Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Vibrational modes of water predict spectral niches for photosynthesis in lakes and oceans

Nature Ecology & Evolution volume 5, pages 55–66 (2021)Cite this article

Subjects

Abstract

Stretching and bending vibrations of water molecules absorb photons of specific wavelengths, a phenomenon that constrains light energy available for aquatic photosynthesis. Previous work suggested that these absorption properties of water create a series of spectral niches but the theory was still too simplified to enable prediction of the spectral niches in real aquatic ecosystems. Here, we show with a state-of-the-art radiative transfer model that the vibrational modes of the water molecule delineate five spectral niches, in the violet, blue, green, orange and red parts of the spectrum. These five niches are effectively captured by chlorophylls and phycobilin pigments of cyanobacteria and their eukaryotic descendants. Global distributions of the spectral niches are predicted by satellite remote sensing and validated with observed large-scale distribution patterns of cyanobacterial pigment types. Our findings provide an elegant explanation for the biogeographical distributions of photosynthetic pigments across the lakes and oceans of our planet.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Absorption properties of the water molecule.
Fig. 2: Spectral niches in the underwater light spectrum created by harmonics of the vibrational modes of the water molecule.
Fig. 3: Absorption spectra of the main cyanobacterial pigments capture the spectral niches created by the harmonics of the water molecule.
Fig. 4: Global distribution of the five spectral niches.
Fig. 5: Comparison of predicted spectral niches and global distributions of cyanobacterial pigment types.

Similar content being viewed by others

Data availability

Datasets of all spectra shown in this study (Figs. 1–3 and Extended Data Figs. 1–5) are available67 at: https://doi.org/10.6084/m9.figshare.c.5140601.v1. Remote sensing data that support the findings of this study are available from the Ocean Color Climate Change Initiative project of the European Space Agency: http://www.oceancolour.org. Relative abundance data of cyanobacterial pigment types are obtained from refs. 5,6,7,9,22 and available in Supplementary Table 2.

Code availability

R scripts used to generate Figs. 2 and 4 are available at: https://github.com/tadzi/spectral_niches_photosynthesis

References

  1. Engelmann, T. W. Über Sauerstoffausscheidung von Pflanzenzellen im Mikrospektrum. Bot. Zeit. 40, 419–426 (1882).

    Google Scholar 

  2. Engelmann, T. W. Farbe und assimilation. Bot. Zeit. 41, 1–29 (1883).

    Google Scholar 

  3. Stomp, M. et al. Adaptive divergence in pigment composition promotes phytoplankton biodiversity. Nature 432, 104–107 (2004).

    CAS  PubMed  Google Scholar 

  4. Stomp, M., Huisman, J., Stal, L. J. & Matthijs, H. C. P. Colorful niches of phototrophic microorganisms shaped by vibrations of the water molecule. ISME J. 1, 271–282 (2007).

    CAS  PubMed  Google Scholar 

  5. Pick, F. R. The abundance and composition of freshwater picocyanobacteria in relation to light penetration. Limnol. Oceanogr. 36, 1457–1462 (1991).

    CAS  Google Scholar 

  6. Vörös, L., Callieri, C., Balogh, K. V. & Bertoni, R. Freshwater picocyanobacteria along a trophic gradient and light quality range. Hydrobiologia 369–370, 117–125 (1998).

    Google Scholar 

  7. Stomp, M. et al. Colourful coexistence of red and green picocyanobacteria in lakes and seas. Ecol. Lett. 10, 290–298 (2007).

    PubMed  Google Scholar 

  8. Ting, C. S., Rocap, G., King, J. & Chisholm, S. W. Cyanobacterial photosynthesis in the oceans: the origins and significance of divergent light-harvesting strategies. Trends Microbiol. 10, 134–142 (2002).

    CAS  PubMed  Google Scholar 

  9. Grébert, T. et al. Light color acclimation is a key process in the global ocean distribution of Synechococcus cyanobacteria. Proc. Natl Acad. Sci. USA 115, E2010–E2019 (2018).

    PubMed  Google Scholar 

  10. Luimstra, V. M., Verspagen, J. M. H., Xu, T., Schuurmans, J. M. & Huisman, J. Changes in water color shift competition between phytoplankton species with contrasting light-harvesting strategies. Ecology 101, e02951 (2020).

    PubMed  PubMed Central  Google Scholar 

  11. Mobley, C. D. Light and Water: Radiative Transfer in Natural Waters (Academic Press, 1994).

  12. Kirk, J. T. O. Light and Photosynthesis in Aquatic Ecosystems 3rd edn (Cambridge Univ. Press, 2011).

  13. Dall’Olmo, G., Westberry, T. K., Behrenfeld, M. J., Boss, E. & Slade, W. H. Significant contribution of large particles to optical backscattering in the open ocean. Biogeosciences 6, 947–967 (2009).

    Google Scholar 

  14. Morel, A. et al. Optical properties of the “clearest” natural waters. Limnol. Oceanogr. 52, 217–229 (2007).

    CAS  Google Scholar 

  15. Pegau, W. S., Gray, D. & Zaneveld, J. R. Absorption and attenuation of visible and near-infrared light in water: dependence on temperature and salinity. Appl. Opt. 36, 6035–6046 (1997).

    CAS  PubMed  Google Scholar 

  16. Sogandares, F. M. & Fry, E. S. Absorption spectrum (340–640 nm) of pure water. I. Photothermal measurements. Appl. Opt. 36, 8699–8709 (1997).

    CAS  PubMed  Google Scholar 

  17. Pope, R. M. & Fry, E. S. Absorption spectrum (380–700 nm) of pure water. II. Integrating cavity measurements. Appl. Opt. 36, 8710–8723 (1997).

    CAS  PubMed  Google Scholar 

  18. Mason, J. D., Cone, M. T. & Fry, E. S. Ultraviolet (250–550 nm) absorption spectrum of pure water. Appl. Opt. 55, 7163–7172 (2016).

    CAS  PubMed  Google Scholar 

  19. Mobley, C. D. & Sundman, L. K. HydroLight 5.3—EcoLight 5.3 (Sequoia Scientific Inc., 2016).

  20. Sathyendranath, S., Brewin, R. J., Jackson, T., Mélin, F. & Platt, T. Ocean-colour products for climate-change studies: what are their ideal characteristics? Remote Sens. Environ. 203, 125–138 (2017).

    Google Scholar 

  21. Neeley, A. R. & Mannino, A. (eds) IOCCG Ocean Optics and Biogeochemistry Protocols for Satellite Ocean Colour Sensor Validation, Volume 1.0. Inherent Optical Property Measurements and Protocols: Absorption Coefficient (IOCCG, 2018).

  22. Farrant, G. K. et al. Delineating ecologically significant taxonomic units from global patterns of marine picocyanobacteria. Proc. Natl Acad. Sci. USA 113, E3365–E3374 (2016).

    CAS  PubMed  Google Scholar 

  23. Chisholm, S. W. et al. Prochlorococcus marinus nov. gen. nov. sp.: an oxyphototrophic marine prokaryote containing divinyl chlorophyll a and b. Arch. Microbiol. 157, 297–300 (1992).

    CAS  Google Scholar 

  24. Partensky, F., Hess, W. R. & Vaulot, D. Prochlorococcus, a marine photosynthetic prokaryote of global significance. Microbiol. Mol. Biol. Rev. 63, 106–127 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Moore, L. R., Goericke, R. & Chisholm, S. W. Comparative physiology of Synechococcus and Prochlorococcus: influence of light and temperature on growth, pigments, fluorescence and absorptive properties. Mar. Ecol. Prog. Ser. 116, 259–275 (1995).

    Google Scholar 

  26. Tandeau de Marsac, N. Phycobiliproteins and phycobilisomes: the early observations. Photosynth. Res. 76, 193–205 (2003).

    PubMed  Google Scholar 

  27. Six, C. et al. Diversity and evolution of phycobilisomes in marine Synechococcus spp.: a comparative genomics study. Genome Biol. 8, R259 (2007).

    PubMed  PubMed Central  Google Scholar 

  28. Watanabe, M. & Ikeuchi, M. Phycobilisome: architecture of a light-harvesting supercomplex. Photosynth. Res. 116, 265–276 (2013).

    CAS  PubMed  Google Scholar 

  29. Sanfilippo, J. E., Garczarek, L., Partensky, F. & Kehoe, D. M. Chromatic acclimation in cyanobacteria: a diverse and widespread process for optimizing photosynthesis. Annu. Rev. Microbiol. 73, 407–433 (2019).

    CAS  PubMed  Google Scholar 

  30. Palenik, B. Chromatic adaptation in marine Synechococcus strains. Appl. Environ. Microbiol. 67, 991–994 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Stomp, M. et al. The timescale of phenotypic plasticity and its impact on competition in fluctuating environments. Am. Nat. 172, E169–E185 (2008).

    Google Scholar 

  32. Hirose, Y. et al. Diverse chromatic acclimation processes regulating phycoerythrocyanin and rod-shaped phycobilisome in cyanobacteria. Mol. Plant 12, 715–725 (2019).

    CAS  PubMed  Google Scholar 

  33. Luimstra, V. M. et al. Blue light reduces photosynthetic efficiency of cyanobacteria through an imbalance between photosystems I and II. Photosynth. Res. 138, 177–189 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Humily, F. et al. A gene island with two possible configurations is involved in chromatic acclimation in marine Synechococcus. PLoS ONE 8, e84459 (2013).

    PubMed  PubMed Central  Google Scholar 

  35. Haverkamp, T. et al. Diversity and phylogeny of Baltic Sea picocyanobacteria inferred from their ITS and phycobiliprotein operons. Environ. Microbiol. 10, 174–188 (2008).

    CAS  PubMed  Google Scholar 

  36. Huisman, J. et al. Cyanobacterial blooms. Nat. Rev. Microbiol. 16, 471–483 (2018).

    CAS  PubMed  Google Scholar 

  37. Chen, F. et al. Phylogenetic diversity of Synechococcus in the Chesapeake Bay revealed by ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO) large subunit gene (rbcL) sequences. Aquat. Microb. Ecol. 36, 153–164 (2004).

    Google Scholar 

  38. Somogyi, B., Felföldi, T., Tóth, L. G., Bernát, G. & Vörös, L. Photoautotrophic picoplankton: a review on their occurrence, role and diversity in Lake Balaton. Biol. Futur. https://doi.org/10.1007/s42977-020-00030-8 (2020).

  39. Kardinaal, W. E. A. et al. Competition for light between toxic and nontoxic strains of the harmful cyanobacterium Microcystis. Appl. Environ. Microbiol. 73, 2939–2946 (2007).

    PubMed  PubMed Central  Google Scholar 

  40. Bricaud, A., Claustre, H., Ras, J. & Oubelkheir, K. Natural variability of phytoplanktonic absorption in oceanic waters: influence of the size structure of algal populations. J. Geophys. Res. 109, C11010 (2004).

    Google Scholar 

  41. Monteith, D. T. et al. Dissolved organic carbon trends resulting from changes in atmospheric deposition chemistry. Nature 450, 537–541 (2007).

    CAS  PubMed  Google Scholar 

  42. Weyhenmeyer, G. A., Müller, R. A., Norman, M. & Tranvik, L. J. Sensitivity of freshwaters to browning in response to future climate change. Clim. Change 134, 225–239 (2016).

    Google Scholar 

  43. Kritzberg, E. S. Centennial‐long trends of lake browning show major effect of afforestation. Limnol. Oceanogr. Lett. 2, 105–112 (2017).

    Google Scholar 

  44. Leech, D. M., Pollard, A. I., Labou, S. G. & Hampton, S. E. Fewer blue lakes and more murky lakes across the continental U.S.: implications for planktonic food webs. Limnol. Oceanogr. 63, 2661–2680 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Ekvall, M. K. et al. Synergistic and species‐specific effects of climate change and water colour on cyanobacterial toxicity and bloom formation. Freshw. Biol. 58, 2414–2422 (2013).

    CAS  Google Scholar 

  46. Urrutia‐Cordero, P. et al. Phytoplankton diversity loss along a gradient of future warming and brownification in freshwater mesocosms. Freshw. Biol. 62, 1869–1878 (2017).

    Google Scholar 

  47. Wilken, S. et al. Primary producers or consumers? Increasing phytoplankton bacterivory along a gradient of lake warming and browning. Limnol. Oceanogr. 63, S142–S155 (2018).

    Google Scholar 

  48. Feuchtmayr, H. et al. Effects of brownification and warming on algal blooms, metabolism and higher trophic levels in productive shallow lake mesocosms. Sci. Tot. Environ. 678, 227–238 (2019).

    CAS  Google Scholar 

  49. Deininger, A., Faithfull, C. L. & Bergström, A. K. Phytoplankton response to whole lake inorganic N fertilization along a gradient in dissolved organic carbon. Ecology 98, 982–994 (2017).

    CAS  PubMed  Google Scholar 

  50. Tan, X., Zhang, D., Duan, Z., Parajuli, K. & Hu, J. Effects of light color on interspecific competition between Microcystis aeruginosa and Chlorella pyrenoidosa in batch experiment. Environ. Sci. Pollut. Res. 27, 344–352 (2020).

    CAS  Google Scholar 

  51. Burson, A., Stomp, M., Greenwell, E., Grosse, J. & Huisman, J. Competition for nutrients and light: testing advances in resource competition with a natural phytoplankton community. Ecology 99, 1108–1118 (2018).

    PubMed  Google Scholar 

  52. Dutkiewicz, S. et al. Dimensions of marine phytoplankton diversity. Biogeosciences 17, 609–634 (2020).

    Google Scholar 

  53. Johnson, Z. I. et al. Niche partitioning among Prochlorococcus ecotypes along ocean-scale environmental gradients. Science 311, 1737–1740 (2006).

    CAS  PubMed  Google Scholar 

  54. Malmstrom, R. R. et al. Temporal dynamics of Prochlorococcus ecotypes in the Atlantic and Pacific Oceans. ISME J. 4, 1252–1264 (2010).

    PubMed  Google Scholar 

  55. Lange, P. K. et al. Scratching beneath the surface: a model to predict the vertical distribution of Prochlorococcus using remote sensing. Remote Sens. 10, 847 (2018).

    Google Scholar 

  56. Wernand, M. R., van der Woerd, H. J. & Gieskes, W. W. C. Trends in ocean colour and chlorophyll concentration from 1889 to 2000, worldwide. PLoS ONE 8, e63766 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Dutkiewicz, S. et al. Ocean colour signature of climate change. Nat. Commun. 10, 578 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Bricaud, A., Morel, A. & Prieur, L. Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains. Limnol. Oceanogr. 26, 43–53 (1981).

    CAS  Google Scholar 

  59. Twardowski, M. S., Boss, E., Sullivan, J. M. & Donaghay, P. L. Modeling the spectral shape of absorption by chromophoric dissolved organic matter. Mar. Chem. 89, 69–88 (2004).

    CAS  Google Scholar 

  60. Babin, M. et al. Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe. J. Geophys. Res. 108, 1–20 (2003).

    Google Scholar 

  61. Babin, M., Morel, A., Fournier-Sicre, V., Fell, F. & Stramski, D. Light scattering properties of marine particles in coastal and open ocean waters as related to the particle mass concentration. Limnol. Oceanogr. 48, 843–859 (2003).

    Google Scholar 

  62. Doxaran, D. et al. Spectral variations of light scattering by marine particles in coastal waters, from the visible to the near infrared. Limnol. Oceanogr. 54, 1257–1271 (2009).

    CAS  Google Scholar 

  63. Nechad, B., Ruddick, K. G. & Park, Y. Calibration and validation of a generic multisensor algorithm for mapping of total suspended matter in turbid waters. Remote Sens. Environ. 114, 854–866 (2010).

    Google Scholar 

  64. Petzold, T. J. Volume Scattering Functions for Selected Ocean Waters (No. SIO-REF-72-78) (Scripps Institution of Oceanography, 1972).

  65. Morel, A. & Gentili, B. Diffuse reflectance of oceanic waters: its dependence on sun angle as influenced by the molecular scattering contribution. Appl. Opt. 30, 4427–4438 (1991).

    CAS  PubMed  Google Scholar 

  66. Sathyendranath, S. et al. An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors 19, 4285 (2019).

    CAS  Google Scholar 

  67. Holtrop, T. et al. Data: vibrational modes of water predict spectral niches for photosynthesis in lakes and oceans. https://doi.org/10.6084/m9.figshare.c.5140601.v1 (2020).

  68. Sanfilippo, J. E. et al. Interplay between differentially expressed enzymes contributes to light color acclimation in marine Synechococcus. Proc. Natl Acad. Sci. USA 116, 6457–6462 (2019).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This article is dedicated to the memory of our late colleagues M. Stomp and H. C. P. Matthijs, who provided a source of inspiration for our understanding of the spectral niches for cyanobacterial photosynthesis. We thank G. Dall’Olmo and R. M. Letelier for constructive comments on previous versions of the manuscript, M. Kehoe (University of Amsterdam) for measuring underwater spectra of the North Atlantic during the STRATIPHYT II cruise, V. M. Luimstra (University of Amsterdam) for help with the cyanobacterial absorption spectra, and F. R. Pick (University of Ottawa) and L. Vörös (Hungarian Academy of Sciences) for sampling of lake stations. We thank the Tara Oceans coordinators and consortium for support, and the captains and crew of the Tara schooner for sampling of the marine stations. This research was funded by the Dutch Research Council (NWO) under grant no. ALW-GO 14-06 and a VENI-grant to M. Stomp, and also by the French Agence Nationale de la Recherche (ANR) programs CINNAMON (ANR‐17‐CE02‐0014‐01) and EFFICACY (ANR-19-CE02-0019).

Author information

Author notes

  1. These authors contributed equally: Tadzio Holtrop, Jef Huisman.

  2. Deceased: Maayke Stomp.

Authors and Affiliations

  1. Department of Water & Climate Risk, Institute for Environmental Studies (IVM), VU University Amsterdam, Amsterdam, the Netherlands

    Tadzio Holtrop, Levi Biersteker, Jeroen Aerts & Hendrik Jan van der Woerd

  2. Department of Freshwater and Marine Ecology (FAME), Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, the Netherlands

    Tadzio Holtrop, Jef Huisman, Maayke Stomp & Levi Biersteker

  3. Research Department UMR 7144—Adaptation and Diversity in the Marine Environment, Station Biologique de Roscoff, Sorbonne Université, CNRS, Roscoff, France

    Théophile Grébert, Frédéric Partensky & Laurence Garczarek

Authors
  1. Tadzio Holtrop
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jef Huisman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maayke Stomp
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Levi Biersteker
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jeroen Aerts
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Théophile Grébert
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Frédéric Partensky
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Laurence Garczarek
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Hendrik Jan van der Woerd
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.S. and J.H. conceived the original idea and designed the study in collaboration with H.J.v.d.W. The radiative transfer model was run by T.H. and H.J.v.d.W. Underwater light spectra were measured by M.S. and J.H. Remote sensing data were analysed by T.H., L.B. and H.J.v.d.W. Absorption spectra of cyanobacteria were measured by M.S., J.H. and L.G. Biogeographical distributions of the pigment types were collected by T.G., F.P. and L.G. for the marine stations and by M.S. and J.H. for the lake stations and Baltic Sea. T.H. made the figures. J.H. and T.H. wrote the manuscript and H.J.v.d.W., L.G., F.P., T.G., J.A. and L.B. commented on the different manuscript versions.

Corresponding author

Correspondence to Jef Huisman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Inherent optical properties of coloured dissolved organic matter (CDOM) and non-algal particles (NAP).

a, Absorption spectrum of CDOM; scattering by CDOM is negligible. b, Absorption and scattering spectrum of NAP used in our application (see Methods for details).

Extended Data Fig. 2 Predictions of a null model in which the vibrational modes of H2O are ignored.

a, In the null model, the absorption spectrum of water (blue-grey line) is replaced by a smooth absorption spectrum (magenta line) without the subtle shoulders of the vibrational harmonics. b, Overlay of 100 underwater scalar irradiance spectra at the euphotic depth for waters with different CDOM concentrations, calculated by the null model. The null model does not predict a spectral landscape with pronounced peaks and valleys (in contrast to models that incorporate the vibrational modes of H2O; see Fig. 2 in the main text).

Extended Data Fig. 3 Absorption spectra of chromatic acclimators grown in different light colours.

a, Fluorescence excitation spectra of Synechococcus A15-62, a chromatic acclimator that adjusts its PUB:PEB ratio. The spectra show excitation wavelengths absorbed by the cells and subsequently emitted by the phycobilisomes as fluorescence at 580 nm, when the cells are grown in blue light (blue line) or green light (green line). b, Absorption spectra of Pseudanabaena CCY9509, a chromatic acclimator that adjusts its PEB:PCB ratio. The absorption spectra are shown for cells acclimated to green light (green line), orange light (orange line), and midway during chromatic acclimation after a switch from green to orange light (black line). The spectra are normalized with respect to (a) the PEB peak at ~540 nm, and (b) the Chl-a peak at 440 nm. Spectra in (a) were measured in this study using methods described in Sanfilippo et al.68, whereas spectra in (b) are from Stomp et al.31. For comparison, grey peaks and valleys in the background show simulated underwater irradiance spectra and vertical dashed lines indicate the harmonics of the water molecule.

Extended Data Fig. 4 Comparison of simulated and measured irradiance spectra, for aquatic ecosystems ranging from the clearest ocean waters to a hypertrophic lake.

a, Simulated planar irradiance spectra at the euphotic depth for a wide range of CDOM concentrations. b, Measured planar irradiance spectra at the euphotic depth in 7 different aquatic ecosystems. The spectra were obtained from (1) the South Pacific gyre (near Easter Island), (2) North Pacific gyre (station ALOHA north of Hawaii), (3) subtropical North Atlantic (Canary Islands), (4) temperate North Atlantic (west of Ireland), (5) Baltic Sea (near Gulf of Finland), (6) lake IJsselmeer (Netherlands) and (7) lake ‘t Joppe (Netherlands). Simulated irradiance spectra in (a) that qualitatively resemble measured irradiance spectra in (b) are indicated by the same colour. The irradiance spectrum of the South Pacific gyre is from Morel et al.14; all other spectra were measured in this study. Locations of the measured spectra are mapped in Fig. 5a. Vertical dashed lines indicate the harmonics of the water molecule.

Extended Data Fig. 5 Relative availability of the spectral niches depends on absorption by CDOM and NAP.

The relative availability of a spectral niche is calculated by the radiative transfer model, as the fraction of the total scalar irradiance at the euphotic depth that falls within this spectral niche (see equation 10 in the Methods). The relative availability of the spectral niches is displayed as function of absorption by dissolved and detrital matter (that is, CDOM and NAP) at 443 nm (adg(443)), which is a variable that can be retrieved by satellite remote sensing.

Extended Data Fig. 6 Relative abundances of cyanobacterial pigment types in the Great Lakes Area of North America, Central European lakes and the Baltic Sea.

a, Great Lakes Area of North America. b, Central European lakes and the Baltic Sea. Relative abundances were estimated using epifluorescence microscopy for the lake stations5,6,7 and flow cytometry for stations in the Baltic Sea7 (Supplementary Table 2). Prochlorococcus and PUB-rich Synechococcus were not found at these stations. Data from nearby stations in the Baltic Sea are aggregated in single pie charts.

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2.

Reporting Summary

Peer Review Information

Supplementary Video 1

Interactive plot, showing scalar irradiance spectra at the euphotic depth for a wide range of CDOM and NAP concentrations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Holtrop, T., Huisman, J., Stomp, M. et al. Vibrational modes of water predict spectral niches for photosynthesis in lakes and oceans. Nat Ecol Evol 5, 55–66 (2021). https://doi.org/10.1038/s41559-020-01330-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-020-01330-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology