Ecophysiological growth characteristics and modeling of the onset of the spring bloom in the Baltic Sea

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Journal of Marine Systems 73 (2008) 323 – 337 www.elsevier.com/locate/jmarsys

Ecophysiological growth characteristics and modeling of the onset of the spring bloom in the Baltic Sea Kristian Spilling a,b,⁎, Stiig Markager c a Finnish Environment Institute, PO Box 140, FIN-00250 Helsinki, Finland Tvärminne Zoological Station, University of Helsinki, FIN-10900 Hanko, Finland National Environmental Research Institute, PO Box 358, DK-4000 Roskilde, Denmark b

c

Received 17 May 2006; accepted 9 October 2006 Available online 23 December 2007

Abstract The onset of spring bloom in temperate areas is a transition period where the low productive, winter phytoplankton community is transformed into a high productive spring community. Downwelling irradiance, mixing depth and the ability of the phytoplankton community to utilize the light, are key parameters determining the timing of the onset of the spring bloom. Knowing these parameters would thus provide tools for modeling the spring bloom and enhance our knowledge of ecophysiological processes during this period. Our main objective with this study was to provide data for the growth characteristics of some key species forming the spring bloom in the Gulf of Finland, and to apply those results in a simple dynamic model for the onset of the spring bloom, in order to test if the timing of the spring bloom predicted by the models corresponds to field observations. We investigated the photosynthetic characteristics of three diatoms and two dinoflagellates (Chaetoceros wighamii, Melosira arctica, Thalassiosira baltica, Scrippsiella hangoei and Woloszynskia halophila), at low temperatures (4–5 °C). All of these species are common during spring bloom in the Baltic Sea. Cultures of these species were acclimated to different irradiance regimes prior to measurements of photosynthesis, respiration, pigment concentration and light absorption. We did not find a positive relationship between respiration and growth rate, and we hypothesize that this relationship, which is well established at higher temperatures, is negligible or absent at low temperatures (b 10 °C). Photosynthetic maximum (Pm), and maximum light utilization coefficient (α) was lowest and respiration (R) highest in the dinoflagellates. We made a model of the onset of the spring bloom in the western part of Gulf of Finland, using the obtained data together with monitoring data of mixing depth and water transparency from this area. Model results were compared to field observations of chlorophyll-a (Chl-a) concentration. There was a good agreement between the model predictions and the observed onset of the spring bloom for the diatoms. S. hangoei, however, was not able to reach positive production in the model, and W. halophila had the similar growth characteristics as S. hangoei. Consequently, these species must have other competition strategies enabling them to exist and grow during spring bloom. © 2007 Elsevier B.V. All rights reserved. Keywords: Photosynthesis; Respiration; Dinoflagellates; Diatoms; Baltic Sea; Spring bloom; Phytoplankton competition

⁎ Corresponding author. Finnish Environment Institute, PO Box 140, FIN-00250 Helsinki, Finland. Tel.: +358 9 40300223; fax: +358 9 40300291. E-mail address: [email protected] (K. Spilling). 0924-7963/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.jmarsys.2006.10.012

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1. Introduction The growth of phytoplankton is essential for the behavior and bio-geochemical cycling of matter in pelagic systems. A correct description of this process is therefore essential in dynamic models for aquatic ecosystems. Successful models must be able to describe not only the overall production of organic matter and the distribution of carbon, nitrogen phosphorous etc., but also the approximate distribution in different size classes and among the major taxonomic groups. We are still only in the early stages of making such models (Anderson, 2005; Flynn, 2005). One of the basic requests for such models is data describing the physiological growth characteristics of species or groups. The onset of the spring bloom starts when one or more species in the winter population of phytoplankton are able to obtain significant positive growth rates, which in a light-limited system means that the carbon fixation rate exceeds respiratory and other loss processes. Carbon fixation can be described by the maximum light utilization coefficient (α) and the photosynthetic maximum (Pm) and there are many possible formulations for modeling the relationship between light and photosynthesis (Jassby and Platt, 1976; Markager, 1994; Behrenfeld and Falkowski, 1997). Both α and Pm can be measured with standard methods such as carbon uptake (Steeman-Nielsen, 1952) or oxygen evolution. The other part of the balance, the respiration and other loss processes, is more difficult to measure and also to incorporate in a model. Radioactive tracer techniques are difficult to apply to respiration, so in practice we must rely on the less sensitive oxygen technique. Moreover, respiration is not only governing growth but is also itself a function of the growth rate; the apparent respiration rate is changing with time and depends on the light history of the cells, their physiological status and temperature, among other factors (Langdon, 1993). The focus of this study is spring bloom situation in the Gulf of Finland. Here it is often observed that dinoflagellates compete successfully with diatoms (Niemi, 1975; Kononen and Niemi, 1984; Heiskanen, 1993), in contrast to most temperate marine systems where diatoms dominate the spring bloom (Smayda, 1980; Smayda and Reynolds, 2003). Dominance of diatoms during the spring bloom corresponds with findings in laboratory experiments where diatoms have higher growth rates at light limitation and in particular a lower respiration rate than dinoflagellates (Falkowski and Owens, 1978; Taylor and Pollingher, 1987). An initial approach for investigating the success of dino-

flagellates during the spring bloom in the Baltic Sea is therefore to test the hypothesis that dinoflagellates in general have lower growth potential than diatoms. Our main objective with this study was to provide data describing the growth characteristics of some key species forming the spring bloom in the Gulf of Finland, and apply those results in a simple dynamic model for the onset of the spring bloom, in order to test if the timing of the spring bloom predicted by the model correspond to field observations. Secondly we attempted to describe the relationship between growth and respiration in the laboratory, and finally we wanted to compare the photosynthetic, respiratory and growth characteristics of the key vernal diatoms and dinoflagellates from the Baltic Sea. 2. Materials and methods 2.1. Phytoplankton species Single cells were isolated from natural communities in the Gulf of Finland in 2003 and clonal, but not axenic, cultures of: Chaetoceros wighamii (Cleve) Brightwell, Melosira arctica (Ehrenberg) Dickie, Thalassiosira baltica Grunow, Scrippsiella hangoei (Schiller) Larsen and Woloszynskia halophila (Biecheler) ElbrächterKremp were established. All of these species are common winter/spring species in the Baltic Sea, particularly C. wighamii, T. baltica and W. halophila normally form a major component of the phytoplankton biomass during the spring bloom (Niemi, 1975; Kremp et al., 2005). Initially we did measurements on C. wighamii, M. arctica T. baltica and S. hangoei cultures. There was, however, an ongoing revision of the taxonomy of the Baltic Sea dinoflagellates at the time of this study, and the commonly reported S. hangoei has probably been confused with W. halophila, as they are hard to distinguish from each other under a light microscope (Kremp et al., 2005). As a consequence of this finding, we did measurements of photosynthetic and growth properties also of W. halophila at a later stage, but not all measurements were conducted for this species and consequently this species was not included in the model. 2.2. Culturing The cultures, except W. halophila, were grown in 2 liter glass bottles (Schott) in f/2 media (Guillard and Ryther, 1962; Guillard, 1975) at 4.5 °C, 12:12 L/D cycle and salinity of 6. We obtained the correct salinity by diluting 0.2 μm filtered, seawater (salinty of 32) with

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Milli-Q water. The water was sterilized at 121 °C, 200 kPA for 30 min, and the nutrient stocks were added afterwards through a 0.2 μm filter. The cultures were bubbled with pre-filtered air (0.2 μm), in order to keep the cells in suspension and provide gas exchange. In order to obtain cultures with different growth characteristics we used four different light treatments: 0 (dark bottle), ~ 20, ~ 40 and ~ 65 μmol photons m− 2 s− 1. The light levels where all in the range where light is limiting for growth since the situation before the spring bloom is characterized by a limiting mean light intensity, although the light intensity in periods can be higher, e.g. close to the surface around noon. The dark bottle was covered with aluminum foil and left for 3 days before measurements were conducted. The light gradient was created with neutral density filters. Irradiance was measured with a spherical light meter (Biospherical Instruments) placed inside a culture bottle filled with Milli-Q water. The per capita division rate was monitored daily, and was calculated as divisions day− 1 according to the equation: A ¼ ððln N  ln N 0 Þ=t Þ= ln 2

ð1Þ

where N is the cell number and t is the time in days. The cells were counted using a 1 ml slide placed under an inverted microscope. At least 300 cells were counted. Samplings took place when μ had been stable (b20% day to day change) over several days (3 to 4 days). 2.3. Pigments, light absorption and POC Samples were taken for chlorophyll-a (Chl-a), HPLC pigment analysis, light absorption and particulate organic carbon (POC) and were filtrated on 25 mm glass fiber filters (Advantec GF 75). Filtration volume was adjusted to get a clearly visible color on the filter. Chl-a was extracted in 10 ml, 96% ethanol for 24 h (Jespersen and Christoffersen, 1987), and the concentration was measured on a spectrophotometer (Shimadzu UV-2401PC) at 665 nm using 750 nm as correction for scattering by particles. Pigment concentrations were determined using the HPLC technique. Water samples were gently filtered onto 25 mm Advantec GF 75 glass fiber filters and immediately stored in liquid nitrogen until extraction and analysis. Filters were subsequently transferred to syringes with 2.5 ml methanol, placed in a beaker with ice, sonicated for 30 s using a Vibra-cell sonicator equipped with a 3 mm diameter probe operating at 80% in 5 s pulses (Wright et al., 1991). The extract was

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filtered through 0.2 μm PFTE syringe filters into HPLCvials and diluted with water to a final concentration of 80% methanol immediately before the injection of 100 μl sample. HPLC analyses were performed on a Shimazu LC 10A system with a Supercosil C18 column (250 × 4.6 mm, 5 μm) using a Shimadzu SPD-M10 AVP diode-array detector and a Shimadzu RF-10 AXL fluorescence detector. Pigments were identified and quantified through comparison with known pigment standards, purchased from DHI Water and Environment, Hørsholm, Denmark. Filters for measuring light absorption by phytoplankton were stored in liquid nitrogen and measured within a month from the sampling day. Spectral absorption (300 to 800 nm by 0.5 nm) was detected using a spectrophotometer (Shimadzu UV-2401PC). The value of a blank filter was subtracted, and the absorption was calculated according to Bricaud and Stramski (1990) including a S-factor to correct for scattering of light within the filter. POC filters were allowed to dry and stored at room temperature (20 °C) until the carbon content was measured with a mass spectrometer (Europa Scientific ANCA-MS 20-20). 2.4. Photosynthesis and respiration Photosynthesis was measured in 60 ml tissue culture (TC) flasks (Nunc) submerged in a linear PE-incubator operated at 4.5 °C. Flasks were rotated during the incubation, and a 400 watt Osram HQI-E lamp was used as the light source. Stainless steel meshes were used to attenuate the light. The incubation period was 3 h. Photosynthesis was measured both as oxygen evolution and by uptake of radioactive labeled carbon (SteemanNielsen, 1952) as modified by Markager et al. (1999). Oxygen concentration was measured using a fiber optic oxygen sensor (PreSens GmbH, Fibox 3), calibrated against 0 and 100% air saturation of oxygen before measurements (anoxic water by adding sodium dithionite and oxygen saturated water by bubbling with air). Photosynthesis and respiration were calculated from oxygen measurements taken in the 60 ml TC flasks before and after the incubation period (3 h). The time course of respiration was also followed in 10 ml glass vials covered with aluminum foil, and with magnetic stirring in order to keep the cells in suspension. The oxygen sensor made measurements every 10 s, and the respiration rate was calculated by linear regression. For 14C uptake, activities of 0.28 to 0.93 MBq, depending on Chl-a concentration, were added to 1 liter pooled sample that were distributed to the tissue culture

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flasks. After the incubation 10 ml of the culture was placed in a glass vial and 200 μl 0.5 M HCl were added, the rest of the culture (50 ml) was filtered onto a filter (Advantec GF 75) and placed in a glass vial with 200 μl 0.1 M HCl. The aqueous solution remained for 48, and the filter for 24 h before the scintillation liquid (Ecoscint A, National Diagnostics for filters and Insta-Gel Plus, Packard for 10 ml samples) was added. The radioactivity was measured using a liquid scintillation counter (Beckman LS-1801). The inorganic carbon concentration was calculated from pH and alkalinity, determined by ion-titration. Photosynthetic rates were measured at 12 irradiances ranging from 0 to 1100 μmol photons m− 2 s− 1 for the 14C method and 10 irradiances ranging from 0 to 800 μmol photons m− 2 s− 1 for the oxygen method. In order to describe the photosynthesis–irradiance relationship (PEcurve) we used the model (Webb et al., 1974): P ¼ Pm ð1 exp ðaE=Pm ÞÞ

ð2Þ

where P is photosynthetic production, Pm is photosynthetic maximum, α is the initial slope of the PE-curve and E is irradiance. All units are presented in Table 1. The photosynthetic quotient (PQ) was calculated in the α region of the PE-curve by dividing α O2 with α C. In the Pm region, respiration was added to Pm-O2 in order to get the gross oxygen production, and subsequently gross Pm-O2 divided with Pm-C to get the PQ at Pm. All statistical analyses were conducted with two tailed Student's t-test assuming equal variance, using Microsoft Excel software. The volume of the cells was measured with a particle counter (Elzone 282 PC). The cells were sonicated for 1 min prior to measurement in order to separate cells, and 1.7 ml of liquid was sampled during 30 s using a Table 1 Units and definitions of symbols used in text Symbols

Units

Kd = attenuation coefficient ac = carbon specific absorption a⁎ = chlorophyll specific absorption Ec = compensation point μ = growth rate α = maximum light utilization coefficient ϕmax = maximum quantum yield Pm = photosynthetic maximum PQ = photosynthetic quotient R = respiration

m− 1 m2 (mol C)− 1 m2 (mg Chl-a)− 1 μmol photons m− 2 s− 1 divisions day− 1 mol m2 (mg Chl-a)− 1 (mol photons)− 1 mol (mol photons)− 1 μmol (mg Chl-a)− 1 h− 1 mol O2 (mol C)− 1 mol O2 (mol C)− 1 day− 1

120 μm orifice diameter. The average cell volume (V) was used to calculate the average equivalent spherical diameter (ESD), using the equation: ESD ¼ 2ð3V =4PÞ1=3 :

ð3Þ

All the cells in the dark treatment had been acclimated to low light similar to the lowest light treatment (~20 μmol photons m− 2 s− 1), before transfer into darkness, with the exception of T. baltica, which had been growing at high irradiance (~ 200 μmol photons m− 2 s− 1) before placed in darkness. 2.5. W. halophila The W. halophila culture were kept in 500 ml TC flasks (Greiner bio-one) with a filter cap, enabling diffusion of gasses. Direct measurements of dissolved inorganic carbon were done before measuring primary production with a carbon analyzer (Unicarbo, Elektro. Dynamo) and there was no sign of CO2 limitation (~ 1.5 mmol dissolved inorganic carbon l− 1 and pH N 8). Growth rate was calculated based on cell counts, using the same procedure as for the other species. When μ had been stable over several days we measured the PEcurve with radiolabled carbon ( 14 C). A total of 0.81 MBq were added to 50 ml sample, after homogenization 3 ml was distributed to each of 16 scintillation vials (7 ml), which subsequently were placed directly in a PE incubator (a prototype constructed by B.G. Mitchell, Scripps Institute of Oceanography, University of California, USA). We used 2 dark bottles and 14 different irradiances ranging from 0–550 μmol photons m− 2 s− 1 . After an incubation period of 1.5 h, 50 μl 1 M HCl was added and the scintillation vials were left open for 3 days, after which 4 ml Hi Safe scintillation liquid was added. The radioactivity was measured using a liquid scintillation counter (Wallac Winspectral 1414). Calculation of the PE parameters and determination of Chl-a and POC was done using the same methods as for the other species. Respiration was measured using Winkler titration, using the automatic method of Williams and Jenkinson (1982). The incubation time was 22 h. 2.6. Modeling the onset of spring bloom For the model exercise we obtained monitoring data for Chl-a concentrations, Secchi depth, salinity and temperature profiles (CTD) and irradiance from the sampling station Längden (59° 46.4′N, 23° 15.8′E), located in the western part of Gulf of Finland. Data for

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Table 2 Photosynthetic, respiration and growth characteristics for Chaetoceros wighamii, Melosira arctica, Thalassiosira baltica and Scrippsiella hangoei R

Ec

a⁎

ac

α O2

αC

ϕmax O2 ϕmax C Pm O2 Pm C PQ α PQ Pm %R of Pm

0.248 0.187 0.243 0.280 0.237

10.1 9.6 11.2 12.5 11.1

0.0158 0.0128 0.0151 0.0138 0.0139

5.2 3.7 4.1 3.5 3.8

0.000874 0.000907 0.001119 0.001376 0.001134

0.000444 0.000635 0.000679 0.000703 0.000672

0.0747 0.0940 0.1009 0.1330 0.1093

0.0380 0.0659 0.0613 0.0680 0.0651

253 195 281 252 243

197 175 230 202 202

2.0 1.4 1.7 2.0 1.7

1.4 1.2 1.3 1.4 1.4

11.9 8.9 9.4 8.4 9.7

0.209 6.3 0.0131 5.0 0.262 6.7 0.0152 4.6 0.494 11.0 0.0161 4.1 0.350 16.2 0.0103 2.2 0.369 11.3 0.0139 3.6

0.000812 0.001249 0.001314 0.000989 0.001184

0.000382 0.000545 0.000499 0.000789 0.000611

0.0849 0.1390 0.1237 0.1460 0.1362

0.0399 0.0607 0.0470 0.1166 0.0748

185 222 292 290 268

123 131 190 198 173

2.1 2.3 2.6 1.3 2.1

1.6 1.9 1.8 1.7 1.8

13.4 15.4 8.8 8.3 11.5

0.00 0.32 0.42 0.52

0.274 0.329 0.171 0.232 0.244

25.9 14.1 6.5 11.3 10.6

0.0123 0.0074 0.0074 0.0109 0.0086

2.1 1.9 2.0 3.5 2.5

0.000904 0.000748 0.000851 0.000536 0.000712

0.000728 0.000583 0.000474 0.000380 0.000479

0.0986 0.1658 0.1787 0.0768 0.1404

0.0796 0.1294 0.0996 0.0545 0.0945

269 197 143 118 153

311 179 192 137 169

1.2 1.3 1.8 1.4 1.5

1.0 1.3 0.8 1.0 1.0

14.0 10.7 13.9 15.4 13.5

−0.13 −0.01 0.19 0.30

0.264 0.454 0.494 0.210 0.386

39.6 30.3 17.6 17.2 21.7

0.0103 0.0068 0.0106 0.0151 0.0108

1.7 1.8 2.4 2.7 2.3

0.000459 0.000544 0.000936 0.000668 0.000716

0.000500 0.000365 0.000448 0.000372 0.000395

0.0684 0.1075 0.1354 0.0675 0.1035

0.0747 0.0721 0.0648 0.0377 0.0582

121 136 160 103 133

150 99 117 74 97

0.9 1.5 2.1 1.8 1.8

1.1 1.9 1.9 1.8 1.7

23.7 26.7 27.9 26.5 26.2

Light acclimation μ (μmol P m− 2 s− 1) C. wighamii 0 21 42 60 Mean M. arctica 0 20 42 60 Mean T. baltica 0 18 43 70 Mean S. hangoei 0 13 43 70 Mean

0.07 0.27 0.40 0.68

−0.09 0.27 0.36 0.50

For definitions of symbols see Table 1.

Chl-a concentrations and Secchi depth were averaged for each Julian day across years from 1987 to 2004 so daily values describing the seasonal succession were obtained. Values for the attenuation coefficient for PAR (Kd) were calculated from Secchi depth as Kd = 2.1/Secchi depth. Values for surface irradiance from 2001 to 2005 were averaged on an hourly basis and used to drive the model. Mixing depth was calculated from depth profiles of temperature and salinity from 1987 to 2004. A model for water column production was established for each species from the physiological measurements and the conditions in the water column assuming a constant biomass of 1 mg Chl-a m− 3. Irradiance was calculated at 0.2 m intervals for each hour from surface irradiance and Kd assuming a surface reflection of 6% (Kirk, 1994). Photosynthesis was then calculated from Eq. (2) with the addition of a loss term for respiration, R: P ¼ Pm ð1  expðaE=Pm ÞÞ  R:

ð4Þ

Daily water column production was summed over 24 h and from surface to the mixing depth or to the bottom.

Values for Pm and α (values from 14C-measurements) and R (assuming a RQ of 1) where obtained from Table 2. However, values for α were adjusted for differences in spectral light distribution according to Markager and Vincent (2001) using values for a⁎ (Table 2) and measured spectra from 400 to 700 nm of the incubator lamp and in the water columns (data not shown) at a location about 59° 46.4′N, 23° 15.8′E. No loss factors, except respiration, were included in the model. Hence, the model is not able to predict the development in biomass but only to estimate under which conditions a positive water column production is possible for each of the four species. 3. Results 3.1. Cell characteristics and growth The examined species had different size, which was reflected in the average carbon content per cell (Fig. 1). The Chl-a content per cell was clearly related to the growth irradiance for M. arctica, S. hangoei and Woloszynskia halophila where the Chl-a content per cell

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Fig. 1. The carbon content (open symbols) and maximum light utilization coefficient (α, filled symbols) plotted against the equivalent spherical diameter (ESD) for Chaetoceros wighamii (●), Melosira arctica ( ), Thalassiosira baltica (▼) and Scrippsiella hangoei (x). ESD was calculated according to Eq. (3). Error bars represent S.D. (n = 4).

▪

increased with decreasing irradiance. For C. wighamii and T. baltica, there was no clear trend over the applied light intensities. In the dark treatment, however, the Chl-a content per cell decreased in all species. The dark acclimated T. baltica culture had been at high light intensity prior to the placement in dark, which is the main

reason for the low Chl-a content per cell and smaller cell size in this treatment. The average C:N ratio was 4.11 ± 0.33 (S.D.) and the C:Chl-a ratio was 31.9± 4.4 (S.D.) for all species, and there was no clear trend over the applied light treatments. The ratio between Chl-a and photoprotective pigments was negatively correlated with

Fig. 2. The growth rate of the diatoms: Chaetoceros wighamii, Melosira arctica and Thalassiosira baltica and the dinoflagellates: Scrippsiella hangoei and Woloszynskia halophila growing at different irradiance (12:12 D/L cycle). Filled symbols indicate diatoms and open symbols indicate dinoflagellates.

K. Spilling, S. Markager / Journal of Marine Systems 73 (2008) 323–337 Table 3 Photosynthetic, respiration and growth characteristics for Woloszynskia halophila Light acclimation (μmol P m− 2 s− 1) μ

R

αC

Pm C

W. halophila 0 10 20 40 80 Mean

nd 0.364 nd 0.328 0.388 0.360

0.000287 0.000331 0.000203 0.000245 0.000217 0.000257

51 62 60 69 59 60

− 0.30 0.00 0.08 0.16 0.17

For definitions of symbols see Table 1; nd = not determined.

increasing cell size. Furthermore, the ratio between chlorophylls (a + c) and carotenoids was similar in M. arctica, T. baltica and S. hangoei: 2.3 ± 0.4 (S.D.), but considerably higher in C. wighamii: 9.3 ± 1.2 (S.D.). The dinoflagellates grew at a lower rate than the diatoms (Fig. 2), and at irradiances above the compensation point (N 15 μmol photons m− 2 s− 1) growth rates of S. hangoei and W. halophila were approximately half of growth rates for the diatoms. The slope of the μE-curve was, however, similar for all five species. Growth of the W. halophila culture was leveling off at the highest irradiance (80 μmol photons m− 2 s− 1) while the other species did not reach the maximum growth rate, which for the diatoms is presented in Spilling (2007). In the C. wighamii culture cell divisions were still taking place even after 3 days in darkness, which is the reason for the positive growth rate in darkness.

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3.2. Respiration Respiration was not clearly related to growth for any of the species (p N 0.1). The average respiration rate per carbon unit was 30 to 40% higher for M. arctica, S. hangoei and W. halophila compared to C. wighamii and T. baltica (Tables 2 and 3) but the variation was high and statistically there was no clear evidence to support that there were different respiration rates between any of the species (p N 0.16). However, the respiration rate, as percentage of the photosynthetic maximum, was about two times higher for S. hangoei compared to the three diatoms (Fig. 3). In addition to the long term dark respiration rate we followed the oxygen development over short time intervals. The oxygen consumption of S. hangoei was much higher in light below the compensation point (b15 μmol photons m− 2 s− 1) compared with the dark respiration. Fig. 4 shows an example of this, where S. hangoei cells acclimated to 70 μmol photons m− 2 s− 1 had over 3 times higher oxygen consumption at 5 μmol photons m− 2 s− 1 compared with the rate in darkness. The diatoms did not show any signs of such increased oxygen consumption in low light. There was furthermore no clear change in the dark respiration following the cycles of increasing irradiances for any of the species. 3.3. Photosynthetic properties The two smallest species (C. wighamii and M. arctica) had the most efficient light absorption, and the α-values were on average 20 to 40% higher for these

Fig. 3. Percentage dark respiration of gross oxygen production (bars), and photosynthetic maximum (●) calculated as carbon fixation for Chaetoceros wighamii, Melosira arctica, Thalassiosira baltica and Scrippsiella hangoei. Error bars represent S.D. (n = 4).

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Fig. 4. One example of oxygen development during light–dark shifts conducted with S. hangoei. The light was turned on and off every 15 min; filled symbols are measured in dark, open symbols in light. The line indicates the sequence of the measurements. The concentration of dissolved oxygen was measured every 10 s, and the consumption/production of oxygen was calculated by linear regression form the measurements conducted during every period. Note the enhanced oxygen uptake at low light which was found repeatedly for this species.

species compared to T. baltica and S. hangoei (Fig. 1). The α values were similar for S. hangoei and T. baltica, as were the average Pm measured as oxygen production. There was, however, evidence suggesting that the Pm measured as carbon incorporation was higher for all the diatoms compared with the dinoflagellates (p = 0.02). The calculated carbon incorporation per carbon units, revealed that S. hangoei and W. halophila were N 30% less productive than the diatoms, which corresponds to the lower growth rate in these species (Fig. 2, Tables 2 and 3). Moreover, S. hangoei had the lowest average maximum quantum yield of C. There was no overall correlation between the compensation point (Ec) and the light intensity for growth of the four species, but C. wighamii and M. arctica revealed an increasing Ec with increasing light acclimation. The photosynthetic quotient (PQ) was higher for the diatoms in the light-limited region (α) of the PE-curve, compared to PQ at light saturation (Table 2). We could not, however, detect this difference in the S. hangoei culture. The average PQ values for the diatoms and S. hangoei were 1.5 to 2.1 at light limitation, and 1.0 to 1.8 at light saturation. The dark uptake of carbon was in general less than 1% of Pm for the diatoms but for S. hangoei and W. halophila this percentage was c. 2–3%. For the diatoms the difference between particulate and total dark uptake was generally negligible or low (b 10%) but for S. hangoei, the total uptake was twice the uptake in particulate form. These observations, together with the

elevated respiration rates below Ec, indicate an enhanced metabolism at low light and in darkness for S. hangoei, probably related to excretion of dissolved compounds causing an enhanced bacterial activity and in turn dark uptake of 14C (Markager, 1998). 3.4. Seasonal development of the spring bloom The spring bloom in the Gulf of Finland occurs between Julian days 90 and 150, with a peak value at about day 120 or May 1 (Fig. 5A). At this time water temperature is typically about 0–2 °C. Before day 90, Chl-a concentrations are rather stable at approximately 1 mg Chl-a m− 3. During the bloom, concentrations reach 40 mg Chl-a m− 3 followed by typical summer values of 1 to 7 mg Chl-a m− 3, in some years with another Chl-a peak in July. The majority of the phytoplankton community during the spring bloom is composed of diatoms and dinoflagellates. Their relative biomass is variable from year to year, and Fig. 6 shows an example from a year when their biomass was approximately equal. The available light for photosynthesis is determined by the intensity of surface irradiance, water transparency, and mixing depth. Daily surface irradiance increases from 5 mol photons m− 2 day− 1 in January to a maximum of 71 mol photons m− 2 day− 1 in June. During the onset of the spring bloom in late March and early April, the level is between 35 and 45 mol photons m− 2 day− 1. The only available measure for water transparency is Secchi depth, which varies from 2.5 to 9.2 m

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Fig. 5. A. Average chlorophyll-a concentrations between 0 and 10 m, Julian day no. 90, 120 and 150 are indicated in the figure. B. Kd-values calculated from Secchi depth observations (Kd = 2.1/Secchi depth). C. Mixing depth estimated from the major pycnocline from CTD-profiles.

with a mean value of 5.2 m. When transformed to Kdvalues the overall mean is 0.45 m− 1. Before mid-March (day 100) most values are below 0.5 m− 1 (mean 0.33 m− 1) with a few much higher values, maybe related to resuspension of particles. After mid-March values are highly variable until the autumn (Fig. 5B). Mixing depth varies between 5 m and mixing to the bottom at ~ 55 m (Fig. 5C). There is a general decrease in mixing depth over time during spring (zmix = 71–0.34 ⁎ day no.) and mixing to the bottom is not recorded after day 105 corresponding to mid April. 3.5. Model results The aim of this model exercise was to evaluate the physiological measurements in an ecological context.

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The focus was on the timing of the transition from negative to positive growth rates for a phytoplankton population living in the upper mixed layer. Positive growth rates are a prerequisite for the onset of the spring bloom and the prediction of this shift can therefore be compared to field measurements of Chl-a. We have not attempted to model the phytoplankton biomass since we do not have data for loss processes like grazing or sedimentation. Fig. 7 shows the calculated growth rates for the four species with a Kd-value of 0.33 m− 1 (mean value before the onset of the spring bloom) and a constant mixing depth of 20 m. The three diatom species attain a positive growth rate between day 83 (C. wighamii) and 91 (T. baltica) whereas the model predicts that S. hangoei is unable to grow at these conditions. The mixing depth is highly variable at the time, but 20 m is a realistic average mixing depth (Fig. 5C). There is an overall agreement between model results and field observations for the diatoms. Mixing depth is clearly the model parameter that is most variable and maximum mixing depth, which allows growth, is therefore calculated for each day for the four species (Fig. 8). Increasing mixing depth postpones the time when net growth is possible. A deeper mixing depth increases the respiratory losses relative to the carbon fixation, and higher surface irradiance is required before a positive balance is obtained. Fig. 8 shows that the best performance is found for C. wighamii and M. arctica which at day 90 can have positive growth with mixing down to ~25 m. T. baltica requires mixing depth of less than 19 m at day 90. The S. hangoei can only growth when mixing depth is less than 11 m at day 90 and growth is never possible at mixing depth deeper than 19 m. Light attenuation is also a factor for growth, and the combined effects of Kd and mixing depth is tested in Fig. 9, showing the ‘window’ for growth predicted by the model at day 90. Growth is possible below and to the left of the lines, and the figure shows that C. wighamii and M. arctica have the widest window for growth, closely followed by T. baltica. Growth of S. hangoei is only possible at lower mixing depths and Kd-values, compared to the diatoms. 4. Discussion 4.1. Effect of size The size of a cell is known to influence the light absorption and the absorption cross-section and it is related to the intracellular pigment packaging. Smaller cells will have a more efficient absorption of the light due to less internal self-shading (Kirk, 1994; Fujiki and Taguchi, 2002). As a consequence, the cell size should

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Fig. 6. An example of temporal biomass distribution is shown in this stacked area plot of the of diatom and dinoflagellate biomass, integrated in the upper 30 m at Längden sampling station during spring 2000. The percentage marks the contribution to the total maximum biomass. Redrawn from Spilling et al. (2006).

have a negative influence on the maximum light utilization coefficient (Kirk, 1994; Finkel and Irwin, 2000), as supported by our study (Fig. 1). The difference in Pm is not related to cell size but rather to the different taxa, as the Pm is similar in the diatoms (C. wighamii, M. arctica and T. baltica) but lower for the dinoflagellates (S. hangoei and W. halophila). The limiting factor of photosynthesis at light saturation is in general the turnover time of rubisco (Falkowski and Raven,

1997), and the lower Pm for S. hangoei and W. halophila is probably due to a lower rubisco to Chl-a ratio compared with the diatoms. 4.2. Photosynthesis, growth and respiration The most obvious result was that the diatoms showed higher growth rates and higher maximum photosynthetic capacity than the dinoflagellates (Fig. 3, Table 2). The

Fig. 7. Model results for net growth for the four species in this study assuming a biomass of 1 mg Chl m− 3, a mixing depth of 20 m and a Kd-value of 0.33 m− 1.

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Fig. 8. Model results showing the Julian day when a positive net growth occur as a function of mixing depth, assuming a biomass of 1 mg Chl-a m− 3 and a Kd-value of 0.33 m− 1. The observed onset of the spring bloom at day 90 is indicated with a vertical dashed line.

maximum utilization of light for growth (the α-slope) was not noticeably different, however (Fig. 2). Falkowski et al. (1985) concluded that interspecific differences in growth rate at a given irradiance were not related to the respiration rate, but rather to variance in a⁎ and the Chl-a:C ratio. Although the C:Chl-a ratio was lower in W. halophila there was no clear variation between S. hangoei and the diatoms, and although a⁎ was highest in the two smallest species (C. wighamii and M. arctica), it cannot explain the difference in growth efficiency between the diatoms and S. hangoei. S. hangoei and W. halophila had lower growth rates due to lower photosynthetic output and higher respiration rates, and we suggest that the respiration rate is a key factor causing the differences seen in growth efficiency. The direct respiration measurements were not notably different due to large variation, but the mean respiration was highest for S. hangoei and W. halophila (Tables 2 and 3), and the percent of maximum gross production used in respiration was clearly higher for S. hangoei (Fig. 3). This finding is supported by observations in the literature where dinoflagellates generally have higher respiration rates than diatoms (Taylor and Pollingher, 1987). The photosynthetic quotient (PQ) is always N1 as some of the energy produced in the photosynthetic process is used for other purposes than fixing carbon, mainly to reduce nitrate (Falkowski and Raven, 1997). Increasing PQ with decreasing light has been reported (Megard et al., 1985; Iriarte, 1999), and storage of nitrogen has been shown to be higher at light limiting conditions compared to light saturation (Needoba and

Harrison, 2004). Megard et al. (1985) suggested that relatively more of the photosynthetic energy is channeled at reducing nitrate at low light. Although their hypothesis has been questioned (Williams and Robertson, 1991), our results for the diatoms support an increased PQ at low light. The relatively low C:N ratio in S. hangoei and the diatoms suggests a high degree of nitrogen storage, and the fact that the C:N ratio was

Fig. 9. Conditions for positive growth rates for the four species in the experiments as predicted by the model at Julian day no. 90. Growth is possible at combinations of mixing depth and Kd-values below and to the left of the lines for each of the four species.

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similar in all species indicates that they all have similar storage capabilities per carbon unit. W. halophila, however, had a C:N ratio close to the Redfield ratio suggesting lower capacity for N storage. In studies of respiration rates, there is usually a positive linear relationship between growth rate and respiration (Langdon, 1993). The slope is the fraction of carbon respired per unit carbon assimilated, and the intercept is the rate of the maintenance respiration. In this study we could not detect such a relationship between growth rate and respiration. With a linear regression, a positive slope was detected only for two of the species, and neither of them had strong statistical support. Most measurements of growth rate and respiration have been conducted at higher temperature (~ 20 °C) than what was used in the present study (Langdon, 1993). Verity (1982) found in his studies of the marine diatom Leptocylindrus danicus Cleve, a positive correlation between growth rate and respiration at temperatures ≥ 10 °C, but no correlation at 5 °C. There are also examples of respiration measurements conducted at low temperature (b5 °C) where a negative relationship between growth rate and respiration has been found (Sakshaug et al., 1991). We do not suggest that the slope is negative, but our results and evidence from literature indicate that the relationship between growth rate and respiration is less pronounced or absent at low temperatures (e.g. below 10 °C). Peculiarly, S. hangoei had oxygen consumption over 3 times higher at light below the Ec (b 10 μmol photons m− 2 s− 1) compared with the dark respiration (Fig. 3). The explanation for this is unknown, but it could be related to active pumping of protons across the thylacoid membrane, which would require ATP from mitochondrial respiration. This type of reverse pumping of protons is known to happen when there is a low pH gradient between the stroma and lumen (Falkowski and Raven, 1997). Another possibility is that light stimulates the production of extracellular enzymes related to uptake of dissolved organic substances, which is supported by the enhanced dark uptake in total production compared to particulate production measurements. Low light has previously been shown to stimulate the uptake of dissolved compounds in macrophytes (Markager and Sand-Jensen, 1990). An uncertainty in the presented respiration measurements is bacterial respiration, which was not directly measured. One way to assess the bacterial activity, however, is to examine the dark uptake of carbon, which was low compared with the phytoplankton uptake during light. We therefore assume that the bacterial respiration was negligible, but an improvement of the respiration method we used would be to measure

simultaneously the respiration in both the culture and a filtered sample, where the filter pore size is adjusted to let the bacteria through while keeping the phytoplankton cells out. 4.3. Model There is a good agreement between the field observations of day c. 90 as the onset of the spring bloom, and the model prediction of day 83 to 91 as the first days with positive growth rates for C. wighamii and M. arctica with a mixing depth of 20 m. However, for S. hangoei the models prediction does not match observations as dinoflagellates do bloom at same time as the diatoms (Fig. 8). W. halophila showed similar growth characteristics as S. hangoei indicating that the same conclusion can be drawn for this species. There are several possible explanations for this. If we look at the physiological parameters used in the models, it is clearly the high respiration rates that lead to low growth performance for S. hangoei at low light conditions. High respiration rates are common for dinoflagellates in the literature (Burris, 1977; Falkowski and Owens, 1978; Taylor and Pollingher, 1987) and consistent in our experiments. It is thus unlikely that this parameter should be wide off the mark in the model. One possible explanation is that the motile dinoflagellates can avoid the deep layers and thereby get more light than the average for the mixed layer (Ault, 2000). Another explanation is that the dinoflagellate is mixotrophic and therefore able to grow at light levels below its requirement for phototrophic growth. We have not found direct evidence of mixotrophy for S. hangoei or W. halophila in the literature, but it has been suggested (Olli et al., 1998; Rintala et al., 2007), and mixotrophy is documented for other related genus like Heterocapsa and Alexandrium (Legrand and Carlsson, 1998; Legrand et al., 1998). Before the peak of spring bloom there are inorganic nutrients available, thus the most likely benefit of mixotrophy under the light-limited condition during spring are as a carbon source (Legrand and Carlsson, 1998). There was a large difference between the dark uptake of carbon in the total uptake and the particulate uptake for S. hangoei, suggesting some heterotrophic activity, binding the 14C to small bacteria (coming through the 0.8 μm filter) or alternatively taken up through mixotrophy by the phytoplankton cells (Stoecker, 1999) and released as DOC. Mixotrophy increases respiration rate (Hansen et al., 2000), and could thus be an explanation for the higher respiration rate in the dinoflagellates compared with the diatoms.

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An improvement of the presented model would be to include a dynamic mixing regime as this is clearly more realistic. Such an addition does, however, come with a cost, as it makes it harder to interpret the results. Mixing depth less than Sverdrup's critical depth (Sverdrup, 1953) would give positive production earlier in the season compared with our model, and this positive production can be determined from our data. However, mixing depth deeper than Sverdrup's critical depth impose a loss process, which is harder to define. In addition to respiration, there are other loss processes like grazing or sedimentation. Although grazing and sedimentation losses are relatively low before and at the onset of spring bloom (Lignell et al., 1993; Heiskanen, 1998) such additional loss processes would make the interpretation of a dynamic mixing depth harder to interpret. If a dynamic mixing regime was to be included in the model, realistic loss rates need to be added as well. As we were only interested in modeling the initiation of the spring bloom we decided to keep the model and interpretation of the results simpler, by using a linearly rising mixing depth based on linear regression of observed mixing depths. Our model does not include any of the loss rates, the predicted time for the onset of positive growth might therefore be too early. Another complicating factor is what the 14C-method actually measures. If it measures gross growth the model is correct, but if it actually measures something between net and gross production (Bender et al., 1987) the respiratory loss is overestimated in the model. The two effects counteract each other, which might be the reason for the good agreement with field observations.

one species described by Kremp et al. (2005). The growth and primary productivity of W. halophila was similar to that of S. hangoei when grown under similar conditions in culture, and our result suggest that W. halophila even has lower maximum growth rate and Pm-values compared with S. hangoei. We would thus not expect W. halophila to perform any better than S. hangoei in the model of the onset of the spring bloom. The S. hangoei and W. halophila cultures does not grow any better in nutrient replete sea water (unpublished data), suggesting that there is not any vital substance lacking in the f/2 media. Other reports of growth rates of W. halophila in culture and in mesocosms are similar to the presented μ-rates for this species (Kremp et al., 2005). The growth characteristics for the species we examined confirm the general hypothesis for competition between diatoms and dinoflagellates: the dinoflagellates are not able to compete with the diatoms on growth (Smayda and Reynolds, 2001, 2003). There must consequently be other reasons for the dinoflagellates to be present during the spring bloom in the Baltic Sea. Mixotrophy, as a carbon source, and vertical positioning could be factors enabling the success of these vernal dinoflagellates. Other possibilities could be that dinoflagellates impose a negative effect on diatom growth, i.e. allelopathy, or alternatively that diatoms are more susceptible to grazing or sedimentation. There is little or no information about allelopathy as a factor during spring bloom in the Baltic Sea, but grazing or sedimentation of diatoms seems unlikely to play a vital role as these processes are marginal, at least in the initial phase of the spring bloom (Lignell et al., 1993; Heiskanen, 1998).

4.4. The spring bloom in the Baltic Sea

5. Conclusions

Historical records identify the chain-forming Peridiniella catenata and a single-cell dinoflagellate as dominant species during spring bloom in Gulf of Finland (Niemi, 1973, 1975; Kononen and Niemi, 1984). Newer records has often identified the single-cell species as S. hangoei (Larsen et al., 1995; Kremp and Anderson, 2000; Höglander et al., 2004). However, there exists an isomorphic dinoflagellate, W. halophila, and the identity of S. hangoei and W. halophila has probably been confused in earlier work; W. halophila produce the dominating dinoflagellate cyst found in the sediment and is probably the dominating of the two species (Kremp et al., 2005). In order to avoid confusion, it should be noted that the S. hangoei culture we used in this study has been positively identified as the S. hangoei described by Larsen et al. (1995), and the W. halophila culture has been positively identified as the

Our main aim of this study was to determine important ecophysiological properties of key vernal phytoplankton species from the Baltic Sea. For the examined diatoms, we have shown that the onset of spring bloom can be modeled reliably, indicating that the presented photosynthetic and growth parameters are good estimates of these characteristics, which can be used in developing better models in the future. The respiration rate did not, as expected, increase linearly with growth rate, and we suggest that this relationship is less pronounced or absent in low temperature (b 10 °C). Finally we have shown that cold water dinoflagellates in the Baltic Sea do not match the growth of diatoms during the spring bloom, supporting the general hypothesis that dinoflagellates have lower growth potential than diatoms. Consequently, there must be other reasons why dinoflagellates are abundant during this period in the Baltic Sea.

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