MATURE: Biogeochemistry of the Maximum TURbidity zone in
Estuaries. A summary report.
Peter.M.J. Herman and Carlo Help
Netherlands Institute of Ecology, Centre for Estuarine and Coastal Ecology, Vierstraat 28,
4401 EA Yerseke, The Netherlands.
21136?
1. Introduction.
1 .a. Aims and objectives.
The EU-environment research programme MATURE (Biogeochemistry of the MAximum
TURbidity zone in Estuaries) was a cooperation project between the following partners:
- N100, Yerseke (coordinator)
- University of Hamburg, Dept. of Marine Chemistry and Biogeochemistry
- University of Hamburg, Center of Marine and Climate Research
- NIOZ, Texel
- TNO, Den Helder
- University of Brussels
- University of Gent
- University of Bordeaux, station of Arcachon
- Institute Tecnico Superior, Lisbon
The objectives of the programme are summarized as follows:
The role of biological processes in the formation and subsequent utilization of particles in the
maximum turbidity zone will be studied in three European estuaries : Gironde, Schelde and
Elbe. Special attention will be given to organic matter and biological processes acting on it.
Nutrients and trace elements will be considered insofar as they are regulating biological
processes in the estuary. Numerical modelling of water and suspended matter transport is an
integrated part of the project.
Specific research questions addressed in the project are:
.
How is the concentration, stability and fate of aggregates influenced by the transformations of organic matter, advected from river and human sources?
.
.
How can formation, sedimentation and resuspension of particles be parameterized and
incorporated into numerical hydrodynamic models?
What is the importance of microbiological processes (primary production, bacterial min-
eralization, protozoan grazing) for the geochemistry of the system; what are the specific
characteristics of the microbial loop on the aggregates; how do they affect the dynamics
of the aggregates?
.
How do the biological processes at higher trophic levels (selective grazing and manipula-
tion of particles in the water column and upper layers of the sediment) affect the particle
dynamics. Reversely, what is the influence of the geochemical environment on these
processes?
1 .b. Organization of the project.
T.tITifi^Ld^rk-f<?lsllle.pr?JlecLhas been centred around six joint field campaigns of appr. 1
week. In Sprin_g 1993 and in Spring 1994 a one-week campaign has been organized in' Elbe,
Schelde and Gironde. A great number of physical, chemical, microbiological and macrobi-
ological measurements have been performed on common stations during these campaigns.
Each of the campaigns started with an along-estuary transect, followed by a 24h cycle with
hourly measurements at a fixed station, and by a second transect along the estuary. Sampling on the common stations was usually concentrated on surface, middle water and bottom
water levels, although not all variables have always been determined at all the levels. CTD
casts provided a better vertical resolution for a number of physical and chemical variables.
After analysis in the laboratory, all results have been gathered at N100 and have been
stored in a common database. This database (Herman & de H
, this report) with a manual
and documentation, has been distributed among the partners~oTfhem
project. In principle, it is
freely available to the scientific community upon completion of the project.
Alongside the field measurements, laboratory experiments have been performed on specific
biological questions, e.g. relating to the feeding biology of the zooplankton, and a mesocosm
setup has been designed for experimental study of environmental stress on estuarine
zooplankton communities.
Finally, 3D hydrodynamic models have been developed for the three estuaries. These models have been calibrated on available field and monitoring data. Their application to the
analysis of the biogeochemical field data is not completed, however.
This scientific summary report highlights the conclusions that can be drawn from the interdisciplinary comparison of the results obtained. For a detailed description of the results of the
different research groups, we refer to the reports of the partners given in appendix. A complete overview of these reports is given at the end of this summary.
2. Description of the MTZ during the field campaigns.
CTD profiles were obtained by the University of Hamburg (see Pfeiffer, this report) at many
stations in and around the MTZ of the estuaries during the field campaigns. Two profiles
along the estuary were sampled with a spatial resolution of around 10 km; during the 24h
stations appr. every hour a CTD cast was made.
Results of the CTD casts are delivered by the University of Hamburg in a number of ASCIL
files. This partner provided a program to draw vertical profiles of the measured variables at
every station (see Pfeiffer, this report). Results were combined in a Paradox data base containing all observations. From this database synoptic pictures were produced that summanze
the data on satinity and light transmission (as a measure of turbidity) in this report. The CTD
database is kept apart from the general biogeochemical database. It is included with the da-
tabase as the files CTD.db (basic data) and CTDSTAT.DB (station data, linked to CTD.DB
through the field "Station").
The(light transmission values[gave clear pictures of the vertical and horizontal distribution of
suspended solids along the estuarine salinity gradients. Figs. 1-11 summarize salinity (upper
picture, a) and light transmission (lower picture, b) in the Elbe in 1993 and 1994, Gironde
1993 and 1994 and Schelde 1993 and 1994. With one exception (Schelde 1993, where graclients of light transmission were hardly visible) there are clear gradients in suspended solids
along the estuarine axis. Vertical gradients in light transmission were clearly visible and most
pronounced where the average transmission was lower than appr. 60 %.
Fig. 12 shows the dependence of depth-averaged light transmission on salinity in the three
estuaries and the two years. The relation of light transmission with salinity is closer than with
position along the axis of the estuary (compare, e.g., the two transects in the Schelde in
1994: figs 10 and 11), indicating that the zone of maximum turbidity moves up and down the
estuary with the tide.
In Gironde and Elbe, peak turbidity values are found at salinities between 0.1 and 4 psu.
Turbidity in the Gironde is so high that transmissometry lacks enough resolution at the highest turbidity values. The situation in the Schelde seems to differ from the other two estuaries.
^.
In 1993 no clear maximum turbidity zone could be detected; in 1994 a peak value was found
at a slightly higher salinity (peak at 4 psu, distinctly lower turbidity in the range 0.1-3). Despite
the difference in river discharge rates between 1993 and 1994, causing the salinity gradient
to be moved further downstream in all three estuaries (figs 1-11), there were no large differences in the relation of transmission with salinity between the two years in Elbe and Gironde.
For the Schelde, again, the picture is less clear due to the lack of a distinct MTZ in 1993.
From these results it can be concluded that the MTZ in, at least, Elbe and Gironde and_BQSs^blyjilspjnthe Schelde, is positioned in the very low salinity range of the estuary. It moves
up- and downstream with the tide, and shifts along the estuarine axis,togetherwith the saJmity gradient, at differing discharge rates. In the field samples the concentration of suspended
material in a maximum turbidity zone is closely linked to the transition zone from zero to ve
low salinities. Even if salinity per se is not the direct cause of the concentration of the suspended matter, it is closely correlated with the factors causing the peak turbidity.
A full description of the biogeochemical variables in the MTZ during the field campaigns is
given by Brockmann et al. (this report) and will not be repeated here. These authors discuss
the differences between the estuaries with respect to nutrients, oxygen and organic loadings.
They conclude that anthropogenic stress in the Gironde is far less pronounced than in the
other two estuaries. Nitrification in the maximum turbidity zone is well expressed in Elbe and
Schelde. Denitrification is important in the Schelde.
Elbe 1993 transect 1
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3. The dynamics of suspended matter in the MTZ - flocculation processes.
3.a. The in situ determination of floc size.
The dynamics of flocs in the MTZ were studied primarily by in situ camera systems (see
Eisma, this report, for full details). Two different camera systems were deployed: a camera
with a 1:1 image, and one with a 1:10 magnification. The volume of water photographed by
the latter camera is appr. 100 times smaller than that covered by the 1:1 camera.
Both camera systems have a different detection window for particle sizes. The mean particle
diameter determined with both systems differs by approximately one order of magnitude,
although both cameras seem to give consistent results. An example is shown in Fig. 13,
where measurements of floc numbers in a size class vs. geometric mean diameter of the
class by the two camera systems in one body of water were overlaid, after proper rescaling
for the volume of water scanned. In the overlap zone between the two detection windows,
averages between both readings were calculated. The consistency shown between the two
camera systems was found in most samples where both cameras were deployed quasisimultaneously.
1E3
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diameter (um)
Fig. 13. Comparison of floc size frequencies observed by the two camera systems in the same body of water.
Number of particles per size class is plotted vs. geometric mean diameter of the size class. Observed numbers of
particles in the 1:1 camera were divided by 1 00 since this camera scans a 100 times larger volume of water. In
the overiap zone, the number of particles was calculated as the average of the rescaled numbers in the two
camera readings.
Nevertheless, the (volume-based) mean particle sizes calculated from both cameras have a
relatively low correlation coefficient of 0.32 (see Fig.14). This may be caused by scatter in
the data, especially in the larger diameter classes which have a large weight in the calcula-
tion of the volume-base mean size.
To filter out as much of the variability as possible, we calculated the geometric mean of the
volume-based particle size of both cameras as a relative measure of particle size to relate
with other measured variables.
In Fig. 15 this geometric mean particle size is plotted against the best predictor we could cal-
culate from the environmental data available in the joint field campaigns. This predictor is
expressed as:
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Vol-based Mean size 1:1 (urn)
Fig. 14. Relation between the volums-based mean floc size determined by two camera systems in the same body
a
of water. All available data where both camera systems have been used are shown.
logic (Particle size) = 1.714 + 0.105 * logic (SPM) + 0.145 * logic (POC) - 0.103 * logio (SAL)
-1
(eq.1)
where SPM = suspended particulate matter concentration (mg.l )-1
POC = particulate organic carbon concentration (mg C.F')
SAL = salinity of the water (psu)
This regression had an i2 of 0.44 (n=97) and was highly significant. In view of the rather tow
correlation between particle sized determined by the two camera systems, its value may actuatly indicate that most of the non-random variation in the particle size is effectively explained by the three environmental variables.
Among the single environmental factors, POC explains most of the variation in particle sizes.
The (log-log) regression of particle size on POC has an r2 of 0.31. This value is 0.21 for SAL,
and 0.15 for SPM.
The result of this regression analysis using the average of the 1:1 and 1:10 camera systems
is qualitatively in accordance with regressions of the particle sizes obtained by the 1:1 camera or the 1:10 camera systems on environmental variables separately. We consistently find
that POC is the single most important variable in the environment explaining the size of the
flocs as observed in the field.
Two main factors are thought to be important for the formation of a floc upon collision between two particles (van Leussen, 1994): the organic coating of the particles and the salinity
of the surrounding water influencing the double layer dynamics. The regression equation
qualitatively indicates the importance of this coating with the positive exponents for POC and
SPM. The influence of salinity is more complex. In the data set particle size decreases with
increasing salinity, contrary to what is expected from theory. However, a closer inspection of
the data reveals that the^nfluence of salinity is non-linear. Fig. 16 shows the residuals of partide size on POC and SPM (i.e. the difference between observations and the part of the pre-
diction equation containing POC and SPM) as a function of salinity. It can clearly be seen
that particle^izes^arejarger than expected at salinities up to 1 psu, but decrease again at
high^salinities._Salinity change (from freshwater to slightly brackish) has a more profound
effect than salinity per se, possibly because a new equilibrium between organic coating and
salmity establishes at higher salinities than appr. 1 psu. It should be noted that, although most
observations in this salinity range come from the Schelde estuary, the Elbe observations are
fully in line with the Schelde data. Moreover, this zone of low salinities corresponds very well
with the observed zones of increased turbulence, as observed in the hydrographic de'scrip-
tions of the estuarine maximum turbidity zones.
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flocs in the laboratory, the exponent is usually in the range of 0.4-1.0 (Gibbs 1985; Hawley,
1982; Kajihara, 1971). The present observations are intermediate between the two extremes.
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Fig. 17. Relation between observed sinking speed of flocs with smallest diameter of the Hoc. The regression line
is a Geometric Mean regression.
The predicted difference in sinking speed of particles is two orders of magnitude over the
size range observed. Combined with the predicted influence of POC on particle size, one can
conclude, at least qualitatively, that the organic matter in estuaries is partly responsible for
the dynamics of the suspended material. The importance of this factor can be estimated from
the exponents of the regressions. Mean particle size variation attributable to POC and SPM
was a factor two in our data base, leading to a predicted variation in sinking speeds of slightly
over a factor two.
Due to technical difficulties (see: Eisma, this report) the number of determinations of particle
setlling velocities was too small to re-appraise the relationship between settling velocity and
particle concentration in the water. This relationship is important for the hydrodynamical and
sediment transport modelling (see: Cancino & Neves, this report). Literature data for the coefficients of the relationship had to be used for the modelling. However, the form of the relationship between settling velocity and suspended matter concentration as used in the modelling (based on Dyer, 1986) is quite similar to the one predicted on the basis of the regression
equations presented here.
3.c. Modelling suspended sediment concentration, deposition and erosion.
3-D hydrodynamical and transport models have been developed for the three estuaries
(Pfeiffer, this report, Cancino & Neves, this report). Numerical experiments, mainly with the
Schelde model, point to several important conclusions.
Local geographical information on sediment composition is important for a correct prediction
(at short term) of the suspended sediment dynamics in the water column. The availability of
erodable sediment and the critical erosion velocity parameters are dependent on the c6mposition of the sediment. It was shown for the Schelde that correct predictions of the along-
estuary gradients in suspended matter concentrations were not possible without allowing for
variable coefticients (based on observed sediment composition) along the estuarine gradient.
Although obviously the composition of the sediments itself is a function of hydrodynamic
factors, modelling at a much longer timescale would be needed to predict it, and thus make
the sediment transport modelling self-contained. Models developed within the project make
predictions essentially at the time scale of a tidal cycle; this can only be succesfully performed with the inclusion of field data on sediment composition.
The three-dimensional topography of the estuary determines essential features such as vertical mixing, salt stratification, upward bottom flows of suspended sediments etc..., leading to
the observed turbidity in the MTZ. This is shown very clearly by the lateral transact in Hansweert (Schelde) taken from the numerical simulations. Vertical gradients result from the
specific structure of the northern and southern flow channels. (Fig. 18). In the Gironde, essentially the same model with similar parameters produced much more vertical stratification
and a considerably higher turbidity in the MTZ.
The transport models were able to produce hindcasts of the suspended matter concentration
and salinity profiles well in accordance with observations during the field work. As such, they
provide excellent tools to investigate further the relative importance of mechanisms of suspended sediment dynamics described by the field studies. Inclusion of these mechanisms
into the models, however, was not possible due to time limitations of the project.
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Fig. 18. Model results showing vertical profile of salinity (psu) during low slack water period in a transversal
section near Hansweert (simulated for the day 28/04/94). Y-axis givel local depth in meters, X-axis transversal
coordinates. (From: Cancino & Neves, this report)
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Fig. 20. Comparison between observed (temperature-corrected) bacterial production rate (ng C I h ) and
predictions based on a linear function of POC and DOC concentrations. Data are labeled by the first letter of the
estuary sampled. For companson the 1:1 line is also given.
With an r2 of 0.77 most of the variation in bacterial production rates is described as a simple
function of substrates available. It was not possible to relate any of the remaining variance to
a measured environmental variable, such as SPM or satinity. Strikingly, the largest deviation
between observation and model occurred in a few samples from the Hoboken-Rupelmonde
region in the Schelde, both in 1993 and in 1994. This is the region of maxima! nitrification
activity, and maybe the deviation bears some relationship to that. However, it was not possible with the data obtained to go deeper into this problem. (Fig. 21)
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Fig. 21. Residuals of bacterial production rate (^g C r1 h-1) from the regression on POC and DOC, plotted versus
salinity. Data are labeled by the first letter of the estuary sampled. Large positive residuals are found in the towsalinity zone of the Schelde estuary only. Based on data by Goosen etal: (this report).
The plot of residuals vs. estimates indicates a slight curvilinearity in the relationship between
bacterial production and substrate. In fact, a slightly better regression could be obtained with
a power function regression: BAPR = A *-DOC8'* POCC;butThe"increasein~fit was not very
high, and this relationship has the disadvantage of being difficult to interpret in biological
terms.
The bacterial production rates are not very high, considering the important amount of organic
matter present in most samples. By assuming that organic matter decay rate = 2* bacterial
production rate, and recalculating units, we conclude that the first order decay rates of DOC
are in the order of 6 yr-1, and for~POC in the order of 2.4 yr-1. These rates are low, and com-
parable to tirst-order degradation rates of organic matter in sediments. This is further evidence that processes in the MTZ are in several respects more comparable to sediment processes than to typical water column processes. The organic matter present is resistant to
bacterial degradation; apart from adsorption onto particulate matter, this is probably also
caused by the lack of local primary production as a consequence of light limitation (see
Kromkamp et al., this report).
The difference in decay rates of POC and DOC is important for the evaluation of the consequences of accumulation of inorganic material in suspension in the MTZ. White the inorganic
material is a factor retaining the organic matter in the MTZ, it is also responsible (through
adsorption) for a slow-downof the degradation of this organic matter. The relative degradation rate (taking the rate of POC as 1) is added to the figure showing the influence of SPM on
organic matter partitioning between POC and DOC. The dependence ot degradability on
SPM is most pronounced in the range of SPM between 0 and 100 mg/l.
The results of this analysis do not lend support to the hypothesis that the high amounts of
suspended matter provide the bacteria with 'reaction centres', through their attachment on
the particulate matter. Enhancement of the degradability at high particulate concentrations is
not found, on the contrary. However, this does not preclude that the availability of substrate
may be of importance to nitrifying bacteria (that have a clear preference for attachment to
particles) or for denitrifying bacteria (that are dependent on anoxic microsites). For bacteria
in general, Hernandez et al. (this report) show that the percentage attached to particulate
material increases with increasing suspended matter concentrations, with decreasing salinity
and with decreasing oxygen concentrations. The cross-correlation between these factors
clearly shows that bacteria in the MTZ are much more attached to particulates than elsewhere in the estuary.
Measurements performed to investigate the effects of attachment on processes did not yield
conclusive evidence. Bonin et al. (this report) report a weak correlation between measured
denitrification activity and suspended matter concentrations in the three estuaries. This correlation holds true in most cases for the location of the peak activity along the estuarine transect only. These authors, however, also show that the highest denitrification activity in depth
profiles of Elbe and Gironde is found in the surface waters. Considering the generally observed depth profiles of suspended matter, with distinctly higher concentrations near the
bottom, this observation would rather point to a negative correlation between denitritication
activity and presence of flocs in the water column. The measurements of denitrifying activity
by N20 production are in general very high. In fact, most are higher than the potential denitrifying enzyme activity measured in the same samples, and the order of magnitude of meas-
ured denitritication rates would, in combination with estuarine residence times in the order of
tens of days, deplete almost all available NOs from the water. Possible interference of nitriti-
cation with the NsO production (used to measure denitrification rate) would need more detailed research.
In general, variation in bacterial density or biovolume was limited (Hernandez et al., this report). Biovqlume per cet varied over a range of appr. 2, while densities generally ranged be-
tween 2.106 ml-1 and 107 ml'1. This variation is considerably less than the variability in bacte-
rial production rates. Bacterial numbers or biomass are a bad predictor of bacterial activity
rates.
5. Quality of organic matter - chemical analysis.
Burdloff et al. (this report) analyzed the quality of organic matter in 6 different estuaries by
chemical analysis. (Fig. 22).They measured bulk organic material, and also the more de-
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production for a standard day (i.e. calculated with the observed P-t relationship, assuming the
same illumination at the water surface and the same daylength, and corrected for temperature differences with a Qio of 2.5) is strongly dependent on light extinction in the water column, as expressed by the dependence of production per unit biomass on the euphotic depth
zeu (Fig. 23).
The relationship between depth-integrated primary production and euphotic depth is not the
same for the three estuaries. There is a significant difference in slope between Gironde on
the one hand, and Schelde and Elbe on the other hand. This difference in slope is surprising
at first sight: the phytoplankton of the Gironde is the worst adapted to low light regimes, while
this estuary has the lowest light intensities in the water column. Most probably the lack of a
genuine estuarine phytoptankton assemblage in the Gironde may explain this pattern. Primary production in the maximum turbidity zone of the Gironde is very low; only at the most
seaward stations there is a substantial production. The low production in the maximum turbidity zone is performed by marine phytoplankton species that are badly adapted to the turbid
estuarine environment, but not replaced by estuarine species because net productivity is
negative even for these species. It should be kept in mind that these measurements are
taken in 1994, when due to high river runoff the maximum turbidity zone was near to the estuarine mouth.
1E4
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Fig. 23 Depth-integrated, daily-integrated pnmary produdtion per unit of chlorophyll-a for a standard day along
transects in the three estuaries (mgC m-2(f1.(mg Chla m-3)-1), plotted versus euphotic depth. Each normalized
production estimation is based on measured light extinction coefficient at the sampling point, and on measured P-l
curves at that point. Slopes for Schelde and Elbe are not significantly different, slope for the Gironde is
significantly higher. Data from Kromkamp et al. (this report)
In general, production per unit of phytoplankton biomass is very low in these samples. As.
suming as an approximation an average depth of 10 m at these stations, a carbon-to-
chlorophyll ratio of 30 for the phytoplankton and the standard light and daylength values used
in these calculations, gross primary production is on average only 7.5 % of phytoplankton
standing stock per day. Taking into account the respiration of the phytoplankton, net primary
production was most probably negative in most stations studied.
The limited role of phytoplankton primary production for the organic matter dynamics in the
MTZ is further emphasized by calculating the ratio of gross primary production on total organic carbon The average ratio for the samples taken is 0.39 % per day. Allowing for phytoplankton respiration, this ratio approaches zero or negative values in most of the stations.
7. Higher trophic levels: occurrence
An interesting observation is made by Gasparini et al. (this report), on the shift of developmental stages of Eurytemora in Gironde and Elbe (Fig. 24), but not in the Schelde. Two alternative hypotheses are brought forward to explain this spatial distribution: (1) nauplii drift
downstream and copepodites move upstream by active swimming, where swimming speed is
dependent on body size or (2) specific gravity of animals increases as they grow older, and
therefore nauplii would preferentially occur in surface waters with a net downstream current,
whereas copepodites would occur in the bottom waters where the residual current is upstream.
Although the available data are not sufficient to reach a conclusion on the mechanism, several factors are in favour of the first hypothesis: measured swimming speeds of Eurytemora
are sufficient for the displacement made; the spatial distribution of Eurytemora with respect
to suspended matter in the estuary is different for the three estuaries (coinciding with MTZ in
the Gironde, upstream of MTZ in the Elbe, downstream of MTZ in the Schelde); in the
Schelde the spatial distribution of the species suggests that it is actively avoiding the low
oxygen zone, even if this has the highest potential food concentration; no clear gradients in
the vertical distribution of the species have been found in long time series in the Gironde.
The investigation of this problem with the aid of the numerical transport models developed
within this project is an interesting topic for future research, since the retention of
autochtonous estuarine populations within or around the MTZ is fundamental to the understanding of the ecological processes in this area.
The occurrence of hyperbenthos along the transects in the three estuaries (Fockedey &
Mees, this report) bears little correlation with the environmental factors measured, except for
the strong avoidance of low oxygen levels in the Schelde. In the Gironde the hyperbenthos
has its peak occurrence in the most upstream station, well ahead of the MTZ. In the Elbe it is
slightly upstream of the MTZ, whereas in the Schelde it is downstream of the MTZ. Densities
in the Schelde are lower than in the other estuaries, but considering the temporal variability
known from this estuary it is unclear whether this difference is significant.
Higher trophic levels: food biology
Several studies have been directed towards the influence of abiotic and biotic factors on the
food uptake by zooplankton and hyperbenthos.
Fockedey & Mees (this report) describe the feeding behaviour of Neomysis integer as selec-
tive towards zooplankton. Besides zooplankton, the animals have (sometimes large) amounts
of non-specified detritus in the guts. Qualitative chemical analysis of the material is currently
going on. In future descriptions of the dynamics of hyperbenthic animals in the estuaries, a
closer connection with the zooplankton stocks is certainly a point of interest. The problem is
important since previous studies have shown that hyperbenthic animals may provide an importantlink between primary production and mineralization processes in the'estuary and fish
productivity.
Tackx et al. (this report) performed experiments on selective feeding by Eurytemora affinis
oniphytoplankton. They showed that the copepod has a significant preference for phytoplank-
ton even in the presence of relatively high concentrations of (partly organic) suspended
matter The species can therefore not be described as a non-selective filter feeder, taking
phytoplankton and detrital food in the proportions offered to them.
Gut fluorescence_measurements in many samples differing in chlorophyll content and suspended matter (Gasparini et al., this report) confirm these observations, but define more
clearly the conditions under which selectivity may operate. As shown in Fig. 25, no correla-
tion was found between gut fluorescence and chlorophyll concentration in the water. A clear
negative_correlationwasobserved between gut fluorescence and suspended matter concentration. Egg production rates are lower at higher suspended matter concentrations, but the
decrease is less pronounced than that of gut fluorescence, showing that the animals com-
pensate for the lack of phytoplankton food by a higher intake of non-fluorescent food particles.
A few simple model calculations, using observed data on POC, SPM and CHLa in the MATURE database (and, thus, taking into account the field correlations between these variables), explore the possible impact of selectivity of the copepods. Assuming that the species
feeds non-selectively, uptake of chlorophyll-a would be proportional to the fraction chl-a in
the total POC. If total uptake rate of POC is described with a Monod function, gut fluorescence would be given by:
GF = a * POC/(K+POC) * chla/POC
where GF is gut fluorescence recalculated as a dimensionless (<?C/gC) ratio, a js a propor
tionality constant, POC is total particulate organic carbon (mg.t ) and chla is chlorophyll a
concentration recalculated to mg C.l-1 with a C/chl-a ratio of 50.
Predicted gut fluorescence under these assumptions is given as a function of SPM and of
chlorophyll^a in Fig. 26a,b (where a=0.04, K=4 mg.l-1);A clear relationship with Chl-a is
shown, contrary to the observations.
As an alternative, one could assume that the copepods' clearance rate (volume swept clear
per individual per unit time) is inversely proportional to chlorophylt-a concentration (animals
look for phytoplankton food in larger volumes when this is rare) and inversely proportional to
POC concentrations (high concentrations demanding more handling time for the particles).
Further, one could assume that the animals take all phytoplankton food from the cleared volume, but only a small proportion of the POC. Gut fluorescence would then be given by:
GF a * chla *
*
K.c + chla
K.+nPOC
p
where Kc (mg C I'1) and Kp (mg C F1) are saturation constants preventing the clearance rates
from becoming infinite, chla Is the phytoplankton concentration recalcutated to mg C I'1,
nPOC is the non-phytoplankton POC (mg C I-1), GF is a non-dimensionalised ratio and a (I
(mg C)~1) is a proportionality constant.
Figs. 26c,d (where a=0.0275 I (mg C)'1, Kc= 0.005 mg/l and Kp
-1
1 mg F) show that this
model cannot be refuted by the data. Both the dependence of gut fluorescence on SPM, and
the non-dependence on chlorophyll-a concentration, are reproduced. The constants of the
model are chosen such that the order of magnitude of predicted gut fluorescence corresponds to the data in Fig. 25. Moreover, taking into account the gut passage time used by
Irigoien et al.(), the clearance rate for phytoplankton was calculated without any other change
to the model than recatculation of units. Its value is in the range measured by Tackx et al.
(this report). The dependencies of phytoplankton clearance rate on phytoplankton biovolume
(shown in Fig. 26e), total particulates volume and ratio phytoplankton/total particutates (not
shown here) faithfully reproduce the figures given by Tackx et at. (this report). These features
corroborate the simple model proposed here.
This model can be interpreted such that the animals take all phytoplankton food particles
from the volume of water cleared per unit of time, and complete their feeding by uptake of a
relatively small proportion of the non-phytoplankton POC in this volume. Assuming that the
maximal gut content (calculated as adimensionless ratio) is in the order of 0.02, one can
calculate the non-phytoplankton gut content as 0.02-GF. Thus an apparent selectivity can be
calculated as the ratio between relative concentration of phytoplankton POC in the gut over
relative concentration of phytoplankton carbon in the water, (relative concentration being
taken_as concentration of phytoptankton divided by total POC, either in the gut or in the water).The apparent selectivity is almost independent of POC, but is a strong negative function
of chlorophyll-a concentration Under this model, we expect the highest apparent selectivity
indices when both chlorophytl-a and POC are low, but an almost equally 'high selectivity at
low chlorophyll-a and high POC.
ELBE
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GIRONDE
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Fig. 24. Distribution of developmental stages of Eurytemora affinis along the axis of the estuaries Elbe, Schelde
and Girpnde. 'Model' values are calculated taking into account size-dependent swimming speeds of the animals.
From: Gasparini et al., this report.
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A
List of sub-reports of the partners.
Herman, P.M.J. and B.J. de Hoop. The MATURE database.
Eisma, D. In-situ measurements of suspended matter floc size and settling velocity in the
estuaries of the Elbe, Schelde and Gironde.
Brockmann, U.H., F. Edelkraut, T. Raabe, K.H. Viehweger. MATURE. Project Organic Matter
and Nutrients. Final report.
Cancino, L. & R. Neves. Mature. Final report (hydrodynamical modelling).
Pfeiffer, K. Hydrographic measurements and numerical modelling contributions compiles and
issued by ZMK.
Muylaert, K. & K. Sabbe. Structure and spatial distribution of spring phytoplankton assemblages in and around the maximum turbidity zone of estuaries: a comparison between the
estuaries of the Elbe (Germany), the Schelde fThe Netherlands/Belgium) and the Gironde
(France).
Kromkamp, J., J. Peene, P. van Rijswijk, P. van Breugel & N. Goosen. A comparison of
phytoplankton primary production in three turbid, European estuaries: The Elbe, Westerschelde and Gironde.
Hernandez, A., N. Raymond, J. Castel & P. Caumette. Spring distribution of bacterioplankton
and production-predation relationships in the Gironde, Elbe and Schelde estuaries.
Bonin, P., N. Raymond, A. Hernandez & P. Caumette. Interaction between nitrate freshwater
input, particulate organic matter and denitrification in the maximum turbidity zone in three
european estuaries: Etbe, Gironde, Scheldt.
Raymond, N., A. Hermandez & P. Caumette. Sulfate reduction in estuarine sediments.
Tackx, M.L.M., R. Billones, L. Zhu, N. Daro & A. Ftachier. Feeding of zooplankton on microplankton and detritus.
Irigoien, X. and J. Castel. A simple mathematical model to estimate net primary production in
the Gironde estuary.
Irigoien, X., D. Burdloff, J. Castel & H. Etcheber. The role of the maximum turbidity zone
controlling phytoplankton production and organic matter quality.
Irigoien, X., J. Castel & S. Gasparini. Gut clearance rate as predictor of food limitation situations. Application to two estuarine copepods, Acartia bitilosa and Eurytemora affinis.
Gasparini S., J. Castel & X. Irigoien. Egg production, growth rate and nutritional activity of
the estuarine copepod Eurytemora affinis in the MTZ.
Gasparini, S.. J. Castel & X. Irigoien. Longitudinal distribution of the copepod Eurytemora
affinis (Poppe) in 3 european estuaries.
Fockedey, N. & J. Mees. Theme 4: Higher trophic levels, partim hyperbenthos.
Jak, R G. & K.J.M. Kramer. Adjustment of low oxygen regimes and effects of hypoxia on an
estuarine plankton community in enclosures.