PII: S0043-1354(00)00360-2
Wat. Res. Vol. 35, No. 4, pp. 901–912, 2001 # 2001 Published by Elsevier Science Ltd. Printed in Great Britain 0043-1354/01/$ - see front matter
POTAMON: A DYNAMIC MODEL FOR PREDICTING PHYTOPLANKTON COMPOSITION AND BIOMASS IN LOWLAND RIVERS E. EVERBECQ1, V GOSSELAIN2, L. VIROUX2 and J.-P DESCY2* 1
Centre for Environmental Study and Modelling, University of Lie`ge, Belgium and 2 Laboratory of Freshwater Ecology, Department of Biology, FUNDP, Namur, Belgium (First received 26 January 2000; accepted in revised form 13 July 2000)
Abstract}POTAMON is a unidimensional, non-stationary model, designed for simulating potamoplankton from source to mouth. The forcing variables are discharge, river morphology, water temperature, available light and nutrient inputs. Given the description of several algal categories, POTAMON allows to simulate algal ‘‘successions’’ at a particular site, as well as longitudinal changes of potamoplankton composition and biomass. The algal categories differ by their physiology, their loss rates, and their sensitivity to grazing by zooplankton. Two zooplankton categories were considered, Brachionuslike and Keratella-like, which differ by their clearance rate, their incipient limiting level, their selectivity towards phytoplankton, and their growth yield. The model simulates satisfactorily the onset and the magnitude of the phytoplankton spring bloom in the Belgian part of R. Meuse, the biomass decrease in early summer, and the autumn bloom. It also renders the major variations of algal assemblages along the river. The model allows to confirm that the main driving variables of potamoplankton dynamics in a eutrophic river are physical factors: discharge and related variables (e.g. retention time), light and temperature. In addition, the simulations confirm that the zooplankton–phytoplankton interaction may result in phytoplankton biomass fluctuations and compositional changes. POTAMON can be useful to explore plankton dynamics in a large river, and it may become a tool to test various management measures. # 2001 Published by Elsevier Science Ltd. Key words}phytoplankton, zooplankton, lowland river, modelling, eutrophication
INTRODUCTION
The understanding of potamoplankton dynamics has been greatly improved, since the basic hypotheses drawn at the beginning of the 20th century (reviewed in Welch, 1952), by several essays and studies highlighting the importance of hydrological processes, among other physical factors (Reynolds, 1988). In particular, heterogeneity of flow and river retentiveness have been proposed as mechanisms for increasing retention time and extending opportunities for development of planktonic organisms (Margalef, 1960; Reynolds et al., 1991; Reynolds and Glaister, 1993; Reynolds, 1995). Other studies focused on the effect of discharge on river plankton, since a negative correlation between discharge and chlorophyll a has been reported many times (e.g. Lack, 1971; Backhaus and Kemball, 1978; Friedrich and Viehweg, 1984; Jones, 1984; Lair and Sargos, 1993; Schmidt, 1994). If this negative relationship can be explained by the
*Author to whom all correspondence should be addressed. Tel.: +32-81-724405; fax: +32-81-230391; e-mail:
[email protected] 901
reduction of retention time as discharge increases, a dilution effect is also involved, and mathematical formulations have been proposed to describe this process (Talling and Rzo´ska, 1967; Descy et al., 1987). Therefore, plankton dynamics in a large river are controlled by processes which occur at the catchment scale (Billen et al., 1994). A typical longitudinal pattern can also be recognised, as a result of the interplay between physical processes and biological responses and interactions, which determines growth and losses of phytoplankton (review in Reynolds and Descy, 1996). Some studies have found a significant relationship between TP and chlorophyll a in particular rivers systems, arguing in favour of a ‘‘predictive model’’ based on regression equations (see Basu and Pick, 1996; Roos and Pieterse, 1996). Other studies have attempted to derive equations from regression analyses of data from rivers around the world, suggesting that general characteristics of the river basin, as watershed area, may be worth taking into account (van Nieuwenhuyse and Jones, 1996). If such equations might seem useful for water quality management (for a review on management issues involving potamoplankton, see Wehr and
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Descy, 1998), it is unlikely, however, that they can be applied successfully to all river systems. Indeed, light limitation, rather than nutrient limitation, is a common feature of turbulent, turbid aquatic systems (Reynolds et al., 1994). Moreover, regression models tend to favour a stationary view of potamoplankton development, which is certainly not adequate to understanding and management of these strongly non-stationary systems. By contrast, simulation models are best suited for representing dynamic situations, making it possible to understand how structure and biomass of river plankton observed at a particular site have been determined by processes which occurred upstream, in the main river and its catchment. Most potamoplankton simulation models have been developed since the end of the seventies (e.g. Dauta, 1983), and concern several European rivers: the R. Lot (The´baut and Qotbi, 1999), the R. Seine (e.g. Billen et al., 1994; Garnier et al., 1995), the R. Rhine (Admiraal et al., 1993), the R. Meuse (Descy et al., 1987). Most models have considered the algal community as a whole, although it is composed of various taxa with contrasting ecological requirements. Besides the interest of addressing the question ‘‘which (algae), and how much’’ (Capblancq and Catalan, 1994), predicting phytoplankton composition in rivers is critical in several management and human health issues, like assessing the risk of harmful algal blooms and designing appropriate control measures (Sherman and Webster, 1994; Bormans et al., 1997; Bormans and Condie, 1998) or managing the effects of eutrophication not only in river systems, but also in coastal zones (Billen and Garnier, 1997; Ghiot et al., 1998). Consequently, there have been several attempts to predict potamoplankton composition, using simulation models (Dauta, 1983; Garnier et al., 1995; Cloot and Leroux, 1997; Bormans et al., 1997; Bormans and Condie, 1998; Bormans and Webster, 1999) or other techniques as multivariate analysis (Ruse and Love, 1997) and artificial neural networks (Whitehead et al., 1997; Recknagel et al., 1997). Although comparison of those models, which have dealt with different time and space scales, is difficult, it appears that the most successful so far were based on a detailed description of the physical properties related to the river flow and of ecophysiological characteristics of the algae. This paper presents an attempt to develop a simulation model for predicting phytoplankton composition in fully non-stationary conditions in a regulated lowland river (R. Meuse, Belgium). Although the emphasis has been placed on trophic relationships and control of phytoplankton by small zooplankton, we have been able to test for silica and phosphorus limitation of algal growth during blooms. The model allows not only to describe algal ‘‘successions’’ at a particular site, but also to simulate longitudinal changes of potamoplankton composition and biomass.
MATERIALS AND METHODS
Environmental and plankton data Most data used in this study were acquired during an ecological study of the R. Meuse, a temperate regulated river, sampled from 1994 through 1996 at the site of La Plante, located in the Belgian part of the river, at 528 km from the source (Gosselain et al., 1998). Representative data on soluble reactive phosphorus (SRP) and silica (SRSi) are given below (Fig. 5); inorganic nitrogen was >3 mg N L ÿ 1 at all times. Analytical methods can be found in Gosselain et al. (1998). Other characteristics relevant to plankton development, i.e. discharge, temperature and light are given below (Fig. 1). Data on changes in phytoplankton biomass and composition were obtained from Gosselain (1998) and Gosselain et al. (1998). Briefly, both years presented a spring diatom bloom dominated by Stephanodiscus hantzschii Grun. and related taxa. This early phase was followed by the progressive development of a more diverse community, with several Chlorococcales achieving significant biomass in late spring. In early summer, as a dense rotifer-dominated zooplankton developed, phytoplankton biomass declined in one or two weeks; grazing by zooplankton has been identified as the main factor involved in this decline. The mid-summer phytoplankton was characterised by an increase in the proportion of large algal units (several taxa of the filamentous diatom genus Aulacoseira, large unicellular centric diatoms and colonies of Chlorococcales). At the end of summer, ‘‘small centrics’’ reappeared and formed an autumn bloom, which was more pronounced in 1996, as low river discharge and good meteorological conditions lasted until November. Total phytoplankton carbon biomass was calculated from chlorophyll a (Chla), using a single C : Chla ratio of 37 (Descy and Gosselain, 1994). Carbon biomass of the algal categories were estimated from algal counts and biovolumes determined in each sample, using the Eppley
Fig. 1. Calculated variations of temperature (8C), discharge (m3 s ÿ 1) and available light in the water column (J cm ÿ 2 d ÿ 1), in the R. Meuse at La Plante, in 1994 and 1996. Available light is PAR, calculated from measured incident solar radiation, divided by the product of mean depth by vertical extinction coefficient; to dampen the amplitude of the day-to-day variations of available light, the moving average over 7 days has been plotted.
POTAMON: modelling phytoplankton in rivers equations (Smayda, 1978). In addition to our data, Chla data used for comparison with calculated values are from two different sources: for both years, frequent measurements carried out by the Brussels Company for Water Supply (CIBE) at a site located a few km upstream of our sampling site. Data collected by the Ministry of the Walloon Region (MRW) have also been used for 1996. All these measurements used extraction in 90% acetone or acetone methanol 5 : 1, and measurement of extract extinction at 665 nm, with a correction for phaeopigments. Model description POTAMON is a uni-dimensional, non-stationary model, designed to simulate plankton dynamics for a whole year, from source to mouth of the main river. The forcing variables are discharge, river morphology, water temperature and incident light energy. All other variables are calculated, or, as for nutrient inputs, derived from another simulation model (see below). Results can be viewed either as temporal variations (day by day) at one site, or as longitudinal variations on a given day. The time step for computing is about 1 h, given that incident light data (provided by the Royal Institute of Meteorology, IRM, Belgium) are available every half hour. Such a small time step enables us to simulate diel cycles, in particular of dissolved oxygen, as well as to represent non-stationary events, like a series of dull days following a sunny period. The model comprises three successive sub-models: *
*
*
The hydrodynamic model, which calculates, at every river point, discharge and related variables (water velocity, water column height, transfer time, dilution rate), on the basis of river morphology (slope, width, presence of dams and of canalised sections) and of discharge measurements at one site; The temperature model, which calculates water temperature in successive stretches, from measured temperature at one site and from warm water discharges to the river; The biological model, which calculates
(i)
phytoplankton production, from incident light as a main forcing variable (see Descy et al., 1987), and the development of phytoplankton, zooplankton and bacterioplankton; (ii) degradation of organic matter from autochthonous production and from allochthonous sources (agriculture, domestic and industrial waste water); transformation of nutrient inputs within the ecosystem is taken into account in order to calculate concentrations of various forms of P and N; four categories of bacteria are described, either autotrophic (nitrifying bacteria), or heterotrophic (bacterioplankton, biofilm bacteria and sediment bacteria); (iii) the oxygen budget in the water column, from metabolic activity of planktonic (photosynthetic production, consumption by biodegradation, respirations) and benthic biomass, taking into account surface reaeration. Each particular constituent of the river can be simulated using the following generic equation (advection–diffusion equation): @ @ @ @ AC þ AUC ¼ Ak C dispersion; @t @x @x @x X þ Di ðCi Þdðx ÿ xi Þ tributaries; þ
i X
Rk dðx ÿ xk Þ
direct releases;
k
þ Ps L þ Pl
surface and linear sources;
þAðINT PROÿINT DISÞ; internal fluxes;
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where C is the concentration of the constituent (g m ÿ 3), k is dispersion coefficient (m2 s ÿ 1), U is flow velocity (m s ÿ 1), A is cross-section of the river (m2), H is height of the river (m), Di is flow rate of the tributary i (m3 s ÿ 1), Ci is concentration in the tributary i (g m ÿ 3), x is distance from the source (m), Rk is direct release of the constituent (g s ÿ 1) Ps ; P‘ is surface and linear releases (g m ÿ 2 s ÿ 1, g m ÿ 1 s ÿ 1), INT_PRO is internal production fluxes (g m ÿ 3 s ÿ 1) and INT_DIS is internal disappearance fluxes (g m ÿ 3 s ÿ 1). Internal fluxes for phytoplankton and zooplankton are described in Table 1. The equation system is resolved using a Lagrangian method. Carbon and nutrients inputs to the river were estimates computed by the PEGASE model (Smitz et al., 1997), designed for calculating surface water quality at the scale of entire watersheds. The PEGASE model takes into account: (i) non-point sources from the watershed, which vary according to land use; (ii) industrial waste water; and (iii) domestic waste water, as estimated from populationequivalents (either direct input of sewage to the river system, or output from treatment plants, in different forms of C, N and P). In the model, carbon and nutrients are split into several forms (organic/inorganic, dissolved/particulate, biodegradable/non-biodegradable). Dissolved silica was described as non-point inputs from the watershed. We detail hereafter the phytoplankton and zooplankton compartment and the major variables controlling their activity and development. Light in the water column was calculated from incident light and from the vertical extinction coefficient (ke ), according to the Lambert–Beer equation. ke (see Table 2) varies as a function of the amount of inorganic suspended matter, of total phytoplankton carbon biomass (with a specific extinction coefficient for each phytoplankton class, see below), and of particulate organic carbon (POC) determined by the model. POC comes from various sources, i.e. inputs from point and non-point sources, as explained above. POTAMON simulates the different components of the biocenosis in terms of carbon biomass. Table 2 gives the values of the parameters used in the model runs. Phytoplankton photosynthesis and growth (Table 1) consume ÿ 3ÿ CO2, nitrogen (NH+ 4 , NO3 ), phosphorus (PO4 ) and silica (SiO2, for diatoms only); the production rate depends on temperature and available light. SiO2 uptake is a function of C uptake by diatoms, according to a SiO2 : C ratio, with a half-saturation constant (KSiO2). Phytoplankton growth also depends on SRP concentration, according to the Monod model, with a half-saturation constant KPO4; no intracellular P storage is taken into account. Parameter values for nutrient-limited growth were derived from published data for different taxa (Tilman and Kilham, 1976; Mechling and Kilham, 1982; van Donk and Kilham, 1990). Phytoplankton losses were computed taking into account, with different parameter values for each algal category j: * * * *
a temperature-dependent mortality rate (tmphyj20, with a Q10 given by qmphyj); a temperature-dependent respiration rate (trphyj20, with a Q10 given by qrphyj); a sedimentation rate (tsphyj); grazing by zooplankton (see below).
In the present application, we considered four categories of phytoplankton, named after the most typical taxa, which are often dominant in the algal assemblages in the R. Meuse: *
Stephanodiscus hantzschii, representing the cold water centric diatoms which dominate in the spring and autumn blooms in many eutrophic temperate rivers;
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Units
Equation
Phytoplankton phyj tpphy
Phytoplankton concentration Production rate
mg C L ÿ 1 dÿ1
Kopt (T).Red_PRed_Si.f(I)
Kopt (T) T Red_P PPO4 Red_Si
Light saturated rate of photosynthesis Temperature Phosphorus limitation factor Phosphate concentration Silica limitation factor
mg C mg C ÿ 1 d ÿ 1 8C } mg P C ÿ 1 }
f ðIÞ
Light limitation factor
}
Iðz; tÞ I0 ðtÞ Ik wmoy ke k1, k2, k3 tmphy qmphy trphy qrphy tsphy Graz
Available light Incident light energy Light saturation constant Average light over the preceding 7 days Extinction coefficient Extinction coefficient parameters Mortality rate Q10 on mortality rate Respiration rate Q10 on respiration rate Sedimentation rate Grazing rate
mE m ÿ 2 s ÿ 1 mE m ÿ 2 s ÿ 1 mE m ÿ 2 s ÿ 1 mE m ÿ 2 s ÿ 1 mÿ1
Zooplankton zooi tfzoom qgzoo tfzoo
Zooplankton concentration Maximum filtration rate Q10 on filtration rate Filtration rate
2
Kopt ðTopt ÞeðÿðTÿTopt Þ
PPO4/(PPO4+KPO4) SiO2/(SiO2+KSiO2) Z H Iðz; tÞ=ð2IkÞ h i dz 2 0 1 þ ðIðz; tÞ=ð2IkÞÞ2 1 I0 ðtÞ I0 ðtÞeÿkeH arctg ÿ arctg ¼ H 2Ik 2Ik I0 ðtÞeÿke z b0 wmoy =ðb1 þ wmoy Þ k1 þ
P j
d
ÿ1
=dtek2 Þ
k2j phyj þ k3 POC
tmphy20 qmphy e½ðTÿ20Þ=10
dÿ1
trphy20 qrphy e½ðTÿ20Þ=10
dÿ1 dÿ1
vsphy/H tfzoo tfzoophy zoo
mg C L ÿ 1 L mg C ÿ 1 d ÿ 1
tfzoo1m20 qgzoo e½ðTÿ20Þ=10
L mg C ÿ 1 d ÿ 1
tfzoom
if phyG Cphyzoo
phyG
Grazable phytoplankton
mg C L ÿ 1
tfzoom phyG=Cphyzoo P tf zoophyj phyj
trzoo qrzoo tmzoo qmzoo
Respiration rate Q10 on respiration rate Mortality rate Q10 on mortality rate
dÿ1
trzoo20 qrzoo e½ðTÿ20Þ=10
dÿ1
tmzoo20 qmzoo e½ðTÿ20Þ=10
if phyG5Cphyzoo
j
*
*
*
Small centrics, corresponding to a more diversified assemblage of unicellular centric diatoms which are present all over the growing season; the assemblage includes several taxa of small centrics (up to 15 mm in diameter), belonging to the genera Stephanodiscus, Cyclotella, Cyclostephanos and Thalassiosira; the physiological type chosen for this category is Cyclotella meneghiniana Ku¨tz; Non-siliceous algae, which include primarily Chlorococcales (Chlorophytes) and occasionally Cryptophytes; small green algae like Chlorella and Scenedesmus served as a model for these algae; Large diatoms, actually representing the large, inedible diatoms which develop best in mid and late summer; the Aulacoseira genus represents these large phytoplankton.
In the studied stretch of the R. Meuse, micro-crustaceans developed only for short periods in late summer, while rotifers may form dense populations from May to September. Therefore, only two zooplankton categories were considered: Brachionus-like rotifers (zoo1) and Keratella-like rotifers (zoo2). The two forms differ by their clearance rate (tfzooi), incipient limiting level (cphyzooi, food concentration under which ingestion rate is foodlimited) and growth yield (yzooi). Growth and grazing rates of rotifers were derived from field and laboratory measurements (Gosselain et al., 1996; Gosselain, 1998). Both rotifers categories also have different temperature-dependent mortality (tmzooi20) and respiration rates (trzooi20).
RESULTS AND DISCUSSION
The four phytoplankton categories differ by their lightsaturated rate of photosynthesis (Koptj), their optimal temperature for photosynthesis (Toptj), and their saturation coefficient for photosynthesis (Ikj). They also have different respiration, mortality and sedimentation rates, and different sensitivities to grazing by zooplankton (tfzoo1phyj and tfzoo2phyj, depending on the zooplankton category). Koptj varies with temperature (depending on a coefficient which determines the shape of the curve, dtej); Ikj varies as a function of available light in the water column, averaged over the 7 preceding days (wmoy). In the present application, given that the tributaries of the R. Meuse carry little or no phytoplankton input from the tributaries to the main river was neglected.
Model simulations and calculated vs. observed comparisons Depending on the availability of sufficiently detailed data on phytoplankton and zooplankton, two years were simulated, 1994 and 1996. The computed values were compared with measured values at the main measurement site, La Plante, located 528 km from the river source. It should be emphasised that there was very little calibration of the model parameters, which were given experimental
Light saturated rate of photosynthesis Optimal temperature for photosynthesis Determines the shape of the Kopt : t curve Parameter of the Ik equation Parameter of the Ik equation Specific extinction coefficient Half saturation constant for P-limited growth Half saturation constant for Si-limited growth Silica : carbon ratio in diatoms Mortality rate (at 208C) Respiration rate (at 208C) Sedimentation velocity Edibility coefficient by Brachionus-like Edibility coefficient by Keratella-like
Maximum filtration rate for Brachionus-like (at 208C) Maximum filtration rate for Keratella-like (at 208C) ILL for Brachionus-like ILL for Keratella-like Growth yield for Brachionus-like Growth yield for Keratella-like Mortality rate (at 208C) of Brachionus-like Mortality rate of Keratella-like (at 208C) Respiration rate of Brachionus-like (at 208C) Respiration rate of Keratella-like (at 208C)
Phytoplankton Kopt (at Topt) Topt dtek b0 b1 k2 KPO4 KSiO2 SiO2 : C tmphy20 trphy20 vsphy tfzoo1phy tfzoo2phy
Zooplankton tfzoo1m20 tfzoo2m20 cphyzoo1 cphyzoo2 yzoo1 yzoo2 tmzoo120 tmzoo220 trzoo120 trzoo220
Description
d dÿ1 dÿ1 dÿ1
ÿ1
L mg C ÿ 1 d ÿ 1 L mg C ÿ 1 d ÿ 1 mg C L ÿ 1 mg C L ÿ 1
mg C mg C ÿ 1 d ÿ 1 8C 8C mE m ÿ 2 s ÿ 1 mE m ÿ 2 s ÿ 1 m2 mg C ÿ 1 mg P L ÿ 1 mg SiO2 L ÿ 1 mg SiO2 mg C ÿ 1 dÿ1 dÿ1 m dÿ1
Units
0.13 0.12 0.13 0.12
1.6 1.35 2.0 1.5 0.15 0.15
5.25 11 9 150 20 0.50 0.01 0.02 0.50 0.17 0.17 1.00 0.95 0.75
Stephanodiscus
Table 2. Parameters of the plankton submodel
0.15 0.15
6.00 19 9 250 25 0.50 0.01 0.06 0.50 0.15 0.15 0.80 0.85 0.75
Small centrics
0.12 0.15
6.00 23 14 350 30 0.50 0.01 0.00 0.00 0.16 0.16 0.60 0.75 1.00
Non-siliceous algae
0.10 0.15
4.00 28 10 80 10 0.75 0.01 0.10 0.75 0.06 0.06 0.90 0.10 0.01
Aulacoseira
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values when available, and published values in other cases. Some fitting was sometimes needed for parameters, but there was never fitting of the calculated state variables with measured values. The calculated vs. observed comparison is made as follows: *
*
*
*
Calculated total phytoplankton biomass, expressed as mg Chla L ÿ 1 (based on a single C : Chla ratio of 37), vs. measured Chla; Calculated carbon biomass of phytoplankton categories vs. carbon biomass of the corresponding categories, estimated from relative biomass from microscope counts and measured Chla (methods are detailed in Gosselain, 1998 and Gosselain et al., in press); Calculated zooplankton biomass vs. biomass of rotifers estimated from counts (Viroux, 2000) Calculated silica and phosphorus concentrations vs. measured values.
Environmental conditions: discharge, temperature and light Figure 1 presents the variations of the main physical factors influencing plankton development, as calculated by the model, for the studied site. Temperature and discharge data are the output of the respective sub-models, for the two years simulated. ‘‘Light’’ is an estimate of the daily light available in the water column, calculated from measured incident solar radiation, divided by the product of mean depth by vertical extinction coefficient; the latter has been computed by the model. Both years were contrasted with respect to flow and meteorology. In particular, the river discharge was lower in 1996 and the lowflow period extended from early April to late November, while this period was more than a month shorter in 1994, starting at mid-May. The temperature profile of the two years was roughly similar, with however higher maximal values in summer 1994. By contrast, light in the water column was globally higher in summer 1996 than in summer 1994. Those factors influenced the timing of the phytoplankton and zooplankton blooms: both communities developed earlier in 1996 (Gosselain et al., 1998). Phytoplankton and zooplankton biomass The profiles of total phytoplankton biomass, for both years, are shown in Fig. 2, and the simulations of the different algal categories in Fig. 3. With regard to the total algal biomass in both years (Fig. 2), the model simulates satisfactorily the onset and the magnitude of the spring bloom, the decrease in early summer, and the lower biomass range observed throughout summer. The correspondence between calculated and measured Chla is, however, better for 1996 than for 1994. For this year, the model fails to render the proper timing of the late summer bloom.
Fig. 2. Variations of chlorophyll a (Chla) in the R. Meuse at La Plante, in 1994 and 1996 (solid line: simulated daily values; dots: measured Chla). Chla data are from CIBE (open circles) and from FUNDP or MRW (closed circles).
It also simulates higher biomass than that actually measured in the river in October. Accordingly, Stephanodiscus hantzschii, the coldwater centric diatom, is well simulated in both years (Fig. 3). Similarly, the biomass variations of small centrics and of Chlorophyceae and Cryptophyceae are mostly well rendered, except for a peak of nonsiliceous algae in spring 1996. The large diatoms are globally less well simulated: in 1994, although the calculated biomass peak fits the observed data, there is an improper timing in the simulation, which explains the discrepancy visible in Fig. 2 for the late summer bloom. For 1996, there is a lack of agreement between the model and the data for the ‘‘large diatoms’’ category. Rotifer biomass is simulated satisfactorily by the model (Fig. 4), in both years, with regard to magnitude and timing of the peaks. This is particularly true for the Brachionus-like category, and for the Keratella-like rotifers in 1996. By contrast, there is a lack of fit between observed and calculated Keratella-like rotifers in 1994. Nutrient concentrations, test for possible limitation of algal growth, and grazing impact The simulation of diatoms growth makes it possible to calculate variations of Si concentrations in the river. The comparison between simulations and measurements was made for 1996, for which sufficient data were available (Fig. 5(b)). There is good agreement with regard to the concentrations and the timing of the peaks. Similarly, simulated phosphorus
POTAMON: modelling phytoplankton in rivers
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Fig. 3. Variations of carbon biomass of the four phytoplankton categories in the R. Meuse at La Plante, in 1994 and 1996 (solid line: simulated daily values; dots: estimated carbon biomass from phytoplankton counts).
concentrations show a good fit with the measurements (Fig. 5(a)). On the basis of this validation of nutrient concentrations in the river, we performed a test for detecting limitation of algal growth, expected during bloom episodes. Indeed, Si limitation of diatom growth (which may be suspected when Si50.22 mg l ÿ 1; Reynolds, 1997) may have occurred in 1996, throughout March and April. Setting the halfsaturation constants to zero mimicked a hypothetical non-nutrient-limited situation. The simulations (Fig. 6) indicate that indeed diatoms may have been limited by low-silica concentrations after the first peak of the spring bloom in 1996, while such a limitation did not occur in 1994. According to similar simulations for phosphorus, potential P limitation might have appeared during the largest blooms in both years. It must be reminded, however, that the algal sub-model does neither take into account P storage in algal cells, nor all the bioavailable P in the
water. Therefore, actual P limitation is unlikely to have occurred. By contrast, setting rotifer grazing rate to zero has a dramatic impact on phytoplankton biomass in both years (Fig. 7), essentially from late spring to late summer: this demonstrates, in agreement with Gosselain et al. (1998), that zooplankton grazing can be a key factor involved in the ‘‘summer decline’’ of phytoplankton biomass in rivers, as long as phytoplankton consists mostly of small, edible algae. The effect of grazing was higher when large rotifer densities were present throughout summer, as in 1996. Conversely, the zooplankton grazing pressure on total algal biomass was reduced when large phytoplankton units, particularly large diatoms, dominated the phytoplankton, as in summer 1994. Longitudinal changes Although a proper validation cannot be achieved as data on variation of phytoplankton composition
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Fig. 4. Variations of carbon biomass of the two zooplankton categories in the R.Meuse at La Plante, in 1994 and 1996 (solid line: simulated daily values; dots and diamonds: estimated carbon biomass from phytoplankton counts).
Fig. 5. Variations of dissolved phosphorus (a) and silica (b) in the studied reach of the R. Meuse, in 1996 (solid line: simulated daily values; dots: measured dissolved reactive phosphorus and silica).
along the river course are missing, the model can be used to explore longitudinal changes in phytoplankton composition and biomass. Several simulations of typical seasonal situations during the year 1996, for a 200 km stretch located mainly in the Belgian part of the river, are presented in Fig. 8. The first panel (Fig. 8(a)) shows a late spring situation, where the centric diatom Stephanodiscus hantzschii dominates the phytoplankton over the whole 200 km stretch. Zooplankton is barely developed, yet there is a conspicuous downstream decrease of phytoplankton, attributable to increasing metabolic losses as depth
and turbidity increase in the regulated stretch comprised between 480 and 620 km (Descy, 1992; Descy and Gosselain, 1994). The early summer situation (Fig. 8(b)) also presents a distinct downstream decline of the algal biomass, a part of which, however, is clearly related to zooplankton development in the simulated strech. Phytoplankton composition differs strikingly from that in spring: here, centric, Cyclotella-like diatoms and non-siliceous algae dominate the algal assemblage, and large diatoms tend to develop further downstream. The zooplankton community consists mainly of Brachionus-like rotifers, which present a progressive decline, as a result of the effect of increasing resource limitation (lower phytoplankton biomass, with a higher share of inedible algae) on reproductive rates (Viroux, 2000). The next panel (Fig. 8(c)) presents a somewhat similar composition of the phytoplankton, which differs from the early summer situation by the occurrence of plankton maxima further upstream. This results mainly from lower flow rate, which allowed more time for phytoplankton growth in the upstream part of the river. Another difference is the greater share of large diatoms in most of the river stretch simulated, increasing downstream. The last panel (Fig. 8(d)) shows a typical autumn simulation, which resembles that of spring, that is, with a phytoplankton community dominated by Stephanodiscus hantzschii. Some non-siliceous algae are present, however, and may increase their relative contribution downstream. Zooplankton development remains low, mainly as a result of lower temperature. The changes are clearly related to typical seasonal variations of temperature and light, while the river flow was still rather low at this time of the year (Fig. 1).
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Fig. 6. Simulations of phytoplankton chlorophyll a in the R. Meuse in 1994 and 1996, with and without Si (left panels) or P (right panels) limitation. The hypothetical non-nutrient-limited situation was obtained by setting the half-saturation constants to zero.
Fig. 7. Simulations of phytoplankton chlorophyll a in the R. Meuse in 1994 and 1996, with and without zooplankton grazing. The hypothetical non-grazing situation was obtained by setting the grazing rates to zero.
CONCLUSIONS
The development of the POTAMON model, designed to simulate plankton dynamics in lowland rivers, enables us to make several observations about the requirements of such models. A first requirement,
which may not be easy to meet, is a reliable data base: indeed, if Chla and several physical and chemical data are often available for routine monitoring of water quality, information on key factors involved in phytoplankton growth, such as river morphology, water transparency, and nutrient inputs, is more difficult to gather. The same is true for phytoplankton data themselves, which must be based on adequate techniques for sampling, counting and estimating biomass of the relevant algal categories (Gosselain et al., in press). A second requirement is the availability of ecophysiological data from which the model parameters can be set up. Paradoxically, despite the large number of experimental studies on algae in culture, it is no easy task to find comprehensive data on metabolic rates, nutrient requirements, sedimentation rate and edibility for several key algal taxa. Moreover, designing a model that includes trophic relationships requires data not only about grazing and growth rates of several rotifer species, but also about their feeding selectivity, which may be affected by a number of factors (e.g., for rotifers, Pourriot, 1977). Moreover, ‘‘herbivorous’’ zooplankton may shift to another kind of prey, including protozooplankton and bacteria (e.g. Sherr and Sherr, 1988). Furthermore, one is often limited by gaps in the knowledge in plankton taxonomy and ecology, and therefore, comprehensive and reliable field data are absolutely needed for model calibration. Despite those problems, the POTAMON model described satisfactorily potamoplankton biomass and composition during two annual cycles in a lowland
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Fig. 8. Longitudinal variations in a 220 km stretch of the R. Meuse, as simulated by the POTAMON model, for the year 1996. (a) situation on May 21 (T=158C, daily solar radiation=2450 J cm ÿ 2; discharge at 570 km: 84 m3 s ÿ 1); (b) situation on July 9 (T=188C, daily solar radiation=1550 J cm ÿ 2; discharge at 570 km : 46 m3 s ÿ 1); (c) situation on August 19 (T=21.28C, daily solar radiation=2260 J cm ÿ 2; discharge at 570 km : 33 m3 s ÿ 1); (d) situation on October 8 (T=13.68C, daily solar radiation=1075 J cm ÿ 2; discharge at 570 km : 42 m3 s ÿ 1). Dots are the measured Chla concentrations.
river, and allowed to explore longitudinal variations of the algal assemblages. In addition to confirming conclusions already drawn from in situ and experimental studies (Gosselain et al., 1998), as well as from other attempts to simulate potamoplankton dynamics (Garnier et al., 1995), the modelling approach is useful to identify the next steps in studies of potamoplankton ecology in lowland river systems. As far as phytoplankton composition is concerned, the main conclusions of the present study are: (i)
Diatoms remained dominant most of the time, relying on their ability to grow at sufficient rates under low light; they benefited from relatively low sedimentation losses given the depth of the studied stretch and from sufficient flow velocity; small unicellular centrics were susceptible to zooplankton grazing, and formed blooms when the grazing pressure was reduced (spring, fall); they were not or scarcely limited by nutrient (Si, P) availability; this diatom dominance confirms the views of Reynolds (1994) regarding mechanisms of algal selection in turbulent turbid systems. (ii) When discharge was low enough to allow phytoplankton to develop, temperature and light variations were the main drivers of algal
‘‘succession’’; the succession was, from spring to autumn: Stephanodiscus hantzschii>other unicellular centrics>Chlorophytes, Cryptophyceae and centric diatoms>large centric diatoms, mainly Aulacoseira spp.; grazing by zooplankton was a significant loss factor involved in those changes, ultimately resulting in large diatom dominance in late summer; in other words, algal size mattered when a dense rotifer community was allowed to develop, depending on flow rate and temperature; there was no evidence for nutrient limitation, and consequently nutrient competition is not likely to play any role in the system studied. (iii) Phytoplankton composition may be quite different at sufficiently distant sites, on the same day, as a result of changes in physical and biotic factors, and on specific responses of the main algal categories. A full agreement between observations and calculations, however, has not been reached in all cases, and one may question whether it would be possible to try to improve the potamoplankton model presented here. A first question may be: how many algae and rotifer categories would be useful? Although it is hard to draw a limit, a maximum of 7–8 algal types
POTAMON: modelling phytoplankton in rivers
(as in Reynolds and Irish, 1997) might be realistic and sufficient to describe most river phytoplankton communities. For instance, splitting the ‘‘large centrics’’ into large unicellular centrics and filamentous diatoms would make it possible to improve the simulation of this category. Similarly, the distinction between Chlorophytes and Cryptophyceae may help improve the description of both algal groups. This may lead to introduce an additional category of small rotifers, Synchetidae, which may develop large populations in rivers and have distinct food requirements (Pourriot, 1977). Finally, the inclusion of large and small cyanobacteria would complete the model and make it more applicable to other river systems, in which dead zones, backwaters or reservoirs allow more time for those usually slow-growing algae. This would also improve the predictive capacity of the model, especially for assessing the risk of cyanobacterial blooms in case of extreme low flow situations. For similar reasons, the zooplankton compartment might be completed by the addition of small-bodied crustaceans, like the cladoceran Bosmina. Also, the grazer compartment could be completed by adding predation by benthic filter-feeders, which can have a dramatic effect on phytoplankton in rivers (Strayer et al., 1996; Roditi et al., 1996). For achieving those improvements, it would be necessary to assign to all categories of algae and zooplankton their respective production and loss rates, nutrient or resource kinetics (including cell quotas for phytoplankton), and their dependence upon environmental factors. This is clearly a challenge, given the scarcity of relevant existing ecophysiological data, as well as of suitable plankton data sets from lowland rivers. REFERENCES
Admiraal W., Mylius S. D., de Ruyter van Steveninck E. D. and Tubbing D. M. J. (1993) A model of phytoplankton production in the lower River Rhine verified by observed changes in silicate concentration. J. Plankton Res. 15, 659–682. Backhaus D. and Kemball A. (1978) Gewa¨ssergu¨teverha¨ltnisse und Phytoplanktonentwicklung im Hochrhein, Oberrhein und Neckar. Arch. Hydrobiol. 82, 166–206. Basu B. K. and Pick F. R. (1996) Factors regulating phytoplankton and zooplankton biomass in temperate rivers. Limnol. Oceanogr. 41, 1572–1577. Billen G. and Garnier J. (1997) The Phison River plume: coastal eutrophication in response to changes in land use and water management in the watershed. Aquat. Microbiol. Ecol. 13, 3–17. Billen G., Garnier J. and Hanset P. (1994) Modelling phytoplankton development in whole drainage networks: the RIVERSTRAHLER model applied to the Seine river system. Hydrobiologia 298, 119–137. Bormans M. and Condie S. A. (1998) Modelling the distribution of Anabaena and Melosira in a stratified river weir pool. Hydrobiologia 364, 3–13. Bormans M. and Webster T. (1999) Modelling the spatial and temporal variability of diatoms in the River Murray. J. Plankton Res. 21, 581–598. Bormans M., Maier H., Burch M. and Baker P. (1997) Temperature stratification in the lower River Murray,
911
Australia: implication for cyanobacterial bloom development. Mar. Freshwater Res. 48, 647–654. Capblancq J. Catalan J. (1994) Phytoplankton: Which, and how much. In Limnology now: A paradigm of planetary problems, ed R. Margalef, pp. 9–36. Elsevier Science B.V., Amsterdam, Cloot A. and Le Roux G. (1997) Modelling algal blooms in the middle Vaal River: a site specific approach. Water Res. 31, 271–279. Dauta A. (1983) Conditions de de´veloppement du phytoplancton. Etude comparative du comportement de huit espe`ces en culture. Cine´tiques d’assimilation et de croissance: e´tude expe´rimentale. Mode´lisation applique´e aux cultures et a` un milieu naturel: le Lot. Ph.D. Thesis, Universite´ Paul Sabatier, Toulouse. 166 pp. Descy J.-P. (1992) Eutrophication in the River Meuse. In Eutrophication: research and application to water supply, eds D. W. Suttcliffe and J. G. Jones, pp. 132–142. Ambleside, U.K. Descy J.-P. and Gosselain V. (1994) Development and ecological importance of phytoplankton in a large lowland river (River Meuse, Belgium). Hydrobiologia 289, 139–155. Descy J. -P., Servais P., Smitz J. S., Billen G. and Everbecq E. (1987) Phytoplankton biomass and production in the River Meuse (Belgium). Water Res. 21, 1557–1566. Friedrich G. and Viehweg M. (1984) Recent developments of the phytoplankton and its activity in the Lower Rhine. Verh. int. Ver. Limnol. 22, 2029–2035. Garnier J., Billen G. and Coste M. (1995) Seasonal succession of diatoms and Chlorophyceae in the drainage network of the Seine river: observations and modeling. Limnol. Oceanogr. 40, 750–765. Ghiot C. B., Bauler P., Beckers J. M., Delhez E. J., Carabin G. J., Dassargues A. G., Delie`ge J. F. and Everbecq E. (1998) SALMON: Sea Air Land Modelling Operational Network. In Hydroinformatics ’98, eds V. Babovic and L. C. Larsen, pp. 371–377. Balkema, Rotterdam, The Netherlands. Gosselain V. (1998) Phytoplancton de la Meuse et de la Moselle et impact du broutage par le zooplancton. Ph.D. Thesis, Department of Biology, FUNDP, Namur, Belgium, 459 pp. Gosselain V., Hamilton P. and Descy J.-P. Estimating phytoplankton carbon from microscope counts: an application to riverine samples. Hydrobiologia, in press. Gosselain V., Joaquim-Justo C., Viroux L., Mena M., Metens A., Descy J.-P. and Thome´ J.-P. (1996) Laboratory and in situ grazing rates of freshwater rotifers and their contribution to community grazing rates. Arch. Hydrobiol. Suppl. 113, 353–361. Gosselain V., Viroux L. and Descy J.-P. (1998) Can a community of small-bodied grazers control phytoplankton in rivers? Freshwater Biol. 39, 9–24. Jones F. H. (1984) The dynamics of suspended algal populations in the lower Wye catchment. Water Res. 18, 25–35. Lack T. J. (1971) Quantitative studies on the phytoplankton of the Rivers Thames and Kennet at Reading. Freshwater Biol. 1, 213–224. Lair N. and Sargos D. (1993) A 10 year study at four sites of the middle course of the river Loire. I}Patterns of change in hydrological, physical and chemical variables in relation to algal biomass. Hydroe´col. Appl. 5, 1–27. Margalef R. (1960) Ideas for a synthetic approach to the ecology of running waters. Int. Revue ges. Hydrobiol. 45, 133–153. Mechling J. A. and Kilham S. S. (1982) Temperature effects on silicon limited growth of the Lake Michigan diatom Stephanodiscus minutus (Bacillariophyceae). J. Phycol. 18, 199–205.
912
E. Everbecq et al.
Pourriot R. (1977) Food and feeding habits of Rotifera. Arch. Hydrobiol. Suppl. 8, 243–260. Recknagel F., French M., Harkonen P. and Yabunaka K.-I. (1997) Artificial neural network approach for modelling and prediction of algal blooms. Ecol. Model. 96, 11–28. Reynolds C. S. (1988) Potamoplankton: paradigms, paradoxes and prognoses. In Algae and the aquatic environment, ed F. E. Round, pp. 285–311. Biopress Ltd., Bristol. Reynolds C. S. (1994) The long, the short and the stalled: on the attributes of phytoplankton selected by physical mixing in lakes and rivers. Hydrobiologia 289, 9–21. Reynolds C. S. (1995) River Plankton: the paradigm regained. In The ecological basis for river management, eds D. M. Harper and A. J. D. Ferguson, pp. 161–174. Wiley, Chichester. Reynolds C. S. (1997) Vegetation processes in the pelagic: a model for ecosystem theory. In Excellence in Ecology, vol. 9, ed O. Kinne, Ecology Institute, Oldendorf/Luhe. 371 pp. Reynolds C. S. and Glaister M. S. (1993) Spatial and temporal changes in phytoplankton abundance in the upper and middle reaches of the River Severn. Arch. Hydrobiol. Suppl. 101, 1–22. Reynolds C. S. and Irish A. E. (1997) Modelling phytoplankton dynamics in lakes and reservoirs: the problem of in-situ growth rates. Hydrobiologia 349, 5–17. Reynolds C. S. and Descy J.-P. (1996) The production, biomass and structure of phytoplankton in large rivers. Arch. Hydrobiol. Suppl. 113, 161–187. Reynolds C. S., Carling P. A. and Beven K. (1991) Flow in river channels: new insights into hydraulic retention. Arch. Hydrobiol. 121, 171–179. Reynolds C. S., Descy J.-P. and Padisa´k J. (1994) Are phytoplankton dynamics in rivers so different from those in shallow lakes? Hydrobiologia 289, 1–7. Roditi H. A., Caraco N. F., Cole J. J. and Strayer D. L. (1996) Filtration of Hudson river water by the zebra mussel (Dreissena polymorpha). Estuaries 19, 824–832. Roos J. C. and Pieterse A. (1996) Seasonal variation of phytoplankton biomass in the Middle Vaal river, South Africa. Water SA 22, 33–42. Ruse L. and Love A. (1997) Predicting phytoplankton composition in the River Thames, England. Regulated rivers: Res. Manage. 13, 171–183. Schmidt A. (1994) Main characteristics of the phytoplankton of the Southern Hungarian section of the River Danube. Hydrobiologia 289, 97–108. Sherman B. S. and Webster I. T. (1994) A model for the light-limited growth of buoyant phytoplankton in a
shallow, turbid waterbody. Aust. J. Mar. Freshwater Res. 45, 847–862. Sherr E. B. and Sherr B. F. (1988) Role of microbes in pelagic food webs: a revised concept. Limnol. Oceanogr. 33, 1225–1227. Smayda T. J. (1978) From phytoplankters to biomass. In Phytoplankton manual, ed A. Sournia, pp. 273–279. UNESCO, Paris. Smitz J., Everbecq E., Delie`ge J.-F., Descy J.-P., Wollast R. and Vanderborght J.-P. (1997) PEGASE, une me´thodologie et un outil de simulation pre´visionnelle pour la gestion de la qualite´ des eaux de surface. Tribune de l’Eau. 588, 73–82. Strayer D. L., Powell J., Ambrose P., Smith L. C., Pace M. L. and Fischer D. T. (1996) Arrival, spread, and early dynamics of a zebra mussel (Dreissena polymorpha) population in the Hudson river estuary. Can. J. Fish. Aquat. Sci. 53, 1143–1149. Talling J. F. and Rzo´ska J. (1967) The development of plankton in relation to hydrological regime in the Blue Nile. J. Ecol. 55, 637–662. The´bault J. -M. and Qotbi A. (1999) A model of phytoplankton development in the Lot River (France). Simulations of scenarios. Water Res. 33, 1065–1079. Tilman D. and Kilham S. S. (1976) Phosphate and silicate growth and uptake kinetics of the diatoms Asterionella formosa and Cyclotella meneghiniana in batch and semicontinuous culture. J. Phycol. 12, 375–383. van Donk E. and Kilham S. S. (1990) Temperature effects on silicon- and phosphorus-limited growth and competitive interactions among three diatoms. J. Phycol. 26, 40–50. Van Nieuwenhuyse E. E. and Jones J. R. (1996) Phosphorus-chlorophyll relationship in temperate streams and its variations with stream catchment area. Can. J. Fish. Aquat. Sci. 53, 99–105. Viroux L. (2000) Dynamique du me´tazooplancton en milieu fluvial. Ph.D. Thesis, Department of Biology, FUNDP, Namur, Belgium, 309 pp. Wehr J. D. and Descy J. -P. (1998) Use of phytoplankton in large river management. J. Phycol. 34, 741–749. Welch P. S. (1952) Limnology, 538 pp. McGraw-Hill, New York. Whitehead P. G., Howard A. and Arulmani C. (1997) Modelling algal growth and transport in rivers: a comparison of time series analysis, dynamic mass balance and neural network techniques. Hydrobiologia 349, 39–46.
