Journal of Plankton Research Vol.21 no.10 pp.1877–1889, 1999
Rapid and versatile routine measurements of plankton biovolumes
with BACCHUS
Corina J.Carpentier1, Henk A.M.Ketelaars2, Arco J.Wagenvoort and Karina
(C.) P.R.Pikaar-Schoonen
Biological Laboratory, Water Storage Company Brabantse Biesbosch,
PO Box 61, NL-4250 DB Werkendam, The Netherlands
1Present
address: Kiwa Research and Consultancy, PO Box 1072, NL-3430 BB
Nieuwegein, The Netherlands
2To
whom correspondence should be addressed
Abstract. A semi-automated biovolume measurement application called BACCHUS (Biovolume
Analysis Cum Calculus and High Utility Statistics), based on an image-analysis program combined
with a database, was developed. With this application, biovolumes of plankton taxa can be determined
while counting routine samples. Statistical analyses of the ln-transformed results are performed online. The width of the 95% confidence interval is calculated and indicates the precision of the calculated mean. By setting limits to the maximum allowed width of the interval, the extent of desired
precision can be defined. For our laboratory, we decided that the limits of the confidence interval must
be <0.2 ln units above or below the mean of the ln-transformed results. As soon as this criterion is
reached, the user can stop measuring, thereby minimizing the number of measurements necessary to
reach a mean biovolume which is sufficiently precise. Other advantages of the BACCHUS application
are a very flexible input of master data, output of stored data and a user-friendly operation environment. BACCHUS is very easy to customize because it does not require any expertise in database
programming.
Introduction
The diameter of phytoplankton cells varies from 1 to >1000 µm (Semina, 1978;
Reynolds, 1984). As is obvious from this large size range, the determination of
algal density by counting cells is an inadequate measure of phytoplankton
biomass (Lohman, 1908; Nauwerck, 1963; Bellinger, 1974; Vilicic, 1985). The size
range of zooplankton (rotifers, copepods and cladocerans) also covers several
orders of magnitude, indicating the need for biomass determinations instead of
merely counting individuals.
Many researchers therefore stressed the use of accurate biomass estimates in
phyto- and zooplankton studies (e.g. Bottrell et al., 1976; Culver et al., 1985;
Sommer et al., 1988). High-performance liquid chromatography (HPLC), which
separates and quantifies indicator pigments for different phylogenetic algal
groups, has often been suggested as an alternative to microscopic phytoplankton
enumeration combined with biovolume measurements (Wilhelm et al., 1991;
Millie et al., 1993). However, taxonomic resolution of HPLC analysis is low,
certain diagnostic pigments occur in more than one algal group, standardization
is difficult to achieve (Millie et al., 1993; Latasa et al., 1996; Roy et al., 1996) and
the method is only applicable for phytoplankton studies. This last drawback also
holds for colour image-analysed fluorescence microscopy. With this method, only
biomass measurements of plankton ranging in size from 1 to 100 µm can be
© Oxford University Press 1999
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performed (Verity and Sieracki, 1993). Another disadvantage of this method is
that it inherently does not provide taxonomic information.
Plankton biovolumes are usually measured using an ocular micrometer, which
is, however, a time-consuming method (Wilhelm et al., 1991; Schmid et al., 1998).
In addition, the use of this instrument may cause errors in measurements due to
variation in the angle and distance between the eye of the observer and the
eyepiece of the microscope. Variation in accuracy of measurements can derive
from fatigue of the observer because this alters the position of the head relative
to the eyepiece, and also from differences among observers (McCauley, 1984). In
addition, the technician must estimate and manually record length data, which is
very time consuming and a motion-intensive process, subject to errors (Roff and
Hopcroft, 1986). In a comparison of the precision of a microcomputer-based
measuring system with a conventional microscope eyepiece micrometer method,
Roff and Hopcroft (1986) found that the precision of measurement of the latter
is much lower, leading to much greater errors in biomass calculations due to
compounded errors.
In the last few decades, (semi-)automated measuring systems have been
described to overcome some of these drawbacks. Sprules et al. (1981) presented
a microcomputer-based technique which uses a calliper system linked to a microcomputer through an analogue–digital converter for measuring a variety of
biological features. This system was also used by Mills and Confer (1986) and
Allen et al. (1994) for zooplankton measurements. Data processing and flexible
output calculations in these systems are limited, however.
Completely automated systems to determine biovolumes and/or length were
developed by Jeffries et al. (1980, 1984), Rolke and Lenz (1984) and Enserink
(1995). The great advantage of this type of measuring system is the speed with
which measurements can be performed. However, this advantage is largely
undone by the time-consuming preparations which are needed to make a sample
suitable for automatic measurement. Enserink (1995) clearly showed that
forming a well-recognizable image is a very laborious task. An important problem
is caused by organisms overlapping each other. All organisms have to be separated first or else the computer will consider them as one. Enserink (1995)
concluded that a completely automated measuring system can only be useful if at
least 50–100 similar shaped organisms are to be measured in one sample.
Allen et al. (1994) also concluded that completely automated measuring
systems are quick, but unsuitable for gathering accurate information at the
species level. Accurate identifications of species can only be achieved with a
microscope. With the help of a semi-automated interactive measuring system,
microscopic counts can easily be combined with measurements of plankton
dimensions. According to Allen et al. (1994), this is probably the fastest and most
accurate way to determine biovolume on a routine basis.
We developed a semi-automated measuring system called BACCHUS (Biovolume Analysis Cum Calculus and High Utility Statistics) which is based on an
image-analysis program linked to a database. With this application, biovolumes
of phyto- and zooplankton taxa can be determined while counting routine
samples. In principle, the biovolume of a given species is determined from a few
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dimensions based on a geometrical approximation to describe its volume. On-line
statistical evaluation of the data limits the number of necessary measurements to
a minimum.
Description
System requirements
Digital image processing and analyses were performed with a QUANTIMET
500IW IBM-compatible computer (300 MHz, 64 Mb internal memory, 170
monitor) (Leica, Cambridge, UK) with absolutely positioned mouse (digitizing
tablet, Wacom, Neuss, Germany), and an inverted microscope (Leica, Wetzlar,
Germany) with either a 1 CCD monochrome or a 3 CCD colour video camera
(Sony, Hoofddorp, The Netherlands) fitted to the drawing tube. Minimal requirements of the PC, however, are 200 MHz speed and 16 Mb internal memory. Any
microscope used for phyto- and zooplankton counting can be used provided that
it can be fitted with a drawing tube. The BACCHUS application uses the database
program Microsoft Access 7.0 for Windows 95 and the image-analysis software
QWin Y2.3a (Leica, Cambridge, UK). Measurement routines were created using
the built-in macro software QUIPS (QUANTIMET image-processing system).
Master tables
For a clear understanding of the names of tables, fields and buttons used in the
application, the following layout has been used: database tables are in bold capitals (e.g. T_CURRENT), table fields are in bold (e.g. sample), buttons are in
bold italic (e.g. Data evaluation).
Before measurements can be performed, taxon-specific data and information
on magnification have to be entered in the database master table (Figure 1).
Eleven different geometrical shapes (and any combination of these) are used to
describe the phyto- and zooplankton biovolumes: sphere, half-sphere, ellipsoid,
cone, circular cylinder, elliptic cylinder, brick, spindle, lens-shaped disc, barrel and
Fig. 1. Form in which the taxon-specific data of BACCHUS are entered.
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ellipsoid segment. All the necessary information to calculate the biovolume of
each taxon is stored in the database master table (MEASURE_LIST). For this list,
geometrical approximations from the literature have been evaluated and, if necessary, improved. In addition, for some taxa, new formulae were derived. Table I
shows some examples; the complete list will be published in another article, but
can also be obtained from the corresponding author. In principle, every threedimensional shape must be described by three dimensions from which the biovolume can be calculated. In the case of a sphere, only one dimension (diameter) has
to be measured, but it is raised to the third power because all three dimensions
have the same value. For cones, barrels, spindles and cylinders, two dimensions
need to be measured because the third dimension equals one of the others. For all
other shapes, length, width and height are necessary to calculate the biovolume.
Because only up to two dimensions can be visualized under the microscope, at
least one dimension has to be derived from one of the others or a fixed value has
to be determined from a number of specimens in a special effort. Therefore, the
program offers the possibility to add a factor or a fixed value. The factor has to be
taken from the literature or (if possible) has to be calculated from one’s own
measurements. For example, for the diatom Actinocyclus normanii (cylinder), we
determined the ratio between height and diameter to be 0.82 (n = 67; minimum =
0.60; maximum = 1.06). For the chlorophycean alga Pediastrum, which always
settles on the flat side, the height was measured by manipulating the algae in glycerine. Height was determined to be 6.1 µm (n = 9; minimum = 4.1 µm; maximum
= 10.2 µm).
The combinations of cylinder + half-sphere, cylinder + cone and cone + halfsphere are added to the program as standard shapes. Any combination of the
basic shapes is possible to a maximum of 10 dimensions. This feature makes it
possible to describe complex shapes. For example, the biovolume of a female
Cyclopoida with eggs can be approximated by a cone + half-sphere (body shape)
and two ellipsoids (the egg sacs). Total biovolume is then calculated using the
following formula:
1
1
—– · p · w2 · (l + w) + 2 · — · p · legg · w2egg
12
6
where w is the body width, l is the body length, legg is the length of the egg sac
and wegg is the width of the egg sac.
Some organisms cannot easily be described by a simple geometrical shape
because they have many appendices, e.g. Ceratium (Dinophyceae) and Staurastrum (Chlorophyceae). In an image of a single taxon, a maximum of 10 different
measurements can be performed.
Measurements can be made in any direction by drawing a straight line using
the digitizer. In addition, the image-analysis system makes it possible to follow
the curvature of curved organisms (e.g. Closterium and Monoraphidium).
Coenobium-forming taxa can either be measured as individual cells or as a unit.
When measuring the coenobium as a unit, a standard counting unit for
a coenobium (4, 8 or 16 cells) is necessary. Together with the actual number of
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Table I. Basic geometrical shapes and examples of formulae for the calculation of biovolumes and
additional factors of 11 taxa in the BACCHUS application
Taxon
Geometrical shape
Formula
Chrysococcus
Actinocyclus normanii
Asterionella formosa
Ankyra
Gymnodinium helveticum
Kephyrion
Ascomorpha
Notholca
Sida crystallina
Daphnia galeata
Copepodite
Sphere
Cylinder (circular [)
Brick
Spindle
Ellipsoid (circular [)
Cone
Cylinder (ellipsoid [)
Segment of ellipsoid
Barrel
Lens-shaped disc
Cone + half-sphere
(1/6) · p · d3
(1/4) · p · d2 · h
l·w·h
(2/15) · p · w2 · l
(1/6) · p · w2 · l
(1/12) · p · w2 · l
(1/4) · p · l · w · h
1/24 · p · (3 · l · w · h + 4 · h3)
2/9 · p · l · w2 + 1/3 · w2
1/3 · (1/8 · p · l · w · h + 1/24 · p · h3)
1/12 · p · w2 · (l + w)
Factor
h = 0.82 · d
w=h
w = 0.18 · l
w = 0.6 · l; h = 0.4 · l
h = 0.8 · w
h = 0.32 · l
d, diameter; h, height; l, length; w, width.
cells in the coenobium, a conversion factor is created to convert the real biovolume to a standardized biovolume. This conversion factor is calculated as the
standard number of cells in a coenobium divided by the actual number of cells in
the coenobium, and is necessary to perform statistical calculations on the results.
Because of cell division behaviour, the number of cells in a coenobium is always
a power of two. Calculation of the mean biovolume of two coenobia, one of 16
cells and one of 32 cells, would result in a useless biovolume of a non-existing
coenobium with 24 cells. This problem is solved by converting the biovolume of
coenobia to standardized biovolumes of a fixed number of cells (a power of two).
The actual number of cells is stored in the database because this information can
be useful when real coenobium size is necessary, e.g. for grazing studies.
One of the master tables also includes all calibration values of the used microscopes and possible magnifications. Every user can adjust this information to
his/her own microscope(s). These calibration values are needed to convert the
number of pixels into units of length. Before starting measurements, the microscope type and magnification are selected. Depending on this choice, the imageanalysis program retrieves a corresponding calibration value from the database.
Distortion effects were tested by measuring a preserved specimen of the alga
Cryptomonas (Cryptophyceae) 25 times in the middle and in the top and bottom
left and right corner of the screen. Analysis of variance (ANOVA) using the
statistical software package Statistica for Windows (Statsoft, 1997) showed no
significant difference (P = 0.28).
Measurements
The communication between the image-analysis program and the database is
established through dynamic data exchange (DDE) links. From the image-analysis program, the database is started automatically. Before the biovolume
measurements can be performed, the microscope set-up has to be checked to
confirm that the conversion factor between pixels and unit of length is still correct
(Figure 2). Without successfully completing this check, it is impossible to start
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Fig. 2. Schematic presentation of the BACCHUS application.
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Measuring plankton biovolumes with BACCHUS
measuring. An officially certified object micrometer is used for this purpose. The
technician draws a straight line of predefined length using a digital image of the
object micrometer. The computer verifies whether the length of this line corresponds with the stored value. In our application, a maximum deviation of 3% of
the predefined length is tolerated. It is impossible to start measuring without
passing the calibration check. After having entered user and sample identification
codes in the appropriate fields [which are sent to the database by a DDE link and
stored in a table (T_CURRENT)], biovolume measurements can be started.
The next step is to choose a taxon from the list, which contains all taxa available in the database. In addition, the used objective with corresponding magnification is selected. This information is also stored in T_CURRENT along with the
user and sample identification code. According to the chosen magnification, the
database finds a corresponding calibration value which is used to convert pixels
into a unit of length. At this point, a timestamp (day, date, hour, minute, second)
is generated in the database and stored in T_CURRENT as well. The combination of the timestamp with the user and sample identification code, taxonname
and magnification creates a unique record for the database.
As soon as the microscopic image is digitized, a window automatically appears
with the first dimension to be measured (retrieved from MEASURE_LIST). The
measured value (number of pixels) is converted to a unit of length using the calibration value mentioned above. The converted value is sent to the database and
also stored in the record of T_CURRENT as value1. A second, third, etc., dimension is measured and stored in the same way, thus generating value2, value3, etc.,
to a maximum of 10 values. After measuring all the necessary dimensions for that
taxon, the program automatically calculates the biovolume of the organism using
the taxon-specific information from MEASURE_LIST. This calculated biovolume
is stored in T_CURRENT and displayed on the screen. The user is able to discard
this record in case anything went wrong during the measurement. Otherwise, the
database will contain incorrect information, which will have to be deleted afterwards. If the value is accepted, the collected data in T_CURRENT are automatically transferred to another table (T_DATA) and stored as one record consisting of
a taxonname, sample identification code, user identification code, timestamp,
value1 to value10 and the calculated biovolume and standardised biovolume.
At the end of the measurement, the calculated biovolume is presented on
screen. After two or more measurements, the running geometric mean of the
biovolume of this taxon is displayed as well, and after 10 or more measurements
the half-width of the 95% confidence interval (CI) and criterion (see below) are
also displayed. However, it is not necessary to measure all individuals of one
taxon before switching to another taxon, because the information is stored per
taxon and new results are calculated and displayed on screen every time a
measurement of that taxon is performed.
Statistical analysis of the results
The statistical analysis is performed in the database where the table T_DATA is
used as a spreadsheet. Analysis of measured biovolumes showed that the mean
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was positively correlated with the variance, which implies a non-normal distribution. A logarithmic transformation makes the variance independent of the
mean (Sokal and Rohlf, 1995). Theil-Nielsen and Søndergaard (1998) also
showed that this type of data (biovolumes/biomass) requires a logarithmic transformation to achieve a normal distribution. All statistical analyses are performed
with ln-transformed results. The mean, standard error (SE), and upper and lower
limit of the 95% CI (UL95% and LL95%) of the ln-transformed biovolumes are
calculated for every taxon, using formulae from Sokal and Rohlf (1995):
Standard deviation (s)
s=
√
n
–
o (Yi – Y )2
i=1
——————
n–1
(1)
–
where Yi is the ith individual result, Y is the mean of i results and n is the total
number of results.
Standard error (SE)
s
SE = ——
—
√n
(2)
Upper limit of the 95% CI (UL95%)
–
UL95% = Y + t · SE
(3)
–
where Y is the mean of the results and t is ta = 0.05; d.f. = n – 1.
Lower limit of the 95% CI (LL95%)
–
LL95% = Y – t · SE
(4)
The value of t varies between 2.26 (d.f. = 9) and 2.05 (d.f. = 29). For practical
reasons, we used the value of 2.1, which is the t value of 20 measurements
(d.f. = 19).
The width of the 95% CI indicates the precision of the calculated mean. The
width of this interval is determined by the number of measurements and the
standard deviation. By performing more measurements, the interval will become
smaller, which means that the mean becomes more precise.
By setting limits to the maximum allowed width of the interval, the extent of
desired precision can be defined. In BACCHUS, the half-width of the 95% CI
and criterion are presented for the first time after 10 measurements. After
performing 30 measurements without reaching the criterion, measurements are
usually stopped, thereby accepting a less precise mean biovolume. For our laboratory, we decided that the limits of the CI must be <0.2 ln units above or below
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Measuring plankton biovolumes with BACCHUS
the mean. Figure 3 shows the change in mean biovolume and 95% CI when the
results show considerable variation. Measurements of small Cryptomonas were
carried out and their individual biovolumes varied between 900 and 4100 µm3;
ln(biovolume) varied between 6.8 and 8.3. As is shown in Figure 3, the 0.2 limit
is reached after 14 measurements.
Finally, the backtransformed mean biovolume is calculated for every taxon (i.e.
the geometric mean).
If many measurements throughout all seasons have been performed, and statistical analysis shows that the biovolume of this particular taxon does not vary
significantly, it can be decided to stop measuring this taxon and use its calculated
fixed value instead.
Data retrieval
The BACCHUS application contains a standard report which can be used to
generate reports about all measured taxa, the number of measured individuals
per taxon, geometric mean, minimum and maximum biovolume per sample. This
report can be printed after each session. For further data processing, all kinds of
combinations of taxa, sampling locations and dates can be retrieved using the
Data evaluation option.
The straightforwardness of the database structure makes it easy to perform
queries for specific evaluations. For example, a simple query was created to
Fig. 3. Running mean biovolume and number of measurements of Cryptomonas. The 95% CI and
allowed width of the interval to reach the desired precision (criterion) are also indicated. d, individual
result; white line, running mean biovolume; grey area, 95% CI; black lines, criterion (0.2 ln units above
or below the mean); the vertical line indicates the point where the width of the 95% CI reaches the
criterion.
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C.J.Carpentier et al.
examine the length–width ratio of the genus Mallomonas (Chrysophyceae). It
appeared that these individuals could be separated into two groups, each with a
more or less specific length–width ratio (Figure 4). Further microscopic examination showed that Mallomonas occurred in ellipsoid and spindle-shaped types,
indicating at least two species.
Discussion
Evident spatial and temporal variations exist in phyto- and zooplankton biovolumes (Ruttner, 1952; Nauwerck, 1963; Ruttner-Kolisko, 1977; Vilicic, 1985). This
makes the use of literature data or single measurements unsuitable for accurate
biovolume determinations (Nauwerck, 1963). With semi-automated measuring
systems (e.g. Sprules et al., 1981; Allen et al., 1994), biovolumes can be measured
much more rapidly on a routine basis.
Compared to semi-automated systems based on the concept of Sprules et al.
(1981), the advantage of using a digitizer is that curved organisms can be
measured (Roff and Hopcroft, 1986). In contrast to the system used by Roff and
Hopcroft, which measures curved organisms by summing up short linear
measurements traced along the curvature, a real curved line can be drawn on
screen by the image-analysis system we used. Another advantage of the system
we used, compared to that of Roff and Hopcroft (1986), is that we use on-line
statistics, which minimizes the number of measurements without losing precision.
As far as we know, this is the only (semi)automated measuring system which uses
on-line statistics. However, biological measuring systems may evolve rapidly and
are updated on a regular basis, without the updates being published (Verity and
Sieracki, 1993; N.D.Yan, personal communication, 1997).
Other advantages of the BACCHUS application are a very flexible input of
master data, output of stored data and a user-friendly operation environment.
BACCHUS is very easy to customize because it does not require any expertise
in database programming.
Fig. 4. Scatter plot of length and width of Mallomonas (Chrysophyceae). d, individual result (n = 88).
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Compared to previously published semi-automated systems, which are inexpensive, the BACCHUS application only runs on a relatively expensive imageanalysis system (hardware, video camera and software cost ~$25 000). It should,
however, be realized that the image-analysis software has many more features
than are used by the BACCHUS application and can be used for many different
purposes. For example, we used the image storage and retrieval application to
build a reference collection of phyto- and zooplankton, and made many small
applications for specific purposes like fully automated length–frequency distributions and macro-invertebrate biomass determinations. Other applications are,
for example, an automated colony count technique for the detection and separation of confluent microbial colonies and colonies of various sizes on Petri dishes
(Corkidi et al., 1998), automated detection of cyanobacteria (Thiel and Wiltshire,
1995) and morphometric analysis of electron micrographs of microalgal cells
(Fisher et al., 1998). In addition, enumeration of the ecologically important
picoplankton (Wilde and Cody, 1998) can be performed on a routine basis.
Roff and Hopcroft (1986) compared their digitized measuring system with a
conventional microscope eyepiece micrometer and found that the precision of
measurement was much lower using an eyepiece micrometer. From our own
measurements, it also became clear that ocular micrometer measurements are
very time consuming. Measurements of Actinocyclus normanii (Bacillariophyceae) with BACCHUS were performed in about half the time necessary to
perform the measurements with the ocular micrometer. Biovolume calculations
(including statistical parameters) with BACCHUS virtually require no extra time
at all. When measuring Scenedesmus (Chlorophyceae), the measurements with
the ocular micrometer required even more time because the organism had to be
reorientated to be able to measure two dimensions perpendicular to each other.
The orientation and position of organisms are unimportant when using
BACCHUS.
Ocular micrometer measurements quickly lead to user fatigue. Therefore, the
more measurements are performed, the less precise they will become. When
using BACCHUS, this loss of precision, caused by fatigue of the user, does not
occur. The practice in our laboratory with many technicians has shown that plankton counts can very easily and comfortably be combined with biovolume
measurements.
We introduced on-line statistical evaluation of the data to minimize the number
of measurements necessary to reach a mean biovolume which is sufficiently
precise. It has to be noted that in routine samples not enough individuals of a
taxon can be present in the sample to reach the desired precision.
Even if no image-analysis system is used to measure biovolumes, the statistical
analysis we propose can be used to reduce analysing time.
A disadvantage of image analysis is that only two dimensions can be measured.
Laser and digital confocal microscopy can acquire images in three dimensions,
but are at present not suitable for routine measurements of plankton biovolumes,
because they are either expensive or require a highly skilled technician (Verity et
al., 1996). In addition, analyses are very time consuming. These microscopes can,
however, be applied for occasionally measuring the third dimension of certain
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plankton taxa in order to check the factors used in BACCHUS for calculating the
third dimension.
A recent development in measuring zooplankton abundance and size is the use
of the optical plankton counter. Although data are obtained in real time, and are
thereby saving time and effort, and large geographical areas can be characterized
relatively easily (Gallienne and Robins, 1998), it does not (yet) form an alternative for the detailed analyses many plankton ecologists need. Identification of
most zooplankters with this system is limited to general taxonomic groups and
only larger or distinctive animals can be identified to species level. In addition,
most rotifers and early developmental stages smaller than 250 µm cannot be
detected (Sprules et al., 1998).
In conclusion, the BACCHUS application allows quick routine measurements
of phytoplankton and zooplankton biovolumes. By using on-line evaluation of
collected data, only a minimum number of measurements are performed to reach
a suitably precise mean biovolume.
Acknowledgements
Ronald Sperber is acknowledged for database programming, Sandra Danuhyarso-Novemsia, Francien Lambregts-van de Clundert, Ger-An de JongePinkster, Meta Frank and Sonja Vernooij for performing measurements, and
Wim Hoogenboezem and Jurgen Volz for critically reviewing an earlier version
of the manuscript. This study was supported in part by a grant from the Dutch
Ministry of Economical Affairs, Research and Development Stimulation
Programme (SO/1996/6073/1/6919).
References
Allen,G., Yan,N.D. and Geiling,W.T. (1994) ZEBRA 2—Zooplankton Enumeration and Biomass
Routines for APIOS: a semi-automated sample processing system for zooplankton ecologists.
Ministry of Environment and Energy Report, Dorset, Ontario, Canada.
Bellinger,E.G. (1974) A note on the use of algal sizes in estimates of population standing crops. Br.
Phycol. J., 9, 157–161.
Bottrell,H.H., Duncan,A., Gliwicz,Z.M., Grygierek,E., Herzig,A., Hillbricht-Ilkowska,A.,
Kurasawa,H., Larsson,P. and Weglenska,T. (1976) A review of some problems in zooplankton
production studies. Norw. J. Zool., 24, 419–456.
Corkidi,G., Diaz-Uribe,R., Folch-Mallol,J.L. and Nieto-Sotelo,J. (1998) COVASIAM: an image
analysis method that allows detection of confluent microbial colonies and colonies of various sizes
for automated counting. Appl. Environ. Microbiol., 64, 1400–1404.
Culver,D.A., Boucherle,M.M., Bean,D.J. and Fletcher,J.W. (1985) Biomass of freshwater crustacean
zooplankton from length-weight regressions. Can. J. Fish. Aquat. Sci., 42, 1380–1390.
Enserink,E.L. (1995) Food mediated life history strategies in Daphnia magna: their relevance to
ecotoxicological evaluations. Thesis, Agricultural University Wageningen, Wageningen, The
Netherlands.
Fisher,T., Berner,T., Gal,A. and Dubinsky,Z. (1998) A comparison of computerized image analysis
and stereology as tools for morphological study of algal cells. Isr. J. Plant Sci., 46, 177–180.
Gallienne,C.P. and Robins,D.B. (1998) Trans-oceanic characterization of zooplankton community
size structure using an optical plankton counter. Fish. Oceanogr., 7, 147–158.
Jeffries,H.P., Sherman,K., Maurer,R. and Katsinis,C. (1980) Computer-processing of zooplankton
samples. In Kennedy,V.S. (ed.), Estuarine Perspectives. Academic Press, New York, pp. 303–316.
Jeffries,H.P., Berman,M.S. and Poularikas,A.D. (1984) Automated sizing, counting and identification
of zooplankton by pattern recognition. Mar. Biol., 78, 329–334.
1888
Measuring plankton biovolumes with BACCHUS
Latasa,M., Bidigare,R.R., Ondrusek,M.E. and Kennicutt,M.C.,II (1996) HPLC analysis of algal
pigments: a comparison exercise among laboratories and recommendations for improved analytical performance. Mar. Chem., 51, 315–324.
Lohman,H. (1908) Untersuchungen zur Feststellung des vollständigen Gehaltes des Meeres an
Plankton. Wiss. Meeresunters., 10, 131–370.
McCauley,E. (1984) The estimation of the abundance and biomass of zooplankton in samples. In
Downing,J.A. and Rigler,F.H. (eds), A Manual on Methods for the Assessment of Secondary
Productivity in Fresh Waters. IBP Handbook 17, 2nd edn. Blackwell Scientific, Oxford, pp. 228–265.
Millie,D.F., Paerl,H. and Hurley,J.P. (1993) Microalgal pigment assessments using High-Performance
Liquid Chromatography: a synopsis of organismal and ecological applications. Can. J. Fish. Aquat.
Sci., 50, 2513–2527.
Mills,E.L. and Confer,J.L. (1986) Computer processing of zooplankton application in fisheries studies.
Fisheries, 11, 24–27.
Nauwerck,A. (1963) Die Beziehungen zwischen Zooplankton und Phytoplankton im See Erken.
Symb. Bot. Upsal., 17, 1–163.
Reynolds,C.S. (1984) The Ecology of Freshwater Phytoplankton. Cambridge University Press,
Cambridge.
Roff,J.C. and Hopcroft,R.R. (1986) High precision microcomputer based measuring system for
ecological research. Can. J. Fish. Aquat. Sci., 43, 2044–2048.
Rolke,M. and Lenz,J. (1984) Size structure analysis of zooplankton samples by means of an automated
image analyzing system. J. Plankton Res., 6, 637–645.
Roy,S., Chanut,J.-P., Gosselin,M. and Sime-Ngando,T. (1996) Characterization of phytoplankton
communities in the lower St. Lawrence Estuary using HPLC-detected pigments and cell microscopy. Mar. Ecol. Prog. Ser., 142, 55–73.
Ruttner,F. (1952) Planktonstudien der Deutschen Limnologischen Sunda-Expedition. Arch.
Hydrobiol., 21, 1–274.
Ruttner-Kolisko,A. (1977) Suggestions for biomass calculation of planktonic rotifers. Arch.
Hydrobiol. Beih., 8, 71–76.
Schmid,H., Bauer,F. and Stich,H.B. (1998) Determination of algal biomass with HPLC pigment
analysis from lakes of different trophic state in comparison to microscopically measured biomass.
J. Plankton Res., 20, 1651–1661.
Semina,H.J. (1978) The size of cells. In Sournia,A. (ed.), Phytoplankton Manual, UNESCO Monographs on Oceanographic Methodology. UNESCO, Paris, Vol. 6, pp. 233–237.
Sokal,R.R. and Rohlf,F.J. (1995) Biometry. The Principles and Practice of Statistics in Biological
Research, 3rd edn. W.H.Freeman and Co., New York.
Sommer,U., Gliwicz,Z.M., Lampert,W. and Duncan.A. (1988) The PEG model of seasonal succession of planktonic events in fresh waters. Arch. Hydrobiol., 106, 433–471.
Sprules,W.G., Holtby,L.B. and Griggs,G. (1981) A microcomputer-based device for biological
research. Can. J. Zool., 59, 1611–1614.
Sprules,W.G., Jin,E.H., Herman,A.W. and Stockwell,J.D. (1998) Calibration of an optical plankton
counter for use in fresh water. Limnol. Oceanogr., 43, 726–733.
Statsoft (1997) Statistica for Windows. Computer program manual. Statsoft Inc., Tulsa.
Theil-Nielsen,J. and Søndergaard,M. (1998) Bacterial carbon biomass calculated from biovolumes.
Arch. Hydrobiol., 141, 195–207.
Thiel,S. and Wiltshire,R.J. (1995) The automated detection of cyanobacteria using digital image
processing techniques. Environ. Int., 21, 233–236.
Verity,P.G. and Sieracki,M.E. (1993) Use of color image analysis and epifluorescence microscopy to
measure plankton biomass. In Kemp,P.F., Sherr,B.F., Sherr,E.B. and Cole,J.J. (eds), Handbook of
Methods in Aquatic Microbial Ecology. Lewis Publishers, Boca Raton, FL, pp. 327–338.
Verity,P.G., Beatty,T.M. and Williams,S.G. (1996) Visualization and quantification of plankton
detritus using digital confocal microscopy. Aquat. Microb. Ecol., 10, 55–67.
Vilicic,D. (1985) An examination of cell volume in dominant phytoplankton species of the central and
southern Adriatic Sea. Int. Rev. Ges. Hydrobiol., 70, 829–843.
Wilde,E.W. and Cody,W.R. (1998) Picoplankton counts greatly alter phytoplankton quantitative
analysis results. J. Freshwater Ecol., 13, 79–85.
Wilhelm,C., Rudolph,I. and Renner,W. (1991) A quantitative method based on HPLC-aided pigment
analysis to monitor structure and dynamics of the phytoplankton assemblage—A study from Lake
Meerfelder Maar (Eifel, Germany). Arch. Hydrobiol., 123, 21–35.
Received on February 28, 1999; accepted on May 21, 1999
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