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BY-NC-ND 4.0 license Open Access Published by De Gruyter November 9, 2018

Limited response of a spring bloom community inoculated with filamentous cyanobacteria to elevated temperature and pCO2

Malin Olofsson, Anders Torstensson, Maria Karlberg, Franciska S. Steinhoff, Julie Dinasquet, Lasse Riemann, Melissa Chierici and Angela Wulff
From the journal Botanica Marina


Temperature and CO2 levels are projected to increase in the future, with consequences for carbon and nutrient cycling in brackish environments, such as the Baltic Sea. Moreover, filamentous cyanobacteria are predicted to be favored over other phytoplankton groups under these conditions. Under a 12-day outdoor experiment, we examined the effect on a natural phytoplankton spring bloom community of elevated temperature (from 1°C to 4°C) and elevated pCO2 (from 390 to 970 μatm). No effects of elevated pCO2 or temperature were observed on phytoplankton biovolumes, but a significantly higher photosystem II activity was observed at elevated temperature after 9 days. In addition, three species of diazotrophic filamentous cyanobacteria were inoculated to test their competitive capacity under spring bloom conditions. The toxic cyanobacterium Nodularia spumigena exhibited an average specific growth rate of 0.10 d−1 by the end of the experiment, indicating potential prevalence even during wintertime in the Baltic Sea. Generally, none of the inoculated cyanobacteria species were able to outcompete the natural phytoplankton species at temperatures ≤4°C. No direct effects were found on heterotrophic bacteria. This study demonstrates the highly efficient resistance towards short-term (12 days) changes in abiotic factors by the natural Baltic Sea spring bloom community.


The Baltic Sea covers an area of 377,000 km2 and is the second largest brackish sea on Earth. Due to climate change, the temperature of the Baltic Sea is predicted to increase by 2–5°C over the coming 100 years (HELCOM 2013), while the global atmospheric partial pressure of CO2 (pCO2) will rise from current values of ca. 400 μatm (National Oceanic and Atmospheric Administration, U.S. Department of Commerce, for December 2017) to >970 μatm (IPCC 2013). Uptake of anthropogenic CO2 is causing changes in the marine carbonate system and will result in a CO2-enriched and more acidic state, and a concomitant elevated temperature may alter the biogenic carbon flow. In particular, in areas of high productivity and with temperatures <20°C (Wohlers et al. 2009), increased temperatures may result in higher respiration rates and decreased net productivity rates. These changes are predicted to affect all trophic levels of the planktonic food web, and thereby nutrient and carbon cycling in areas such as the Baltic Sea (Andersson et al. 2015).

Phytoplankton have flexible strategies to cope with environmental heterogeneities, e.g. changes in light conditions and nutrient supply (Boyd et al. 2016). Baltic Sea phytoplankton communities experience large daily variation in pH, e.g. ranging from early morning values of 7.8–8.4 in the afternoon during a summer bloom (Wulff et al. 2018), similar to pH ranges measured in natural aggregates of filamentous cyanobacteria (Ploug 2008). Hence, natural phytoplankton thrive despite large fluctuations in pH and pCO2. Diatoms and dinoflagellates usually dominate the spring bloom (March–May) in the Baltic Proper (Andersson et al. 1994, Wasmund et al. 1998, Groetsch et al. 2016). Thereafter, N2-fixing cyanobacteria like Aphanizomenon sp. (A. Morren ex É. Bornet and C. Flauhault 1886 ‘1888’), Nodularia spumigena (Mertens ex Bornet and Flauhault 1888) and Dolichospermum spp. ((Ralfs ex Bornet and Flauhault) P. Wacklin, L. Hoffmann and J. Komárek 2009) (Larsson et al. 2001) proliferate and contribute with a large input of fixed N2 to the system (Klawonn et al. 2016) and act as a local supply of NH4+ to surrounding organisms (Ploug et al. 2011). Aphanizomenon sp. and N. spumigena may be observed in low concentrations all year round (Suikkanen et al. 2010, Wasmund 2017), with high rates of N2-fixation by Aphanizomenon spp. down to 10°C (Svedén et al. 2015). Whether N2-fixation is carried out at even lower temperatures in the Baltic Sea remains unknown, but expression of nitrogenase genes has been observed locally (Bentzon-Tilia et al. 2015) and in the Arctic (Blais et al. 2012) at temperatures close to freezing. Also, potentially toxic filamentous cyanobacteria formed winter blooms in a temperate lake system at 3°C (Wejnerowski et al. 2018). A satellite-data based study of surface temperature predicts an earlier start of the summer season in the Baltic Sea (Kahru et al. 2016), with an earlier initiation of the spring bloom (Sommer et al. 2012). This may also induce an earlier and more extensive cyanobacterial summer bloom, as warmer temperatures are predicted to favor various groups of cyanobacteria (Fu et al. 2007, Paerl and Huisman 2008, Davis et al. 2009, Karlberg and Wulff 2013).

The multitude of environmental factors and complex interactions between trophic levels affected by climate change hamper qualified predictions on the future biomass and composition of phytoplankton in the Baltic Sea Proper, and on the success of various cyanobacterial groups. To start disentangling these complex interactions, we performed a bi-factorial experiment with a microplanktonic Baltic Sea community and inoculated strains of key diazotrophic filamentous cyanobacteria. Hereby, we aimed to (1) test the resistance of a natural spring bloom community, in terms of biovolume and species composition, to increased temperature and elevated pCO2, and (2) test the potential of diazotrophic filamentous cyanobacteria to compete with the natural spring bloom community under predicted future conditions.

Materials and methods

Experimental setup and sampling procedure

The outdoor experiment was conducted between 23 March and 4 April 2011. The water was collected at the Baltic Sea Proper monitoring station B1 on 17 March 2011 (58°49′N, 17°38′E), with a salinity of 6.6 and a sea surface temperature of ca. 1°C. The collected water was gently sieved through a 200-μm mesh to exclude large grazers. Cultures of the following diazotrophic filamentous cyanobacterial strains were obtained from the Kalmar Algae Collection, Linnaeus University, Kalmar, Sweden: Aphanizomenon sp. (A. Morren ex É. Bornet and C. Flauhault 1886‚ 1888‘), Nodularia spumigena (Mertens ex Bornet and Flauhault 1888) and Dolichospermum spp. ((Ralfs ex Bornet and Flauhault) P. Wacklin, L. Hoffmann and J. Komárek 2009) (KAC16, formerly known as Anabaena sp.), isolated from the Baltic Sea and kept at 10°C, were inoculated into the natural community. The cyanobacteria were pre-cultured for 1 week under nutrient replete conditions, 6°C and ca. 20 μmol photons m−2 s−1. The proportion of inoculated cyanobacteria to the natural community was based on in situ conditions at the nearby monitoring station B1 in June with a ratio of 1:10 between cyanobacteria and diatoms+dinoflagellates (from the database of the Swedish Meteorological and Hydrological Institute, for June 2011), hereby adding ca. 0.1 mm3 l−1 of the three cyanobacteria species all together. Biovolume (mm3 l−1) is recommended by HELCOM as a proxy for Baltic Sea phytoplankton biomass (Olenina et al. 2006).

After filtration and inoculation, the water was divided into 12×4-l Plexiglas aquaria. Triplicates of four treatments were established by combining ambient temperature (1°C) and elevated temperature (4°C) with ambient pCO2 (390 μatm) and elevated pCO2 (970 μatm) in an orthogonal design. The elevated temperature and CO2 levels are according to the predictions by HELCOM (2013) and IPCC (2013) for the coming 100 years. The aquaria were randomly placed in two temperature controlled water baths, recorded with two loggers per basin (HOBO Pendant, Onset Computer Corporation, USA). The basins were exposed to sunlight and covered with green plastic mesh to reduce direct solar irradiance including ultraviolet radiation, mimicking photosynthetically active radiation (PAR, 400–700 nm) at 1 m water depth at station B1. PAR was measured with a LI-1000 datalogger equipped with a Li-Cor UWQ5201 PAR sensor (Li-COR, Lincoln, USA). A Li-COR radiometer equipped with a 2π PAR sensor was used to record irradiances under the mesh throughout the experiment.

The two pCO2 treatments were established by connecting each aquarium with a tube, constantly providing synthetic air (AGA Gas, Linköping, Sweden). The gas was dispersed to the water by plastic air diffusers at a flow rate of ~15 ml min−1. The aquaria were sealed with Plexiglas lids, where small holes were maintained for gas outlet to prevent backpressure build-up. In addition, each aquarium was provided with a submerged tube connected to an external syringe, which was used to transfer subsamples from the aquaria without opening the lids and thus disturbing the pCO2 of the headspace (ca. 5 cm).

Each of the aquaria were subsampled for phytoplankton growth and species composition, bacterial abundance and production, dissolved inorganic nutrients, and carbonate system parameters at around 08:00 h on days 0, 3, 6, 9 and 12. On days 0 and 12, samples for photosynthetic pigment analysis and stoichiometry were collected. After analysis of in situ conditions, additional PO43− of ca. 4 μm of was added at day 1 to avoid limitation. For each of the four treatments, one additional aquarium containing only 0.2-μm filtered seawater was sampled for pH, total alkalinity, temperature and inorganic nutrients, to control for non-organismal effects on the carbonate system.

Determination of the carbonate system

Total alkalinity (AT) and pH were established according to Dickson et al. (2007). Samples for pH determination were 0.2-μm filtered and analyzed spectrophotometrically (Shimadzu UV-2100) at 19.0°C on the total scale (pHT), using a 2-mm solution of m-cresol purple as indicator (Clayton et al. 1995). The pH of the indicator solution was measured and the perturbation of the sample pH by the addition of indicator was corrected according to Chierici et al. (1999).

AT was determined using a potentiometric titration with 0.05 m HCl (Haraldsson et al. 1997). The equivalence point was estimated using Gran evaluation (Gran 1952) and manual titration with a dosimat (876 Dosimat plus, Metrohm) and a pH sensitive electrode (Aquatrode with Pt1000 Thermistor, Metrohm). The accuracy was tested using certified reference material (CRM) batch 97 supplied by A. Dickson, San Diego, USA (Dickson et al. 2007), with an average value of 2206±5.3 μmol kg−1.

Calculations of pCO2, [HCO3], [CO32−] and [CO2] were performed with the chemical speciation model CO2SYS (Pierrot et al. 2006). Measured data for pH at 19.0°C, AT, [PO4], [Si(OH)4], salinity, and temperature were used in the calculation together with the constant for HSO4 determined by Dickson (1990) and the total hydrogen ion scale (pHT). The dissociation constants used for carbonic acid (K1 and K2) were determined as described by Mehrbach et al. (1973) and refitted by Dickson and Millero (1987).

Stoichiometry and analyses of dissolved inorganic nutrients

To determine the concentration of particulate organic carbon (POC) and nitrogen (particulate organic nitrogen, PON), 150-ml samples were filtered onto pre-combusted (400°C, 4 h) GF/C filters (25 mm). The filters were frozen at −20°C, freeze-dried, ground (MM301, Retsch, Haan, Germany), and analyzed on an EA 1108 (CHNS-O, Fisons Instruments, Thermo Fisher Scientific, Waltham, MA, USA) applying 2,5-bis-[5-ert.-butyl-bensoaxzol-2-yl]-thiophen as standard. To determine the concentration of dissolved inorganic NO3+NO2 (dissolved inorganic nitrogen, DIN), PO43− (dissolved inorganic phosphorus, DIP) and Si (dissolved inorganic silicate, DISi), 20 ml of sample was filtered through a 0.2-μm pore-size filter and stored at −80°C, until analyzed within 3 weeks on an autoanalyzer (Grasshoff et al. 1999).

Photosynthetic activity

Photosynthetic activity was estimated by variable chlorophyll fluorescence measurements in photosystem II (PSII), with a WATER-PAM (Pulse amplitude modulated fluorometer) equipped with red light emitting diodes (Walz Mess- und Regeltechnik, Effeltrich, Germany). Samples were dark adapted at in situ temperature for a minimum of 20 min before measurements. Minimum fluorescence (F0′) was determined by applying a low level of light and the maximum fluorescence (Fm′) by exposing the sample to a short saturation pulse of light (>4000 μmol photons m−2 s−1 for 0.6 s). Variable fluorescence (Fv=Fm′–F0′) and maximum quantum yield (Fv/Fm) were determined for all samples. Rapid light curves (RLCs) were performed by measurement of ΔF/Fm′ ((Fm′−F0)/Fm′) of quasi-adapted (15 s) cells at nine levels of actinic light (0, 51, 76, 110, 165, 250, 368, 571 and 851 μmol photons m−2 s−1) in the emitter-detector unit. Relative electron transport rate (rETR) was calculated by effective quantum yield (ΔF/Fm′) multiplied by PAR irradiance. Photosynthetic parameters (rETRmax, Ek, and αPSII) were calculated according to Jassby and Platt (1976), fitted by the Nelder-Mead method in the R package phytotools (Silsbe and Malkin 2015, R Core Team 2016).

Microplanktonic composition and specific growth rates

Samples (50 ml) were preserved with alkaline Lugol’s solution, stored in darkness, and analyzed within 3 months using the Utermöhl method according to HELCOM (2014a). Half of the chamber was viewed at 100× magnification (Zeiss Axiovert 40CFL, Germany) and organisms >30 μm were counted and identified to species level or group (e.g. pennate diatom). Lengths and widths were measured and biovolume (mm3 l−1) was calculated according to Hillebrand et al. (1999). At 200× and 400× magnification, a transect diagonally across the chamber bottom (1000 and 500 μm wide, respectively) was analyzed and organisms >8 μm were counted. The biovolume (mm3 l−1) of organisms >8 μm was thereafter converted into POC based on Menden-Deuer and Lessard (2000). The calculated POC based on microscopy analysis was compared to the measured POC analyzed on GF/C filters, in order to estimate the fraction of organisms or organic material not identifiable by microscopy (ca. 1–8 μm). Specific growth rate was calculated for diatoms, dinoflagellates, and each cyanobacterial species separately, for days 6–12. The specific growth rate (μ d−1)=(ln DB–ln DA)/(tB–tA), where DA is the biovolume (mm3 l−1) at the first day of a period and DB the biovolume at the end of the period, and tA is day A and tB is day B.

Heterotrophic bacterial abundance and productivity

1.5 ml of sample were fixed with 1% glutaraldehyde and stored at −80°C. The samples were thawed, stained with SybrGreen (Invitrogen) and counted on a FASCanto II flow cytometer (Becton Dickinson; Gasol and del Giorgio 2000) using fluorescent beads (True counts, Becton Dickinson) to calibrate the flow rate. Bacterial productivity was measured by [3H]-thymidine incorporation (Fuhrman and Azam 1982) as modified for microcentrifugation by Smith and Azam (1992). Duplicate 1.7 ml aliquots were incubated with [methyl-3H]-thymidine (20 nm final conc., GE Healthcare) in sterile 2.0-ml capacity polypropylene tubes for ca. 1 h at in situ temperature. Duplicate blanks (killed control) were prepared by adding 5% trichloracetic acid prior to the addition of isotopes. Productivity was calculated using 1.4×1018 cells mole−1 thymidine incorporated (average calculated from published Baltic Sea data, SE=0.1×1018 cells mole−1 thymidine, n=73; HELCOM 2014b) and 20 fg carbon cell−1 (Lee and Fuhrman 1987).

Statistical analyses

All data were analyzed for effects of elevated pCO2 and temperature by linear mixed effects (LME) modeling using the package nlme in R (R Core Team 2016, Pinheiro et al. 2017). All models were fitted using restricted maximum likelihood, where sampling day was included as a fixed, categorical factor, and aquarium was included as a random factor (random intercept) to account for repeated measurement over time. Response variables were biovolume of the cyanobacterial species, diatoms, dinoflagellates, ciliates, POC, PON, heterotrophic bacterial abundance and productivity and photosynthetic activity (Fv/Fm, rETRmax, Ek, and αPSII). Multiple comparisons of significant factors were performed by Tukey’s HSD test using the lsmeans package (Lenth 2016) in R. Parametric assumptions were visually verified using boxplots and Q-Q plots (Quinn and Keough 2002) and, if necessary, data were either log (POC, PON and POC:PON) or square root [cell-specific bacterial productivity (CSP), rETRmax, Ek] transformed. In addition, regression analysis was performed between bacterial abundance and biovolume of phytoplankton groups, POC:PON, POC and PON, using Pearson correlation for day 12 (SPSS). Significance was set to p<0.05. Additional multivariate analyses were performed to detect differences between treatments or time on species level and are included as Supplementary material.


In summary, a significantly higher Fv/Fm value was found at elevated temperatures at days 9 and 12, as compared to ambient conditions. For bacterial abundance, interaction effects between temperature and pCO2 were found at day 12, and a higher POC:PON ratio at elevated temperatures as compared to ambient. No treatment effects on the biovolume of diatoms, dinoflagellates or filamentous cyanobacteria were observed. The results are presented in detail below.

Temperature and the carbonate system

As desired, our manipulation generated ambient and elevated temperature conditions from day 1 onwards (Figure 1A). The average pH was 7.70 in all treatments at day 0 (before starting the pCO2 treatment), thereafter the elevated pCO2 remained ca. 0.2 pH units below ambient pCO2 (Table 1). The average total alkalinity (AT) was 1527 μmol kg−1 at day 0, and 1573 at day 12, with no difference between the treatments (Table 1). The pCO2 at day 12 was lower in the treatments with organisms as compared to the controls without organisms.

Figure 1: Environmental conditions during the experiment.(A) Temperature (°C) at ambient (blue: 1°C) and elevated (red: 4°C) conditions. (B) Radiation conditions (PAR, 400–700 nm) during the experiment. Gray areas (A) and error bars (B) indicate standard deviation, n=2.

Figure 1:

Environmental conditions during the experiment.

(A) Temperature (°C) at ambient (blue: 1°C) and elevated (red: 4°C) conditions. (B) Radiation conditions (PAR, 400–700 nm) during the experiment. Gray areas (A) and error bars (B) indicate standard deviation, n=2.

Table 1:

Experimental conditions and treatments, including pHT (presented at in situ temperature), temperature (°C), total alkalinity (AT; μmol kg−1) and pCO2 (μatm).

DayTreatmentpHTTemperature (°C)AT (μmol kg−1)pCO2 (μatm)
01°C 390 μatm7.70±0.023.41520±12802±44
1°C 970 μatm7.71±0.033.41529±6804±47
4°C 390 μatm7.70±0.024.41537±19813±41
4°C 970 μatm7.71±0.014.41520±30797±24
31°C 390 μatm7.81±0.011.01548±10602±6
1°C 970 μatm7.60±0.041.01542±131002±97
4°C 390 μatm7.74±0.044.61546±13746±62
4°C 970 μatm7.57±0.034.61576±111153±85
61°C 390 μatm7.84±0.050.71575±8576±68
1°C 970 μatm7.61±0.050.71579±12988±115
4°C 390 μatm7.84±0.034.51566±5598±43
4°C 970 μatm7.61±0.044.51570±101043±98
ControlNo data4.5/0.7No dataNo data
91°C 390 μatm7.86±0.021.41557±26539±23
1°C 970 μatm7.63±0.051.41567±11933±107
4°C 390 μatm7.80±0.014.41574±5669±22
4°C 970 μatm7.59±0.024.41569±81070±58
121°C 390 μatm7.85±0.020.81556±49548±10
1°C 970 μatm7.58±0.020.81573±61053±50
4°C 390 μatm7.87±0.014.41584±9564±14
4°C 970 μatm7.64±0.054.41580±5978±119

  1. pCO2 was derived from pHT, AT, temperature, salinity (6.6 throughout the experiment), inorganic phosphate and silicate concentration using CO2SYS (Pierrot et al. 2006). Data are presented for each treatment and controls (390 μatm/970 μatm for pHT, AT and pCO2, and 1°C/4°C for temperature). Controls represent aquaria incubated without the addition of any microplankton. Standard deviation is included when available, n=3.

Stoichiometry and dissolved inorganic nutrients

There was a significant interaction between sampling day and temperature for POC and PON concentration [p=0.006 and 0.002, F1,8=13.9 and 16.6, respectively, linear mixed effects (LME)]. The concentrations of POC and PON (Table 2) did not differ between temperatures at day 0 (p>0.132, Tukey’s test), but were significantly higher at ambient temperature as compared to elevated temperature at day 12 (p=0.010 and p<0.0001, respectively, Tukey’s test). The measured POC (>1 μm) was ca. 20–40 times higher than the calculated POC based on microscopy (>8 μm) analysis at day 0, and ca. 46–128 times higher at day 12 (Table 2). There was a significant interaction between sampling day and pCO2 for the POC:PON ratio (p<0.018, F1,8=8.9, LME). Thus, the interaction between sampling day and pCO2 was reflected by a significant difference between pCO2 treatments for POC:PON ratio at day 0 (p=0.005, Tukey’s test), but no difference at day 12 (p=0.999, Tukey’s test). Also, the POC:PON ratio was, overall, significantly higher at elevated temperature than at ambient temperature (p=0.002, F1,8=21.0, LME, Table 2).

Table 2:

Nutrient stoichiometry and POC:PON ratios.

01°C 390 μatm144.5±10.117.3±0.98.4±0.238.8±0.96.1±1.0
1°C 970 μatm168.5±16.319.6±1.68.6±0.236.4±2.06.7±1.6
4°C 390 μatm176.7±43.920.6±4.88.5±0.341.0±3.06.4±1.0
4°C 970 μatm230.7±51.820.8±5.311.3±2.338.5±1.76.4±1.0
31°C 390 μatmNo dataNo dataNo data1.7±0.13.5 ±0.6
1°C 970 μatmNo dataNo dataNo data2.0±0.23.9±0.7
4°C 390 μatmNo dataNo dataNo data<0.1±0.03.6±0.2
4°C 970 μatmNo dataNo dataNo data<0.1±0.03.3±0.9
61°C 390 μatmNo dataNo dataNo data0.1±0.02.4±0.7
1°C 970 μatmNo dataNo dataNo data0.1±0.21.9±0.3
4°C 390 μatmNo dataNo dataNo data0.1±0.11.8±0.8
4°C 970 μatmNo dataNo dataNo data0.1±0.02.0±0.7
91°C 390 μatmNo dataNo dataNo data0.09±0.023.0±0.7
1°C 970 μatmNo dataNo dataNo data0.10±0.082.2±0.5
4°C 390 μatmNo dataNo dataNo data0.12±0.012.4±1.3
4°C 970 μatmNo dataNo dataNo data0.09±0.012.0±0.7
121°C 390 μatm178.0±42.624.9±5.27.1±0.50.1±0.12.9±1.3
1°C 970 μatm193.2±45.828.4±6.66.8±0.10.1±0.01.5±0.5
4°C 390 μatm127.5±25.115.3±3.28.4±0.10.1±0.02.8±0.2
4°C 970 μatm132.0±10.015.0±2.08.9±0.50.1±0.02.2±0.8

  1. Particulate organic carbon (POC) and particulate organic nitrogen (PON) are presented as μm, POC:PON ratio and dissolved inorganic nitrogen:phosphate ratio (DIN:DIP) as mol:mol, and POC organisms, based on microalgal biovolume calculation from microscopy analysis, as μm. Standard deviation is included, n=3.

The concentration of DIN (NO3+NO2) was around 8 μm at day 0 and decreased to below the detection limit (<0.2 μm) at elevated temperatures at day 3, but with a concentration >6 μm at ambient temperature (Figure 2A). Thereafter the concentration of DIN decreased in all treatments, to an average of 0.31 μm at day 12. The concentration of DISi (Si) remained >10 μm in all treatments throughout the experiment (Figure 2B), with lower concentrations at elevated temperature at day 12 as compared to ambient. The concentration of DIP (PO43−) was <0.25 μm in all treatments day 0. From day 1, the concentration of PO43− (DIP) remained >2 μm in all treatments throughout the experiment (Figure 2C). The addition of DIP changed the DIN:DIP ratio from an average of 38.66 at day 0 to an average of 0.03 at elevated temperature and 1.85 at ambient temperature at day 3 and, from day 3 and onwards, the average DIN:DIP ratio remained <0.14 in all treatments (Table 2).

Figure 2: Inorganic nutrient concentrations in different temperature and pCO2 treatments.(A) Dissolved inorganic nitrogen (DIN; NO3−+NO2−). (B) Dissolved inorganic silica (DISi). (C) Dissolved inorganic phosphate (DIP). Error bars indicate standard deviation, n=3.

Figure 2:

Inorganic nutrient concentrations in different temperature and pCO2 treatments.

(A) Dissolved inorganic nitrogen (DIN; NO3+NO2). (B) Dissolved inorganic silica (DISi). (C) Dissolved inorganic phosphate (DIP). Error bars indicate standard deviation, n=3.

Photosynthetic activity and PAR levels

There was a significant interaction between sampling day and temperature on Fv/Fm (p<0.0001, F4,32=28.9, LME). The Fv/Fm decreased from day 0 to day 6 (Figure 3A) and did not differ between temperature treatments at these sampling days (p>0.990, Tukey’s test). Fv/Fm increased from day 9, with a significantly higher ratio at increased temperature as compared to ambient at days 9 and 12 (p<0.0001, Tukey’s test). Interactions between temperature and sampling day were also observed for the RLC-derived photosynthetic parameters (Figure 3B–D), αPSII (p=0.012, F3,19=4.7, LME), rETRmax (p=0.040, F3,19=3.4, LME) and Ek (p=0.004, F3,19=6.1, LME). PAR was on average 150 μmol photons m−2 s−1 at midday during the initiation of the experiment and ranged from 100 to 250 photons m−2 s−1 on average until day 8, when PAR decreased to around 50 μmol photons m−2 s−1 until the end of the experiment (Figure 1B).

Figure 3: Photosynthetic activity of phytoplankton in different temperature and pCO2 treatments.(A) Maximum quantum yield: Fv/Fm. (B) Initial slope of the rapid light curve (photosynthetic efficiency): αPSII. (C) Minimum saturation irradiance: Ek (μmol photons m−2 s−1). (D) Maximum relative electron transport rate: rETRmax. Error bars indicate standard deviation, n=3.

Figure 3:

Photosynthetic activity of phytoplankton in different temperature and pCO2 treatments.

(A) Maximum quantum yield: Fv/Fm. (B) Initial slope of the rapid light curve (photosynthetic efficiency): αPSII. (C) Minimum saturation irradiance: Ek (μmol photons m−2 s−1). (D) Maximum relative electron transport rate: rETRmax. Error bars indicate standard deviation, n=3.

Microplanktonic composition and specific growth rates

Shannon’s diversity index was significantly affected by time (p=0.001, F4,32=6.3, LME) but not by temperature and pCO2; the index was significantly higher at day 0 compared to the following days (p<0.01, Tukey’s test). The biovolume of both centric and pennate diatoms was also significantly affected by time (p<0.0001, F4,32=77.8 and 46.5, respectively, LME). The biovolume of all diatoms (Figure 4B) significantly decreased between days 0 and 3 (p<0.0001, Tukey’s test) but then remained stable in all treatments until day 12. Neither the biovolume of pennate nor of centric diatoms showed any treatment effects at day 12 (Figure 4B). No treatment effects were found for dinoflagellates and ciliates day 12. No differences between the treatments were found for the specific growth rate of diatoms and dinoflagellates between days 6 and 12.

Figure 4: Biovolume (mm3 l−1) of phytoplankton in different temperature and pCO2 treatments (a: 1°C 390 μatm, b: 1°C 970 μatm, c: 4°C 390 μatm, and d: 4°C 970 μatm).(A) Biovolume of filamentous cyanobacteria. (B) Biovolume of ciliates, dinoflagellates, pennate diatoms and centric diatoms. Error bars indicate standard deviation, n=3.

Figure 4:

Biovolume (mm3 l−1) of phytoplankton in different temperature and pCO2 treatments (a: 1°C 390 μatm, b: 1°C 970 μatm, c: 4°C 390 μatm, and d: 4°C 970 μatm).

(A) Biovolume of filamentous cyanobacteria. (B) Biovolume of ciliates, dinoflagellates, pennate diatoms and centric diatoms. Error bars indicate standard deviation, n=3.

The biovolume of the three inoculated filamentous cyanobacteria species (Nodularia spumigena, Aphanizomenon sp. and Dolichospermum sp.; Figure 4A) was significantly affected by time (p<0.0001, F4,32=9.1, 1991.6 and 148.7, respectively, LME), and significantly decreased between days 0 and 6 (p<0.0001, Tukey’s test). Thereafter, the biovolume of N. spumigena slightly increased, but there was no significant difference between treatments at day 12. Aphanizomenon sp. and Dolichospermum sp. were almost non-detectable between days 3 and 9, and cells were only occasionally found at elevated temperature at day 12. For N. spumigena, an average specific growth rate of 0.10±0.13 d-1 was observed between days 6 and 12, with no significant difference between the treatments. No specific growth rate could be calculated for Aphanizomenon sp. and Dolichospermum sp. due to lack of filaments at day 6.

Heterotrophic bacterial abundance and productivity

The bacterial abundance increased for all treatments on days 0–6, followed by a decrease until day 12 (Figure 5A). The bacterial abundance was significantly affected by sampling day (p<0.0001, F4,29=312.4, LME) and pCO2 (p=0.003, F1,8=17.8, LME), with an interaction effect (p<0.0001, F4,29=60.3, LME). There were also interaction effects for day and temperature (p<0.0001, F4,29=76.0, LME), pCO2 and temperature (p<0.0001, F1,8=71.0, LME), and day, temperature and CO2 (p<0.0001, F4,29=44.8, LME). No significant correlations were found between bacterial abundance and groups of phytoplankton. However, significant correlations were found between bacterial abundance and POC:PON ratio (R2=0.41, p=0.024, n=12), POC (R2=0.35, p=0.042, n=12), and PON (R2=0.48, p=0.013, n=12), with high bacterial abundance at high POC and PON concentrations and low POC:PON ratio. The cell-specific productivity (CSP; Figure 5B) was significantly affected by sampling day (p=0.026, F4,32=3.2, LME), gradually decreasing until day 9 (p<0.01, Tukey’s test), and remaining stable on days 9 and 12 (p=0.99, Tukey’s test).

Figure 5: Heterotrophic bacterial abundance and bacterial production in different temperature and pCO2 treatments.(A) Bacterial abundance (109 cells l−1). (B) Cell-specific production (CSP, 10−9 μg C cell−1 d−1). Error bars indicate standard deviation, n=3.

Figure 5:

Heterotrophic bacterial abundance and bacterial production in different temperature and pCO2 treatments.

(A) Bacterial abundance (109 cells l−1). (B) Cell-specific production (CSP, 10−9 μg C cell−1 d−1). Error bars indicate standard deviation, n=3.


This bi-factorial study aimed to test the resistance of a natural spring bloom community to increased temperature and elevated pCO2. In addition, we have tested whether diazotrophic filamentous cyanobacteria, normally dominating the Baltic Sea summer bloom, can compete with the natural spring bloom species under future climate change conditions. Interestingly, neither the biovolume of the natural spring bloom community nor the inoculated filamentous cyanobacteria were affected by elevated pCO2 or temperature conditions after being exposed for 12 days, thus, suggesting at least short-term resistance to fluctuations in the abiotic factors tested. Phytoplankton are known to have strategies to cope with environmental heterogeneities, such as changes in light conditions and nutrient supply, as they are mixed in the water column (e.g. Boyd et al. 2016). While Baltic Nodularia spumigena usually thrives at high temperatures and is mostly found at high densities during late summer (Suikkanen et al. 2010, Klawonn et al. 2016), we found it to be persistent and capable of growing at low temperature (≤4°C). Overall, the filamentous cyanobacteria did not outcompete the natural spring bloom species under these conditions.

Natural spring bloom community response to elevated temperature and pCO2

All PSII parameters (Fv/Fm, αPSII, rETRmax and Ek) decreased together with the drop in DIN concentration and phytoplankton biovolume but recovered by the end of the experiment. The initial decrease in DIN concentration at elevated temperature can possibly be explained by a relatively higher affinity for NO3 in the phytoplankton (Reay et al. 1999). Also, the sudden drop in photosynthetic parameters suggests that the cells became nutrient stressed early on in the experiment (Beardall et al. 2001). Fv/Fm, αPSII, rETRmax and Ek were significantly higher at elevated temperatures as compared to ambient levels after 12 days, indicating that even though the total biovolume of phytoplankton was not yet affected by temperature, the cells were more active. The slow increase in photosynthetic parameters may have been triggered by regeneration of N through the decomposition of the initial standing stock of phytoplankton, or potentially from N2-fixation by Nodularia spumigena. This strain of N. spumigena was recently shown to perform high rates of N2-fixation in culture (Olofsson et al. 2016). From days 9 to 12 we observed a decrease in Si concentration at elevated temperature, suggesting that diatoms were the main contributor to the increase in photosynthetic activity, but this was not reflected in the biovolume (or cell numbers) of diatoms. Silica-scaled chrysophytes have been observed in the Baltic Sea, but a more likely explanation may be that diatoms attached to the walls of the aquaria were missed out in the sampling. Also, the assimilation of Si by diatoms increases with temperature, possibly explaining the decrease from day 9 to day 12 at elevated temperature (Martin-Jézéquel et al. 2000).

No short-term effects of elevated pCO2 were observed on the biovolumes of diatoms and dinoflagellates. Previously documented short-term effects on diatoms include enhanced photosynthesis and carbon production (Wu et al. 2010, Yang and Gao 2012, Liu et al. 2017), while long-term studies have reported decreased growth rate and increased rates of respiration (7 months incubation in Torstensson et al. 2015, 1800 generations in Li et al. 2017). In a large-scale Baltic Sea mesocosm experiment, no effect on primary productivity at elevated pCO2 (ca. 1330 μatm) was observed, while a decreased respiration rate was detected after 31 days (Spilling et al. 2016). These previous studies point at the large variation between species, incubation times and environmental conditions affecting the outcome of experimental treatments. Our study highlights the resistance to short-term fluxes by the phytoplankton community, where no major effects by elevated pCO2 were found after 12 days. However, minor effects on species levels were detected, where the pennate diatom (10–40 μm) and the dinoflagellate Peridiniella catenata (Levander) Balech 1977 were negatively affected by the elevated temperature and pCO2, respectively (Supplementary Tables S1, S2). At day 12, there were higher concentrations of POC and PON at ambient temperature as compared to elevated, which may be related to organisms of 1–8 μm, like nano- and picoflagellates, present in the Baltic (Kuuppo 1994), which was not in focus in this study.

Response of filamentous cyanobacteria to elevated temperature and pCO2

Filamentous cyanobacteria are important N-sources in the Baltic Sea during summer. Overall, Aphanizomenon sp. contributes the most to N2-fixation due to its high biomass (Klawonn et al. 2016) and sustains high N2-fixation rates already at 10°C during early spring (Svedén et al. 2015). In the present study, the DIN:DIP ratio was <3 from day 3 onwards, similar to late-spring conditions, which may stimulate growth of cyanobacteria (Larsson et al. 2001). Anyhow, the filamentous cyanobacteria in our study were not able to outcompete the natural spring bloom species at low temperatures. Recovery was occurring faster at higher temperature, which was interestingly not (yet) reflected in biovolume and specific growth rates. The early acclimation of PSII is most likely occurring faster than what can be observed in growth, as the light-dependent reactions generally respond rapidly and growth encompasses many more processes than photochemistry.

An earlier start of the summer season has recently been predicted in a future climate change scenario (Kahru et al. 2016). Both Aphanizomenon sp. and Nodularia spumigena are present in low concentrations during winter in the Baltic Sea, but so far very little is known about their wintertime activity (Suikkanen et al. 2010, Wasmund 2017). During winter, Aphanizomenon sp. appears in higher abundance than N. spumigena in the Baltic Sea and, thus, possibly exhibits different survival strategies in terms of initial biomass in a pre-bloom situation (Wasmund 2017). Even though the low temperature (≤4°C) is far from optimal growth temperatures for the filamentous cyanobacteria (Suikkanen et al. 2010), the increase in biomass of N. spumigena from day 6 onwards indicates rapid acclimation to the experimental conditions in this strain. Thus, we suggest that N. spumigena has a potential to grow during spring bloom conditions. The biovolume of Aphanizomenon sp. decreased at the initiation of the experiment, as also observed by Wulff et al. (2018), thus, no temperature effects could be observed.

Manipulations of the carbonate system using air bubbling to mimic future climate change scenarios have previously been used successfully (Gattuso et al. 2010, Karlberg and Wulff 2013, Torstensson et al. 2013, 2015, Wulff et al. 2018). A pH difference was established from day 3 onwards, even though specific pCO2 values are difficult to control because pCO2 also varies with photosynthesis and respiration rates (Wulff et al. 2018). Nevertheless, no effects of elevated pCO2 were found on the filamentous cyanobacteria. These results are consistent with some previous studies (Paul et al. 2016, Wulff et al. 2018), while others show increased growth (Wannicke et al. 2012, Eichner et al. 2014). Also, filamentous cyanobacteria are known to cope with daily fluctuations in pH measured both in aggregates (Ploug 2008) and in situ conditions (Wulff et al. 2018).

Response of heterotrophic bacteria to elevated temperature and pCO2

Growth of heterotrophic bacteria is limited by temperature during most of the year in the Baltic Sea (Hagström and Larsson 1984). Nevertheless, no response in bacterial abundance to higher temperature was observed in our experiment, which may indicate that in early spring an increase of 3°C would not stimulate bacterial growth. Consistent with earlier work (Burrell et al. 2017), bacterial abundance was not affected by the increase in pCO2. The observed early increase in abundance and productivity in all treatments (up to day 6) may be due to the release of DOC from surrounding phytoplankton (Engel et al. 2013, Torstensson et al. 2015). After day 6, the decline in bacterial abundance may suggest that bacterial mortality agents (grazers or viruses) increased, especially at higher temperature. Indeed, the grazing by heterotrophic flagellates has been shown to increase with temperature in the Baltic Sea Proper (Riemann et al. 2008). In addition, other work suggests that virus-induced bacterial mortality might be particularly sensitive to temperature variations in cold environments (Vaqué et al. 2017). Hence, while we did not examine viruses and flagellates (<8 μm) in the present experiment, they may potentially play central roles for the biogeochemical cycling of nutrients and energy through the planktonic microbial loop, particularly at the low temperatures prevailing in early spring.

In conclusion, increased pCO2 and temperature did not enhance filamentous cyanobacteria over the natural spring bloom species, but the overall higher photosynthetic activity at elevated temperatures during the latter part of the experiment indicated more active cells within the community. For the toxic Nodularia spumigena, a positive specific growth rate during the second half of the experiment reflects its successful acclimation to temperatures ≤4°C and indicates a putative ecological relevance also during wintertime in the Baltic Sea. Our results highlight the resistance of the natural spring bloom community to short-term variations in abiotic conditions and, as such, suggest only limited microplankton responses to the pCO2 and temperature levels predicted.


This study was supported by the Association of European Marine Biological Laboratories (ASSEMBLE, no. 227799) and by a guest scholarship of the Swedish Institute (00185/2010). We thank Nina Ekstrand for chemical analyses at Kristineberg, and My Mattsdotter Björk for helping out with data analysis. Also, thanks to Monica Appelgren for keeping the algal bank and providing cultures of filamentous cyanobacteria, to the staff at Askö marine field station for helping with water collection, and also to the staff at Sven Lovén centre, Kristineberg, for assistance during the experiment. The funding source did not have a role in the data collection, analysis and interpretation nor in the writing of the present publication.


Andersson, A., P. Haecky and Å. Hagström. 1994. Effect of temperature and light on the growth of micro- nano- and pico-plankton: impact on algal succession. Mar. Biol. 120: 511–520.10.1007/BF00350071Search in Google Scholar

Andersson, A., H.E.M. Meier, M. Ripszam, O. Rowe, J. Wikner, P. Haglund, K. Eilola, C. Legrand, D. Figueroa, J. Paczkowska, E. Lindehoff, M. Tysklind and R. Elmgren. 2015. Projected future climate change and Baltic Sea ecosystem management. AMBIO 44: S345–S356.10.1007/s13280-015-0654-8Search in Google Scholar

Beardall, J., E. Young and S. Roberts. 2001. Approaches for determining phytoplankton nutrient limitation. Aquat. Sci. 63: 44–69.10.1007/PL00001344Search in Google Scholar

Bentzon-Tilia, M., S.J. Traving, M. Mantikci, H. Knudsen-Leerbeck, J.L. Hansen, S. Markager and L. Riemann L. 2015. Significant N2 fixation by heterotrophs, photoheterotrophs and heterocystous cyanobacteria in two temperate estuaries. ISME J.9: 273–285.10.1038/ismej.2014.119Search in Google Scholar

Blais, M., J.-E. Tremblay, A.D. Jungblut, J. Gagnon, J. Martin, M. Thaler and C. Lovejoy. 2012. Nitrogen fixation and identification of potential diazotrophs in the Canadian Arctic. Glob. Biogeochem. Cycl. 26: GB3022.10.1029/2011GB004096Search in Google Scholar

Boyd, P.W., C.E. Cornwall, A. Davison, S.C. Doney, M. Fourquez, C.L. Hurd, I.D. Lima and A. McMinn. 2016. Biological responses to environmental heterogeneity under future ocean conditions. Global Change Biol. 22: 2633–2650.10.1111/gcb.13287Search in Google Scholar

Burrell, T.J., E.W. Maas, D.A. Hulston and C.S. Law. 2017. Variable response to warming and ocean acidification by bacterial processes in different plankton communities. Aquat. Microb. Ecol. 79: 49–62.10.3354/ame01819Search in Google Scholar

Chierici, M., A. Fransson and L.G. Anderson. 1999. Influence of m-cresol purple indicator additions on the pH of seawater samples: correction factors evaluated from a chemical speciation model. Mar. Chem. 65: 281–290.10.1016/S0304-4203(99)00020-1Search in Google Scholar

Clayton, T.D., R.H. Byrne, J.A. Breland, R.A. Feely, F.J. Millero, D.M. Campbell, P.P. Murphy and M.F. Lamb. 1995. The role of pH measurements in modern oceanic CO2 system characterizations: precision and thermodynamic consistency. Deep-Sea Res. Pt II 42: 411–429.10.1016/0967-0645(95)00028-OSearch in Google Scholar

Davis, T.W., D.L. Berry, G.L. Boyer and C.J. Gobler. 2009. The effects of temperature and nutrients on the growth and dynamics of toxic and non-toxic strains of Microcystis during cyanobacteria blooms. Harmful Algae 8: 715–725.10.1016/j.hal.2009.02.004Search in Google Scholar

Dickson, A.G. 1990. Standard potential of the reaction: AgCl(s)+ 1/2H2(g)=Ag(s)+HCl(aq), and the standard acidity constant of the ion HSO4 in synthetic sea water from 273.15 to 318.15 K. J. Chem. Thermodyn. 22: 113–127.10.1016/0021-9614(90)90074-ZSearch in Google Scholar

Dickson, A.G. and F.J. Millero. 1987. A comparison of the equilibrium constants for the dissociation of carbonic acid in seawater media. Deep-Sea Res. I 34: 1733–1743.10.1016/0198-0149(87)90021-5Search in Google Scholar

Dickson, A.G., C.L. Sabine and J.R. Christian. 2007. Guide to Best Practices for Ocean CO2 Measurements, PICES Special Publication 3, North Pacific Marine Science Organization, Sidney, Canada.Search in Google Scholar

Eichner, M., B. Rost and S.A. Kranz. 2014. Diversity of ocean acidification effects on marine N2 fixers. J. Exp. Mar. Biol. Ecol. 457: 199–207.10.1016/j.jembe.2014.04.015Search in Google Scholar

Engel, A., C. Borchard, J. Piontek, K.G. Schulz, U. Riebesell and R. Bellerby. 2013. CO2 increases 14C primary production in an Arctic plankton community. Biogeosciences 10: 1291–1308.10.5194/bg-10-1291-2013Search in Google Scholar

Fu, F.-X., M.E. Warner, Y. Zhang, Y. Feng and D.A. Hutchins. 2007. Effects of increased temperature and CO2 on photosynthesis, growth, and elemental ratios in marine Synechococcus and Prochlorococcus (Cyanobacteria). J. Phycol. 43: 485–496.10.1111/j.1529-8817.2007.00355.xSearch in Google Scholar

Fuhrman, J.A. and F. Azam. 1982. Thymidine incorporation as a measure of heterotrophic bacterioplankton production in marine surface waters: evaluation and field results. Mar. Biol. 66: 109–120.10.1007/BF00397184Search in Google Scholar

Gasol, J.M. and P.A. del Giorgio. 2000. Using flow cytometry for counting natural planktonic bacteria and understanding the structure of planktonic bacterial communities. Scientia Marina 64: 197–224.10.3989/scimar.2000.64n2197Search in Google Scholar

Gattuso, J.P., K. Gao, K. Lee, B. Rost and K.G. Schulz. 2010. Approaches and tools to manipulate the carbonate chemistry. In: (U. Riebesell, V.J. Fabry, L. Hansson and J.P. Gattuso, eds) Guide to best practices for ocean acidification research and data reporting. Publications Office of the European Union, Luxembourg. pp. 41–52.Search in Google Scholar

Gran, G. 1952. Determination of the equivalence point in potentiometric titrations. Part II. Analyst 77: 661–671.10.1039/an9527700661Search in Google Scholar

Grasshoff, K., K. Kremling and M. Ehrhardt. 1999. Methods of seawater analysis. 3rd edition, Wiley-VHC, Weinheim, DE.10.1002/9783527613984Search in Google Scholar

Groetsch, P.M.M., S.G.H. Simis, M.A. Eleveld and S.W.M. Peters. 2016. Spring blooms in the Baltic Sea have weakened but lengthened from 2000 to 2014. Biogeosciences 13: 4959–4973.10.5194/bg-13-4959-2016Search in Google Scholar

Hagström, Å. and U. Larsson. 1984. Diel and seasonal variation in growth rates of pelagic bacteria. In: (J.E. Hobbie and P.J.L. Williams, eds) Heterotrophic activity in the sea. Plenum, New York. pp. 249–262.Search in Google Scholar

Haraldsson, C., L.G. Anderson, M. Hassellov, S. Hulth and K. Olsson. 1997. Rapid, high-precision potentiometric titration of alkalinity in the ocean and sediment pore waters. Deep-Sea Res. I 44: 2031–2044.10.1016/S0967-0637(97)00088-5Search in Google Scholar

Hillebrand, H., C.-D. Dürselen, D. Kirschtel, U. Pollingher and T. Zohary. 1999. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35: 403–424.10.1046/j.1529-8817.1999.3520403.xSearch in Google Scholar

HELCOM. 2013. Climate change in the Baltic Sea Area: HELCOM thematic assessment in 2013, Balt. Sea Environ. Proc. No. 137.Search in Google Scholar

HELCOM. 2014a. Annex C-6: Guidelines concerning phytoplankton species composition, abundance and biomass. In: Manual for Marine Monitoring in the COMBINE Programme of HELCOM. Accessed June 2018.Search in Google Scholar

HELCOM. 2014b. Annex C-11: Guidelines concerning bacterioplankton growth determination. In: Manual for Marine Monitoring in the COMBINE Programme of HELCOM. Accessed June 2018.Search in Google Scholar

IPCC. 2013. Climate change: the physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. In: (T.F. Stocker, D. Qin, G-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A.Y.X. Nauels, V. Bex and P.M. Midgley, eds) Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.Search in Google Scholar

Jassby, A.D. and T. Platt. 1976. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol. Oceanogr. 21: 540–547.10.4319/lo.1976.21.4.0540Search in Google Scholar

Kahru, M., R. Elmgren and O.P. Savechuk. 2016. Changing seasonality of the Baltic Sea. Biogeosciences 13: 1009–1018.10.5194/bg-13-1009-2016Search in Google Scholar

Karlberg, M. and A. Wulff. 2013. Impact of temperature and species interaction on filamentous cyanobacteria may be more important than salinity and increased pCO2 levels. Mar. Biol. 160: 2063–2072.10.1007/s00227-012-2078-3Search in Google Scholar

Klawonn, I., N. Nahar, J. Walve, B. Andersson, M. Olofsson, B.J. Svedén, S. Littmann, M.J. Whitehouse and H. Ploug. 2016. Cell-specific nitrogen- and carbon-fixation of cyanobacteria in a temperate marine system (Baltic Sea). Environ. Microb. 18: 4596–4609.10.1111/1462-2920.13557Search in Google Scholar

Kuuppo, P. 1994. Annual variation in the abundance and size of heterotrophic nanoflagellates on the SW coast of Finland, the Baltic Sea. J. Plankton Res. 16: 1525–1542.10.1093/plankt/16.11.1525Search in Google Scholar

Larsson, U., S. Hajdu, J. Walve and R. Elmgren. 2001. Baltic Sea nitrogen fixation from the summer increase in upper mixed layer total nitrogen. Limnol. Oceanogr. 46: 811–820.10.4319/lo.2001.46.4.0811Search in Google Scholar

Lee, S. and J.A. Fuhrman. 1987. Relationship between biovolume and biomass of naturally derived marine bacterioplankton. Appl. Environ. Microbiol. 53: 1298–1303.10.1128/aem.53.6.1298-1303.1987Search in Google Scholar

Lenth, R.V. 2016. Least-squares means: the R package lsmeans. J. Stat. Softw. 69: 1–33.10.18637/jss.v069.i01Search in Google Scholar

Li, F., J. Beardall, S. Collins and K. Gao. 2017. Decreased photosynthesis and growth with reduced respiration in the model diatom Phaeodactylum tricornutum grown under elevated CO2 over 1800 generations. Global Change Biol. 23: 127–137.10.1111/gcb.13501Search in Google Scholar

Liu, X., Y. Li, Y. Wu, B. Huang, M. Dai, F. Fu, D.A. Hutchins and K. Gao. 2017. Effects of elevated CO2 on phytoplankton during a mesocosm experiment in the sothern eutrophicated coastal water of China. Sci. Rep. 7: 6868.10.1038/s41598-017-07195-8Search in Google Scholar

Martin-Jézéquel, V., M. Hildebrand and M.A. Brzezinski. 2000. Silicon metabolism in diatoms: implications for growth. J. Phycol. 36: 821–840.10.1046/j.1529-8817.2000.00019.xSearch in Google Scholar

Mehrbach, C., C.H. Culberson, J.E. Hawley and R.M. Pytkowicz. 1973. Measurement of the apparent dissociation constants of carbonic acid in seawater at atmospheric pressure. Limnol. Oceanogr. 18: 897–907.10.4319/lo.1973.18.6.0897Search in Google Scholar

Menden-Deuer, S. and E.J. Lessard. 2000. Carbon to volume relationship for dinoflagellates, diatoms and other protest plankton. Limnol. Oceanogr. 45: 569–579.10.4319/lo.2000.45.3.0569Search in Google Scholar

National oceanic and atmospheric administration (NOAA), U.S. Department of commerce., for December 2017.Search in Google Scholar

Olenina, I., S. Hajdu, L. Edler, A. Andersson, N. Wasmund, S. Busch, J. Göbel, S. Gromisz, S. Huseby, M. Huttunen, A. Jaanus, P. Kokkonen, I. Ledaine and E. Niemkiewicz. 2006. Biovolumes and size-classes of phytoplankton in the Baltic Sea. HELCOM Balt. Sea Environ. Proc. 106: 144.Search in Google Scholar

Olofsson, M., J. Egardt, A. Singh and H. Ploug. 2016. Inorganic phosphorus enrichments in Baltic Sea water have large effects on growth, carbon- and N2-fixation by Nodularia spumigena. Aquat. Microb. Ecol. 77: 111–123.10.3354/ame01795Search in Google Scholar

Paerl, H.W. and J. Huisman. 2008. Blooms like it hot. Science 320: 57–58.10.1126/science.1155398Search in Google Scholar

Paul, A.J., E.P. Achterberg, L.T. Bach, T. Boxhammer, J. Czerny, M. Haunost, K.-G. Schulz, A. Stuhr and U. Riebesell. 2016. No observed effect of ocean acidification on nitrogen biogeochemistry in a summer Baltic Sea plankton community. Biogeosciences 13: 3901–3913.10.5194/bg-13-3901-2016Search in Google Scholar

Pierrot, D., E. Lewis and D.W.R. Wallace. 2006. MS Excel Program developed for CO2 system calculations, ORNL/CDIAC-105. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, USASearch in Google Scholar

Pinheiro, J., D. Bates, S. DebRoy and D. Sarkar. 2017. R Core Team. nlme: linear and nonlinear mixed effects models. R package version 3.1–137. in Google Scholar

Ploug, H. 2008. Cyanobacterial surface blooms formed by Aphanizomenon sp. and Nodularia spumigena in the Baltic Sea: small-scale fluxes, pH, and oxygen microenvironments. Limnol. Oceanogr. 53: 914–921.10.4319/lo.2008.53.3.0914Search in Google Scholar

Ploug, H., B. Adam, N. Musat, T. Kalvelage, G. Lavik, D. Wolf-Gladrow and M.M.M. Kuypers. 2011. Carbon, nitrogen and O2 fluxes associated with the cyanobacterium Nodularia spumigena in the Baltic Sea. ISME J. 5: 1549–1558.10.1038/ismej.2011.20Search in Google Scholar

Quinn, G.P. and M.J. Keough. 2002. Experimental design and data analysis for biologists. Cambridge University Press, Cambridge. pp. 173–207.10.1017/CBO9780511806384.009Search in Google Scholar

R Core Team. 2016. R: a language and environment for statistical computing. In R Core Team., Version 3.1.1, R Foundation for Statistical Computing, Vienna, Austria.Search in Google Scholar

Reay, D.S., D.B. Nedwell, J. Priddle and C. Ellis-Evans. 1999. Temperature dependence of inorganic nitrogen uptake: reduced affinity for nitrate at subopimal temperatures in both algae and bacteria. App. Environ. Microb. 65: 2577–2584.10.1128/AEM.65.6.2577-2584.1999Search in Google Scholar

Riemann, L., C. Leitet, T. Pommier, K. Simu, K. Holmfeldt, U. Larsson and Å. Hagström. 2008. The native bacterioplankton community in the central Baltic Sea is influenced by freshwater bacterial species. Appl. Environ. Microbiol. 74: 503–515.10.1128/AEM.01983-07Search in Google Scholar

Silsbe, G.M. and S.Y. Malkin. 2015. Phytotools: Phytoplankton Production Tools., Version 1.0 ed.Search in Google Scholar

Smith, D.C. and F. Azam. 1992. A simple, economical method for measuring bacterial protein synthesis rates in seawater using 3H-leucine. Marine Microbial Food Webs 6:102–114.Search in Google Scholar

Sommer, U., N. Aberle, K. Lengfellner and A. Lewandowska. 2012. The Baltic Sea spring phytoplankton bloom in a changing climate: an experimental approach. Mar. Biol. 159: 2479–2490.10.1007/s00227-012-1897-6Search in Google Scholar

Spilling, K., A.J. Paul, N. Virkkala, T. Hastings, S. Lischka, A. Stuhr, R. Bermúdez, J. Czerny, T. Boxhammer, K.G. Schulz, A. Ludwig and U. Riebesell. 2016. Ocean acidification decreases plankton respiration: evidence from a mesocosm experiment. Biogeosciences 13: 4707–4719.10.5194/bg-13-4707-2016Search in Google Scholar

Suikkanen, S., H. Kaartokallio, S. Hällfors, M. Huttunen and M. Laamanen. 2010. Life cycle strategies of bloom-forming, filamentous cyanobacteria in the Baltic Sea. Deep-Sea Res. PT II 57: 199–209.10.1016/j.dsr2.2009.09.014Search in Google Scholar

Svedén, J.B., B. Adam, J. Walve, N. Nahar, N. Musat, G. Lavik, M.J. Whitehouse, M.M.M. Kuypers and H. Ploug. 2015. High cell-specific rates of nitrogen and carbon fixation by the cyanobacterium Aphanizomenon sp. at low temperatures in the Baltic Sea. FEMS Microbiol. Ecol. 91: fiv131.10.1093/femsec/fiv131Search in Google Scholar

Torstensson, A., M. Hedblom, J. Andersson, M.X. Andersson and A. Wulff. 2013. Synergism between elevated pCO2 and temperature on the Antarctic sea ice diatom Nitzschia lecointei. Biogeosciences 10: 6637–6669.10.5194/bg-10-6391-2013Search in Google Scholar

Torstensson, A., M. Hedblom, M.M. Björk, M. Chierici and A. Wulff. 2015. Long-term acclimation to elevated pCO2 alters carbon metabolism and reduces growth in the Antarctic diatom Nitzschia lecointei. P. Roy. Soc. B-Biol. Sci. 282: 20151513.10.1098/rspb.2015.1513Search in Google Scholar

Vaqué, D., J. Boras, F. Torrent-Llagostera, S. Agusti, J.M. Jesús, E. Lara, Y.M. Castillo, C.M. Duarte and M.M. Sala. 2017. Viruses and protists induced-mortality of prokaryotes around the Antarctic Peninsula during the Austral summer. Front Microb. 8: 241.10.3389/fmicb.2017.00241Search in Google Scholar

Wannicke, N., S. Endres, A. Engel, H.P. Grossart, M. Nausch, J. Unger and M. Voss. 2012. Response of Nodularia spumigena to pCO2 – part 1: growth, production and nitrogen cycling. Biogeosciences 9: 2973–2988.10.5194/bg-9-2973-2012Search in Google Scholar

Wasmund, N. 2017. Recruitment of bloom-forming cyanobacteria from winter/spring populations in the Baltic Sea verified by a mesocosm approach. Boreal Env. Res. 22: 445–455.Search in Google Scholar

Wasmund, N., G. Nausch and W. Matthäus. 1998. Phytoplankton spring blooms in the southern Baltic Sea–spatio-temporal development and long-term trends. J. Plankton Res. 20: 1099–1117.10.1093/plankt/20.6.1099Search in Google Scholar

Wejnerowski, L., P. Rzymski, M. Kokocinski and J. Meriluoto. 2018. The structure and toxicity of winter cyanobacterial bloom in a eutrophic lake of the temperate zone. Ecotoxicology27: 752–760.10.1007/s10646-018-1957-xSearch in Google Scholar

Wohlers, J., A. Engel, E. Zöllner, P. Breithaupt, K. Jürgens, H.-G. Hoppe, U. Sommer and U. Riebsell. 2009. Changes in biogenic carbon flow in response to sea surface warming. PNAS 106: 7067–7072.10.1073/pnas.0812743106Search in Google Scholar

Wu, Y., K. Gao and U. Riebesell. 2010. CO2-induced seawater acidification affects physiological performance of the marine diatom Phaeodactylum tricornutum. Biogeosciences 7: 2915–2923.10.5194/bg-7-2915-2010Search in Google Scholar

Wulff, A., M. Karlberg, M. Olofsson, A. Torstensson, L. Riemann, F.S. Steinhoff, M. Mohlin, N. Ekstrand and M. Chierici. 2018. Ocean acidification and desalination: climate-driven change in a Baltic Sea summer microplanktonic community. Mar. Biol. 165: 63.10.1007/s00227-018-3321-3Search in Google Scholar

Yang, G. and K. Gao. 2012. Physiological responses of the marine diatom Thalassiosira pseudonana to increased pCO2 and seawater acidity. Mar. Environ. Res. 79: 142–151.10.1016/j.marenvres.2012.06.002Search in Google Scholar

Supplementary Material

The online version of this article offers supplementary material (

Received: 2018-01-25
Accepted: 2018-10-02
Published Online: 2018-11-09
Published in Print: 2019-02-25

©2019 Malin Olofsson et al., published by De Gruyter, Berlin/Boston

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