, Volume 778, Issue 1, pp 61–74 | Cite as

Threshold quantification and short-term forecasting of Anabaena, Aphanizomenon and Microcystis in the polymictic eutrophic Lake Müggelsee (Germany) by inferential modelling using the hybrid evolutionary algorithm HEA

  • Friedrich Recknagel
  • Rita Adrian
  • Jan Köhler
  • Hongqing Cao


Forecasting models for Anabaena, Aphanizomenon and Microcystis have been developed for the hypertrophic phase from 1979 to 1990 and the eutrophic phase from 1997 to 2012 of the polymictic Lake Müggelsee by means of the hybrid evolutionary algorithm HEA. Comparisons of limnological parameters of the two phases revealed not only a distinct seasonal extension of N-limitation but also higher water temperatures that rose earlier and lasted longer between spring and autumn from 1997 to 2012. These differences were reflected by threshold conditions and sensitivity functions of the cyanobacteria-specific models evolved by HEA for the two phases. Seven-day-ahead forecasts matched well timings of peaking biomass observed for the three cyanobacteria but partially failed to predict accurate magnitudes, whereby coefficients of determination r 2 ranged between 0.48 and 0.76 for models in Phase I and between 0.42 and 0.69 in Phase II. The threshold conditions of the models quantified ranges of key predictor variables such as water temperature and transparency, concentrations of NO3-N and PO4-P that were symptomatic for sudden outbreaks of high biomass of the three cyanobacteria. Sensitivity functions extracted from 20 best performing models for each of the three cyanobacteria in both phases indicated different abundances between N-fixing Anabaena and Aphanizomenon compared to non-N-fixing Microcystis in response to strengthened N-limitation in Phase II.


Polymictic eutrophic lake Cyanobacteria HEA Forecasting Ecological thresholds Sensitivity analysis Interrelationships 



We thank two anonymous reviewers for their instructive comments that have significantly improved the manuscript. This research was partially funded by the EU Project LIMNOTIP under the FP7 ERA-Net Scheme (Biodiversa 01LC1207A).


  1. Adrian, R., R. Deneke, U. Mischke, R. Stellmacher & P. Lederer, 1995. A long-term study of Heiligensee (1975–1992). Evidence for effects of climate change on the dynamics of eutrophied lake ecosystems. Archive for Hydrobiology 133: 315–337.Google Scholar
  2. Adrian, R., C. M. O’Reilly, H. Zagarese, S. B. Baines, D. O. Hessen, W. Keller, D. M. Livingstone, R. Sommaruga, D. Straile, E. van Donk, G. A. Weyhenmeyer & M. Winder, 2009. Lakes as sentinels of climate change. Limnology and Oceanography 54: 2283–2297.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Adrian, R., D. Gerten, V. Huber, C. Wagner & S. R. Schmidt, 2012. Windows of change: temporal scale of analysis is decisive to detect ecosystem responses to climate change. Marine Biology 159: 2533–2542.CrossRefGoogle Scholar
  4. APHA, 2005. Standard Methods for the Examination of Water and Wastewater, 21st ed. American Public Health Association, Washington, DC.Google Scholar
  5. Cao, H., F. Recknagel & P. Orr, 2013. Enhanced functionality of the redesigned hybrid evolutionary algorithm HEA demonstrated by predictive modelling of algal growth in the Wivenhoe Reservoir, Queensland (Australia). Ecological Modelling 252: 32–43.CrossRefGoogle Scholar
  6. Cao, H., F. Recknagel & P. T. Orr, 2014. Parameter optimisation algorithms for evolving rule models applied to freshwater ecosystem. IEEE Transactions on Evolutionary Computation 18(6): 793–806.CrossRefGoogle Scholar
  7. Huber, V., R. Adrian & D. Gerten, 2008. Phytoplankton response to climate warming modified by trophic state. Limnology and Oceanography 53(1): 1–13.CrossRefGoogle Scholar
  8. Elliott, J. A., I. D. Jones & S. J. Thackeray, 2006. Testing the sensitivity of phytoplankton communities to changes in water temperature and nutrient load, in a temperate lake. Hydrobiologia 559: 401–411.CrossRefGoogle Scholar
  9. Holland, J. H., 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbour, MI.Google Scholar
  10. Holland, J.H., et al., 1986. Induction. Process of Inference, Learning, and Discovery. MIT Press, Cambridge.Google Scholar
  11. Huber, V., R. Adrian & D. Gerten, 2010. A matter of timing: heat wave impact on crustacean zooplankton. Freshwater Biology 55: 1769–1779.Google Scholar
  12. Huber, V., C. Wagner, D. Gerten & R. Adrian, 2012. To bloom or not to bloom: contrasting responses of cyanobacteria to different heat waves explained by critical thresholds of abiotic drivers. Oecologia 169: 245–256.CrossRefPubMedGoogle Scholar
  13. Huisman, J., J. Sharples, J. M. Stroom, P. M. Visser, W. E. A. Kardinaal, J. M. H. Verspagen & B. Sommeijer, 2004. Changes in turbulent mixing shift competition for light between phytoplankton species. Ecology 85: 2960–2970.CrossRefGoogle Scholar
  14. Jöhnk, K. D., J. Huisman, J. Sharples, B. Sommeijer, P. M. Visser & J. M. Stroom, 2008. Summer heatwaves promote blooms of harmful cyanobacteria. Global Change Biology 14: 495–512.CrossRefGoogle Scholar
  15. Köhler, J., H. Behrendt & S. Hoeg, 2000. Long-term response of phytoplankton to reduced nutrient load in the flushed Lake Müggelsee (Spree system, Germany). Archiv für Hydrobiologie 148: 209–229.CrossRefGoogle Scholar
  16. Köhler, J., S. Hilt, R. Adrian, A. Nicklisch, H. P. Kozerski & N. Walz, 2005. Long-term response of a shallow, moderately flushed lake to reduced external phosphorus and nitrogen loading. Freshwater Biology 50: 1639–1650.CrossRefGoogle Scholar
  17. Livingstone, D. M. & R. Adrian, 2009. Modeling the duration of intermittent ice cover on a lake for climate-change studies. Limnology and Oceanography 54(5): 1709–1722.CrossRefGoogle Scholar
  18. Mooij, W. M., L. N. De Senerpont Domis & J. H. Janse, 2009. Linking species- and ecosystem-level impacts of climate change in lakes with a complex and a minimal model. Ecological Modelling 220: 3011–3020.CrossRefGoogle Scholar
  19. Paerl, H. W., 1988. Nuisance phytoplankton blooms in coastal, estuarine and inland waters. Limnology and Oceanography 33: 823–847.CrossRefGoogle Scholar
  20. Recknagel, F., M. Hosomi, T. Fukushima & D.-S. Kong, 1995. Short- and long-term control of external and internal phosphorus loads in lakes—a scenario analysis. Water Research 29(7): 1767–1779.CrossRefGoogle Scholar
  21. Recknagel, F., H. Cao, C. van Ginkel, D. van der Molen, H. Park & N. Takamura, 2008. Adaptive agents for forecasting seasonal outbreaks of blue-green algal populations in lakes categorised by circulation type and trophic state. Verhandlungen Internationale Verein Limnologie 30(2): 191–197.Google Scholar
  22. Recknagel, F., I. Ostrovsky, H. Cao, T. Zohary & X. Zhang, 2013. Ecological relationships, thresholds and time-lags determining phytoplankton community dynamics of Lake Kinneret, Israel elucidated by evolutionary computation and wavelets. Ecological Modelling 255: 70–86.CrossRefGoogle Scholar
  23. Recknagel, F., I. Ostrovsky & H. Cao, 2014a. Model ensemble for the simulation of plankton community dynamics of lake Kinneret (Israel) induced from in situ predictor variables by evolutionary computation. Environmental Modelling & Software 61: 380–392.CrossRefGoogle Scholar
  24. Recknagel, F., P. Orr & H. Cao, 2014b. Inductive reasoning and forecasting of population dynamics of Cylindrospermopsis raciborskii in three sub-tropical reservoirs by evolutionary computation. Harmful Algae 31: 26–34.CrossRefGoogle Scholar
  25. Recknagel, F., I. Ostrovsky, H. Cao & Q. Chen, 2014c. Hybrid evolutionary computation quantifies environmental thresholds for recurrent outbreaks of population density. Ecological Informatics 24: 85–89.CrossRefGoogle Scholar
  26. Reynolds, C., 1984. The Ecology of Freshwater Phytoplankton. Cambridge University Press, Cambridge.Google Scholar
  27. Storn, R. & K. Price, 1997. Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11: 341–359.CrossRefGoogle Scholar
  28. Utermoehl, H., 1958. Zur Vervollkommnung der quantitativen Phytoplankton Methodik. Mitt. Internationale Ver. Theoretische und Angewandte Limnologie 9: 1–38.Google Scholar
  29. Wilhelm, S. & R. Adrian, 2008. Impact of summer warming on the thermal characteristics of a polymictic lake: consequences for oxygen, nutrients and phytoplankton. Freshwater Biology 53: 226–237.CrossRefGoogle Scholar
  30. Wagner, C. & R. Adrian, 2009. Cyanobacteria blooms: quantifying the effects of climate change. Limnology and Oceanography 54(6): 2460–2468.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Friedrich Recknagel
    • 1
  • Rita Adrian
    • 2
  • Jan Köhler
    • 2
  • Hongqing Cao
    • 1
  1. 1.School of Biological SciencesUniversity of AdelaideAdelaideAustralia
  2. 2.Leibniz-Institute of Freshwater Ecology and Inland FisheriesBerlinGermany

Personalised recommendations