Z NQSi:C
2.3 Model Validation
Microalgal growth and primary productivity were the focus of many studies in Antarctic sea ice [e.g. Grossi et al. 1987, Rivkin and Putt 1987, Cota and Sullivan 1990, Thomas et al.
1992, Gleitz and Thomas 1993, Grossmann and Dieckmann 1994]. However, there is no available complete time-series of sea ice microalgal carbon, chlorophyll-a , N:C and Si:C cellular quotas for the pack-ice. Most of the data collected in the field are composed of short snapshots on the chlorophyll-a standing crop, or a few primary production measurements.
On the other hand, some short-term studies in the Arctic were conducted in the last few years [e.g. Gosselin et al. 1997, Sherr et al. 1998]. Mock and Gradinger [2000] did experimental work with sea ice microalgae collected in the Barents Sea at the end of the melting season, between May-June 1997. Since the biological processes that regulate primary production in the Arctic and Antarctic sea ice are comparable, the experimental results of Mock and Gradinger [2000] are used to validate the model.
In their experimental design, three sea ice microalgal batch cultures were obtained from the bottom 5 cm of 33 ice cores and stored at -1.0◦C and salinity 32.4 under continuous illumination (≈ 15 µmol photons m−2 s−1). The cultures were filtered through a 100 µm sieve to exclude large grazers and after three days of acclimation the cultures were enriched with a nutrient mixture and sub sampled in intervals of 2 to 5 days through 25 days. Nutri-ent concNutri-entrations were analyzed according to the standard seawater procedure [Grasshoff et al. 1983], chlorophyll-a by the fluorimetric method [Arar and Collins 1992] and organic particulates (POC and PON) using a Haereus CHN-O-Rapid analyzer. The biogenic silica
in the cultures was calculated over the time variation of dissolved Si concentrations. Data collection and experimental design are discussed in detail by Mock and Gradinger [2000].
Table 2.4: Model parameters found by multidimensional fitting with experimental data and initial conditions for model integration.
Model Parameters
Symbol Value Definition Units
fCmax 0.042 Maximum C-specific light-saturated photosynthetic rate h−1
AN:Cmax 0.083 Maximum C-specific nitrogen uptake rate g N (g C)−1h−1 ASi:Cmax 0.030 Maximum C-specific silicate uptake rate g Si (g C)−1h−1
kN 1.0 Half-saturation constant for N uptake µM [N]
kSi 2.0 Half-saturation constant for Si uptake µM [Si]
αchl 2.5×10−5 Chl-specific photosynthetic efficiency g C (g Chl)−1m2. (µmol photons)−1 QChl:Cmax 0.035 Maximum Chlorophyll-a:Carbon ratio g Chl (g C)−1 QN:Cmin 0.075 Minimum Microalgal N:C cellular quota g N (g C)−1 QN:Cmax 0.190 Maximum Microalgal N:C cellular quota g N (g C)−1 QSi:Cmin 0.090 Minimum Microalgal Si:C cellular quota g Si (g C)−1 ξSi:N 3.8 Dumping factor for Maximum Si:C cellular quota dimensionless
β 10.0 Shape parameter for functionγ dimensionless
n 0.4 Shape parameter for functionAlim dimensionless
k3 0.132 Half-saturation constant for functionγ g N (g C)−1 Initial Conditions
[N]0 43.0 Dissolved nitrogen concentration µM[N]
[Si]0 65.0 Silicate concentration µM[Si]
P0C 53.6 Microalgal carbon biomass µM C
P0Chl 7.14 Microalgal chlorophyll-a biomass µg Chl l−1
P0N 4.41 Microalgal organic nitrogen biomass µM N
P0Si 6.4 Microalgal biogenic silica µM Si
Model parameters were calculated over the average results of the three experiments us-ing the multidimensional fittus-ing method (Downhill Simplex) described by Nelder and Mead [1965]. The maximum carbon-specific light-saturated photosynthetic rate was taken from the experimental data described in the section 2.2.1 (see figure 2.2) and the maximum C-specific nutrient uptake rates were adjusted byANmax:C =fCmaxQNmax:CandASimax:C =fCmaxQSimax:Cto keep the maximum uptake of nutrients in accordance with the maximum carbon assimilation rate.
The model equations where integrated by a Fortran driver adapted from the classical fifth-order Runge-Kutta method with a Cash-Karp adaptive step-size scheme2. Table 2.4
summa-2Fortran algorithms for ODE integration can be found on http://www.netlib.org, including the original
rizes the model parameters found by the multidimensional fitting procedure and the initial conditions used in the model integration. The minimum and maximum nitrogen-to-carbon quotas agree well with observed values commonly found for Antarctic sea ice microalgae and phytoplankton [e.g. Nelson et al. 1989, Garrison and Close 1993, Kristiansen et al.
1992], as well as the Si:C quotas [e.g. Sommer 1991, Nelson and Tr´eguer 1992]. The half-saturation constant for nutrient uptake (kN andkSi) are also in the range of values reported for Antarctic diatoms [Nelson and Tr´eguer 1992, Kristiansen et al. 1992]. However,kSi is considerably lower than previously values found by Jacques [1983] (≈12µM [Si] for Frag-ilariopsis kerguelensis) and Sommer [1991] (≈8µM [Si] for F. cylindrus). Methodological difficulties in using mono-algal cultures can be responsible for the disagreement of values [Nelson and Tr´eguer 1992] and nutrient kinetic studies in sea ice are still necessary to give a better estimate of these parameters. Maximum chlorophyll-to-carbon ratio (QChlmax:C) and the Chl-specific photosynthetic efficiency (αChl) are also in the range of reported values for sea ice microalgae [e.g. Rivkin and Putt 1987, Lizotte and Sullivan 1991; 1992, Thomas et al.
1992].
The model was integrated for 25 days with an adaptive stepsize. The results of the model integration compared with observations are shown in the Figures 2.6 and 2.7. Simulated microalgal carbon biomass and chlorophyll-a present the characteristic exponential growth (Fig. 2.6a) reaching values up to 700 µM C (=8.4 mg C l−1) and 140 µg Chl l−1, respec-tively, showing good agreement with observations. Nutrients are strongly depleted after 11 days (Fig. 2.6b) and completely exhausted after 14 days. Silicate becomes limited at day 11, followed by dissolved nitrogen two days later. The transition between dissolved nutrient limitation and cellular nutrient quota limitation can be better delineated by observing the time evolution of cellular nutrient quotas (Fig. 2.7a). The microalgal cells are apparently nutrient depleted at the beginning of the experiment, increasing their cellular quotas in 3-4 days. When silicate becomes limiting, the Si:C cellular quota starts to decrease, followed by the N:C quota. The exponentially decreasing rate of cellular nutrient pools are followed by a reduction in the growth rate approaching the stationary phase at day 20. The same behavior is also visible when analyzing the chlorophyll-to-carbon and the chlorophyll-to-nitrogen ratios (Fig. 2.7b), where the Chl:N ratio can be used as a proxy to identify the time when nitrogen
Runge-Kutta (RK45) with Fehlberg’s method for timestep splitting. The driver used in this work was adapted from the RK45 described in Press et al. [1992].
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PC
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PChl
a
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[N]
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[Si]
b
Figure 2.6: Model results (solid lines) and experimental data for (a) microalgal carbon biomass (circles) and chlorophyll-a (squares), and (b) dissolved nitrogen (circles) and silicate (squares). The dotted line indicate the time when dissolved nutrients become limited and the dashed line when both dissolved nutrients were completely exhausted. See text for explanation.
becomes limiting. It is important to note that after both dissolved nutrients were exhausted, microalgal biomass is still in exponential growth due to supply of nutrients from the cellular pools. This is the main advantage of Droop’s quota model over typical Michaelis-Menten models [e.g. Fasham et al. 1990, Arrigo et al. 1991], showing that incorporation of carbon biomass can take place even when the medium are totally depleted of dissolved nutrients.
However, the decrease in the chlorophyll-to-carbon ratio (Fig. 2.7b) suggested that microal-gae also lost photosynthetic efficiency (due to the unbalance in the photoacclimation term in equation 2.12 - subsection 2.2.2). Such behavior can be simulated only by coupling the photoacclimation with nutrient co-limitation models. Cullen and Lewis [1988] demonstrated that first-order kinetic models with self-adapting mechanisms are essential to represent rapid responses of algal physiology to transient changes in the light and nutrient supply. The cel-lular nutritional status and the carbon assimilation change as a result of unbalanced growth,
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Q N:C
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a
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Q Chl:C
b
Figure 2.7: Model results (solid lines) and experimental data for (a) cellular N:C quota (circles) and Si:C quota (squares), and (b) chlorophyll-to-nitrogen (circles) and Chl-a:C ratios (squares).
Dotted and dashed lines as in the Fig. 2.6.
but on much longer time scales than the light-harvesting complex. This fact suggests that self-adaptive models are better suited to represent realistic environmental conditions.
Unfortunately, the experiment data exclude heterotrophic protists and there was no in-formation available about the total biomass of protozoa in the sea ice samples. To test the ability of the model to represent grazing, a simulation including a generic protozoa was con-ducted. Model parameters were maintained as described above and initial protozoa biomass was taken to be 1 % of microalgal C-biomass (≈0.53µM C) with a cellular nitrogen concen-tration of 0.08µM N. The maximum ingestion and respiration rates were calculated using equations described in the subsection 2.2.4 with a mean protozoa body mass of 3500 pg C cell−1, and optimum N:C ratioδN:C=0.176, equivalent to the Redfield ratio 6.6 C:N.
The time evolution of microalgal biomass when grazing is included also shows an expo-nential growth following the availability of nutrients (Fig. 2.8a). However, a strong decrease in the microalgal carbon and chlorophyll-a concentrations is observed after nutrients are
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Z C
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P Chl
a
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N-excreted
b
Figure 2.8: Model results including grazing. (a) microalgal carbon biomass (PC), chlorophyll-a (PChl) and protozoa carbon biomass (ZC) as indicated by the arrows. (b) dissolved nitrogen and silicate where the indicated fraction is excreted nitrogen.
hausted. In general, sea ice microalgae grow faster than heterotroph protists under optimal conditions, but growth conditions are unbalanced under limiting resources and the effect of grazing is noticeable [Garrison and Buck 1989]. Dissolved nutrients decrease exponentially accompanying the microalgal growth with a pronounced increment of excreted nitrogen after 20 days (Fig. 2.8b). However, silicate is already limiting and no further microalgal growth is evident.
The nutritional status of microalgae is not affected by grazing (compare Fig. 2.8a and Fig.
2.7). It is interesting to note that the nutritional status of protozoa, represented byQZN:C, is well correlated with the microalgae N:C quota (QN:C) due to the mechanism of selective N-excretion explained in the subsection 2.2.5. A strong signal in the nitrogen-to-carbon and nitrogen-to-chlorophyll ratio can be observed after day 22, when the few microalgal cells still alive begin to take up the excreted nitrogen (Fig. 2.9a).
The model has a relative complex design with a large number of parameters, but its ability
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Figure 2.9: Model results with grazing for (a) microalgal cellular N:C (QN:C) and Si:C quotas (QSi:C), protozoa N:C ratio (QZN:C) and (b) chlorophyll-to-nitrogen and Chl-a:C ratios.
to represent mechanistic processes of self-adaptation in the model components is evident.
The good agreement between model and observations suggests that the partial decoupling of carbon and dissolved nutrient fluxes, as well as the introduction of the photoacclimation model provide the essential information to simulate the time evolution of carbon biomass, chlorophyll-a and nutrients. Such an approach is strongly recommended to study sea ice microbial communities under realistic conditions.
The ability of the model to well represent nutrient dynamics in biological sea ice com-munities in an important aspect to be observed. Nutrient fluctuations within sea ice are much variable and difficult to interpret due to the complexity of the physical processes involved in the flux of brine and the time evolution of biological processes. Brine pockets are al-most closed systems, where the ice permeability is controled by the temperature and sea ice salinity (see Chapter 4). Therefore, dissolved nutrients in brine can be depleted very fast when primary production is high. Dieckmann et al. [1991b] showed that recycling of
nitro-gen takes place within the brine pockets, but dissolution and renitro-generation of silicate occurs problably at very low rates. These facts suggest that the cellular N- and Si-pools play a key role in the primary production when dissolved nutrients are completely exhausted. Since the biological model simulates the uptake of dissolved nutrients decoupled from the carbon pro-duction, it is possible to compute the maximal biomass production based only on the cellular N- and Si-pools.