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How does ocean acidification impact and structure? Aliza Ray1 and Joe Vallino2 1Bard College, Annandale-On-Hudson NY 12504 2Marine Biological Laboratory, Center, Woods Hole MA 02543

1 Abstract

Increasing atmospheric CO2 concentration will result in the acidifying and CO2 fertilization of our oceans. Phytoplankton assemblages will be among the affected by theses changes. We tested how coastal New England phytoplankton community photosynthetic productivity and community composition will be changed. This microcosm experiment manipulated the CO2 concentrations to be 370µatm, 925µatm, and 3700µatm over a 3 week incubation period. Treatments showed reduced pH and changed dissolved inorganic levels relative to each treatment. Measured parameters included , Chl a, pCO2, enumeration, and quantification of phytoplankton taxa using and . The present study revealed a coastal community that was slightly affected by ocean acidification. Changes in respiration across the course of a 24 hour period showed net consumption cycles to be greatest at elevated CO2 levels. A species shift was seen from control treatments to elevated treatments and over a 5 day period. We have concluded that future research needs to be done to accurately determine how phytoplankton will be affected by ocean acidification changes.

Key Words:

Ocean acidification, phytoplankton, primary productivity, community composition

Introduction

Elevated atmospheric CO2 levels, primarily due to fuel combustion, has led to increased CO2 fertilization of oceans (Brewer 2009), also known as ‘ocean acidification’.

Currently the atmospheric CO2 concentration is 390ppm, and is expected to increase to 750 ppm or higher by the end of the century (Raven et al. 2005). Global elemental cycles are driven by biological activity. Assessing the impact of ocean acidification on marine is important for understanding how aquatic systems will react. Observing microbe-driven ecosystem function changes to elevated CO2 has proven challenging (Liu et al. 2010) and (Joint et al. 2011). Phytoplankton play an important role as primary producers of aquatic systems. Direct effects between ocean acidification and photosynthetic ability have been observed to increase under elevated pCO2 (Rost et al. 2008). The net rate of organic carbon production determines support for higher trophic levels. Natural phytoplankton assemblages have been shown to enhance under elevated pCO2 (Egge et al. 2009). This study will focus on coastal phytoplankton community because they contribute significantly to global primary productivity (Field et al. 1998).

Changes in community composition can also be caused by elevated pCO2. Global changes in can alter ecosystem services and disturb biogeochemical cycles, such as control the CO2 taken up by the oceans. Different species play different roles in ecosystem

2 dynamics. For example, the size of phytoplankton determines grazing efficiencies and can alter the population structure of grazers. Microbial community structure shifts can mean a loss of biodiversity, compromised of ecosystem robustness, and potentially major consequences for higher trophic levels. Studies that have observed the effects of increased CO2 inputs on phytoplankton systems have found mixed results on phytoplankton assemblages (Nielsen et al. 2010). It is likely that different phytoplankton taxa react differently to ocean acidification. The effects of elevated pCO2 on microzooplankton have shown no consistent effect to ocean acidification on microbial biodiversity and community composition (Suffrian et al. 2008). In the following we describe the responses of natural fall coastal phytoplankton groups to changes in the carbonate chemistry of a microcosm system to determine how future lowered pH levels and increased CO2 may alter functioning and composition. This study focuses on the changes to phytoplankton photosynthetic productivity and community assemblages. To examine the effects we created semi-continuous microcosm culture techniques in which CO2 manipulated environments were periodically pulsed by pulling sampling and returning medium with nutrients and filtered seawater. Here, we report on the results of a 3 week experiment demonstrating effects of elevated CO2 levels of phytoplankton productivity on local North-west Atlantic Ocean assemblages. We discuss the potential ecological and biogeochemical implications of our findings.

Methods

A coastal seawater sample was obtained from Woods Hole, MA (41.5264° N, 70.6736° W) during November. The sample was filtered through 200µm mesh. 1L of sample was allocated to each microcosm with nutrients. concentrations in the microcosms remained at 36µM

KNO3, 52µM NaSiO3, and 2.3µM KH2PO4. The experiment was preformed on triplicates at 925µatm, 3700µatm, and controlled atmospheric levels of 370µatm. Microcosms were incubated at 20˚C on a 12 hour light cycle and were continuously stirred. CO2 and air input were bubbled into the sample at 23 mL min-1. Each day, 10% of the sample was removed and the same volume of 0.45µm filtered seawater and nutrients were added back in. All air and water samples were taken +/- 1 hour of growth lights turning on each day. The microcosms sat undisturbed for 4 days prior to initiation of the pulse chemostat method.

3 10mL of sample was used to measure pH with Accumet pH/conductivity meter (Fisher Scientific). 10mL was used for fluorescence using a Fluorometer (Turner Designs). 80mL of was filtered using 25mm GF/F filters for nutrients. was measured using a modification of the phenol-hypochlorite method (Solorzano 1969) analyzed with a Cary UV Visible Spectrophotometer (Varian). Phosphate was analyzed by a modification of the method of Murphy and Riley (1962) using UV-VIS Spectrophotometer (Shimadzu). was measured using QuickChem Flow Injection Analyzer (LACHET). Filters were dried, and analyzed for molar carbon and with a PerkinElmer 2400 Series II CHN Elemental Analyzer. 10mL of sample was used to measure dissolved inorganic carbon levels. 20mL of

Ascarite scrubbed CO2-free air was drawn into the syringe, 0.2mL of H2SO4 was added, and sample was shaken for 1 minute prior to injection of air. pCO2 in the microcosm head space and

CO2 input to the system was also measured. CO2 consumption was calculated by subtracting -1 pCO2 input from output, then multiplying by flow rate (23mL min ). Dissolved inorganic carbon (DIC) and microcosm air was measured using gas (GC-8A Shimadzu). 50mL of sample was drawn for microscopy. were fixed in alkaline Lugol’s solution (10g iodine, 20g potassium iodide, 10g sodium acetate, in 140ml distilled water) for a concentration of 0.1%, followed by borate-buffered formalin addition of 2.4%, and 3% sodium thiosulfate for a final concentration of 0.1% (Sherr and Sherr, 1993). Preserved sample were filtered onto 25mm white 0.8µm membrane filters (Osmonics). Taxa were identified to the nearest genus or species. Identification was done using differential interference and bright field light microscopy (Zeiss Axio Imager.M2) at 20X and 40X. Additional samples (50mL) were fixed to a final concentration of 5% glutaric dialdehyde. Direct DAPI (4',6-diamino-2-phenylindole dihydrochloride) counts were taken from the glutaraldehyde preserved samples. 1 ml of sample and 50µl of 200µg/ml working solution DAPI was incubated for 5 minutes then drawn onto 1µm black polycarbonate filters. Phosphate buffered saline was used to rinse. Samples were viewed under 20X magnification using blue fluorescence. Samples were quantified: (cells/field of view)´(area of filter covered by sample) cells ml-1 = (field of view area)´(preseravtion dilution factor)´(ml filtered) Flow cytometry was done on live, unpreserved samples using a flow cytometer (BD FACSCalibur) using CellQuest Pro software. 1mL samples were drawn and filtered using 35µm

4 mesh, and 5µL of 1µm beads were added to the sample. Particles were enumerated based on size, complexity, and fluorescence.

Results

Measured pH during the experimental period remained constant once a steady pH was reached (Figure 1). There were significant differences in pH between treatments. The 370µatm (control) maintained a pH of 8.1, 925µatm was 7.6, and 3700µatm was 7.3. Seawater sampled at the start of the experiment measured a pH of 8. The initial level of DIC of the sampled water was 2700µM, and for all treatment levels the DIC changes occurred during the experimental period (Figure 2). Nutrient profiles phosphate and ammonium were measured throughout the course of the experiment. Initial concentrations of ammonium were undetectable, phosphate measured 0.66µM, and nitrate was also undetectable in the seawater sampled. Ammonium levels gradually increased in all treatments over the course of the experiment (Figure 3A). Phosphate levels gradually decreased in all treatments (Figure 3B). Levels of nitrate after day 5 were below detection limit (Figure 3C). No statistically significant differences were found between treatments. a changes between treatments reflect changes based on treatment. Final Chl a measurements reflect significant differences between treatments, with the 3700µatm treatment having the highest Chl a values, and the ambient CO2 treatment measuring the lowest (Figure 4). Molar carbon and nitrogen levels were not significant between treatments, all treatments showed increasing fluctuation of C and N (Figure 5). Measured consumption showed significant difference between treatments.

Positive CO2 consumption values and slopes indicate net photosynthetic productivity, while negative values and slopes indicate net CO2 production. Initial pCO2 values show net production, while CO2(g) values after a 5 day acclimation period show increased consumption. Consumption decreased in both elevated CO2 treatments. The control treatment reached a steady CO2 consumption rate (Figure 6). A diurnal pCO2 cycle shows significantly different consumption values between treatments. Increased consumption is seen in each treatment during the 12 hours the lights were on, except the highest CO2 treatment saw increased CO2 production from the peak of midday until lights turn off at night. All treatments have negative consumption during

5 the 12 hours the lights were not on (Figure 7). Net CO2 consumption values due to photosynthesis (during the period the lights were on) are increased from the 925µatm and

3700µatm treatments (Table 1). Net CO2 consumption during the course of 24 hours is negative only for the 3700µatm treatment, meaning there is more CO2 produced than was consumed. Changes to species composition from the control were seen in both between treatments and changed over a 5 day period. Samples analyzed on day 10 and 15 showed variance between treatments, and some variance between replicates. Sixteen genus’ and one were identified. On day 10, control and 925µatm treatments were dominated by a diverse assemblage of including Skeltonema spp, Guinardia spp, Cylindrotheca spp, spp, Coscinodiscus spp, Leptocylindrus spp, Pleurosigma spp, and Rhizolenia spp. The 3500µatm had additional of Asterionellopsis glacialis and Thalassionema spp (Figure 8). A succession and a shift in abundances occurred during the following 5 day period. Leptocylindrus spp and Thalassionema spp showed increased dominance in all treatments at each CO2 level. Cylindrotheca spp, previously seen on day 10 in all treatments, was no longer seen in the 3700µatm microcosms. Asterionellopsis glacialis became abundant in the elevated 3700µatm treatment, and Skeletonema spp remained dominant at all treatment levels (Figure 9). The dionglagellate Ceratium spp was present in one replicate at 925µatm on day 10. Nanoplankton and community structure dynamics show no significant difference between treatments. All treatments experienced a shift from a picoplankton dominance to an nanoplankton dominance (Figure 10). In all treatments nanoplankton abundance increased, while picoplankton abundance decreased (Table 2).

Discussion

The aim of the study was to how predicated ocean acidification would affect primary productivity and community composition of coastal phytoplankton assemblages. Microcosms experienced increased acidity and altered DIC levels respective to each treatment level. This established that the microcosm environment achieved a state that mimicked carbonate processes occurring due to ocean acidification.

6 Despite relatively low nitrate levels, there was growth in the microcosms. Daily nitrate additions of nutrient stock were enough to sustain growth, despite levels being undetectable 24 hours later. Growth of phytoplankton in each treatment was observed. The initial drop in DIC over the 5 day assimilation period occurred because of extreme growth in all treatments.

Photosynthetic causes a CO2 and DIC decrease. Phosphate levels decreased significantly as well in all treatments, indicating growth, due to phosphate uptake. In addition, Chlorophyll a levels of each treatment level had shown significant differences by the end of the experiment. Increased ammonium can mean more grazing on phytoplankton by larger , however, based on microcopy, no grazers were identified in any of the treatments. Nitrate uptake by phytoplankton has been shown to inhibit by ammonium levels (l'Helguen et al. 2008).

Phytoplankton productivity, measured by the consumption of CO2, in all microcosms increased during the first half of the experimental period. Decreased productivity in the elevated

CO2 treatments does not mean that consumption was not occurring, only that consumption was lowered. Further examination of the carbonate chemistry of the systems would allow for calculations of CO2(aq) in the water. The 24 hour diurnal cycle shows the fluctuations of CO2 consumption throughout the day. CO2 consumption is greatest in the elevated treatments. The

3700µatm treatment experienced net CO2 production during the course of 24 hours. When photosynthesis was occurring during the lights on cycle, CO2 consumption occurs in all treatments. CO2 consumption was greatest at the 3700µatm treatment level. Further works need to be done to determine the CO2(aq) of the microcosms and carbonate chemistry changes to the systems. Diatom dominated systems have shown 27% to 39% CO2 uptake increases from control (~370µatm) (Riebesell et al. 2007). Molar carbon level differences between treatments do not indicate elevated carbon content at higher CO2 treatment levels. This could be due to carbon content difference based on diversity of species seen. The initial species dominance shift showed differences between treatments, primarily diatoms. Resilience of Skeletonema spp in 3700µatm treatments was consistent with findings of previous studies (Nielsen et al. 2012). The abundance of Asterionellopsis glacialis under elevated CO2 was especially apparent. Individual phytoplankton physiology or nutrient

7 availability have not been closely assessed. Coastal phytoplankton communities have also been observed to be impervious to CO2 elevated changes (Nielsen et al. 2010). While we acknowledge the changes to species composition changes we saw, we also suggest that coastal phytoplankton species could be tolerant of broad level pH fluctuations due to respiratory and photosynthetic processes (Hansen 2002). Our phytoplankton may have been showing resilience to abrupt CO2 changes, previously seen (Vogt et al. 2008). Flow cytometry results did not yield significant changes between treatment levels of nanoplankton (2-20µm) and picoplankton (0.2-2µm) populations. Results were evidence of a microcosm experiment, in which all treatments experienced a bottle-effect, in which nanoplankton abundance increased, and picoplankton abundance decreased. Previous studies have found only slight changes to picoplankton community composition under elevated CO2 levels (Newbold et al. 2012).

In a high CO2 ocean, phytoplankton may over-consume CO2, increasing their C:N ratio (Toggweiler 1993). Elevated molar carbon level increased in all treatments, and the 925µatm and 3700µatm microcosm did not show significant differences in higher molar C compared to control treatments. Increased CO2 and increased light have been found to decrease at light intensities representative of surface layer light levels (Gao et al 2012).

Further research on the effect of increasing CO2 levels on coastal, estuarine, and open- ocean phytoplankton community assemblages. Our research is an example of one coastal scenario. Both individual species effects and community productivity and resilience impacts need to be further examined. Multiple environmental factors, such as nutrient level changes, need to be examined in isolation of other variables, because of phytoplankton sensitivity to light and nutrient levels. The ability of certain species to adapt to sudden pH changes is largely unknown. It is important to study the impacts of ocean acidification to better understand its implications.

Acknowledgements

This exploration would not have been possible without the generosity of everyone at the Marine Biological Lab. Joe Vallino for all his help, tremendous guidance, and carbonate chemistry lessons. Hugh Ducklow, Matthew Erickson, Hap Garritt, and Ken Foreman for sharing their lab and equipment. Jim McIlvain for endless Zeiss microcopy tutorials. Rich McHorney, Alice Carter, and Carrie Harris for their endless patience.

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Literature Cited

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Field C.B., Behrenfeld, M.J., Randerson, J.T., Falkowski, P. 1998. Primary production of the : integrating terrestrial and oceanic components. Science 281: 237-40.

Gao, K., Xu, J., Gao, G., Li, Y., Hutchins, D.A., Huang, B., Wang, L., Zheng, Y., Jin, P., Cai, X., Hader, D.P., Li, W., Xu, K., Liu, N., Ribesell, U. 2012. Rising CO2 and increased light exposure synergistically reduce marine primary productivity. Climate Change 2: 519-523

Hansen P.J. (2002) Effect of high pH on the growth and survival of marine phytoplankton: implications for species succession. Aquatic Microbial 28: 79-88

Joing, I., Doney, S.C., Karl, D.M. 2011. Will ocean acidification affect marine microbes? The ISME Journal, 5: 1-7.

Kemp, P.F., Cole, J.J., Sherr, B.F., Sherr, E.B., 1993. “Handbook of Methods in Aquatic ”. Lewis Publishers, Ann Arbor- Lugol’s protocol

L'Helguen, S., Maguer, J.F., Caradec, J. 2008. Inhibition kinetics of nitrate uptake by ammonium in size-fractionated oceanic phytoplankton communities: implications for new production and f-ratio estimates. Journal Plankton Research. 2008 30: 1179-1188

Lau, J., Weinbauer, M.G., Maier, C., Dai, M., Gattuso, J.P. 2010. Effect of ocean acidification on microbial diversity and on microbe-driven biogeochemistry and ecosystem functioning. Aquatic Microbial Ecology, 61: 291-305.

Murphy, J., Riley, J.P, 1962. A modified single solution method for the determination of phosphate in natural waters. Analytica Chemica Acta 27: 31-6

Newbold, L.K., Oliver, A.E., Booth, T., Tiwari, B., DeSantis, T., Maguire, M., Andersen, G., van der Gast, C.J., Whiteley, A.S. 2012. The response of marine picoplankton to ocean acidification. Environmental 14(9): 2293-307

Nielsen, L.T., Jakobsen, H.H., Hansen, P.J. 2010. High resilience of two coastal plankton communities to twenty-first century seawater acidification: evidence from microcosm studies. Marine Research: 6(6): 542-55

9 Nielsen, L.T., Hallegraeff, G.M., Wright, S.W., Hansen, P.J. 2012. Effects of experimental seawater acidification on an estuarine plankton community. Aquatic Microbial Ecology 65: 271-85

Raven, J., Caldeira, K., Elderfield, H., Hoeg-Guldberg, O. 2005. Ocean acidification due to increasing atmospheric carbon dioxide. Policy Document 12/05, The Royal Society, London, available at: www.royalsoc.ac.uk

Riebesell, U., Schulz, K.G., Bellerby, R.G.J., Botros, M., Fritsche, P., Meyerhofer, M., Neill, C., Nondal, G., Oschlies, A., Wohlers, J., Zollner, E. 2007. Enhanced biological carbon consumption in a high CO2 ocean. Nature, 450: 545-8.

Rost, B., Zondervan, I., Wolf-Gladrow, D. 2008. Sensitivity of phytoplankton to future changes in ocean carbonate chemistry: current knowledge, contradictions and research directions. Mar Ecological Progress Series, 373: 227-37.

Suffrian, K., Simonelli, P., Nejstgaard, J.C., Putzeys, S., Carotenuto, Y., Antia, A.N. 2008. Microzooplankton grazing and phytoplankton growth in marine mesocosms with increased CO2 levels. Biogeosciences Discussions 5: 411-433.

Solórzano, L. 1969. Determination of in Natural Waters by the Phenol Hypochlorite Method. and Oceanography 14.5: 799-801

Toggweiler, J.R. 1993. Carbon overconsumption. Nature 363: 210-11

Vogt, M., Steinke, M., Turner, S., Paulino, A., Meyerhofer, M., Riebesell, U., LeQuere, C. and Liss, P. 2008. Dynamics of dimethylsulphoniopropionate and dimethylsulphide under different CO2 concentrations during a mesocosm experiment. Biogeosciences 5: 407-419.

10 Figures and Tables

Figure 1. pH levels of each treatment over time. Each treatment reached a constant pH, statistical significance is reached. The control treatment, 370µatm pH 8.1, 925µatm pH 7.6, and 3700µatm pH 7.2. Symbols and bars are mean ± SD (n=3) Figure 2. Treatment profiles of dissolved inorganic carbon levels, differences between treatments are statistically significant. Symbols and bars are mean ± SD (n=3) Figure 3. Comparison of ammonium, phosphate, and nitrate levels. Increased ammonium (A), decreased phosphate (B), and decreased nitrate (C) levels to the point of no detection were observed in all treatments, no significantly significant differences were seen. Symbols and bars are mean ± SD (n=3) Figure 4. Chlorophyll a levels between treatments. The elevated CO2 treatments reached statistically significant differences from the control treatment. Symbols and bars are mean ± SD (n=3) Figure 5. Molar carbon (A) and nitrogen (B) levels of each treatment profile. Symbols and bars are mean ± SD (n=3)

Figure 6. Treatment profiles of CO2 consumption. Positive values indicate consumption of CO2,

while negative values indicate CO2 production. Symbols and bars are mean ± SD (n=3) Figure 7. Diurnal cycle. First arrow indicates lights turned on at 8AM and second arrow is when lights turned off at 8PM. Symbols and bars are mean ± SD (n=3) Table 1. Net CO2 consumption during the day and over 24 hours (subtracting respiration). Standard deviation (n=3) Figure 8. Phytoplankton community composition at day 10. Genus’s were identified and enumerated. Figure 9. Phytoplankton community composition at day 15. Genus’ were identified and enumerated. Figure 8 and 9 legend. Figure 10. Community composition change of picoplankton and nanoplankton. (n=3) Table 2. Changes in abundance of the nanoplankton and picoplankton population in each treatment. (n=3)

11 Appendix

8.4

8.2

8

7.8

pH 7.6

7.4

7.2

7 0 7 14 21 Time (Days)

370µatm 925µatm 3700µatm

Figure 1: pH levels of each treatment over time. Each treatment reached a constant pH, statistical significance is reached. The control treatment, 370µatm pH 8.1, 925µatm pH 7.6, and 3700µatm pH 7.2. Symbols and bars are mean ± SD (n=3)

12 3100

2900

2700

2500

2300 [CO2] µM µM [CO2]

2100

1900

1700 0 2 4 6 8 10 12 14 16 18 Time (Days)

370µatm 925µatm 3700µatm

Figure 2: Treatment profiles of dissolved inorganic carbon levels, differences between treatments are statistically significant. Symbols and bars are mean ± SD (n=3)

13 20

15

] (µM) ] 10

+

4 [NH 5

0 0 5 10 15 20 A 1.00

0.80

0.60

] (µM) ]

3 -

4 0.40 [PO

0.20

0.00 0 5 10 15 20 B 10

8

6

] (µM) ] -

3 4 [NO 2

0 0 5 10 15 20 Time (Days)

370µatm 925µatm 3700µatm C Figure 3: Comparison of ammonium, phosphate, and nitrate levels. Increased ammonium (A), decreased phosphate (B), and decreased nitrate (C) levels to the point of no detection were observed in all treatments, no significantly significant differences were seen. Symbols and bars are mean ± SD (n=3)

14

9.00

7.00

)

1 -

5.00 Chl a a (µg L Chl

3.00

1.00 9 11 13 15 17 Time (Days)

370µatm 925µatm 3700µatm

Figure 4: Chlorophyll a levels between treatments. The elevated CO2 treatments reached statistically significant differences from the control treatment. Symbols and bars are mean ± SD (n=3)

15

800

600

400

200 Molar carbon carbon Molar (µmol C)

0 0 3 6 9 12 15 18 A

50

40

30

20

10 Molar nitrogen nitrogen Molar (µmol N) 0 0 3 6 9 12 15 18 Time (Days)

370uatm 925uatm 3700uatm B Figure 5: Molar carbon (A) and nitrogen (B) levels of each treatment profile. Symbols and bars are mean ± SD (n=3)

16

600

)

1 -

day 400

1 1 - 200

0 0 5 10 15 20 -200

-400

consumption consumption (µmol L

2

CO -600 Days

370µatm 925µatm 3700µatm

Figure 6: Treatment profiles of CO2 consumption. Positive values indicate consumption of CO2, while negative values indicate CO2 production. Symbols and bars are mean ± SD (n=3)

17

400

1) - 200

0 0 8 16 24 -200

-400

-600

-800 CO2 consumption consumption CO2 (µmol day -1000 Time (Hours)

370µatm 925µatm 3700µatm

Figure 7: Diurnal cycle. First arrow indicates lights turned on at 8AM and second arrow is when lights turned off at 8PM. Symbols and bars are mean ± SD (n=3)

18 370µatm 925µatm 3700µatm Net CO2 consumption during 104 388 380 the day (µmol day- 1) Net CO2 consumption over 20 318 -172 24 hours SD 9 40 20 Table 1: Net CO2 consumption during the day and over 24 hours (subtracting respiration). Standard deviation (n=3)

19 370µatm replicates

925µatm replicates

3700µatm replicates

Figure 8: Phytoplankton community composition at day 10. Genus’s were identified and enumerated.

20 370µatm replicates

925µatm replicates

3700µatm replicates

Figure 9: Phytoplankton community composition at day 15. Genus’ were identified and enumerated.

Skeletonema spp- Diatom Thalassionema spp- Diatom Guinardia spp- Diatom Ceratium spp- Dinoflagellate Cylindrotheca spp- Diatom Actinoptychus spp- Diatom Chaetoceros spp- Diatom Ditylum spp- Diatom Coscinodiscus spp- Diatom Odontella spp- Diatom Leptocylindrus spp- Diatom Unidentified Pleurosigma spp- Diatom Pseudo-nitzschia spp- Diatom Rhizosolenia spp- Diatom Attheya spp- Diatom Asterionellopsis glacialis- Diatom Figure 8 and 9 legend

21 370µatm 925µatm 3700µatm

100% 100% 100%

75% 75% 75%

50% 50% 50%

25% 25% 25%

0% 0% 0% 0 6 14 17 20 0 6 14 17 20 0 6 14 17 20 Time (Days) Time (Days) Time (Days)

Pico Nano Pico Nano Pico Nano

Figure 10: Community composition change of picoplankton and nanoplankton. (n=3)

22 Nanoplankton Change in Abundance SD 320µatm 5E+04 4E+03 925µatm 8E+04 1E+03 3700µatm 8E+04 4E+03 Picoplankton Change in Abundance SD 320µatm -1E+04 1E+03 925µatm -1E+04 4E+02 3700µatm -1E+04 7E+02 Table 2: Changes in abundance of the nanoplankton and picoplankton population in each treatment. (n=3)

23