modelling the impact of zooplankton grazing on phytoplankton biomass during a dinoflagellate bloom...

Ecological Engineering 16 (2001) 373–394

Modelling the impact of zooplankton grazing onphytoplankton biomass during a dinoflagellate bloom in the

Swan River Estuary, Western Australia

Sandra L. Griffin a,*, Michael Herzfeld b, David P. Hamilton b

a School of En6ironmental Biology, Curtin Uni6ersity of Technology, GPO Box U1987, Perth, 6102, WA, Australiab Department of En6ironmental Engineering, Centre for Water Research, The Uni6ersity of Western Australia, Nedlands, 6907,

WA, Australia

Accepted 12 July 2000

Abstract

Ingestion rates of zooplankton were measured in the Swan River Estuary and in the laboratory. These data wereused together with data from the literature on phytoplankton and zooplankton physiological parameters, to provideinput data to a model of phytoplankton and zooplankton dynamics in the Swan River Estuary. The model also usedmeasured environmental data (nutrient concentrations, light, water temperature and salinity) to simulate phytoplank-ton biomass (differentiated as four groups) and zooplankton biomass (differentiated as three size classes) at a site inthe Swan River Estuary. Zooplankton grazing was shown to be highly important in attenuating a dinoflagellatebloom that occurred over a 3-week model simulation period. © 2001 Elsevier Science B.V. All rights reserved.

Keywords: Ecosystem model; Zooplankton grazing; Phytoplankton biomass; Top-down control

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1. Introduction

Restoration and management of the health ofaquatic ecosystems can only occur if the ecologyof the flora and fauna is well understood and ifthis knowledge can be applied to practical solu-tions. Once the biological, chemical and physicalcomponents of a system have been identified, andtheir interactions quantified, managers can pro-

ceed with an integrated approach, utilising theexpertise of engineers, hydrologists, ecologists,chemists and modellers to achieve an appropriateoutcome.

An example of such an approach is illustratedin a major multi-disciplinary investigation into thephysical and biochemical dynamics of the SwanRiver Estuary. This investigation was initiated bythe Western Australian Estuarine Research Foun-dation in 1995, with a view to providing practicalmanagement solutions to deteriorating waterquality within the estuary (Hamilton, 1996). Thepresent study is one component of this inter-disci-

* Corresponding author. Tel.: +61-8-92663129; fax: +61-8-92662495.

E-mail address: [email protected] (S.L. Griffin).

0925-8574/00/$ - see front matter © 2001 Elsevier Science B.V. All rights reserved.

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S.L. Griffin et al. / Ecological Engineering 16 (2001) 373–394374

plinary study and focuses on zooplankton dynam-ics and their impact on phytoplankton biomass.

The impact of zooplankton grazing on phyto-plankton biomass and the significance of thisimpact have been the subject of many investiga-tions in which phytoplankton blooms have beenshown to influence water quality (Griffiths andCaperon, 1979; Stearns et al., 1987; Svensson andStenson, 1991; Cyr and Pace, 1992). The SwanRiver Estuary zooplankton have also been thefocus of several ecological and physiological stud-ies (e.g. Hodgkin and Rippingale, 1971; Rippin-gale and Hodgkin, 1974a,b, 1977; Rippingale,1987, 1994). However, this is the first investiga-tion that has attempted to quantify the trophicrelations between zooplankton and phytoplank-ton in the Swan River Estuary.

There has been considerable debate about therole of zooplankton in controlling algal bloomsthrough grazing (Harris, 1986; Peters, 1991;DeMelo et al., 1992; Boon et al., 1994). Variousstudies have shown the depletion of phytoplank-ton biomass to be dependent on the density andsize of the zooplankton grazers (Martin, 1970;Gamble, 1978; Lampert and Taylor, 1985; Vanni,1987; Turner and Graneli, 1992), diel variation inzooplankton feeding rates (Gauld, 1953; McAllis-ter, 1971; Mackas and Bohrer, 1976; Lampert andTaylor, 1985; Peterson et al., 1990), and phyto-plankton abundance (Mullin and Brooks, 1970;Frost, 1972; Reeve and Walter, 1977; Ambler,1986; Durbin and Durbin, 1992). All of the fac-tors listed above may be an artefact of the abun-dance of higher order consumers (Carpenter et al.,1985). These authors assert that the abundanceand composition of piscivores can influence thestructure of fish and plankton communities, andtherefore influence the ability of zooplankton toremove phytoplankton biomass through grazing.Here, the impact of zooplankton grazing on phy-toplankton biomass has been assessed by examin-ing grazing rates of different size classes ofzooplankton, during the different phases of analgal bloom in the Swan River Estuary.

The Swan River Estuary is a focal point for thecity of Perth, Western Australia, providing aes-thetic, economic and recreational benefits to thecity residents. The catchment is approximately

190 000 km2, encompassing a wide range of landuses, including urban, agricultural, industrial andforestry. The multitude of different land develop-ments since European settlement in the 1840s hasculminated in the Swan River Estuary showingsigns of eutrophication, indicated by phytoplank-ton blooms in the upper reaches of the estuary inrecent years (John, 1994; Thompson and Hosja,1996). Although toxic species of phytoplanktonhave not occurred in the Swan River Estuary todate, toxic blooms have occurred in a majortributary of the Swan (Hosja and Deeley, 1994),and high nutrient levels (Douglas et al., 1995) andwarm water temperatures in summer could beconducive to blooms by toxic species in thefuture.

The Swan River Estuary is a highly seasonal,micro-tidal system (Kurup et al., 1999), located ina Mediterranean climate zone where most rainfalls in the winter during June, July and August.The estuary has a seasonal cycle of saline tidalencroachment during summer, extending :42km up the river, and fluvial flow during winter,when the saline waters are flushed to the lowerestuary.

A characteristic series of algal blooms occursmost years within the estuary, associated withchanges in salinity. Winter blooms of diatoms(Bacillariophyta) are followed by high biomassspring blooms of chlorophytes and summer andautumn blooms of dinoflagellates (Dinophyceae)(John, 1987). During this cycle of phytoplanktonsuccession, both nutrients derived from the catch-ment and internally regenerated nutrients areavailable for phytoplankton growth (Douglas etal., 1995). The dynamics of this annual cycle ofphytoplankton succession are the focus of anintegrated ecological project (Hamilton, 1996)which seeks to provide advice on the effect ofmanagement practices, based on a detailed scien-tific knowledge of the dynamics of the Swan RiverEstuary. The use of an ecosystem model, Compu-tational Aquatic Ecosystem Dynamics Model(CAEDYM), is an integral part of this process.

Numerical models of ecological processes areincreasingly being used as management tools(Walters, 1986; Schladow and Hamilton, 1995;Harris, 1997). When dealing with a complex

S.L. Griffin et al. / Ecological Engineering 16 (2001) 373–394 375

ecosystem, such as an estuary, models allow forexploration of possible outcomes of various man-agement options (e.g. destratification of the watercolumn, oxygenation of the water column, andremoval of components of the biota) withoutinadvertently causing an undesirable change in theecosystem. However, Walters (1986) suggests thatmodels may not represent the most efficientmethod available to predict the complex relation-ships and interactions between each ecosystemcomponent, because standard scientific investiga-tions fail to resolve many of the uncertaintiesimplicit in models. Here, we use a combination ofexperimental and modelling approaches in adopt-ing a mechanistic approach to resolving many ofthe uncertainties associated with models. We de-termined whether a numerical model is able toaccurately predict the measured dynamics of phy-toplankton and zooplankton populations in a spe-cific location within the Swan River Estuary inJanuary 1996. We then used the model output todetermine whether experimentally determinedzooplankton ingestion of phytoplankton was thefactor contributing most to the measured loss ofphytoplankton biomass.

2. Methods

2.1. Study site

The model was used to predict the impact ofzooplankton grazing at Ron Courtney Island(RCI), approximately 38 km from the mouth ofthe Swan River Estuary. This site has been thesubject of extensive investigations into phyto-plankton and zooplankton dynamics (Bhuiyan,1966; Rippingale, 1987; Thompson and Hosja,1996; Hamilton et al., 1998), nutrient dynamics(Douglas et al., 1995), and hydrodynamics(Stephens and Imberger, 1996). At this site waterdepth is approximately 6 m, substantially deeperthan most of the upper reaches of the estuary(approximately 2–3 m). Salinity ranges seasonallyfrom B5 to \30 ppt, with vertical stratificationof typically 4–5 ppt in early summer, when a saltwedge propagates through RCI. High river flowsat the onset of winter then flush the wedge down-

stream and the water column becomes fullymixed.

2.2. Zooplankton data

Quantitative zooplankton samples for abun-dance estimates were collected every 3 h for 24 h,from discrete depths (surface: 0.5 m and bottom:5–6 m) within the water column, on 9, 16, 23 and30 January, using a 6.28-l water trap. The watertrap was developed specifically for the collectionof mesozooplankton, and was constructed using aclear acrylic tube (i.d. 14 cm, length 40.8 cm),with a remotely operated closing device. The wa-ter trap was designed to be held on a horizontalaxis in the water column (parallel to the sedimentsurface) and to allow flow–through of water untilthe end-caps are closed.

Abundance samples were preserved in 4% form-aldehyde (Wetzel and Likens, 1991), until enumer-ation. At this time, samples were concentrated to20 ml, and a minimum of three subsamples wascounted using a 1-ml, Sedgewick–Rafter Cham-ber. Subsamples were obtained by mixing thesample thoroughly and immediately removing 1ml with a wide-mouthed pipette. Reliability ofthis method was indicated by low (B5%) stan-dard errors between subsample counts.

In both the field and laboratory, zooplanktoningestion rates (defined here as the mass of phyto-plankton carbon consumed per mass of zooplank-ton carbon per day) were measured using thetechnique and equations of Frost (1972), modifiedfor chlorophyll a instead of cell counts. Chloro-phyll a was measured fluorometrically againstchlorophyll a standards, after sonification andsubsequent extraction in 90% v/v acetone for 24 hin the dark at 4°C. Chlorophyll a was convertedto carbon using a conversion factor of 50 (Bougis,1976; Dagg and Wyman, 1983). Zooplanktonbiomass was estimated from length–carbon re-gressions (Uye and Sano, 1995; Liang et al., 1996;Santer 1996) and used to express ingestion rate asa function of mass of zooplankton carbon.

Zooplankton ingestion rates were measured insitu on 9, 23 and 30 January 1996. A size fraction-ation technique was used, in which the wholezooplankton community, collected using the


plankton trap described above, was divided intothree size classes, by pouring the samples througha series of Nitex® screens (300, 100 and 44 mm).This allowed the relative importance of grazing byeach of the dominant zooplankton classes to bequantified. The size classes used did not fit con-ventional size categories (i.e. 2–20, 20–200, 200–2000 mm; Dussart, 1965; Seiburth et al., 1978)because the dominant zooplankton groups couldbe more readily classified into the sizes 44–100mm (small rotifers, e.g. Synchaeta sp.), 100–300mm (larger rotifers, e.g. Brachionus sp. and cope-pod nauplii) and \300 mm (late copepodite andadult calanoid and cyclopoid copepods).

Each size fraction sample was gently rinsed into500-ml acid-washed glass incubation bottles, andmade up to 500 ml volume with filtered seawaterof ambient salinity. The bottles were sealed andreturned to the depth of collection, thereby min-imising variation attributable to the effect of lightattenuation, temperature and water pressure.Replicate experimental bottles and controls wereincubated for 12 h. At the end of each experiment,the bottles were removed from the water, and thecontents screened through a 44-mm Nitex® screento remove zooplankton. The contents of thescreen were rinsed into storage vials using filteredwater of ambient salinity and preserved with 4%buffered formaldehyde (Wetzel and Likens, 1991).These samples were later counted to providegrazer abundance estimates for the experiments.The filtrate was used to measure chlorophyll aconcentrations. A series of preliminary trials wasundertaken to determine the loss of chlorophyll aincurred by the screening process (B1% totalchlorophyll a, Griffin, unpublished data). Maxi-mum ingestion rates for the two smaller sizegroups were based on the maximum rate mea-sured in situ, with natural concentrations of thephytoplankton assemblages present. Additionally,individual zooplankton specimens were visuallyexamined for evidence of phytoplankton cells intheir guts.

In addition to measurement of ingestion ratesin the field, maximum ingestion rates of thelargest size fraction were measured in the labora-tory on a locally occurring calanoid copepod,Gladioferens imparipes Thomson. These experi-

ments were conducted at 20°C, using commonlyoccurring phytoplankton genera in the SwanRiver Estuary (Chlorophyta: Chlamydomonas glo-bosa ; Dinophyceae: Scrippsiella sp., Gyrodiniumsp.; Bacillariophyta: Skeletonema costatum Cleve;and Cryptophyta: Cryptomonas sp.) at three dif-ferent experimental concentrations (indicated bychlorophyll a concentration). Whenever possible,algal cultures were maintained using Swan Estu-ary species; otherwise cultures were obtained fromthe CSIRO Algae Supply Service.

During laboratory experiments, 500-ml food-quality plastic containers were used as experimen-tal containers, which were rotated at 0.1 m s−1 ona plankton wheel. Each container received 50–100individuals of G. imparipes and then was filledwith the experimental food. Replicate experimen-tal containers and controls were incubated on theplankton wheel for 12 h in darkness. At this time,the copepods were removed from the sample us-ing a 44-mm Nitex® screen, and the remainingfood mixture was filtered onto glass fibre filterpapers (nominal pore size of 1.2 mm) for chloro-phyll a analysis, as outlined above.

A feeding preference index was not measuredexplicitly, but was estimated from the differencesin maximum ingestion rates on different foodtypes of the same concentration as measured inthe laboratory at constant temperature. For thesmaller size fractions, feeding indices were esti-mated from literature studies and consideration ofthe size of both the food particles and the grazerconcerned.

2.3. Phytoplankton data

Phytoplankton biomass at RCI was estimatedfrom cell counts of weekly depth-integrated phy-toplankton samples collected by the Water andRivers Commission of Western Australia (WRC).These samples were collected using a flexible rub-ber hose (i.d. 10 mm) which was lowered throughthe water column, sealed and returned to thesurface. Repeated samples were mixed in a vessel,pre-rinsed with sample water, and a 120-ml sub-sample was removed and preserved with approxi-mately 2 ml, of Lugol’s solution. Foridentification, samples were shaken and 1-ml


aliquots were injected into 1-ml Sedgewick–Raftercounting chambers. The cells were counted at×200 magnification from random grids whendense blooms were present, or horizontal or verti-cal transects when cells were less abundant. Phyto-plankton cell volumes were calculated and anestimate of biomass obtained by converting tocarbon using the equations of Parsons et al. (1984).

2.4. Water quality data

Water quality data, including nutrients (ammo-nium, NH4

+; nitrate, NO3−; phosphate, PO4

3−;silica, Si), temperature, salinity and dissolved oxy-gen also were measured weekly at RCI by theWRC. Samples for nutrient analysis were taken at0 m, 0.5 m and near the bottom (5–6 m), and sentto a professional analytical laboratory for mea-surement. All other data were measured in situ at0.5 m depth intervals using a Hydrolab MultiprobeLogger.

2.5. Model description

The CAEDYM model (Hamilton, 1996) in-cludes a nutrient–phytoplankton–zooplanktonsub-model. The model resolved RCI as nine verti-cal layers. A complete coupling of the phytoplank-ton–zooplankton model with a three-dimensionalhydrodynamic model is planned as part of along-term study (Hamilton, 1996). The parametersof this model are described in detail in AppendixA, and Tables 1 and 2. Water quality andzooplankton and phytoplankton biomass datarecorded at RCI on 9 January were used to provideinitial conditions for the model.

Forcing data inputs for the model simulations(measured data used to set the boundaries of themodel) included water temperature, salinity, con-centrations of phosphate, ammonium, nitrate andsilicon, and light. The phytoplankton model differ-entiated between four main groups of phytoplank-ton found in the Swan River Estuary over thesimulated period; dinoflagellates (e.g. Gymno-dinium sp.), marine diatoms (e.g. Skeletonermcostatum Cleve), chlorophytes (e.g. Chlamy-domonas globosa) and cryptophytes (e.g. Cryp-tomonas sp.). Phytoplankton biomass for each

group was represented in terms of chlorophyll aconcentration.

The growth-loss rate function for biomass wasrepresented by an equation to determine thechange in chlorophyll a of each phytoplanktongroup over the model time step of duration 10 min:

(Chla(t

=mf(G)f(T)1Chla−kr f(T)2 f(S)Chla

−((6sChla)(h

−M (1)

where Chlai is the concentration of phytoplanktongroup i in a layer, m is the maximum potentialgrowth rate of phytoplankton and f(G) is com-puted for diatoms as a minimum of growth limita-tion functions for light, phosphorus, nitrogen andsilica:

f(G)=minimum

�1−exp−

� IIK

�,

PO4

PO4+KP

,NH4+NO3

NH4+NO3+KN

orSi

Si+KSi

n(2)

and for the remaining three groups as

f(G)=minimum

�1−exp−

� IIK

�, 1−

kIP

QP

, 1−kIN

QN

n(3)

where I is the mean light over each time step, IK isa parameter that controls the initial slope of thephotosynthesis-irradiance curve, the light term inEqs. (2) and (3) is integrated over the depth of thelayer, and KP, KN and KSi are half saturationconstants for growth dependence on phosphorus,nitrogen and silica, respectively. The light in eachlayer was computed through a Beer’s Law attenu-ation which included a dynamic light extinctioncoefficient that accounted for self-shading (Hamil-ton and Schladow, 1997). Surface irradiance wasrepresented as a diurnal distribution of light withthree-eighths cloud cover. Eq. (3) includes internalnutrients of phosphorus (QP) and nitrogen (QN)and an assigned minimum internal nutrient con-centration for nitrogen and phosphorous, kIN andkIP.


Other parameters in Eq. (1) are the respirationrate constant (kr), the settling rate (ks), which wasapplied only for diatoms, the layer depth ((h), arespiration response to salinity [ f(S)], grazingmortality (M), and f(T)1 and f(T)2, which weretemperature functions of form qT−20 but withf(T)1 also including an inhibition component forhigh temperatures. The inhibition component wasa continuous function which included specifica-tion of a temperature at which there was nolonger an exponential increase in production with

temperature, an optimal temperature for produc-tion and an upper temperature limit where therewas no production. It should also be noted thatsettling of diatoms in each time step was both asource and a sink of the associated chlorophyll afor all layers below the surface. Settling into thelayer from above acted as a source and settlingout of the layer as a sink.

The term f(S), the respiration response to salin-ity, varied according to the phytoplankton group.Many water quality models neglect the direct

Table 1Phytoplankton parameters used in the model

Units Literature ranges andParameter Assigned valuesvalues

Dinoflagellates Chlorophytes Cryptophytes Marine diatoms

2.52.02.52.0bp –7.8×10−4ec4 1.1×10−4m s−1 1.1×10−4 1.1×10−4 –

c5 –5.5×10−55.5×10−55.5×10−52.7×10−4em s−1

60.060.060.0 90.0105–697g, 40–100hmE m−2 s−1IK

5.08–15gINmax 6.5mg N mg 5.0 5.0Chla−1

mg N mg 1.5–4g 2.8 2.5 2.5 2.5INmin

Chla−1

m2 mg Chla−1 0.01–0.03gkc 0.02 0.02 0.02 0.020.0230.02 0.030–0.5gg m−3KN 0.023

0.005KP 0.0050.0051–0.03g 0.016g m−3

0.050.001–0.171gday−1 0.2kr,p 0.15 0.120.028hKSi –mg l−1 – – 0.14

day−1 0.3–1.8h, 1.3–3.63g 0.5Pmax 1.4 1.90.720.0 30.014.018.0pptSopt

°C 33–40i 39.0Tmax 31.0 39.0 32.0Topt 27.033.025.033.020–30j, 32i°C

°C 19.0Tsta 20.0– 18.020.0upg 1.08 1.08 1.081.08– 1.02–1.14g

1.02–1.14g 1.08upr 1.08– 1.08 1.08Ws m s−1 1.4×10−6, 5.8×10−6k – – – 3.0×10−6

40.0g C g Chla−1YCC 40.060a, 45b, 30c 50d, 40.0 40.020–200f

a Bougis (1976).b Dagg and Wyman (1983).c Gifford and Dagg (1988).d Raymont (1980).e Hamilton et al. (1998).f Geider (1987).g Hamilton and Schladow (1997).h Tyrell and Taylor (1996).i Riley and Stefan (1987).j Scavia (1980).k Jørgensen et al. (1986).


Table 2Zooplankton parameters used in the modela

Parameter Literature ranges and valuesUnits Assigned value

Size 1 Size 2 Size 3(100–300 mm) (\300 mm)(40–100 mm)

0.36c, 0.3d, 0.2d, 0.63g, 0.28– 0.18A 0.190.1–0.4j

– 0.0 0.0bz 2.0–0.1–0.13h, 0.04f, 0.08e 0.2day−1 0.2kr 0.2

0.72b1.9h, 0.4–2.7k, 0.4–1.7l,g phyto C (g zoop C)−1kI 1.92b 1.63b

day−1 2.3–7.1m

0.06–1.6iKi 0.825bg phyto C m−3 1.26b 1.4b

kz day−1 0.08e, 0.04f, 0.06–1.6i 0.08 0.06 0.040.11b– 0.25bPij ( j=1) 0.28b

–Pij ( j=2) 0.33b 0.25b 0.15b

0.23b 0.20b 0.17bPij ( j=3) –0.33b– 0.30bPij ( j=4) 0.12b

0.0bPik (i=1) 0.0b– 0.23b

0.0b– 0.0bPik (i=2) 0.05b

0.0bPik (i=3) 0.0b– 0.0b

39.0°C 39.0Tmax 39.033.0Topt 33.0°C 33.020.0°C 20.0Tsta 20.0

uz – 1.07 1.07 1.07

a See Appendix A for definitions of parameters. Phytoplankton groups are denoted by j=1 (dinoflagellates), j=2 (diatoms), j=3(cryptophytes) and j=4 (chlorophytes) and zooplankton groups are denoted by i=1 (size class 1), i=2 (size class 2) and i=3 (sizeclass 3).

b Griffin (this study).c Rivier et al. (1985).d Barnes and Hughes (1988).e Fenchel and Finlay (1983).f Kiørbe et al. (1985).g Conover (1966) for size 3.h McAllister (1971) for size 3.i Hamilton and Schladow (1997).j Hein et al. (1993).k Capriulo and Carpenter (1980) for sizes 1 and 2.l Sautour et al. (1996) for size 3.m Thompson et al. (1994) for size 3.

effects of salinity on phytoplankton but in theSwan River Estuary there is a marked successionbetween freshwater and saltwater species (Thomp-son and Hosja, 1996) which necessitates such aterm. For the freshwater-adapted chlorophytes ittook the form

f(S)=1.0 for S5Sopt

f(S)=�b−1

Sopt2 S2−

2(b−1)Sopt

S+bn

for S\Sopt

(4)

where Sopt is the optimal salinity, at which thevalue of f(S) is 1.0, and b is the value of f(S)when salinity is 2×Sopt. This expression isparabolic, so that the effect of f(S) is to increasethe respiration rate as salinities increase aboveSopt. For the remaining three groups (dinoflagel-lates, cryptophytes and marine diatoms), whichare essentially saltwater-adapted, the salinity re-sponse is given by

f(S)=1.0 for S]Sopt


f(S)=�b−1

Sopt2 S2−

2(b−1)Sopt

S+bn

for SBSopt

(5)

Here b is the value of f(S) when the salinity iszero and Sopt is the salinity at which f(S) is 1.0 (i.e.the minimum value of f(S)). This expression is alsoparabolic, with respiration rate increasing as salin-ities decrease below Sopt.

Internal nutrients were also represented dynam-ically in the dinoflagellates, chlorophytes and cryp-tophytes. The equation for internal phosphorus isgiven by

(QP

(t=UPmax

�f(T)1

kMP−QP PO4

kMP−kIPKP+PO4

n−�

kr f(T)2 f(S)+M

Chla�

QP (6)

and for internal nitrogen by

(QN

(t=UNmax

�f(T)1

kMN−QN NO3+NH4

kMN−kINKN+NO3+NH4

n−�

kr f(T)2 f(S)+M

Chla�

QN (7)

where UPmax and UNmax are maximum rates ofuptake of phosphorus and nitrogen and kMP andkMN are maximum internal stores of phosphorusand nitrogen. The purpose of using an internalnutrient function was to include in the modelvertical migration of the chlorophytes, crypto-phytes and dinoflagellates, based on an earliermodel (Hamilton et al., 1998) which had success-fully reproduced the migration of phytoflagellatesin the Swan River Estuary and which relied onspecification of internal nutrient concentrations.The migration model moved phytoplankton to-wards the bottom of the water column as theirinternal nutrient stores were depleted, typicallyduring the night, and towards the surface accordingto the distribution of light in the water column andprovided there were adequate stores of internalnutrients (for details see Hamilton et al., 1998).

The grazing mortality term for each phytoplank-ton group, M, is a sum of the grazing by the threesize classes (i ) of zooplankton,

M=%i=1,3(ki f(Z)i f(W)i Zi f(T)3(i ))

QC

(8)

where ki is the grazing rate, QC is the ratio ofphytoplankton carbon to chlorophyll a, f(T)3(i ) isa temperature multiplier for one of the threezooplankton groups and is of the same form as thephytoplankton production multiplier f(T)1, Zi isthe zooplankton biomass of the zooplanktongroup, f(Z)i is a Michaelis–Menten grazing func-tion for each zooplankton group, which is given by

f(Z)i=%j=1,4Chlaj

Ki+%j=1,4Chlaj

(9)

where j denotes one of the four phytoplanktongroups, Ki is the half saturation constant forgrazing by each of the three groups of zooplankton,and f(W)i is a grazing preference function thatregulates whether phytoplankton or otherzooplankton groups are preferentially consumed.

f(W)i=PijChlaj QC

%(PijChlaj QC)+%PikZk

(10)

Here Pij and Pik denote the preference ofzooplankton group i for phytoplankton group j andfor zooplankton group I on another zooplanktongroup k, respectively. In this study Pik is set to zerofor the two smaller zooplankton size classes, so thatonly the largest size class of zooplankton could feedon the other groups.

The change in zooplankton biomass is given inthe model by

(Zi

(t=ki f(Z)i Ai Zi f(T)3(i )−kr f(S)f(T)4Zi

(11)where the grazing parameters in this expression aregiven above except for the inclusion of an assimi-lation efficiency, Ai. The second term on the right-hand side represents respiration and includes kr, therespiration rate coefficient, f(T)4, the temperaturemultiplier for the respiration rate, which is of thesame form as f(T)2 and a function to regulaterespiration rate according to salinity. For the twosmaller size classes of zooplankton, there was noapparent effect (i.e. f(S)=1.0) of the observedrange of salinities in the Swan River


Estuary on physiological response (Griffin, un-published data) but for the larger size class aresponse was applied in the model as follows:

f(S)=1.0 for S]Sopt

f(S)=�b−1

Sopt2 S2−

2(b−1)Sopt

S+bn

for SBSopt

(12)

Here b exceeds 1.0 and is the value of f(S)when the salinity is zero. Therefore the largest sizeclass is more likely to be found in marine salinitiesand respiration increases as salinity is reducedbelow Sopt.

Preliminary simulations indicated that the fielddata could not be replicated accurately withoutthe inclusion of a horizontal advective flux ofphytoplankton biomass into the domain. The fullequation describing the rate of change of a non-conservative constituent is:

(C(t

+u·9u=9·(A ·9C)+F (13)

where C is the concentration of the constituent, 9is the gradient operator, u is the velocity vector, Ais the eddy diffusivity and F is the non-conserva-tive rate of change of C. In this application,vertical advection is assumed negligible in com-parison with vertical migration, across-river ad-vection is assumed negligible, horizontal turbulentmixing is also assumed to be small and verticaldiffusion is explicitly modelled under conditionsof mild mixing (Kv=10−5 m2 s−1). The transportis predominantly due to along-river advection,and hence Eq. (13) can be simplified to:

(C(t

+u(C(x

=(

(zKv

(C(z

+F (14)

where u is now the along-river velocity, x is thealong-river co-ordinate and Kv is the vertical eddycoefficient. The non-conservative function variesaccording to the constituent modelled, and is de-scribed for phytoplankton and zooplankton.

Available data were sufficient only to executethe model in a one-dimensional capacity. Despitethis, in the absence of along-river velocities andbiomass gradients, the advective component can-not be directly calculated to solve Eq. (14) in a

prognostic manner. However, assuming that thenon-conservative functions represent the biologi-cal processes accurately, and given the physiologi-cal constants used in these non-conservativefunctions are the best available from the literatureand direct measurements in the field and thelaboratory, then the advective component (i.e.u(C/(x) can be simply calculated as the differencebetween modelled and observed biomass as afunction of time. To achieve this, biomass must bederived as a continuous function of time. The fielddata were used to fit curves to the temporalphytoplankton and zooplankton distributions(SPSS, 1997) thus allowing biomass to be com-puted at any time during the experiment. Theresultant regression characteristics, each of whichhad a coefficient of determination of r2=1, aregiven below.

Dinoflagellates (four-parameter modifiedGaussian):

C=y0+a.exp�

−12��x−x0�

b�cn

where a=16.35, b=3.42, c=1.68, x0=14.81 andy0=1.74

Marine diatoms (three-parameter modifiedGaussian):

C=a.exp�

−12��x−x0�

b�cn

where a=3.52, b=2.66, c=1.29, and x0=14.26.Cryptophytes (four-parameter sine):

C=y0+a.sin�2px

b+c

nwhere a=1.91, b=11.23, c=4.30, and y0=2.16.

Chlorophytes (four-parameter log normal):

C=y0+a.exp�

−12�

ln� x

x0

�,bn2n

where a=1.47, b=0.11, x0=21.4 and y0=0.01.Zooplankton Size 1 (three-parameter sigmoid):

C=a

1+exp�

−x−x0

b�

where a=0.26, b=3.06, and x0=16.77.Zooplankton Size 2 (cubic):


C=y0+ax+bx2+cx3

where a= −5.4e−4, b= −9.9e−7, c=1.6e−6

and y0=0.03.Zooplankton Size 3 (three-parameter single ex-

ponential growth):

C=y0+a.exp(bx)

where a=2.4e−5, b=0.27, and y0=0.05.Two scenarios were simulated by the model

(using a time step of 10 min), corresponding tothe alternative assumptions that zooplanktongrazing reduces the phytoplankton stock and thatzooplankton grazing has no effect on the phyto-plankton stock. The transports required to pro-duce results that correlate with the biomassregressions under these two grazing conditionswere subsequently calculated.

The derived transports constitute the amount ofbiomass that must be advected into the study areaat every time step in order for the measured andsimulated biomass to agree. It is reasonable thatthese transports may vary significantly temporallyand spatially (as either along-river velocity or thebiomass gradients change), and if the conditionsof grazing or no grazing are in fact good assump-tions, then the model should remain stable underconditions of differing transport. Two scenarioswere simulated under conditions of (1) nozooplankton grazing in conjunction withzooplankton grazing-derived transports, and (2)zooplankton grazing in conjunction with trans-ports derived under conditions of no zooplanktongrazing, to examine the stability of the systemunder the different grazing assumptions.

Two factors were not considered in the presentmodel. The first was the effect of dissolved oxy-gen, as the measured water column oxygen con-centrations did not drop below levels which wereconsidered to impart stress to the organisms (B2mg l−1) over the chosen simulation period (Tidey,1997). The second was zooplankton vertical motil-ity, which was not developed in the model formu-lation as it was not possible to discern a regularpattern in the vertical distribution of any of thethree zooplankton groups (Griffin, unpublisheddata). However, zooplankton transport into andout of the domain was included as part of the

model, and zooplankton distribution through thenine vertical layers was assumed to be similar, asindicated by field collections (Griffin, unpublisheddata).

3. Results

3.1. Model parameters

The phytoplankton and zooplankton parametersymbols that were used in the model are definedin Appendix A. Values were entered for each ofthese parameters, based on experimental datafrom the present study, data from previous physi-ological and numerical studies, or in some cases,calibration of output to field data (Tables 1 and2). Initial values for the model simulation areshown in Table 3.

3.2. Zooplankton ingestion rates and feedingpreferences

Ingestion rates and feeding preference indicesare shown in Table 2. The smallest size class ofzooplankton (principally Synchaeta sp.) had thehighest measured ingestion rate (k=1.92 g phytoC (g zoop C)−1 day−1), followed by the middlesize class (principally Brachionus sp. and copepodnauplii (k=1.63 g phyto C (g zoop C)−1 day−1),and then the largest size class (late copepodite andadult calanoid and cyclopoid copepods (k=0.72g phyto C (g zoop C)−1 day−1).

Table 3Initial values for model simulation

mg C l−1Zooplankton biomass, 44–100 mm 0.0220.022 mg C l−1Zooplankton biomass, 100–300 mm

mg C l−10.05Zooplankton biomass, \300 mm7.51Dinophyceae biomass mg Chla l−1

0.01Chlorophyta biomass mg Chla l−1

1.81Cryptophyta biomass mg Chla l−1

Bacillariophyta biomass 1.22 mg Chla l−1

Extinction coefficient 0.06 m−1

0.033NH4+-N mg l−1

mg l−10.005NO3−-N

0.08PO43−-P mg l−1

Silica 2.0 mg l−1

15.0Salinity ppt29.0Temperature °C


3.3. Calibration procedure

The most sensitive parameters, in terms of theireffect on model output, were phytoplanktongrowth rate, zooplankton respiration rate, grazingrate and feeding preference, and the half saturationconstant for grazing. All other parameters werefixed from the literature, field or experimentalmeasurements, except for derived transports, whichwere used as a deficit to tune the model output tothe observed data.

3.4. Model simulations and field measurements

The model simulated a 3-week period at RCI inthe Swan River Estuary. Figs. 1 and 2 show thechange in biomass of phytoplankton (Fig. 1a–d)and zooplankton (Fig. 2a–c) over the 3-weeksimulation period, using model simulations, mea-sured field data, and curves fitted to the field data.Each plot represents the surface layer only, atmidday, at RCI.

Field measurements and model simulations ofdinoflagellate biomass (Fig. 1a), represented bychlorophyll a, indicated that they were the mostabundant phytoplankton group at RCI over the3-week period. Estimates from field measurementsshowed dinoflagellate biomass peaked at 18.1 mgChla l−1 in the surface layer on 14 January (modelprediction of 17.5 mg Chla l−1), declined rapidly to3.3 mg Chla l−1 after 2 weeks (model prediction of3.1 mg Chla l−1) and further declined to 1.8 mg Chlal−1 during the third week (model prediction of 1.7mg Chla l−1). Estimates from field measurementsshowed that diatom biomass (Fig. 1b) also peakedon 14 January at 3.5 mg Chla l−1 (model predictionof 3.5 mg Chla l−1). Diatom biomass then declinedover the remaining period to a minimum of 0.02 mgChla l−1 (model prediction of 0.02 mg Chla l−1).Cryptophyte biomass (Fig. 1c) oscillated during thesimulation period, with field estimates indicatingtwo peaks of biomass of 4.1 mg Chla l−1, occurringon 17 and 28 January, respectively (model predic-tions of 3.9 mg Chla l−1 for each date). Chlorophytebiomass (Fig. 1d) remained low throughout the3-week period, with field estimates showing a peakof 1.5 mg Chla l−1 on 21 January (model predictionof 1.4 mg Chla l−1).

Fig. 1. Phytoplankton biomass (chlorophyll a) at midday atthe surface. Measured data represent weekly measurements atRon Courtney Island, fitted with a regression curve. Modelleddata represent the daily model output for the 3-week period.(a) Dinophyceae, (b) Bacillariophyta, (c) Cryptophyta, and (d)Chlorophyta. Solid line, modelled; dotted line, measured; �,fitted.


Field estimates at the surface at RCI showedthat zooplankton biomass, represented byzooplankton carbon, increased during the 3-weekperiod. The biomass of the smallest zooplanktonsize class (Fig. 2a) was greater than the other twosize classes and peaked at 0.26 mg C l−1 on 30January (model prediction of 0.26 mg C l−1). Thebiomass of the middle size class of zooplankton(Fig. 2b) also peaked at 0.05 mg C l−1 on 30

January (model prediction of 0.05 mg C l−1), asdid the largest size class (Fig. 2c), with a peak of0.13 mg C l−1 (model prediction of 0.13 mg Cl−1). Although field estimates of biomass of eachsize class showed peaks at the same time, biomassof the smallest size class started to plateau after25 January, whereas the biomass of the other twosize classes showed an exponential increase inbiomass (Fig. 2a–c).

The hydrodynamic transports required to pro-duce results that correlate with the biomass re-gressions under the alternative conditions ofzooplankton grazing and no zooplankton grazingwere plotted for each of the phytoplankton andzooplankton groups. Examples of these plots areshown for dinoflagellates (Fig. 3a), diatoms (Fig.3b) and the smallest zooplankton size class (Fig.3c). All other plots showed patterns similar thoseillustrated here, in that both scenarios generatedtransports that resemble reasonable magnitudes.

The model was run using the derived trans-ports, in order to examine the stability of thesystem under the assumptions of zooplanktongrazing or no zooplankton grazing. The first sce-nario (no zooplankton grazing with transportsderived with zooplankton grazing) is shown foreach of the phytoplankton groups (Fig. 4a–d),and each of the zooplankton groups (Fig. 4e–f).The alternative scenario (zooplankton grazingwith transports derived without zooplankton gaz-ing) also is shown for each of the phytoplankton(Fig. 5a–d) and zooplankton (Fig. 5e–f) groups.

3.5. Dinophyceae biomass

The dynamics of the Dinophyceae bloom wereexamined more closely, in order to determinewhich factors contributed most to loss of biomass.Daily dinoflagellate production at the surface(which includes the effects of light, temperatureand nutrients) is shown relative to losses fromrespiration and zooplankton grazing (Fig. 6a) andtransport and migration (Fig. 6b). The contribu-tion per day in both figures represents the propor-tional increase (due to production, migration ortransport) or decrease (due to grazing, respiration,migration or transport) in biomass, expressed as adaily rate. For example, a production rate of 0.5

Fig. 2. Zooplankton biomass (carbon) at midday at the sur-face. Measured data represent weekly measurements at RonCourtney Island, fitted with a regression curve. Modelled datarepresent the daily model output for the 3-week period. (a)Zooplankton size class 1 (44–100 mm); (b) zooplankton sizeclass 2 (100–300 mm), and (c) zooplankton size class 3 (\300mm). Solid line, modelled; dotted line, measured; �, fitted.


Fig. 3. Derived transports advected into the study area at10-min time steps, showing values calculated with and withoutzooplankton grazing. (a) Dinophyceae, (b) Bacillariophyta,and (c) zooplankton size class 1 (44–100 mm). Solid line, withgrazing; dotted line, without grazing.

contributing to both losses and gains in dinoflag-ellate biomass.

4. Discussion

4.1. Predicti6e ability of model

The model reproduced the general trends ofphytoplankton biomass over the 3-week period(Fig. 1a–dFig. 2a–c). However, simulatedbiomass plots of the flagellated phytoplanktonspecies exhibit diurnal migration which cannot beresolved with the fitted field data, as the regres-sion was based on midday data when the majorityof phytoplankton biomass resides at the surface.The modelled and measured data were expectedto show good agreement as any difference inbiomass is compensated with the transport com-ponent (Fig. 3a–c), hence making the hydrody-namic transport under the different grazingconditions the interesting diagnostic. However,not much can be inferred about grazing controlfrom biomass simulations alone since both graz-ing scenarios produced transports within reason-able magnitudes.

All of the simulated transports exhibited diur-nal oscillation, which is consistent with tidal cy-cles (Fig. 3a–c). Along-river transport wasdominant in each case, with transports generallyoscillating around zero, indicating changes in cur-rent direction. Where oscillation was not aroundzero, it may indicate other transports (e.g. verti-cal, compensation for insufficient mixing, oracross-river advection).

The plots of simulated plankton biomass underdifferent scenarios of transport show the stabilityof the system under different grazing assumptions.For the first scenario (no zooplankton grazingwith transports derived with zooplankton grazing)zooplankton biomass undergoes unstable expo-nential growth, fuelled by a large increase inmarine diatom biomass (Fig. 4a–f). The secondscenario (zooplankton grazing with transportsderived without zooplankton grazing) shows thatall biomass self-adjusts to the change in transport.Biomass is bounded and cryptophytes, chloro-phytes and the zooplankton species follow the

represents an increase in biomass of half theoriginal biomass.

Respiration rate of dinoflagellates remainedfairly constant throughout the simulation period.The loss due to grazing by the three zooplanktonsize classes increased progressively over the simu-lation period, however, and peaked near the endof the 3 weeks. Dinoflagellate production de-creased rapidly at the end of the simulation. Mi-gration and transport showed daily fluctuations,


general trends observed in the field data (Fig.5a–f). The failure of the model to perform underthe assumption of no zooplankton grazing in the

presence of a perturbed transport field indicatesthat under all the conditions prescribed (i.e. nutri-ent levels, physiological constants, etc.), the as-

Fig. 4. Biomass of phytoplankton (chlorophyll a) and zooplankton (carbon) at midday at the surface. Measured data representweekly measurements at Ron Courtney Island, fitted with a regression curve. Modelled data represent the daily model output forthe 3-week period under the scenario of no zooplankton grazing with transports derived with zooplankton grazing. (a) Dinophyceae,(b) Bacillariophyta, (c) Cryptophyta, (d) Chlorophyta, (e) zooplankton size class 1 (44–100 mm), (f) zooplankton size class 2(100–300 mm), and (g) zooplankton size class 3 (\300 mm). Solid line, modelled; dotted line, measured; �, fitted.


Fig. 5. Biomass of phytoplankton (chlorophyll a) and zooplankton (carbon) at midday at the surface, at RCI. Measured datarepresent weekly measurements at Ron Courtney Island, fitted with a regression curve. Modelled data represent the daily modeloutput for the 3-week period under the scenario of zooplankton grazing with transports derived with no zooplankton grazing. (a)Dinophyceae, (b) Bacillariophyta, (c) Cryptophyta, (d) Chlorophyta, (e) zooplankton size class 1 (44–100 mm), (f) zooplankton sizeclass 2 (100–300 mm), and (g) zooplankton size class 3 (\300 mm). Solid line, modelled; dotted line, measured; �, fitted.

sumption that zooplankton grazing has negligibleimpact on the phytoplankton stock is not a goodone.

Model output may be enhanced with the inclu-

sion of zooplankton diurnal vertical migration.The model did not include an algorithm for verti-cal migration of zooplankton because initial ex-amination of zooplankton distributions within the


water column did not reveal any clear patterns ofmigration. This may be an artefact of the typicallyturbid estuarine environment; other researchershave found that estuarine zooplankton may notshow strong patterns of diurnal vertical migrationor diurnal feeding in turbid water (Hansen andBoekel, 1991) or when food availability is low(Roman et al., 1988; Sautour et al., 1996). Simi-larly, tidal movement of water displaceszooplankton communities, thereby making itdifficult to ascertain whether diurnal vertical mi-gration is occurring when samples are collectedfrom a fixed point in the estuary. Zooplanktonmigratory behaviour in the Swan River Estuaryrequires further investigation.

The predictive ability of models is partiallydependent on the integrity of calibration data

used and data obtained from populations col-lected in situ are preferable to data from othersources. The field measurements of zooplanktonand phytoplankton biomass during this studywere not replicated; therefore sampling error isunknown. This may have an effect on the modeloutput, as the model was calibrated to singlepoint estimates rather than average biomassvalues.

Some of the zooplankton calibration data usedin this model have been generated from in situand laboratory experiments, using the naturalphytoplankton and zooplankton communities. Areview of related literature produced comparableresults for grazing rates for each of the three sizeclasses (Table 2). However, a substantial amountof the data used in this model was generated from

Fig. 6. Simulated Dinophyceae biomass budget at midday at the surface, at Ron Courtney Island. (a) Production, respiration andzooplankton grazing (Solid line, dinophyceae production; dotted line, respiration; dot–dashed line, grazing), and (b) Production,migration and transport (Solid line, dinophyceae production; dotted line, transport; dot–dashed line, migration).


research undertaken in the Northern Hemisphere,and in coastal or inland rather than estuarinewaters. This may not be of great consequence forphytoplankton parameters, as the kinetics of phy-toplankton growth have been thoroughly docu-mented. Zooplankton parameters, however, arerelatively poorly quantified, and the existing dataare generally highly variable and closely associ-ated with habitat variables (see Huntley, 1988, fora review).

4.2. Loss of phytoplankton biomass

The surface layer phytoplankton budgets underconditions of zooplankton grazing indicate thatsignificant loss of biomass is due to uptake byzooplankton. Although biomass budgets are onlypresented for the dominant dinoflagellate group(Fig. 6a,b), similar trends were seen for each ofthe other three phytoplankton groups. Respira-tion contributed approximately half as much aszooplankton grazing. The effect of the grazing,when combined with the respiration rate, was toexceed the phytoplankton production rate in thelatter period of the simulation.

Respiration rate of dinoflagellates (Fig. 6a) re-mained fairly constant for most of the simulationperiod as a result of the relatively small observedfluctuations in water temperature and salinity(refer to Eq. (11)). However, Dinophyceae pro-duction decreased rapidly at the end of the simu-lation when nitrogen limitation of production wasincreased by a factor of more than 10-fold, inresponse to depletion of inorganic nitrogen fromthe water column. Nitrogen limitation in combi-nation with the effect of grazing (Fig. 6a) resultedin rapid depletion of Dinophyceae biomass (referto Fig. 1a).

While zooplankton grazing contributed most tothe loss of phytoplankton biomass, the large diur-nal fluctuation in migration for the flagellatedspecies, as well as fluctuations in biomass due totidal advection, easily accounts for the greatestdaily variation in biomass. Diurnal vertical migra-tion coincides with production peaks during theday at the surface, and negligible production atnight at the bottom (Hamilton et al., 1998). Simi-larly, zooplankton grazing on dinoflagellates is

likely to peak early in the morning, when theymay come in contact with the dinoflagellates mi-grating upward (as shown in Fig. 6b). The grazinginteractions between zooplankton and dinoflagel-lates, in the context of the migration patterns ofeach, have not been reported in the literature, andwarrant further investigation.

Several studies have demonstrated the impactof zooplankton grazing on phytoplanktonbiomass, but these investigations generally refer toloss of diatom biomass. Landry and Hassett(1982) demonstrated that marine microzooplank-ton (equivalent to size classes 1 and 2 in thisstudy) were able to remove 17–52% of dailydiatom production (6–24% of standing phyto-plankton biomass) through grazing. Bautista andHarris (1992) found that coastal copepods grazed5–8% of the total phytoplankton standing stock,and that 30–40% of the \10 mm fraction (di-atoms) was grazed during the exponential growthphase of a coastal spring bloom.

The present study is perhaps unusual in thatsignificant loss of dinoflagellate biomass throughzooplankton grazing is rarely reported in the liter-ature. Most studies have concentrated on toxicdinoflagellates, and have concluded thatzooplankton will avoid grazing these species (e.g.Huntley et al., 1986; Carlsson et al., 1995; Turriffet al., 1995). Santer (1996) reported that calanoidcopepods were unable to feed and survive on adiet consisting of the dinoflagellate Ceratium fur-coides, whereas cyclopoid copepods will, but thatit was not a preferred food type. The impact ofthe calanoid copepod Acartia hudsonica Pinhey onthe dinoflagellate Heterocapsa triquetra was mini-mal during an enclosure study (Turner andGraneli, 1992) and it was concluded that ciliatesB90 mm in length were the major contributors toa decline in dinoflagellate biomass. In contrast,Nielsen (1991) concluded that ciliates did notcontribute greatly to grazing pressure on Ceratiumspp.; this group of dinoflagellates was removedfrom the water column by large copepods, whichwere able to consume 30–70% of Ceratium pro-duction per day. It is unclear in the present inves-tigation which size class contributed most to thereduction in dinoflagellate biomass, and this war-rants further investigation. However, the small


rotifer Synchaeta sp. (44–100 mm size class) wasnumerically dominant at the time of this investi-gation (peak biomass of 0.26 mg C l−1 after 3weeks).

The effect of dinoflagellates, as a food type, onthe reproductive success of zooplankton, espe-cially copepods, can be important for maintainingzooplankton productivity. Miralto et al. (1995)reported a higher egg development and viabilityfor a copepod maintained on a diet of the di-noflagellates Gonyaulax polyhedra and Prorocen-trum minimum. At the time of the present study,the dominant dinoflagellates in the water at RCIwere Gyrodinium spp., Prorocentrum spp. andScrippsiella spp. (Water and Rivers Commissionof Western Australia, unpublished data). Thesespecies also may be suitable food types forzooplankton production. Although there is apaucity of studies on the nutritional suitability ofdinoflagellates for Australian zooplankton, quali-tative laboratory experiments with the estuarinecopepod Gladioferens imparipes and the rotiferBrachionus sp. have indicated that both Prorocen-trum lima and Scrippsiella sp. are adequate asfood sources for reproduction and survival(Griffin, unpublished data). The suitability ofthese latter dinoflagellates as food for smallerzooplankton groups, such as ciliates and bivalvelarvae, has not been investigated here, but thework of Turner and Graneli (1992) suggests thatdinoflagellates may be a suitable food type forsmaller zooplankton.

Dinoflagellate blooms typically occur in theSwan River Estuary during late summer and earlyautumn most years (John, 1987; WRC, unpub-lished data). This normally coincides with theperiod of peak copepod biomass (Bhuiyan, 1966;Rippingale, 1987). Therefore, it is plausible thatdinoflagellates are an adequate food source forthe resident estuarine zooplankton, and they maybe able to exert ‘top-down’ control on thebiomass of the dinoflagellates.

4.3. Zooplankton biomass

We measured field data and the model simula-tions show clear evidence that each zooplanktongroup increased its biomass during the 3-week

period (refer to Fig. 2a–c). The smaller size classof zooplankton (44–100 mm) showed the greatestincrease in biomass (from 0.02 to 0.26 mg C l−1),whereas the two larger size classes had lowerinitial and final biomass. The middle zooplanktonsize class (100–300 mm) increased in biomass from0.02 to 0.05 mg C l−1, and the largest zooplank-ton size class increased in biomass from 0.05 to0.13 mg C l−1. The biomass of the two largerzooplankton groups was lower than reported inprevious studies (Bhuiyan, 1966; Rippingale,1987).

The biomass peaks of each zooplankton groupoccurred after the decrease in dinoflagellatebiomass (compare Fig. 1a with Fig. 2a–c). Thispattern of prey biomass increase and subsequentcrash, followed by predator biomass increase, is aclassic example of the Lotka–Volterra predator–prey cycle (Krebs, 1985). Although these preda-tor–prey patterns have been widely reported inthe literature for many years, such trophic rela-tionships have not previously been described be-tween planktonic predators and prey in the SwanRiver Estuary, and may have important implica-tions for other Australian estuarine systems withsimilar habitat conditions.

As stated earlier, there are no measured data tosuggest which size class of zooplankton con-tributed most to the observed decline in phyto-plankton biomass. However, it is reasonable toassume that the smaller group (principally Syn-chaeta sp.) had the greatest impact onDinophyceae biomass, because it had the highestingestion rate, the highest biomass, and becausesubsequent microscopic examination of individualspecimens of Synchaeta sp. revealed the presenceof dinoflagellates in their gut. A search of theliterature did not produce any publications deal-ing with the preferred particle size for ingestion bythis rotifer. However, the literature reports thatzooplankton, such as copepod nauplii and otherrotifers, prefer particles ranging in width from 10to 30 mm (e.g. Bougis, 1976; Dumont, 1977; Pour-riot, 1977; Turner and Graneli, 1992). Therefore,it is reasonable to suggest that the size range ofthe dinoflagellate cells present (10–40 mm wide;Huntley et al., 1986) is suitable for ingestion bysmall rotifers, further supporting the suggestion


that this group of zooplankton exerted relativelymore control over Dinophyceae biomass than theother two zooplankton size classes.

5. Conclusion

The one-dimensional model (CAEDYM) accu-rately predicted the observed changes in phyto-plankton biomass over a 3-week period at RonCourtney Island, in the Swan River Estuary. Thesimulated output closely followed the measuredfield data and fitted regression curves, and pro-vided information about diurnal patterns of phy-toplankton production, respiration and migration,and hydrodynamic transport which was not avail-able from field data.

The model clearly demonstrated the impact ofzooplankton grazing on phytoplankton biomass,despite its one-dimensional vertical resolution.When examining the mechanisms behind loss ofphytoplankton biomass, loss due to respirationwas relatively constant and insufficient to causethe decline seen in the in situ data. Loss due tohydrodynamic transport and diurnal migrationwas greatest over small (daily) time scales andoscillated around zero. The net effect of advectionwas not as great as the net effect of zooplanktongrazing, which increased over the 3-week simula-tion period and appeared to be the majorfactor contributing to loss of phytoplanktonbiomass overall. It is also speculated that thesmallest size class (44–100 mm) contributed mostto the loss of Dinophyceae biomass. It is con-cluded that the resident zooplankton were able toexert ‘top-down’ control on the phyto-plankton through their grazing action, and thatthis, in conjunction with a decrease in nutrientavailability and therefore primary productivity,resulted in the observed decrease in phytoplank-ton biomass.

Acknowledgements

This work was supported by the Western Aus-tralian Estuarine Research Foundation and theSchool of Environmental Biology, Curtin Univer-

sity of Technology. The Water and Rivers Com-mission of Western Australia generously providedwater quality data. Dr Rob Rippingale is ac-knowledged for his support and advice through-out aspects of this investigation, as well as forcomments and suggestions on earlier drafts of thismanuscript. The authors also gratefully acknowl-edge the field and laboratory assistance providedby Rod Jacobson, who ably assisted in the collec-tion of zooplankton grazing data. Two anony-mous reviewers are also acknowledged for theirconstructive comments on an earlier draft of thismanuscript.

Appendix A. Phytoplankton and zooplanktonparameters used in the model, and their symbols

Phytoplanktonsymbols

value of f(S) when salinity isbep

2×Sopt (freshwater) or zero(marine)

c4 maximum upward migrationvelocitymaximum downward migrationc5

velocityIK initial slope for photosynthesis-

irradiance curvemaximum internal NINmax

concentrationminimum internal NINmin

concentrationkc specific light attenuation coeffi-

cient for chlorophyll aKN half saturation constant for

nitrogenhalf saturation constant forKP

phosphorouskr,p rate coefficient for respirationKSi half saturation constant for

silicaPmax maximum potential growth rateSopt optimum salinityTmax maximum temperature, where

growth rate is zeroTopt optimum temperature, where

growth rate is maximal


minimum temperature, whereTsta

growth rate is no longerexponentialtemperature multiplier forupg

respirationupr settling velocityWs temperature multiplier for

growthratio of carbon to chlorophyll aYCC

Zooplankton symbolsassimilation rateAvalue of f(S) when salinity isbz

zerokr respiration rate coefficientki grazing rate

half saturation constant forKi

grazingfraction of total loss con-kz

tributed by excretionpreference of zooplanktonPij

group i for grazing phytoplank-ton group jpreference of zooplanktonPik

group i for grazing zooplanktongroup ktemperature dependenceuz

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modelling the impact of zooplankton grazing on phytoplankton biomass during a dinoflagellate bloom...

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