IN SHALLOW COASTAL WATERS :
-
CAN DILUTION EXPERIMENTS REFLECT IN-
SITU GROWTH PROCESSES?
Abstract
A sequence of nine dilution experiments was conducted from March until October 2004 to estimate phytoplankton growth rates as well as zooplankton grazing impact in the northern Wadden Sea. Phytoplankton growth rates varied between 0 - 1.1 d-1, with highest values in March (0.4 d-1) and August (1.1 d-1). Zooplankton grazing showed a seasonal cycle: From March until the end of April no grazing was observed. It started in May (0.66 d-1) and increased until August (1.22 d-1). In October zooplankton grazing was low again (0.17 d-1). Phytoplankton growth rates measured in natural waters were compared to data from dilution experiments. Comparisons were made before and after adapting experimental net growth rates to the in-situ water column light field. Adapted growth rates correlated significantly with in-situ growth rates but slightly overestimated the natural biomass development on average by 4%. This overestimation was similar to reported phytoplankton assimilation by the benthos of the Wadden Sea. Wee conclude, that data obtained by dilution experiments can be used for short-term predictions on in-situ phytoplankton dynamics.
From summer until autumn a high proportion of phytoplankton biomass remained in pelagic carbon pathways by zooplankton grazing, while the diatom spring bloom apparently was not controlled by pelagic grazing. As the observed phytoplankton maximum was lower than could be expected from the dissolved nutrient supply, it is further suggested that during spring benthic assimilation was the main sink for phytoplankton biomass.
Introduction
For the Wadden Sea several time series provide data on temporal and spatial phytoplankton biomass distribution (POREMBA ET AL. 1999, TILLMANN ET AL. 2001, CADÉE & HEGEMAN 2002, VAN BEUSEKOM & DE JONGE 2002) Field studies on grazing impact of the copepod Temora longicornis on Phaeocystis globosa were conducted in the Marsdiep by HANSEN & VAN BOEKEL (1991) and HANSEN (1995). KLEIN BRETELER
& KOSKI (2003) tested a similar low impact of Temora nauplii on Phaeocystis globosa in experiments. TILLMANN & HESSE (1998) made estimates on the grazing impact of heterotrophic microplankton the northern German Wadden from phytoplankton biomass and microplankton carbon data. But only little information is available about the annual cycle of net phytoplankton growth rates (compare Figure 1) and the zooplankton community grazing impact.
At least three processes affect phytoplankton biomass development in a shallow coastal ecosystems (1) phytoplankton growth, (2) zooplankton grazing and (3) benthic assimilation of phytoplankton.
P Bm time 1
P Bm time 2
Sediment / benthic filter feeders Zooplankton
app.growth
Figure 1. Schematic diagram showing 3 processes affecting in-situ phytoplankton biomass (= P Bm) development in a shallow coastal system. Zooplankton grazing is shown by the grey arrow, the brown arrow shows the influence of benthic grazing, and the green arrow the apparent phytoplankton growth. The net growth rate of phytoplankton is the sum of these 3 processes.
The latter includes benthic suspension feeders as well as sediment percolation, both are of high impact in the Wadden Sea (DAME ET AL. 1998, DE BEER ET AL. 1998) and assumed to filter the entire water column within a few weeks (VERWEY 1952).
Therefore, information on changes in phytoplankton biomass (termed as apparent phytoplankton growth rates) do not give any information of the aforementioned loss factors, and measuring net phytoplankton community growth rates in the field is hardly possible. To separate these processes, sea water can be incubated in experiments thereby excluding any benthic impact. An advanced experimental method is the dilution method developed by LANDRY & HASSETT (1982). It allows to separate apparent and net population growth rates since also the grazing rate by zooplankton is quantified. This method has been carried out since two decades to estimate the impact of zooplankton grazing in oceanic and coastal waters (CALBET & LANDRY 2004). Still the extrapolation of experimental results from this method to in-situ processes is frequently discussed:
Critics include: (1) The possibility of artefacts by changed grazer population dynamics due to dilution and incubation effects (DOLAN ET AL. 2000). (2) The direct proportionality between grazing and prey density (GALLEGOS 1989, LANDRY ET AL. 1995) and for this purpose several mathematical models –assuming different response of the grazing impact to the dilution process- were used to evaluate experimental results (EVANS & PARANJAPE 1992, REDDEN ET AL. 2002, LAWS 2003). Indications that the dilution method gives meaningful growth rates were given by MOIGIS & GOCKE (2003) who showed that primary production estimates based on dilution experiments gave similar results as for the 14C method.
In this study a sequence of 9 dilution experiments was conducted between March and October 2004 in the Northern Wadden Sea. Phytoplankton growth rates and zooplankton grazing impact was quantified. Experimental data were compared to in-situ observed phytoplankton growth. The in-situ phytoplankton biomass was measured twice weekly as part of an ongoing time series.
It will be shown that during a Phaeocystis globosa bloom in late spring and in summer zooplankton grazing was of high impact. After adapting experimental net growth rates to the in-situ water column light field, experimental resulted apparent phytoplankton growth rates were in good agreement with in-situ measured phytoplankton biomass development. The phytoplankton loss due to zooplankton grazing and benthic assimilation is compared.
Methods
Study Site
The study was conducted in the List tidal basin, a 404 km2 semi-enclosed bight in the northern Wadden Sea. The basin is connected to the open North Sea by a single tidal inlet (Fig. 2).
To the north and to the south the basin is closed by two dams connecting the Island of Rømø and the Island of Sylt to the mainland. Eastwards the basin closed by the mainland. Water volume at the mean tidal level is about 845 mio m3. The mean water depth is 2 m but reaches up to 40 meter in the main tidal channel. The water column is homogenously mixed. Tides are semidiurnal; the mean tidal range is about two meter. During
low tide, about 50% of the area is exposed to air. About 95 % of the area is covered by sandy sediments. Salinity ranges between 27-32 psu, temperature between -1°C in winter and 22°C in summer. Detailed descriptions for the area are given by GÄTJE &
REISE (1998).
Seawater Sampling and water analysis
Seawater was sampled by ship at a routine station from 1m depth using a 5 L Niskin bottle. For dilution experiments samples were collected in large carboys. The in-situ phytoplankton biomass was determined as chlorophyll a according to JEFFREY &
HUMPHREY (1976) each time dilution experiments were conducted and subsequently after 2-7 days (mean = 4 days). These measurements were part of the Sylt long term time series.
Light attenuation
Light attenuation in the water column was measured with a LI-COR-Sensor (LI-COR;
LI-193) attached to a CTD measuring 4 data sets s-1. Lowering speed was ~ 10cm s-1. The vertical attenuation coefficient (a) was calculated using
)
0exp( ad
I
I
d = − [4]whereby Id is the light intensity at a given depth (d) and I0 reflects the surface light intensity. The attenuation coefficient was used for calculating a vertical light profile for the mean daily (= light hours) surface irradiance. As surface irradiance, global radiation measurements from the Deutsche Wetterdienst Station List/ Sylt were taken, assuming a conversion factor of 1 W m-2 = 4.14 µM photons m-2 s-1 (TILLMANN ET AL. 2001).
Figure 1. The study site.
Sampling sites are indicated as black dots.
List tidal basin 55°N / 8.40°E
10 km List tidal
basin 55°N / 8.40°E
10 km
Primary production
Primary production was measured as photosynthesis-versus-irradiance (P/I) -curves with the light- and dark bottle oxygen method under controlled laboratory conditions.
Incubation bottles were filled bubble-free directly on shipboard through a silicon tube with surface seawater from the Niskin-bottles. Initial oxygen content was measured in triplicate. In the lab eight bottles were wrapped with different neutral density light filters and two bottles with aluminium foil and attached to rotating wheel in a water bath with in-situ temperature. Incubation irradiance was provided by a cool light fluorescent emitter (Norka). Before each experiment, irradiance was measured inside the incubations bottles using a mini-light sensor (LIC-COR) and re-adjusted if necessary.
Incubation irradiance was between 70 and 780 µmol photons s-1 m-2. Incubation time was between 4 and 7 hours depending on the expected increase of oxygen concentration. Oxygen was measured with the Winkler-technique (GRASSHOFF ET AL. 1983) using an automatic titration apparatus (Metrohm Multi-Dosimat 645). With this method an accuracy of ± 0.45 µmol O2 L-1 can be reached. Respiration rates were determined from dark bottles and used to calculate gross primary production. Results were converted from oxygen to carbon assuming a conversion factor of 1.3 (ASMUS ET AL. 1998). P/I-curves were fitted according to PLATT ET AL. (1980) using STATISTICA
6.0 (STATSOFT). From the fit, the maximum photosynthesis rate Pmax (mg C mg Chl-1 h
-1), the initial slope of the curve α (mg C mg Chl-1 h-1(µmol photons m-2 s-1)-1) and the saturation parameter Ek (Pmax/ α) were calculated. Primary production in experimental water depth (= 0.55m), was related to water column primary production: Depth integrated primary production was calculated from P/I-curves and vertical light profiles (Sakshaug et al. 1997). PP55 was defined as the mean primary production rate for experimental water depth and PP200 was defined as the mean primary production rate for the entire water column (= 2 m). From the ratio of PP200/PP55 a conversion factor fd was calculated as shown below.
Grazing experiments
A series of 9 dilution experiments was carried out from March to October 2004 according to LANDRY & HASSET (1982) without pre-screening the undiluted fraction to maintain nearly natural conditions. Seawater for dilution was filtered through a Whatman cellulose-nitrate filter with 0.45 µm pore size. Four dilution steps were prepared in large tubs. The ratio of seawater/ unfiltered seawater was 1:3, 1:1, 3:1, 1:0.
No nutrients were added. Each dilution step was carried out in duplicate. The dilution
experiments were carried out in 2.5 L polycarbonate bottles. The bottles were attached to a wheel rotating in a water basin. The bottles rotated between a depth of 5- 55cm.
The experiments were carried out outside for 24 h at situ irradiance and controlled in-situ temperature conditions. Incubations started between 10 a.m. and 11 a.m. in the morning. Chlorophyll a was determined before and after the incubation process in four replicates for each dilution step.
Dilution experiments allow to determine three variables describing marine plankton dynamics: apparent growth rate (µ), net growth rate (k) reflecting growth without any grazing impact, and the grazing rate (g), where
µ = k - g [1a]
The rate of change of phytoplankton biomass (dB) over time (dt) is expressed by
gB kB dt
dB/ = − [1b]
biomass increase follows equitation [2]
( )
[
k g t]
B
Bt = 0exp − [2]
and changes of phytoplankton biomass were calculated as growth rates according equation [3]
[
ln( / 0)]
−1= Bt B t
µ [3]
The grazing rate (g) is calculated as the slope of a regression line for apparent growth rates against dilution steps, while the net growth rate (k) is reflected by the intersection of the regression line with the y-axis (see LANDRY & HASSETT 1982). All mentioned abbreviations are summarised in Table 1.
Table1. Abbreviations used in this chapter
g grazing rate of heterotrophic plankton
k55 theoretical growth rate of phytoplankton estimated by dilution experiments given a mean water depth of 55 cm
k200 theoretical growth rate as calculated for the mean water depth of 200 cm fd conversion factor between k55 and k200
t Time interval between t1-t0 (days) B0is Phytoplankton biomass in-situ at time t0
Btis Phytoplankton biomass in-situ at time t
Btsim Phytoplankton biomass at time tsimulated without using f for depth correction (=Eq. [2])
Btsim(f) Phytoplankton biomass at time tsimulated by including f for depth correction (=Eq. [4])
µis Apparent in-situ phytoplankton growth rate
µ Apparent growth rate, estimated by dilution experiments µf Apparent growth rate, calculated from (k55*f)-g
Pmax Parameter describing the maximum photosynthetic activity Ek Irradiance at which Pmax is reached
a light attenuation coefficient
PP55 In-situ primary production, estimated for experimental water depth of 55 cm PP200 In-situ primary production, estimated for the mean water depth of the study site
(200 cm)
Comparison of experimental data with in-situ phytoplankton development
In this study, the experimental set of the incubations reflects in-situ surface irradiance at 5- 55 cm water depth but not of the entire in-situ mean water column (200 cm). Thus, experimental results are likely to overestimate in-situ phytoplankton growth rates.
Converting ‘incubation growth rates’ into ‘in-situ growth rates’ may significantly improve the comparison with natural water column processes.
For the proceeding of this study, growth rates were assumed to be proportional to primary production the latter being a function of irradiance and water depth. This implies a simplifying assumption of a constant Chl/C ratio like it is applied in balanced growth models (MADDEN &KEMP 1996, SOETAERT & HERMAN 2001). Thus, the ratio of growth rates within experimental water depth (=5- 55 cm) and the entire water column (=0- 200 cm) (k200/ k55) equals the ratio of primary production within experimental waterdepth and primary production (PP200/ PP55) within the entire water column. This ratio can be expressed as fd:
fd
k k PP
PP20055 ≅ 20055 = [3a]
fd scales the experimental growth rates to the in situ growth rates as shown in equation [3b]
k200= k55 fd [3b]
Including equation [3b] into equation [2] results in equation [4], describing the in-situ biomass growth Btsim(f) using a growth rate µf scaled to the in situ water column irradiance.
[
f k g t]
B
Btsim(f) = 0exp( d 55− ) [4]
Zooplankton grazing is related to prey density (LANDRY & HASSET 1982). In this study zooplankton and phytoplankton are assumed to be equally distributed throughout a mixed water column. Equation [2] and equation [4] were used to calculate a short term biomass prediction (Btsim and Btsim(f)). Results were compared to in-situ measured phytoplankton biomass after time t ( = Btis ).
Results
Annual dynamics of in-situ phytoplankton biomass
From January until March chlorophyll a concentrations remained between 2-8 µg L-1.
During a diatom spring bloom from late March until April, chlorophyll a concentrations increased up to 18 µg L-1. A Phaeocystis globosa bloom followed from May to June, with chlorophyll a concentrations up to 24 µg L-1. In summer and autumn chlorophyll a concentrations remained between 2-8 µg L-1. Figure 3 shows the annual dynamics of phytoplankton biomass.
0 5 10 15 20 25 30 35 40 45
01-04 02-04
03-04 04-04
05-04 06-04
07-04 08-04
09-04 10-04
11-04 12-04 month Chlorophyll a (µg L-1 )
Exp 1 Exp 2
Exp 3
Exp 4
Exp 5 Exp 6
Exp 7 Exp 8
Exp 9 Diatom
bloom
Phaeocystis bloom
Figure 3. Annual cycle of chlorophyll a concentrations. Dates at which dilution experiments were carried out are indicated.
Grazing experiments
Zooplankton grazing rates (g) showed a seasonal pattern: From March until May no zooplankton grazing occurred. Grazing started in the middle of May (0.66 d-1) with a peak in August (1.22 d-1) and slowly decreased until October (0.23 d-1) (Fig. 4 & 5 &
Table 2). From May until the beginning of August grazing rates (Table 2) correspond to a loss of phytoplankton standing crop between approximately 51-122% per day. The highest value of 122% was observed in May in EXP 6 at the end of a Phaeocystis globosa bloom with decaying Phaeocystis cells (Fig. 4).
17/03/04 Exp_1
y = 0.0353x + 0.4685
0.0 0.2 0.4 0.6 0.8 1.0
0 0.4 0.8 1.2
29/03/04 Exp_2
y = -0.0098x + 0.2075
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.4 0.8 1.2
07/04/04 Exp_3 y = -0.0089x + 0.192
0.0 0.2 0.4 0.6 0.8 1.0
0 0.4 0.8 1.2
15/04/04 Exp_4
y = -0.0239x + 0.1786
0.0 0.2 0.4 0.6 0.8 1.0
0 0.4 0.8 1.2
29/04/04 Exp_5
y = -0.0058x + 0.0959
0.0 0.2 0.4 0.6 0.8 1.0
0 0.4 0.8 1.2
17/05/04 Exp_6 y = -0.6565x - 0.0492
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2
0 0.4 0.8 1.2
01/06/04 Exp_7
y = -0.5126x + 0.5712
0.0 0.2 0.4 0.6 0.8 1.0
0 0.4 0.8 1.2
05/08/04 Exp_8
y = -1.2285x + 1.0829 -0.4
-0.2 0.0 0.2 0.4 0.6 0.8 1.0
0 0.4 0.8 1.2
14/10/04 Exp_9
y = -0.1685x + 0.2666
0.0 0.2 0.4 0.6 0.8 1.0
0 0.4 0.8 1.2
Figure 4. Results of dilution experiments. Y-axes show the apparent growth (µ), the X-axes represent the fraction of undiluted seawater. The slope of regression lines reflects the grazing rate (g) and the intercept with the Y-axis is the net growth rate k.
Table 2. Net growth rates (k) apparent growth rates (µ) and grazing rates (g) obtained from dilution experiments.
A seasonal pattern could also be observed for phytoplankton growth rates. The highest net growth rates (k) were measured in March (0.4 d-1) and in August (1.1 d-1). Lowest growth rates occurred at the end of April (0.1 d-1) and in May in EXP 6, when the negative growth rate (-0.05 d-1) indicated a decay of cells at the end of the Phaeocystis globosa bloom. Since this strong biomass decrease was unique short-term process, results from EXP 6 were not used for biomass simulations.
-0.8 -0.4 0.0 0.4 0.8 1.2
0 2-04
0 3-04
0 4-04
0 5-04
0 6-04
0 7-04
0 8-04
0 9-04
1 0-04
growth rate (d-1 ) net growth rate apparent growth rate
Figure 5. Annual cycling of phytoplankton net growth rates (k) and apparent growth rates (µ), dates of conducting experiments were shown as points. Zooplankton grazing by is indirectly shown as the difference between both lines.
Date name k (d-1) g (d-1) µ (d-1)
18.03.04 EXP_1 0.469 -0.035 0.504
29.03.04 EXP_2 0.208 0.010 0.198
07.04.04 EXP_3 0.192 0.009 0.183
15.04.04 EXP_4 0.179 0.024 0.155
29.04.04 EXP_5 0.096 0.006 0.090
17.05.04 EXP_6 0.049 0.657 -0.608
01.06.04 EXP_7 0.571 0.513 0.058
05.08.04 EXP_8 1.083 1.229 -0.146
14.10.04 EXP_9 0.267 0.169 0.098
Light attenuation and primary production measurements for estimating a conversion factor for depth dependent growth rates
Parameters describing the P/I-curves (Pmax, Ek) and the attenuation coefficient in the field are given in Table 3. The attenuation coefficient varied between 0.31 and 3.47 m-1. Pmax showed highest values during the Phaeocystis bloom (13.5 mg C mg Chla-1 h-1) and lowest (3.3 mg C mg Chla-1 h-1) in autumn.
Table 3. Light attenuation coefficient (a) at the sampling site and the mean daily surface irradiance during the dilution experiments. Mean primary production was calculated for a waterdepth of 0.55 meter (PP55) and 2.0 meter (P200) using the data in column 3-6. fd is the ration between PP55 and P200.
in-situ values
P/I-parameters Mean primary production
PP200/
PP55 No date a
m-1
mean surface irradiance µmol m-2s-1
Pmax mgC mg Chla-1 h-1
Ek
µmol photons
m-2 s-1
PP55 mgC mg Chla h L-1
PP200 mgC mg Chla h L-1
fd
1 18.03.04 3.47 455 4.26 195 2.50 0.87 0.35
2 29.03.04 2.03 529 5.69 142 5.06 2.56 0.51
3 07.04.04 2.26 245 6.82 188 3.44 1.41 0.41
4 15.04.04 1.63 623 8.85 213 6.04 3.63 0.60
5 29.04.04 0.79 177 5.14 192 1.66 1.14 0.68
6 17.05.04 1.07 514 12.01 149 7.11 6.21 0.87
7 01.06.04 1.35 629 13.50 107 11.63 6.24 0.53
8 05.08.04 0.50 554 12.17 360 5.46 5.05 0.93
9 14.10.04 1.82 157 3.26 213 1.19 0.53 0.45
The ratio of primary production estimated for 200cm waterdepth and for experimental water depth (PP200 /PP55), expressed by the conversion factor (fd), varied between 0.34 and 0.89 (Table 3). The light attenuation coefficient and fd were strongly correlated (r2= 0.81), as shown in Figure 6.
y = -0.2243x + 0.9976 R2 = 0.8126
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
0 0.4 0.8 1.2 1.6 2 2.4 2.8 3.2 3.6 4
light attenuation coefficient a [m -1]
conversion factor fd
Figure 6. The relation between fd (= PP200/ PP55) and the light attenuation coefficient.
Comparison of data from dilution experiments with in-situ observed phytoplankton biomass development
Phytoplankton biomass predictions were made on the basis of growth rates from dilution experiments using equation [2] and equation [4]. The initial biomass was measured at date t0, Predictions were made for the time interval t (mean= 4 days). The results are shown in Table 4. Simulated biomass concentrations were compared by linear regression to in-situ measured phytoplankton biomass concentrations.
Experimental growth rates scaled to water column irradiance (using Eq. [4]) correlated much more with the in-situ growth rates than raw experimental data.
Table 4. Observed and simulated phytoplankton biomass concentrations.
initial in-situ biomass
growth rate obtained from dilution experiments and calculated biomass following
Eq. [2] and Eq. [4]
Date t0
days
t B0is
chl a µg L-1
Btis
chl a µg L-1
µ
(d-1)
Btsim
chl a µgL-1
µsim(f) d-1
Btsim(f) chl a µgL-1 18.03.04
29.03.04 07.04.04 15.04.04 29.04.04 01.06.04 05.08.04 14.10.04
4 3 6 4 4 2 4 4
5.9 5.62 12.95 15.37 7.34 10.05
2.88 6.82
8.99 7.65 17.77 11.19 8.85 10.59
2.30 3.75
0.504 0.198 0.183 0.155 0.090 0.058 -1.46 0.098
44.26 10.17 38.85 28.57 10.52 11.29 1.61 10.11
0.199 0.096 0.070 0.083 0.059 0.102 -0.221 -0.048
13.09 7.50 19.69 21.46 9.31 8.20 1.19 5.62
following Eq. [2] y = 2.2678x - 0.7278 R2 = 0.4756
0 10 20 30 40 50
0 5 10 15 20
biomass observed (chla µg L-1)
biomass simulated (chla µg L-1)
following Eq. [4] y = 1.2205x - 0.0888 R2 = 0.7004
0 10 20 30 40 50
0 5 10 15 20
biomass observed (chla µg L-1)
biomass simulated (chla µg L-1)
Figure 7. In-situ observed phytoplankton biomass development compared with the biomass development estimated from the dilution experiments. a) following equation [2]; b) following equation [4]. The time interval over which the change in biomass was observed and estimated was on average 4 days. Results from Exp 6 were not included.
Biomass simulation by using equation [2] (= experimental growth rates without conversion) (Fig. 7a) are on average 130% higher than the in-situ biomass development during time-interval t (mean = 4 days). Simulated biomass is significantly correlated with in-situ biomass (r2 = 0.48; Fig 7a). Experimental growth rates scaled to the water column light conditions on the basis of Equation [4] gave better results (Fig. 7b): Still they overestimated the biomass development within time interval t with 23 % but the correlation with in-situ rates is much better (r2 = 0.70).
The biomass overestimation of 130% and 22% for time t (mean =4 days) correspond to daily biomass overestimations of ~ 33% and ~ 6%.
a)
b)
Discussion
Annual cycle of growth and grazing rates
Mostly species specific data on zooplankton grazing in the Wadden Sea exist (e.g.
HANSEN & VAN BOEKEL 1991, HANSEN 1995) and none of them cover the entire growth season. TILLMANN & HESSE (1998) investigated the seasonal distribution and biomass of heterotrophic microplankton in the northern German Wadden Sea. On the basis of phytoplankton biomass and microplankton carbon data, they made rough estimates for the grazing impact of heterotrophic microplankton: They found heterotrophic microplankton to have the potential to daily graze between 12% and 50 % of phytoplankton standing stock in spring and 16- 32% in summer. Results of our study indicate higher grazing impact between late spring and summer than estimated by TILLMANN & HESSE (1998).
From March until the end of April no grazing impact was observed and a diatom spring bloom developed without significant loss of biomass to zooplankton (Fig. 4 & Fig. 5).
Since no grazing impact was observed at the end of the bloom, spring bloom biomass may have entered to the benthic layer as aggregates (SMETACEK 1985) and became a source of the benthic food web by filtering activity of permeable sediments and benthic suspension feeders as e.g. mussels and lugworms. A minor grazing impact on a spring bloom was also reported for the Skagerrak by MAAR ET AL. (2002). Two weeks later, during a Phaeocystis globosa bloom, zooplankton grazing had a high impact and about 50-65 % of the Phaeocystis globosa standing crop was grazed by zooplankton. In the mid of May grazing exceeded phytoplankton growth and a high proportion of the Phaeocystis biomass remained in the planktonic food web. The role of Phaeocystis globosa in the food web is still unclear: Studies in the Southern North Sea showed that Phaeocystis globosa was the major component of the diet of calanoid copepods (HAMM
& ROUSSEAU 2003). In contrast, HANSEN (1995) showed for the Marsdiep (Western Dutch Wadden Sea), that the dominant copepod Temora longicornis preferentially fed on microzooplankton and therefore enhanced Phaeocystis blooms. Grazing by microzooplankton is assumed to shift the community composition from smaller to larger Phaeocystis cells (STELFOX-WIDDICOMBE ET AL. 2004).In our study the highest phytoplankton net growth rate was measured in August with almost one doubling of phytoplankton biomass per day. But since the grazing impact was in the same range as growth rates were, no accumulation of algal biomass occurred.
Especially the experiments during summer demonstrate the importance of information about grazing impact for understanding the organic matter cycling, since data on phytoplankton biomass alone provide no information about the high carbon turn over in the pelagic ecosystem during that period.
Are in-situ growth processes reflected by experimental data?
Since in-situ measurements of net phytoplankton growth rates and zooplankton grazing impact are not possible, the indirect determination via dilution experiments is an alternative method to get information about these processes. In this study, the grazing rates and phytoplankton growth rates found in dilution experiments were compared with data observed in the field. Experimentally derived estimates of the in-situ phytoplankton development agreed well with actual in-situ phytoplankton development after adapting experimental growth rates to water column irradiance. The conversion factor fd implicates the physiological response of the respective phytoplankton community to the water column light field. Also the respective light field strongly influences fd as shown by the covariance between fd and the light attenuation coefficient.
Since dilution experiments exclude the impact of benthic assimilation, the resulted overestimation of phytoplankton biomass is in agreement with a certain loss of biomass to the benthos as often observed in shallow coastal sites (DAME ET AL. 1998):
Benthic assimilation includes two sub-processes (1) active benthic filter feeding, (2) filtering by permeable sediments. (1) Benthic filter feeders are known to have a high impact on the pelagic system particularly in estuarine and coastal areas (DAME ET AL. 1998, RIJSGAARD ET AL. 2004). Mussel beds, oyster reefs, endobenthic cockles and polychaetes are dominant benthic inhabitants of the study site (REISE & LACKSCHEWITZ
1998). In the List tidal basin mussel beds as well as Arenicola sand flats are sinks for large amounts of particulate organic carbon including phytoplankton biomass (ASMUS ET AL. 1998 ) the uptake being positively related to the phytoplankton concentration (ASMUS & ASMUS 1991). For the Wadden Sea, benthic filter feeders are assumed to filter the whole water volume within a few weeks (VERWEY 1952, POSTMA 1981). This is equivalent to a daily filtering rate of 7-14 % of the whole water body. (2) DE BEER ET AL. (2005) estimated that sandy sediment in the Wadden Sea may filter up to 160-500 L m-2 d-1. As a result the water of the List tidal basin would be filtered through the
sediment within 4-12 days, which corresponds to daily sediment filtering rates of 8-25
% of the mean water body of 2 m.
From the above it is concluded that the overestimation of phytoplankton biomass increase by about 6% on the basis of dilution experiments is in a good agreement with potential loss of phytoplankton biomass to the benthic of 7-25 %per day. Together these results show, that at least in our area data from dilution experiments reflect growth and grazing rates as they occur in natural waters.
Relative importance of zooplankton grazing and benthic assimilation in the seasonal cycle of phytoplankton
Zooplankton grazing and benthic assimilation are two major sinks for phytoplankton biomass in the List tidal basin. A comparison of pelagic grazing rates observed in this study and benthic assimilation rates (values from literature, in a range between 7-20%) gives the following seasonal pattern (Figure 9):
In spring, no zooplankton grazing was observed and benthic assimilation seemed to be the main sink for the diatom spring bloom. Both, sedimentation after silicate-limitation of the spring bloom and active filtration contribute to this transfer. From summer until autumn the proportion of pelagic grazing was higher than the proportion of benthic assimilation, indicating most phytoplankton biomass was retained within the pelagic food web. This estimate does not include information on spatial and temporal variability of benthic assimilation: The activity of benthic filter feeders changes with season and environmental influences like temperature, tidal cycle, and suspended matter concentrations and quality (WIDDOWS ET AL. 1979, DAME ET AL. 1980, BANE &
NEWELL 1993, BAYNE ET AL. 1993, HEIP ET AL. 1995). But despite these uncertainties, results of this study provide a first hint on the approximate range of phytoplankton biomass pathways between spring and autumn.
PBm March
PBm
peak in April
Sediment / benthic filter feeders
PBm end of
April
Phaeocystis globosa bloom
PBm beginning of
May
PBm
peak in June
Sediment / benthic filter feeders Zooplankton
PBm June
Summer/ Autumn
PBm October
Sediment / benthic filter feeders Zooplankton
PBm August
PBm October
Figure 9. The impact of zooplankton grazing (grey arrow), apparent phytoplankton growth (green arrow) and benthic assimilation (brown arrow) on phytoplankton biomass (=PBm) development. Net growth rate result from the sum of the 3 arrows. Since no seasonal-specific information is available on benthic assimilation by the sediment/zoobenthos, this feature is included into the diagram as a constant factor.
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