Advertisement

Coupling high-resolution measurements to a three-dimensional lake model to assess the spatial and temporal dynamics of the cyanobacterium Planktothrix rubescens in a medium-sized lake

  • Elisa Carraro
  • Nicolas Guyennon
  • David Hamilton
  • Lucia Valsecchi
  • Emanuela C. Manfredi
  • Gaetano Viviano
  • Franco Salerno
  • Gianni Tartari
  • Diego Copetti
PHYTOPLANKTON
Part of the Developments in Hydrobiology book series (DIHY, volume 221)

Abstract

In a medium-sized pre-alpine lake (North Italy) the cyanobacterium Planktothrix rubescens has strongly dominated the phytoplankton assemblage since 2000, similar to many pre-alpine lakes, despite improvements in water quality. The objective of this study was to determine the factors governing the spatial distribution of P. rubescens, including the major hydrodynamic processes and the influence of long-term reduction in nutrient concentrations during a period of climate warming. We used an intensive field campaign conducted from February 2010 to January 2011, to evaluate distributions of phytoplankton phyla, as well as P. rubescens, using spectrally resolved fluorescence measurements. These data provided highly spatially and temporally resolved phytoplankton population data suitable to calibrate and validate a coupled three-dimensional hydrodynamic (ELCOM) and ecological model (CAEDYM) of the lake ecosystem. The simulations revealed the fundamental role of physiological features of P. rubescens that led to observed vertical patterns of distribution, notably a deep chlorophyll maximum, and a strong influence of lake hydrodynamic processes, particularly during high-discharge inflows in summer stratification. The simulations are used to examine growth-limiting factors that help to explain the increased prevalence of P. rubescens during re-oligotrophication.

Keywords

Metalimnion Hydrodynamics Deep chlorophyll maximum Phytoplankton ELCOM–CAEDYM 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ambrosetti, W. & L. Barbanti, 1999. Deep water warming in lakes: an indicator of climatic change. Journal of Limnology 58: 1–9.CrossRefGoogle Scholar
  2. American Public Health Association (APHA), 1992. Standard Methods for Examination of Water and Wastewater, 18th ed. American Public Health Association, Washington, DC.Google Scholar
  3. Arhonditsis, G. B. & M. T. Brett, 2004. Evaluation of the current state of mechanistic aquatic biogeochemical modelling. Marine Ecology Progress Series 271: 13–26.CrossRefGoogle Scholar
  4. Arhonditsis, G. B., S. S. Qian, C. A. Stow, E. C. Lamon & K. H. Reckhow, 2007. Eutrophication risk assessment using Bayesian calibration of process-based models: application to a mesotrophic lake. Ecological Modelling 208: 215–229.CrossRefGoogle Scholar
  5. Balestrini, R., L. Galli & G. Tartari, 2000. Wet and dry atmospheric deposition at prealpine and alpine site in Northern Italy. Atmospheric Environment 4: 1455–1470.CrossRefGoogle Scholar
  6. Beutler, M., K. H. Wiltshire, B. Meyer, C. Moldaenke, C. Lüring, M. Meyerhöfer, U. P. Hansen & H. Dau, 2002. A fluorometric method for the differentiation of algal populations in vivo and in situ. Photosynthesis Research 72: 39–53.PubMedCrossRefGoogle Scholar
  7. Boegman, L., J. Imberger, G. N. Ivey & J. P. Antenucci, 2003. High-frequency internal waves in large stratified lakes. Limnology and Oceanography 48: 895–919.CrossRefGoogle Scholar
  8. Bright, D. I. & A. E. Walsby, 2000. The daily integral of growth by Planktothrix rubescens calculated from growth rate in culture and irradiance in Lake Zürich. New Phytologist 146: 301–316.Google Scholar
  9. Brookes, J. D. & C. C. Carey, 2011. Resilience to Bloom. Science 334: 46–47.PubMedCrossRefGoogle Scholar
  10. Bruce, L. C., D. Hamilton, J. Imberger, G. Gal, M. Gophen, T. Zohary & K. D. Hambright, 2006. A numerical simulation of the role of zooplankton in C, N and P cycling in Lake Kinneret, Israel. Ecological Modelling 193: 412–436.Google Scholar
  11. Bürgi, H. & P. Stadelmann, 2002. Change of phytoplankton composition and biodiversity in Lake Sempach before and during restoration. Hydrobiologia 469: 33–48.CrossRefGoogle Scholar
  12. Carmichael, W. W., 2001. Health effects of toxin producing cyanobacteria: the ‘CyanoHABS’. Human and Ecological Risk Assessment 7: 1393–1407.CrossRefGoogle Scholar
  13. Copetti, D., G. Tartari, G. Morabito, A. Oggioni, E. Legnani & J. Imberger, 2006. A biogeochemical model of the Lake Pusiano (North Italy) and its use in the predictability of phytoplankton blooms: first preliminary results. Journal of Limnology 65: 59–64.CrossRefGoogle Scholar
  14. Cuypers, Y., B. Vinçon-Leite, A. Groleau, B. Tassin & J. F. Humbert, 2011. Impact of internal waves on the spatial distribution of Planktothrix rubescens (cyanobacteria) in an alpine lake. The ISME Journal 5: 580–589.PubMedCrossRefGoogle Scholar
  15. D’Alelio, D., A. Gandolfi, A. Boscaini, G. Flaim, M. Tolotti & N. Salmaso, 2011. Planktothrix populations in subalpine lakes: selection for strains with strong gas vesicles as a function of lake depth, morphometry and circulation. Freshwater Biology 56: 1481–1493.CrossRefGoogle Scholar
  16. Dokulil, M. T. & K. Teubner, 2000. Cyanobacterial dominance in lakes. Hydrobiologia 438: 1–12.CrossRefGoogle Scholar
  17. Eilers, P. H. & J. J. Goeman, 2004. Enhancing scatterplots with smoothed densities. Bioinformatics 20: 623–628.Google Scholar
  18. Elliot, J. A., 2010. The seasonal sensitivity of Cyanobacteria and other phytoplankton to changes in flushing rate and water temperature. Global Change Biology 16: 864–876.CrossRefGoogle Scholar
  19. Ernst, B., S. J. Hoeger, E. O’Brien & D. R. Dietrich, 2009. Abundance and toxicity of Planktothrix rubescens in the pre-alpine Lake Ammersee, Germany. Harmful Algae 8: 329–342.CrossRefGoogle Scholar
  20. Feuillade, J., M. Feuillade & P. Blanc, 1990. Alkaline phosphatase activity fluctuactions and associated factors in a eutrophic lake dominated by Oscillatoria rubescens. Hydrobiologia 207: 233–240.CrossRefGoogle Scholar
  21. Gal, G., M. R. Hipsey, A. Parparov, U. Wagner, V. Makler & T. Zohary, 2009. Implementation of ecological modelling as an effective management and investigation tool: Lake Kinneret as a case study. Ecological Modelling 220: 1697–1718.CrossRefGoogle Scholar
  22. Gorham, E., J. W. G. Lund, J. E. Sanger & W. E. Dean, 1974. Some relationships between algal standing crop, water chemistry and sediment chemistry in the English lakes. Limnology and Oceanography 19: 601–617.CrossRefGoogle Scholar
  23. Grayson, R. B., G. Blöschl, A. W. Western & T. A. McMahon, 2002. Advances in the use of observed spatial patterns of catchment hydrological response. Advances in Water Resources 25: 1313–1334.CrossRefGoogle Scholar
  24. Gupta, H. V., S. Sorooshian & P. O. Yapo, 1999. Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. Journal of Hydrologic Engineering 4: 135–143.CrossRefGoogle Scholar
  25. Hamilton, D. P. & S. G. Schladow, 1997. Prediction of water quality in lakes and reservoirs. Part I – model description. Ecological Modelling 96: 91–110.CrossRefGoogle Scholar
  26. Hamilton, D. P., K. R. O’Brien, M. A. Burford, J. D. Brookes & C. G. McBride, 2010. Vertical distributions of chlorophyll in deep, warm monomictic lakes. Aquatic Sciences 72: 295–307.CrossRefGoogle Scholar
  27. Hipsey, M.R., 2008. The CWR Computational Aquatic Ecosystem Dynamics Model CAEDYM. User Manual. Centre for Water Research, The University of Western Australia.Google Scholar
  28. Hodges, B. R., J. Imberger, A. Saggio & K. B. Winters, 2000. Modelling basin scale waves in a stratified lake. Limnology and Oceanography 45: 1603–1620.CrossRefGoogle Scholar
  29. Horsburgh, J. S., A. Spackman Jones, D. K. Stevens, D. G. Tarboton & N. O. Mesner, 2010. A sensor network for high frequency estimation of water quality constituent fluxes using surrogates. Environmental Modelling & Software 25: 1031–1044.CrossRefGoogle Scholar
  30. Howarth, R. J. & S. A. M. Earle, 1979. Application of a generalized power transformation to geochemical data. Mathematical Geology 11: 45–62.CrossRefGoogle Scholar
  31. Huisman, J. M., H. C. P. Matthijs & P. M. Visser, 2005. Harmful Cyanobacteria. Springer Aquatic Ecology Series 3, Dordrecht, The Netherlands.Google Scholar
  32. Ibelings, B. W., M. Vonk, F. J. Los, D. T. Van Der Molen & W. M. Mooij, 2003. Fuzzy modeling of cyanobacterial surface water-blooms, validation with NOAA-AVHRR satellite images. Ecological Applications 13: 1456–1472.CrossRefGoogle Scholar
  33. Krivtsov, V., J. Corliss, E. Bellinger & D. Sigee, 2000. Indirect regulation rule for consecutive stages of ecological succession. Ecological Modelling 133: 73–82.CrossRefGoogle Scholar
  34. Laval, B., J. Imberger, B. R. Hodges & R. Stocker, 2003a. Modeling circulation in lakes: spatial and temporal variations. Limnology and Oceanography 48: 983–994.CrossRefGoogle Scholar
  35. Laval, B., B. R. Hodges & J. Imberger, 2003b. Reducing numerical diffusion effects with pycnocline filter. Journal of Hydraulic Engineering 129: 215–224.CrossRefGoogle Scholar
  36. Leboulanger, C., U. Dorigo, S. Jacquet, B. Leberre, G. Paolini & J.-F. Humbert, 2002. Application of a submersible spectrofluorometer for rapid monitoring of freshwater cyanobacterial blooms: a case study. Aquatic Microbial Ecology 30: 83–89.CrossRefGoogle Scholar
  37. Legnani, E., D. Copetti, A. Oggioni, G. Tartari, M. T. Palumbo & G. Morabito, 2005. Planktothrix rubescens seasonal and vertical distribution in Lake Pusiano (North Italy). Journal of Limnology 64: 61–73.CrossRefGoogle Scholar
  38. Livingstone, D. M., 2003. Impact of secular climate change on the thermal structure of a large temperate central European lake. Climatic Change 57: 205–225.CrossRefGoogle Scholar
  39. Mackay, E., D. J. Ian, A. M. Folkard & S. J. Thackeray, 2011. Transition zones in small lakes: the importance of dilution and biological uptake on lake-wide heterogeneity. Hydrobiologia 678: 85–97.CrossRefGoogle Scholar
  40. Mellard, J. P., K. Yoshiyama, E. Litchman & C. A. Klausmeier, 2011. The vertical distribution of phytoplankton in stratified water columns. Journal of Theoretical Biology 269: 16–30.PubMedCrossRefGoogle Scholar
  41. Mieleitner, J. & P. Reichert, 2008. Modelling functional groups of phytoplankton in three lakes of different trophic state. Ecological Modelling 211: 279–291.CrossRefGoogle Scholar
  42. Missaghi, S. & M. Hondzo, 2010. Evaluation and application of a three–dimensional water quality model in a shallow lake with complex morphometry. Ecological Modelling 221: 1512–1525.CrossRefGoogle Scholar
  43. Mooij, W. M., et al., 2010. Challenges and opportunities for integrating lake ecosystem modelling approaches. Aquatic Ecology 44: 633–667.CrossRefGoogle Scholar
  44. Nash, J. E. & J. V. Sutcliffe, 1970. River flow forecasting through conceptual models. Part I. A discussion of principles. Journal of Hydrology 10: 282–290.CrossRefGoogle Scholar
  45. Padisák, J., L. O. Crossetti & L. Naselli-Flores, 2009. Use and misuse in the application of the phytoplankton functional classification: a critical review with updates. Hydrobiologia 621: 1–19.CrossRefGoogle Scholar
  46. Paerl, H. W. & J. Huisman, 2008. Blooms like it hot. Science 320: 57–58.PubMedCrossRefGoogle Scholar
  47. Pannard, A., B. E. Beisner, D. F. Bird, J. Braun, D. Planas & M. Bormans, 2011. Recurrent internal waves in a small lake: potential ecological consequences for metalimnetic phytoplankton populations. Limnology & Oceanography: Fluids & Environments 1: 91–109.Google Scholar
  48. Reynolds, C. S., 1971. The ecology of planktonic blue-green algae in the North Shropshire meres. Field Studies 3: 409–432.Google Scholar
  49. Reynolds, C. S., 2006. The Ecology of Phytoplankton. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
  50. Reynolds, C. S., V. Huszar, C. Kruk, L. Naselli-Flores & S. Melo, 2002. Towards a functional classification of the freshwater phytoplankton. Journal of Plankton Research 24: 417–428.CrossRefGoogle Scholar
  51. Rigosi, A., R. Marcé, C. Escot & F. J. Rueda, 2011. A calibration strategy for dynamic succession models including several phytoplankton groups. Environmental Modelling & Software 26: 697–710.Google Scholar
  52. Rinke, K., P. Yeates & K. O. Rothhaupt, 2010. A simulation study of the feedback of phytoplankton on thermal structure via light extinction. Freshwater Biology 55: 1674–1693.Google Scholar
  53. Robson, B. J. & D. P. Hamilton, 2004. Three-dimensional modelling of a Microcystis bloom event in the Swan River estuary, Western Australia. Ecological Modelling 174: 203–222.CrossRefGoogle Scholar
  54. Robson, B. J., D. P. Hamilton, I. T. Webster & T. Chan, 2008. Ten steps applied to development and evaluation of process-based biogeochemical models of estuaries. Environmental Modelling & Software 23: 369–384.CrossRefGoogle Scholar
  55. Salerno, F. & G. Tartari, 2009. A coupled approach of surface hydrological modelling and Wavelet Analysis for understanding the baseflow components of river discharge in karst environments. Journal of Hydrology 376: 295–306.CrossRefGoogle Scholar
  56. Salmaso, N., 2010. Long-term phytoplankton community changes in a deep subalpine lake: responses to nutrient availability and climatic fluctuations. Freshwater Biology 55: 825–846.CrossRefGoogle Scholar
  57. Serra, T., J. Vidal, J. Colomer, X. Casamitjana & M. Soler, 2007. The role of surface vertical mixing in phytoplankton distribution in a stratified reservoir. Limnology and Oceanography 52: 620–634.CrossRefGoogle Scholar
  58. Trolle, D., H. Skovgaard & E. Jeppesen, 2008. The Water Framework Directive: setting the phosphorus loading target for a deep lake in Denmark using the 1D lake ecosystem model DYRESM–CAEDYM. Ecological Modelling 219: 138–152.CrossRefGoogle Scholar
  59. Van Nes, E. H. & M. Scheffer, 2005. A strategy to improve the contribution of complex simulation models to ecological theory. Ecological Modelling 185: 153–164.CrossRefGoogle Scholar
  60. Vilhena, L. C., I. Hillmer & J. Imberger, 2010. The role of climate change in the occurrence of algal blooms: Lake Burragorang, Australia. Limnology and Oceanography 55: 1188–1200.CrossRefGoogle Scholar
  61. Vuillermoz, E., E. Legnani, D. Copetti & G. Tartari, 2006. Limnological evolution of Pusiano Lake (1972–2004). Verhandlungen des Internationalen Verein Limnologie 29: 2009–2014.Google Scholar
  62. Walsby, A. E. & M. J. Booker, 1980. Changes in buoyancy of a planktonic blue-green alga in response to light intensity. European Journal of Phycology 15: 311–319.CrossRefGoogle Scholar
  63. Walsby, A. E. & F. Schanz, 2002. Light-dependent growth rate determines changes in the population of Planktothrix rubescens over the annual cycle in Lake Zurich, Switzerland. New Phytologist 154: 671–687.CrossRefGoogle Scholar
  64. Walsby, A. E., P. K. Hayes, R. Boje & L. J. Stal, 1997. The selective advantage of buoyancy provided by gas vesicles for planktonic cyanobacteria in the Baltic Sea. New Phytologist 136: 407–417.CrossRefGoogle Scholar
  65. Walsby, A. E., F. Schanz & M. Schmid, 2006. The Burgundy-blood phenomenon: a model of buoyancy change explains autumnal waterblooms of Planktothrix rubescens in Lake Zurich. New Phytologist 169: 109–122.PubMedCrossRefGoogle Scholar
  66. Wurtsbaugh, W. A., H. P. Gross, P. Budy & C. Luecke, 2001. Effects of epilimnetic versus metalimnetic fertilization on the phytoplankton and periphyton of a mountain lake with a deep chlorophyll maxima. Canadian Journal of Fisheries and Aquatic Sciences 58: 2156–2166.CrossRefGoogle Scholar
  67. Zhang, W. & G. B. Arhonditsis, 2009. A Bayesian hierarchical framework for calibrating aquatic biogeochemical models. Ecological Modelling 220: 2142–2161.CrossRefGoogle Scholar
  68. Zhang, M., H. Duan, X. Shi, Y. Yu & F. Kong, 2011. Contributions of meteorology to the phenology of cyanobacterial blooms: implications for future climate change. Water Research. doi: 10.1016/j.watres.2011.11.013 .

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Elisa Carraro
    • 1
  • Nicolas Guyennon
    • 2
  • David Hamilton
    • 3
  • Lucia Valsecchi
    • 1
  • Emanuela C. Manfredi
    • 1
  • Gaetano Viviano
    • 1
  • Franco Salerno
    • 1
  • Gianni Tartari
    • 1
  • Diego Copetti
    • 1
  1. 1.Water Research InstituteBrugherioItaly
  2. 2.Water Research InstituteRomeItaly
  3. 3.Department of Biological SciencesUniversity of WaikatoHamiltonNew Zealand

Personalised recommendations