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Physiological responses of a Southern Ocean diatom to complex future ocean conditions

Abstract

A changing climate is altering many ocean properties that consequently will modify marine productivity. Previous phytoplankton manipulation studies have focused on individual or subsets of these properties. Here, we investigate the cumulative effects of multi-faceted change on a subantarctic diatom Pseudonitzschia multiseries by concurrently manipulating five stressors (light/nutrients/CO2/temperature/iron) that primarily control its physiology, and explore underlying reasons for altered physiological performance. Climate change enhances diatom growth mainly owing to warming and iron enrichment, and both properties decrease cellular nutrient quotas, partially offsetting any effects of decreased nutrient supply by 2100. Physiological diagnostics and comparative proteomics demonstrate the joint importance of individual and interactive effects of temperature and iron, and reveal biased future predictions from experimental outcomes when only a subset of multi-stressors is considered. Our findings for subantarctic waters illustrate how composite regional studies are needed to provide accurate global projections of future shifts in productivity and distinguish underlying species-specific physiological mechanisms.

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Figure 1: A reaction norm of P. multiseries, expressed as growth rate at each temperature divided by maximum observed growth rate for the norm.
Figure 2: Experimental design to mimic a future ocean and assess the individual and interactive physiological effects of temperature.
Figure 3: Summary of physiological metrics sampled during exponential growth (Supplementary Fig. 1) from each of treatments A–D (represented again by bar graphs within colour-coded circles depicting culture conditions detailed in Table 1).
Figure 4: Individual versus the interactive physiological effects of warming on our study diatom.
Figure 5: Representations of the different outcomes of treatments A–D.

References

  1. Doney, S. C. The growing human footprint on coastal and open-ocean biogeochemistry. Science 328, 1512–1516 (2010).

    Article  CAS  Google Scholar 

  2. IPCC Climate Change 2014: The Physical Science Basis (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).

    Google Scholar 

  3. Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).

    Article  Google Scholar 

  4. Dutkiewicz, S., Scott, J. R. & Follows, M. J. Winners and losers: Ecological and biogeochemical changes in a warming ocean. Glob. Biogeochem. Cycles 27, 463–477 (2013).

    Article  CAS  Google Scholar 

  5. Karl, D. M. et al. in Nitrogen in the Marine Environment (eds Capone, D. G., Bronk, D. A., Mulholland, M. R. & Carpenter, E. J.) 705–769 (Academic Press, 2008).

    Book  Google Scholar 

  6. Boyd, P. W., Strzepek, R. S., Fu, F. X. & Hutchins, D. A. Environmental control of open-ocean phytoplankton groups: Now and in the future. Limnol. Oceanogr. 55, 1353–1376 (2010).

    Article  CAS  Google Scholar 

  7. Collins, S., Rost, B. & Rynearson, T. A. Evolutionary potential of marine phytoplankton under ocean acidification. Evol. Appl. 7, 140–155 (2014).

    Article  CAS  Google Scholar 

  8. Boyd, P. W. & Hutchins, D. A. Understanding the responses of ocean biota to a complex matrix of cumulative anthropogenic change. Mar. Ecol. Prog. Ser. 470, 125–135 (2012).

    Article  Google Scholar 

  9. Riebesell, U. & Tortell, P. in Ocean Acidification (eds Gattuso, J. P. & Hansen, L.) 99–116 (Oxford Univ. Press, 2011).

    Google Scholar 

  10. Hoppe, C. J. M. et al. Iron limitation modulates ocean acidification effects on southern ocean phytoplankton communities. PLoS ONE 8, e79890 (2013).

    Article  Google Scholar 

  11. Garcia, N. S., Fu, F. X. & Hutchins, D. A. Co-limitation of the unicellular photosynthetic diazotroph Crocosphaera watsonii by phosphorus, light and carbon dioxide. Limnol. Oceanogr. 58, 1501–1512 (2013).

    Article  CAS  Google Scholar 

  12. Boyd, P. W. Beyond ocean acidification. Nature Geosci. 4, 273–274 (2011).

    Article  CAS  Google Scholar 

  13. Dunne, J. P. A roadmap on ecosystem change. Nature Clim. Change 5, 20–21 (2015).

    Article  Google Scholar 

  14. Boyd, P. W., Lennartz, S. T., Glover, D. M. & Doney, S. C. Biological ramifications of climate-change mediated oceanic multi-stressors. Nature Clim. Change 5, 71–79 (2015).

    Article  Google Scholar 

  15. Marchetti, A. et al. Phytoplankton processes during a mesoscale iron enrichment in the NE subarctic Pacific: Part 1-Biomass and assemblage. Deep-Sea Res. II 53, 2095–2113 (2006).

    Article  Google Scholar 

  16. Lehmann, J. T. Interacting growth and loss rates: The balance of top-down and bottom-up controls in plankton communities. Limnol. Oceanogr. 36, 1546–1554 (1991).

    Article  Google Scholar 

  17. Smith, W. O. Jr (ed.) Polar Oceanography, Chemistry, Biology and Geology 477–517 (Academic Press, 1990).

  18. Moore, J. K. et al. Marine ecosystem dynamics and biogeochemical cycling in the Community Earth System Model CESM1(BGC). J. Clim. 26, 9291–9321 (2013).

    Article  Google Scholar 

  19. Nunn, B. L. et al. Diatom proteomics reveals unique acclimation strategies to mitigate Fe limitation. PLoS ONE 8, e75653 (2013).

    Article  CAS  Google Scholar 

  20. Poulson-Ellestad, K. L. et al. Metabolomics and proteomics reveal impacts of chemically mediated competition on plankton. Proc. Natl Acad. Sci. USA 111, 9009–9014 (2014).

    Article  CAS  Google Scholar 

  21. Fletcher, D. & Dillingham, P. Model-averaged confidence intervals for factorial experiments. Comput. Stat. Data Anal. 55, 3041–3048 (2011).

    Article  Google Scholar 

  22. Berges, J. A., Varela, D. E. & Harrison, P. J. Effects of temperature on growth rate, composition and nitrogen metabolism in the marine diatom Thalassiosira pseudonana (Bacillariophyceae). Mar. Ecol. Prog. Ser. 225, 139–146 (2002).

    Article  Google Scholar 

  23. Strzepek, R. F. & Price, N. M. Influence of irradiance and temperature on the iron content of the marine diatom Thalassiosira weissflogii. Mar. Ecol. Prog. Ser. 206, 107–117 (2000).

    Article  CAS  Google Scholar 

  24. Toseland, A. et al. The impact of temperature on marine phytoplankton resource allocation and metabolism. Nature Clim. Change 3, 979–984 (2013).

    Article  CAS  Google Scholar 

  25. Sugie, K. & Yoshimura, T. Effects of p CO 2 and iron on the elemental composition and geometry of the marine diatom Pseudo-nitzschia pseudodelicatissima. J. Phycol. 49, 475–488 (2013).

    Article  CAS  Google Scholar 

  26. Marchetti, A. & Harrison, P. J. Coupled changes in the cell morphology and the elemental (C, N and Si) composition of oceanic and coastal species of the pennate diatom Pseudo-nitzschia due to iron deficiency. Limnol. Oceanogr. 52, 2270–2284 (2007).

    Article  CAS  Google Scholar 

  27. Nunn, B. L. et al. Deciphering diatom biochemical pathways via whole-cell proteomics. Aquat. Microb. Ecol. 55, 241–253 (2009).

    Article  Google Scholar 

  28. Bissinger, J. E., Montagnes, D. J. S., Sharples, J. & Atkinson, D. Predicting marine phytoplankton maximum growth rates from temperature: Improving on the Eppley curve using quantile regression. Limnol. Oceanogr. 53, 487–497 (2008).

    Article  Google Scholar 

  29. Boyd, P., LaRoche, J., Gall, M., Frew, R. & McKay, R. M. L. Role of iron, light, and silicate in controlling algal biomass in subantarctic waters SE of New Zealand. J. Geophys. Res. 104, 13395–13408 (1999).

    Article  CAS  Google Scholar 

  30. Goldman, J. C. & Carpenter, E. J. A kinetic approach to the effect of temperature on algal growth. Limnol. Oceanogr. 19, 756–766 (1974).

    Article  Google Scholar 

  31. Price, N. M. The elemental stoichiometry and composition of an iron-limited diatom. Limnol. Oceanogr. 50, 1159–1171 (2005).

    Article  CAS  Google Scholar 

  32. Martin-Jezequel, V. M., Hildebrand, V. M. & Brzezinki, M. A. Silicon metabolism in diatoms: Implications for growth. J. Phycol. 36, 821–840 (2000).

    Article  CAS  Google Scholar 

  33. Rose, J. M. et al. Synergistic effects of iron and temperature on Antarctic phytoplankton and microzooplankton assemblages. Biogeosciences 6, 3131–3147 (2009).

    Article  CAS  Google Scholar 

  34. Raven, J. A. & Geider, R. J. Temperature and algal growth. New Phytol. 110, 441–461 (1988).

    Article  CAS  Google Scholar 

  35. Kolber, Z. S. et al. Iron limitation of phytoplankton photosynthesis in the equatorial Pacific Ocean. Nature 371, 145–149 (1994).

    Article  CAS  Google Scholar 

  36. Morel, F. M. M. & Price, N. M. The biogeochemical cycles of trace metals in the oceans. Science 300, 944–946 (2003).

    Article  CAS  Google Scholar 

  37. Bertrand, E. M., Saito, M. A., Lee, P. A., Dunbar, R. B. & DiTullio, G. R. Iron limitation of springtime bacterial and phytoplankton community in the Ross Sea: Implications for vitamin B12 nutrition. Front. Microbiol. 10.3389/fmicb.2011.00160 (2011)

  38. Shi, D. A., Xu, Y., Hopkinson, B. M. & Morel, F. M. M. Effect of ocean acidification on iron availability to marine phytoplankton. Science 327, 676–679 (2010).

    Article  CAS  Google Scholar 

  39. Sunda, W. G. & Huntsman, S. A. Interactive effects of light and temperature on iron limitation in a marine diatom: Implications for marine productivity and carbon cycling. Limnol. Oceanogr. 56, 1475–1488 (2011).

    Article  CAS  Google Scholar 

  40. Geider, R. J., MacIntyre, H. L. & Kana, T. M. A dynamic regulatory model of phytoplankton acclimation to light, nutrients, and temperature. Limnol. Oceanogr. 43, 679–694 (1998).

    Article  CAS  Google Scholar 

  41. Klausmeier, C. A., Litchman, E., Daufresne, T. & Levin, S. A. Phytoplankton stoichiometry. Ecol. Res. 23, 479–485 (2008).

    Article  Google Scholar 

  42. Trimborn, S., Brenneis, T., Sweet, E. & Rost, B. Sensitivity of Antarctic phytoplankton species to ocean acidification: Growth, carbon acquisition, and species interaction. Limnol. Oceanogr. 58, 997–1007 (2013).

    Article  CAS  Google Scholar 

  43. Tew, K. S. et al. Effects of elevated CO2 and temperature on the growth, elemental composition, and cell size of two marine diatoms: Potential implications of global climate change. Hydrobiologia 741, 79–87 (2014).

    Article  CAS  Google Scholar 

  44. Reinfelder, J. R. Carbon dioxide regulation of nitrogen and phosphorus in four species of marine phytoplankton. Mar. Ecol. Prog. Ser. 466, 57–67 (2012).

    Article  CAS  Google Scholar 

  45. Burkhardt, S., Zondervan, I. & Riebesell, U. Effect of CO2 concentration on C:N:P ratio in marine phytoplankton: A species comparison. Limnol. Oceanogr. 44, 683–690 (1999).

    Article  CAS  Google Scholar 

  46. Boyd, P. W. et al. Climate-mediated changes to mixed-layer properties in the Southern Ocean: Assessing the phytoplankton response. Biogeosciences 5, 847–864 (2008).

    Article  CAS  Google Scholar 

  47. Thomas, M. K., Kremer, C. T., Klausmeier, C. A. & Litchman, E. A global pattern of thermal adaptation in marine phytoplankton. Science 741, 79–87 (2012).

    Google Scholar 

  48. Trimborn, S. et al. Inorganic carbon acquisition in potentially toxic and non-toxic diatoms: The effect of pH-induced changes in seawater carbonate chemistry. Physiol. Plant. 133, 92–105 (2008).

    Article  CAS  Google Scholar 

  49. Ou, L. et al. Comparative study of phosphorus strategies of three typical harmful algae in Chinese coastal waters. J. Plank. Res. 30, 1007–1017 (2008).

    Article  CAS  Google Scholar 

  50. Chan, L. L. et al. Proteomic study of a model causative agent of harmful algal blooms, Prorocentrum triestinum II: The use of differentially expressed protein profiles under different growth phases and growth conditions for bloom prediction. Proteomics 4, 3214–3226 (2004).

    Article  CAS  Google Scholar 

  51. Hillebrand, H. et al. Biovolume calculation for pelagic and benthic microalgae. J. Phycol. 35, 403–424 (1999).

    Article  Google Scholar 

  52. Price, N. M. et al. Preparation and chemistry of the artificial algal culture medium Aquil. Biol. Oceanogr. 6, 443–461 (1988/89).

    Google Scholar 

  53. Thomas, W. H., Scotten, H. L. & Bradshaw, J. S. Thermal gradient incubators for small aquatic organisms. Limnol. Oceanogr. 8, 357–360 (1963).

    Article  Google Scholar 

  54. McGraw, C. M. et al. An automated pH-controlled culture system for laboratory-based ocean acidification experiments. Limnol. Oceanogr. 8, 686–694 (2010).

    CAS  Google Scholar 

  55. Box, G. E. P., Hunter, W. G. & Hunter, J. S. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building (Wiley, 1978).

    Google Scholar 

  56. Strzepek, R. F., Maldonado, M. T., Hunter, K. A., Frew, R. D. & Boyd, P. W. Adaptive strategies by Southern Ocean phytoplankton to lessen iron limitation: Uptake of organically complexed iron and reduced cellular iron requirements. Limnol. Oceanogr. 56, 1983–2002 (2011).

    Article  CAS  Google Scholar 

  57. Strzepek, R. F., Hunter, K. A., Frew, R. D., Harrison, P. J. & Boyd, P. W. Iron-light interactions differ in Southern Ocean phytoplankton. Limnol. Oceanogr. 57, 1182–1200 (2012).

    Article  CAS  Google Scholar 

  58. Hoffmann, L. J. et al. A trace-metal clean, pH-controlled incubator system for ocean acidification incubation studies. Limnol. Oceanogr. 11, 53–61 (2013).

    Article  CAS  Google Scholar 

  59. Law, C. S. et al. No stimulation of nitrogen fixation by non-filamentous diazotrophs under elevated CO2 in the South Pacific. Glob. Change Biol. 18, 3004–3014 (2012).

    Article  Google Scholar 

  60. Paasche, E. Silicon and the ecology of marine plankton diatoms. I. Thalassiosira pseudonana (Cyclotella nana) grown in chemostat with silicate as limiting nutrient. Mar. Biol. 19, 117–126 (1973).

    Article  CAS  Google Scholar 

  61. Solórzano, L. & Sharp, J. H. Determination of total dissolved phosphorus and particulate phosphorus in natural waters. Limnol. Oceanogr. 25, 754–758 (1980).

    Article  Google Scholar 

  62. Zhang, H. N. & Byrne, R. H. Spectrophotometric pH measurements of surface seawater at in-situ conditions: Absorbance and protonation behavior of thymol blue. Mar. Chem. 52, 17–25 (1996).

    Article  CAS  Google Scholar 

  63. Hunter, K. A. SWCO2; http://neon.otago.ac.nz/research/mfc/people/keith_hunter/software/swco2 (2009)

  64. Mehrbach, C., Culberson, C. H., Hawley, J. E. & Pytkowicz, R. M. Measurement of the apparent dissociation constants of carbonic acid in seawater at atmospheric pressure. Limnol. Oceanogr. 18, 897–907 (1973).

    Article  CAS  Google Scholar 

  65. Dickson, A. G. & Millero, F. J. A comparison of the equilibrium-constants for the dissociation of carbonic-acid in seawater media. Deep-Sea Res. I 34, 1733–1743 (1987).

    Article  CAS  Google Scholar 

  66. Choi, H., Fermin, D. & Nesvizhskii, A. Significance analysis of spectral count data in label-free shotgun proteomics. Mol. Cell Proteomics 7, 2373–2385 (2008).

    Article  CAS  Google Scholar 

  67. Fermin, D., Basrur, V., Yocum, A. K. & Nesvizhskii, A. I. Abacus: A computational tool for extracting and pre-processing spectral count data for label-free quantitative proteomic analysis. Proteomics 11, 1340–1345 (2011).

    Article  CAS  Google Scholar 

  68. Oksanen, J. et al. vegan: Community Ecology Package R package version 2.0-8 (2013); http://www.bioconductor.org/packages/release/bioc/html/MSstats.html

  69. Schilling, B. et al. Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline: Application to protein acetylation and phosphorylation. Mol. Cell Proteomics 11, 202–214 (2012).

    Article  CAS  Google Scholar 

  70. R Core Team R: A Language and Environment for Statistical Computing (Foundation for Statistical Computing, 2014); http://www.R-project.org

    Google Scholar 

  71. MacLean, B. D. M. et al. Skyline: An open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).

    Article  CAS  Google Scholar 

  72. Choi, M., Chang, C.-Y. & Vitek, O. MSstats.daily: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments R package version 2.3.5 (2014)

  73. Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd edn (Springer, 2002).

    Google Scholar 

  74. Link, W. A. & Barker, R. J. Model weights and the foundations of multimodel inference. Ecology 87, 2626–2635 (2006).

    Article  Google Scholar 

  75. Chatfield, C. Model uncertainty, data mining and statistical inference. J. R. Stat. Soc. Ser. A 158, 419–466 (1995).

    Google Scholar 

  76. Buckland, S. T., Burnham, K. P. & Augustin, N. H. Model selection: An integral part of inference. Biometrics 53, 603–618 (1997).

    Article  Google Scholar 

  77. Turek, D. & Fletcher, D. Model-averaged Wald confidence intervals. Comput. Stat. Data Anal. 56, 2809–2815 (2012).

    Article  Google Scholar 

  78. Welch, B. L. The generalization of Student’s problem when several different population variances are involved. Biometrika 34, 28–35 (1947).

    CAS  Google Scholar 

  79. Sieracki, M. E., Verity, P. G. & Stoecker, D. K. Plankton community response to sequential silicate and nitrate depletion during the 1989 North Atlantic spring bloom. Deep-Sea Res. II 40, 213–225 (1993).

    Article  CAS  Google Scholar 

  80. Allen, J. T. et al. Diatom carbon export enhanced by silicate upwelling in the northeast Atlantic. Nature 437, 728–732 (2005).

    Article  CAS  Google Scholar 

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Acknowledgements

P.W.B. acknowledges support from IMAS and the ACE-CRC. C.L.H. received Marsden funding (UOO0914, Royal Society of New Zealand) to support M.Y.R., C.E.C. and Y.-y.F. B.L.N. and E.T.-S. were supported by National Science Foundation grants OCE-1060300 (B.L.N.) and OCE-1233014 (E.T.-S.) and the University of Washington Proteomics Bioinformatics Team (UWPR95794). We thank K.J.M. Dickinson and E. Breitbarth for provision of laboratory culture facilities and expertise in incubation set-up, respectively.

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P.W.B. conceived and designed the experiments; E.A.A., C.E.C., C.M.Mc.G., M.Y.R. and P.W.B. performed the experiments; B.L.N. and E.T.-S. conducted the proteomics analysis; P.W.D. developed the statistical experimental design and carried out the biostatistical analysis; C.L.H. and M.R.-G. contributed materials/analysis tools; P.W.B., B.L.N., P.W.D., C.M.Mc.G., E.A.A., C.L.H. and E.T.-S. wrote the paper.

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Correspondence to P. W. Boyd.

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Boyd, P., Dillingham, P., McGraw, C. et al. Physiological responses of a Southern Ocean diatom to complex future ocean conditions. Nature Clim Change 6, 207–213 (2016). https://doi.org/10.1038/nclimate2811

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