Professor Friedrich Recknagel

Professor  Friedrich Recknagel
 Position Professor
 Org Unit School of Biological Sciences
 Telephone +61 8 8313 3953
 Mobile +61 4 3350 0089
 Location Floor/Room 1 ,  Benham ,   North Terrace
  • Teaching Interests

    • Course coordinator of 'Integrated Catchment Management'
    • Course coordinator of 'Ecosystem Modelling'
    • Professional short course in 'Ecology, Modelling and Management of Catchment-Lake Systems'
    • Professional short course in 'Machine and Deep Learning for Explanatory and Predictive Modelling in Ecology'
  • Research Interests

    • My lab 'Watershed Ecology and Ecoinformatics' focuses on following topics:
    • Inductive reasoning of ecology and phenology of freshwater phyto- and zooplankton
    • Process-based and inferential lake and watershed modelling
    • Eutrophication management in lakes and watersheds
    • Inductive reasoning and forecasting of Cyanobacteria Blooms
    • Predictive bioassessment and threshold identification of aquatic habitats
    • Ecological applications of evolutionary computation and artificial neural networks
    • Ecological applications of machine and deep learning
    • Ecological modelling and informatics
  • Publications



      Google Scholar:

      Citations - 4626, h-index - 37, i10-index - 90                                                                           

      Book Publications: 

    • Recknagel, F. and W. Michener (eds.), 2018. Ecological Informatics. Data management and knowledge discovery. 3rd Edition. Springer International, 1-482.(
    • Jorgensen, S.E., Chon, T.S. and F. Recknagel, 2009. Handbook of Ecological Modelling and Informatics. WIT Press, Southampton, UK
    • Recknagel, F. (ed.), 2006. Ecological Informatics. Scope, Techniques and Applications. 2nd Edition. Springer, New York, 496 pp.
    • Recknagel, F., 2003. Ecological Informatics. Understanding Ecology by Biologically-Inspired Computation. Springer-Verlag, Berlin, Heidelberg, New York, 1 – 402.
    • Recknagel, F., 1989. Applied Systems ecology. Approach and case Studies in Aquatic ecology. Akademie-Verlag, Berlin, 1-138.

              Peer-reviewed Journal Articles: 

    • Kim, H.G., Cho, K.H. and F. Recknagel, 2024. Bibliometric network analysis of scientific research on early warning signals for cyanobacterial blooms in lakes and rivers. Ecological Informatics 80, 202503.
    • Kim, H.G., Cho, K.H. and F. Recknagel, 2023. Time-series modelling of harmful cyanobacteria blooms by convolutional neural networks and wavelet generated time-frequency images of environmental driving variables. Water Research 246.
    • Recknagel, F., 2023. Cyberinfrastructure for sourcing and processing ecological data. Ecological
    • Recknagel, F., Park, H., Sukenik, A. and T. Zohary, 2022. Dissolved organic nitrogen, dinoflagellates and cyanobacteria in two eutrophic lakes: An analysis by inferential modelling. Harmful Algae 114. 102229.
    • Kim, H.G., Recknagel, F., Park, H.W. and G.J. Joo, 2021. Implications of flow regulation for habitat conditions and phytoplankton populations of the Nakdong River, South Korea. Water Research 207. 
    • Recknagel, F., Adrian, R. and J. Koehler, 2021. Quantifying phenological asynchrony of phyto- and zooplankton in response to changing temperature and nutrient conditions in Lake Mueggelsee (Germany) by means of evolutionary computation. Environmental Modelling and Software 146.
    • Sultana, J., Recknagel, F. and H. Nguyen, 2020. Species-specific macroinvertebrate response to climate and land-use scenarios in a Mediterranean catchment revealed by an integrated modelling approach. Ecological Indicators 118.
    • Hong, S., Recknagel, F., et al., 2020. Relationships of otter populations with fish, macroinvertebrates and water quality across three Korean rivers revealed by inferential modelling based on evolutionary computation. Ecological Informatics 59.
    • Sultana, J., Tibby, J., Recknagel, F., Maxwell, S. and P. Goonan, 2020. Comparison of two commonly used methods for identifying water quality thresholds in freshwater ecosystems using field and synthetic data. Science of Total Environment 724.
    • Recknagel, F., Zohary, T., Rueker, J., Orr, P., Castello Branco, C. and B. Nixdorf, 2019. Causal relationships of Raphidiopsis (formerly Cylindrospermosis) with water temperature and N:P-ratios: A meta-analysis across lakes with different climates based on inferential modelling. Harmful Algae 84, 222-232. 
    • Sultana, J., Recknagel, F., Tibby, J. and S. Maxwell, 2019. Comparison of water quality thresholds for macroinvertebrates in two Mediterranean catchments by the inferential techniques TITAN and HEA. Ecological Indicators 101, 867-877. 
    • Nguyen, H.H., Recknagel, F., Meyer, W., Frizenschaf, J., Ying, H. and M.S. Gibbs, 2019. Comparison of the alternative models SOURCE and SWAT for predicting catchment streamflow, sediment and nutrient loads uder the effect of land use changes. Science of the Total Environment 662, 254-265.  
    • Rocha, M.I.A., Recknagel, F., Minoti, R.T., Huszar, V.L.M., Kozlowski-Suzuki, B., Cao, H., Starling, F. and C.W.C. Branco, 2019. Assessing the effects of abiotic varibales and zooplankton on picocyanobacterial dominance in two tropical mesotrophic reservoirs by means of evolutionary computation. Water Research 149, 120-129.   
    • Nguyen, H.H., Recknagel, F. and W. Meyer, 2018. Effects of urbanisation and climate change on flow and nutrient loads of a Mediterranean catchment in South Australia. Ecohydrology and Hydrobiology (in press).
    • Nguyen, H.H., Recknagel, F. and W. Meyer, 2018. Water quality control options in response to catchment urbanization: A scenario analysis by SWAT. Water (in press).
    • Recknagel, F., Orr, P., Bartkow, M., Swanepoel, A. and H. Cao, 2017. Early warning of limit-exceeding concentrations of cyanobacteria and cyanotoxins in drinking water reservoirs by inferential modelling. Harmful Algae 69, 18-27.
    • Nguyen, H.H., Recknagel, F., Meyer, W., Frizenschaf, J. and M. Shretsha, 2017. Modelling the impacts of altered management practices, land use and climate changes on the water quality of the Millbrook catchment-reservoir system in South Australia. Journal of Environmental Management  202, 1-11.
    • Recknagel, F., Kim, D.K., Joo, G.-J. and H. Cao, 2017. Response of Microcystis and Stephanodiscus to alternative flow regimes of the regulated River Nakdong (South Korea) quantified by model ensembles based on the hybrid evolutionary algorithm (HEA). River Research and Applications 33, 949-958. 
    • Shrestha, M., Recknagel, F., Frizenschaf, J. and W. Meyer, 2017. Future climate and land use effects on flow and nutrient loads of a Mediterranean catchment in South Australia. Science of Total Environment 590-591:186-193.
    • Nguyen, H.H., Recknagel, F., Meyer, W. and J. Frizenschaf, 2017. Analysing the effects of forest cover and irrigatioin farm dams on stream flows of water-scarce catchments in South Australia through the SWAT model. Water 9, 33, 1-16. 
    • Cao, H., Recknagel, F. and M. Bartkow, 2016. Spatially-explicit forcasting of cyanobacteria assemblages in freshwater lakes by multi-objective hybrid evolutionary algorithms. Ecological Modelling 342, 97-112.
    • Recknagel, F., Adrian, R., Kohler, J. and H. Cao, 2016. Threshold quantification and short-term forecasting of Anabaena, Aphanizomenon and Microcystis in the polymictic, eutrophic Mueggelsee (Germany) by inferential modelling using the hybrid evolutionary algorithm HEA. Hydrobiologia 778, 61-74.
    • Shrestha, M.K., Recknagel, F., Frizenschaf, J. and W. Meyer, 2016. Assessing SWAT models based on single and multi-site calibration for the simulation of flow and nutrient loads in the semi-arid Onkaparinga Catchment in South Australia. Agricultural Water Management 175, 61-71.
    • Chen, Q., Guan, T., Yun, L., Li, R. and F. Recknagel, 2015. Online forecasting chlorophyll a concentrations by an auto-regressive integrated moving average model: Feasibilities and potentials. Harmful Algae 43, 58–65.
    • Zhang, X., Recknagel, F., Chen, Q., Cao, H. and R. Li, 2015. Spatially-explicit modelling and forecasting of cyanobacteria growth in Lake Taihu by evolutionary computation. Ecological Modelling 306, 216-225.
    • Recknagel, F., Branco, C.W., Cao, H., Huszar, V.L. and I.F. Sousa-Filho, 2015. Modelling and forecasting the heterogeneous distribution of picocyanobacteria in the tropical Lajes Reservoir (Brazil) by evolutionary computation. Hydrobiologia 749, 53-67.
    • Recknagel, F., Ostrovsky, I. and H. Cao, 2014. 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.
    • Chen, Q., Zhang, C., Recknagel, F., Guo, J.  and K. Blanckaert, 2014.  Adapatation and multiple parameter optimisation of the simulation model SALMO as prerequisite for scenario analysis on a shallow eutrophic lake. Ecological Modelling 273, 109-116.
    • Recknagel, F., Orr, P. and H. Cao, 2014. Inductive reasoning and forecasting of population dynamics of Cylindrospermopsis raciborski in three sub-tropical reservoirs by evolutionary computation. Harmful Algae 31, 26-34.
    • Recknagel, F., 2013. Current scope, case studies and future directions of ecologcial informatics. Journal of Environmental Informatics 21, 1, 1-9.
    • Recknagel, F., Ostrovsky, I., Cao, H., Zohary, T. and X. Zhang, 2013. Ecological relationships, thresholds and time lags determining phytoplankton community dynamics in Lake Kinneret, Israel elucidated by evolutionary computation and wavelets. Ecological Modelling 255, 70-86.
    • Cao, H., Recknagel, F. and P. Orr, 2013. Enhanced functionality of the hybrid evolutionary algorithm HEA demonstrated by predictive modelling of algal growth in the Wivenhoe Reservoir, Queensland (Australia). Ecological Modelling 252, 32-43.
    • Recknagel, F., 2011. Ecological informatics: A discipline in the making. Ecological Informatics 6, 1-3.
    • Recknagel, F., 2008. Ecological Informatics: Overview. In: Jorgensen, S.E. and B.D. Faith (Editors), 2008. Encyclopedia of Ecology, Oxford, Elsevier, Vol. 2, pp. 1041-1058.
    • Sierp, M., Qin, J.G. and F. Recknagel, 2008. Biomanipulation: a review of biological control measures in eutrophic waters and the potential for Murray cod Maccullochella peelii peelii to promote water quality in temperate Australia. Reviews in Fish Biology and Fisheries, 1-23.
    • Qua, S., Chen, Q. and F. Recknagel, 2008.  Cellular automata based simulation of random versus selective harvesting strategies in predator–prey systems. Ecological Informatics 3, 252-258.

    • Recknagel, F., van Ginkel, C., Cao, H., Cetin, L. and B. Zhang, 2008. Generic Limnological Models on the Touchstone: Testing the Lake Simulation Library SALMO-OO and the Rule-based Microcystis Agent for Warm-monomictic Hypertrophic Lakes in South Africa. Ecological Modelling 215, 144-158.
    • Recknagel, F., Cetin, L.T. and Zhang B., 2008. Process-based Simulation Library SALMO-OO for Lake Ecosystems. Part 1: Object-oriented Implementation and Validation. Ecological Informatics 3,2,, 170-180.
    • Cao, H., Recknagel, F,  Cetin,  L. and B. Zhang, 2008. Process-based Simulation Library SALMO-OO for Lake Ecosystems. Part 2: Multi-objective Parameter Optimisation by Evolutionary Algorithms. Ecological Informatics, 181-190.
    • Recknagel, F., Cao, H., van Ginkel, C., van der Molen, D., Park, H. and N. Takamura, 2008. Adaptive agents for forecasting seasonal outbreaks of blue-green algal populations in lakes categorised by circulation type and trophic state. Verh. Internat. Verein. Limnol. 30, 2, 191-197.
    • Welk, A., Recknagel, F., Cao, H., Chan, W.S. and A. Talib, 2008. Rule-based agents for forecasting algal population dynamics in freshwater lakes discovered by hybrid evolutionary algorithms. Ecological Informatics 3, 1, 46-54.
    • van Ginkel, C., Cao, H., Recknagel, F.  and S. du Plessis, 2007. Forecasting of dinoflagellate blooms in warm-monomictic hypertrophic reservoirs in South Africa by means of rule-based agents. Water SA 33, 531-538.
    • Chan, W. S., Recknagel, F., Cao, H. and H.D. Park, 2007. Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms. Water Research 41, 2247-2255

    • Kim, D.-K., Cao, H., Jeong, K.-S., Recknagel, F. and G.-J. Joo, 2007. Predictive function and rules for population dynamics of Microcystis aeruginosa in the regulated Nakdong River (South Korea), discovered by evolutionary algorithms. Ecological Modelling 203, 147-156.
    • Wen, L. and F. Recknagel, 2006. Balancing phosphorus adsorption and consumption processes in experimental treatment ponds designed for agricultural drainage water. Ecological Engineering 28, 14-24
    • Atanasova, N., Todorovski, L., Dzeroski, S., Rekar Remec, S., Recknagel, F. and B. Kompare, 2006. Automated modelling of a food web in lake Bled using measured data and a library of domain knowledge. Ecological Modelling, 194, 1-3, 37-48
    • Recknagel, F., Kim, B. and A. Welk, 2006. Unravelling ecosystem behaviour of lake Soyang (South Korea) in response to climate and management by means of artificial neural networks. Journal Internat. Verein. Limnol. 29, 3, 1497-1502
    • Recknagel, F. , Talib, A. and D. van der Molen, 2006. Phytoplankton Community Dynamics of Two Adjacent Dutch Lakes in Response to Seasons and Eutrophication Control Unravelled by Non-Supervised Artificial Neural Networks. Ecological Informatics 1, 3, 277-286
    • Cao, H., Recknagel, F., Joo, G.-J., D.-K. Kim, 2006. Rule set discovery for the prediction and explanation of chlorophyll -a dynamics in the Nakdong River (Korea) by means of a hybrid evolutionary algorithm. Ecological Informatics 1, 43-53
    • Whigham, P., Dick, G. and F. Recknagel, 2006. Exploring seasonal patterns using process modelling and evolutionary computation. Ecological Modelling 159, 1-2, 146-152
    • Recknagel, F, Kim, B., Takamura, N. and A. Welk, 2006. Unravelling and Forecasting Algal Population Dynamics in Two Lakes Different in Morphometry and Eutrophication by Neural and Evolutionary Computation. Ecological Informatics 1, 2, 133-151
    • Recknagel, F., 2006. Ecological applications of adaptive agents. In: Recknagel, F. (ed.), 2006. Ecological Informatics. 2nd Edition. Springer, New York, 109-124
    • Jeong, K.-S., Recknagel, F. and G.-J. Joo, 2006. Prediction and elucidation of population dynamics of the blue-green algae Microcystis aeruginosa and the diatom Stephanodiscus hantzschii in the Nakdong river-reservoir system (South Korea) by a recurrent artificial neural network.Recknagel, F. (ed.), 2006. Ecological Informatics. 2nd Edition. Springer, New York, 256-273
    • Horrigan, N., Bobbin, J., Recknagel, F. and L. Metzling, 2005. Patterning, prediction and explanation of stream macroinvertebrate assemblages in Victoria (Australia) by means of artificial neural networks and genetic algorithms. In: Lek, S., Scardi, M., Verdonschot, P.F.M., Descy, J.-P. and Y.-S. Park (eds.), 2005. Modelling Community Structures in Freshwater Ecosystems. Springer-Verlag, Berlin, Heidelberg, New York, 252-260
    • Horrigan, N., Choy, S., Marshall, J. and F. Recknagel, 2005. Assessing the effect of salinisation on stream macroinvertebrate communities in Queensland. Australian Freshwater and Marine Research 56, 1-9
  • Professional Associations

    • Guest Profesor of the Federal University of Rio de Janeiro (Brazil)
    • Guest Professor of the Chinese Academy of Sciences, Beijing, China
    • Extraordinary Professor of the North-West University at Potchefstroom, South Africa (
    • Editor-in-Chief of the international journal Ecological Informatics: 2018 Impact Factor 2.511  ( published by Elsevier B.V.
    • Member of the Editorial Board of the international journal Environmental Modelling and Software (https/ published by Elsevier B.V.
    • Member of the Editorial Board of the international journal Ecological Modelling ( published by Elsevier B.V.
    • Recent invitations as keynote speaker at international conferences: - Global Conference of the International Society for Ecological Modelling, 2 - 6 May 2023, Toronto, Canada; X. Int Conference on Ecological Informatics, 25-28 Oct 2018, Jena, Germany; - Int Conference on Ecological Informatics, 27th - 28th June 2013, Seoul, Korea;  - 7th International Conference of the Int Society for Ecological Informatics, 4th - 7th Dec 2012, Brasilia, Brazil; - 4th International Ecosummit, 30th Sep - 5th Oct 2012, Columbus, Ohio; - 18th Conference of the Int Society for Ecological Modelling, 20 - 23 Sep 2011, Beijing, China; - 31st Congress of the Int Society of Limnology SIL 2010, 15 - 20 Aug 2010, Capetown, South Africa (; - International Conference on Ecological Informatics and Ecosystem Conservation ISEIS 2010, 27 - 29 Aug 2010, Beijing, China (; - 2nd IWA Asian Pacific Young Professionals Conference, 4 - 6 Nov 2009, Beijing, China; - International Conference EnviroInfo 2009, 9 - 11 Sep 2009, Berlin, Germany; - 4th Symposium on Information Technologies in Environmental Engineering, 28 - 29 May 2009, Thessaloniki, Greece
    • Member of the International Scientific Committee of the EcoSummit 2012, 30 September - 5 October 2012, Collumbus Ohio, USA (
    • Member of Water Quality Research Australia (
    • Member of the Environment Institute of the University of Adelaide (
  • Files

  • Media Expertise

    ExpertiseAlgae - toxic; freshwater ecology; organic pollution and algal blooms; limnology of lakes; drinking water reservoirs and wetlands; blue-green algae; water quality; carp
    NotesAlt phone: (08) 8303 3999

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Entry last updated: Friday, 9 Feb 2024

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