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a more limited scale and exported to statistical and presentational packages in many popular formats such
as text, rich text, comma separated, MS Excel, MS Access, MS Word, Fox Pro, Dbase, Lotus and to
SQL compliant databases. High performance numerical computation and visualisation software provides a
powerful tool for the management of large geographical datasets such as the Continuous Plankton Recorder
database. Our study constitutes the first development of an easy access and interactive long-term database
for plankton in the North Sea.
Future developments will need to validate output data on a regional scale with independent plankton
sampling mechanisms. Thereafter an extension of the area covered by the database might be achieved and
new species can be added by a stepwise sequence of operations. A Microsoft compatible version of the
browser for both CD and web distribution, which will make the dataset even more accessible, is currently
under development and should be completed in 2003.
Acknowledgements
We wish to thank all the past and present members and supporters of the Continuous Plankton Recorder
survey, whose efforts have allowed the long-term establishment and maintenance of the CPR dataset. The
authors are particularly indebted to Dr. Benjamin Planque for his advice in the programming of the geostatistical algorithms. We also wish to acknowledge the work of Paul Dowland, Nathan Clarke, and Maria
Papadaki of the Network Research Group, University of Plymouth, who have translated the MATLAB CD
into an easily accessible Microsoft-compatible version using a Windows Wizard interface for CD and
web application.
References
Beaugrand, G., Reid, P. C., Ibañez, F., & Planque, B. (2000). Biodiversity of North Atlantic and North Sea calanoid copepods.
Marine Ecology Progress Series,(204), 299–303.
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