Skip to main content

Advertisement

SpringerLink
Log in

Discussion of skill improvement in marine ecosystem dynamic models based on parameter optimization and skill assessment

  • Biology
  • Published:
Chinese Journal of Oceanology and Limnology Aims and scope Submit manuscript

Abstract

Marine ecosystem dynamic models (MEDMs) are important tools for the simulation and prediction of marine ecosystems. This article summarizes the methods and strategies used for the improvement and assessment of MEDM skill, and it attempts to establish a technical framework to inspire further ideas concerning MEDM skill improvement. The skill of MEDMs can be improved by parameter optimization (PO), which is an important step in model calibration. An efficient approach to solve the problem of PO constrained by MEDMs is the global treatment of both sensitivity analysis and PO. Model validation is an essential step following PO, which validates the efficiency of model calibration by analyzing and estimating the goodness-of-fit of the optimized model. Additionally, by focusing on the degree of impact of various factors on model skill, model uncertainty analysis can supply model users with a quantitative assessment of model confidence. Research on MEDMs is ongoing; however, improvement in model skill still lacks global treatments and its assessment is not integrated. Thus, the predictive performance of MEDMs is not strong and model uncertainties lack quantitative descriptions, limiting their application. Therefore, a large number of case studies concerning model skill should be performed to promote the development of a scientific and normative technical framework for the improvement of MEDM skill.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Arhonditsis G B, Adams-Vanharn B A, Nielsen L, Stow C A, Reckhow K H. 2006. Evaluation of the current state of mechanistic aquatic biogeochemical modeling: citation analysis and future perspectives. Environmental Science & Technology, 40(21): 6547–6554.

    Article  Google Scholar 

  • Arhonditsis G B, Brett M T. 2004. Evaluation of the current state of mechanistic aquatic biogeochemical modeling. Marine Ecology Progress Series, 271: 13–26.

    Article  Google Scholar 

  • Athias V, Mazzega P, Jeandel C. 2000. Selecting a global optimization method to estimate the oceanic particle cycling rate constants. Journal of Marine Research, 58(5): 675–707.

    Article  Google Scholar 

  • Bagley J D. 1967. The behavior of adaptive systems which employ genetic and correlation algorithms. University of Michigan, Ann Arbor, MI, USA.

    Google Scholar 

  • Bennett A F, McIntosh P C. 1982. Open ocean modeling as an inverse problem: tidal theory. Journal of Physical Oceanography, 12(10): 1004–1018.

    Article  Google Scholar 

  • Beven K J, Binley A. 1992. The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes, 6(3): 279–298.

    Article  Google Scholar 

  • Bouttier F, Courtier P. 1999. Data assimilation concepts and methods. In: ECMWF. Meteorological Training Course Lecture Series. p.1–75.

    Google Scholar 

  • Campolongo F, Cariboni J, Saltelli A. 2007. An effective screening design for sensitivity analysis of large models. Environmental Modelling & Software, 22 (10): 1509–1518.

    Article  Google Scholar 

  • Cariboni J, Gatelli D, Liska R, Saltelli A. 2007. The role of sensitivity analysis in ecological modelling. Ecological Modelling, 203(102): 167–182.

    Article  Google Scholar 

  • Carroll D L. 1996. Chemical laser modeling with genetic algorithms. AAIA Journal, 34(2): 338–346.

    Article  Google Scholar 

  • Chen C S, Ji R B, Zheng L Y, Zhu M Y, Rawson M. 1999. Influences of physical processes on the ecosystem in Jiaozhou Bay: a coupled physical and biological model experiment. Journal of Geophysical Research, 104(C12): 29925–29949.

    Article  Google Scholar 

  • Chu P C, Ivanov L M, Margolina T M. 2007. On non-linear sensitivity of marine biological models to parameter variations. Ecological Modelling, 206(3–4): 369–382.

    Article  Google Scholar 

  • Chu-Agor M L, Muñoz-Carpena R, Kiker G, Emanuelsson A, Linkov I. 2011. Exploring vulnerability of coastal habitats to sea level rise through global sensitivity and uncertainty analyses. Environmental Modelling & Software, 26(5): 593–604.

    Article  Google Scholar 

  • Cossarini G, Solidoro C. 2008. Global sensitivity analysis of a trophodynamic model of the Gulf of Trieste. Ecological Modelling, 212(1–2): 16–27.

    Article  Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A. 1991. Ant System: An Autocatalytic Optimization Process. Technical Report 91106 Revised, Department of Electronic, Politecnico of Milano, Milan, Italy.

    Google Scholar 

  • Epstein E S. 1969. Stochastic dynamic prediction. Tellus, 21(6): 739–759.

    Article  Google Scholar 

  • Esposito W R, Floudas C A. 2000. Deterministic global optimization in nonlinear optimal control problems. Journal of Global Optimization, 17(1–4): 97–126.

    Article  Google Scholar 

  • Evensen G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5): 10143–10162.

    Article  Google Scholar 

  • Fan W, Lv X Q. 2009. Data assimilation in a simple marine ecosystem model based on spatial biological parameterizations. Ecological Modelling, 220(17): 1997–2008.

    Article  Google Scholar 

  • Fennel K, Losch M, Schröter J, Wenzel M. 2001. Testing a marine ecosystem model: sensitivity analysis and parameter optimization. Journal of Marine Systems, 28(12): 45–63.

    Article  Google Scholar 

  • Flynn K J. 2005. Castles built on sand: dysfunctionality in plankton models and the inadequacy of dialogue between biologists and modellers. J. Plankton Res., 27(12): 1205–1210.

    Article  Google Scholar 

  • Fogel L J, Burgin G H. 1969. Competitive Goal-seeking Through Evolutionary Programming. Decision Science Inc., San Diego, California.

    Google Scholar 

  • Friedrichs M A M, Carr M E, Barber R T, Scardi M, Antoine D, Armstrong R A, Asanuma I, Behrenfeld M J, Buitenhuis E T, Chai F, Christian J R, Ciotti A M, Doney S C, Dowell M, Dunne J, Gentili B, Gregg W, Hoepffner N, Ishizaka J, Kameda T, Lima I, Marra J, Mélin F, Moore J K, Morel A, O’Malley R T, O’Reilly J, Saba V S, Schmeltz M, Smyth T J, Tjiputra J, Waters K, Westberry T K, Winguth A. 2009. Assessing the uncertainties of model estimates of primary productivity in the tropical Pacific Ocean. Journal of Marine Systems, 76(1–2): 113–133.

    Article  Google Scholar 

  • Friedrichs M A M, Dusenberry J A, Anderson L A, Armstrong R A, Chai F, Christian J R, Doney S C, Dunne J, Fujii M, Hood R, McGillicuddy D J, Moore J K, Schartau M, Spitz Y H, Wiggert J D. 2007. Assessment of skill and portability in regional marine biogeochemical models: role of multiple planktonic groups. Journal of Geophysical Research, 112(C8): C08001.

    Article  Google Scholar 

  • Friedrichs M A M. 2002. Assimilation of JGOFS EqPac and SeaWiFS data into a marine ecosystem model of the Central Equatorial Pacific Ocean. Deep Sea Research Part II: Topical Studies in Oceanography, 49 (1–3): 289–319.

    Google Scholar 

  • Fulton E A. 2010. Approaches to end-to-end ecosystem models. Journal of Marine Systems, 81(1–2): 171–183.

    Article  Google Scholar 

  • Geem Z W, Kim J H, Loganathan G V. 2001. A new heuristic optimization algorithm: harmony search. Transactions of the Society for Modeling and Simulation International, 76(2): 60–68.

    Article  Google Scholar 

  • Gibson G A, Spitz Y H. 2011. Impacts of biological parameterization, initial conditions, and environmental forcing on parameter sensitivity and uncertainty in a marine ecosystem model for the Bering Sea. Journal of Marine Systems, 88(2): 214–231.

    Article  Google Scholar 

  • Glover F. 1986. Future paths for integer programming and links to artificial intelligence. Computers & Operations Research, 13(5): 533–549.

    Article  Google Scholar 

  • Gregg W W, Friedrichs M A M, Robinson A R, Rose K A, Schlitzer R, Thompson K R, Doney S C. 2009. Skill assessment in ocean biological data assimilation. Journal of Marine Systems, 76(1–2): 16–33.

    Article  Google Scholar 

  • Holland J H. 1975. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. U. Michigan Press.

    Google Scholar 

  • Hood R R, Laws E A, Armstrong R A, Bates N R, Brown C W, Carlson C A, Chai F, Doney S C, Falkowski P G, Feely R A, Friedrichs M A M, Landry M R, Moore J K, Nelson D M, Richardson T L, Salihoglu B, Schartau M, Toole D A, Wiggert J D. 2006. Pelagic functional group modeling: progress, challenges and prospects. Deep-Sea Research II: Topical Studies in Oceanography, 53(5–7): 459–512.

    Article  Google Scholar 

  • Huret M, Gohin F, Delmas D, Lunven M, Garçon V. 2007. Use of SeaWiFS data for light availability and parameter estimation of a phytoplankton production model of the Bay of Biscay. Journal of Marine Systems, 65 (1–4):509–531.

    Article  Google Scholar 

  • Hurtt G C, Armstrong R A. 1996. A pelagic ecosystem model calibrated with BATS data. Deep-Sea Research II: Topical Studies in Oceanography, 43(2–3): 653–683.

    Article  Google Scholar 

  • Hurtt G C, Armstrong R A. 1999. A pelagic ecosystem model calibrated with BATS and OWSI data. Deep-Sea Research II: Topical Studies in Oceanography, 46(1): 27–61.

    Article  Google Scholar 

  • Iman R L. 2008. Latin Hypercube Sampling. John Wiley & Sons, Ltd.

    Book  Google Scholar 

  • Ishizaka J. 1990. Coupling of Coastal Zone Color Scanner data to a physical—biological model of the southeastern U. S. continental shelf ecosystem. 3. Nutrient and phytoplankton fluxes and CZCS data assimilation. Journal of Geophysical Research, 95(C11): 20201–20212.

    Article  Google Scholar 

  • Jones R H. 1965. Optimal estimation of initial conditions for numerical prediction. Journal of the Atmospheric Sciences, 22(6): 658–663.

    Article  Google Scholar 

  • Kalman R E. 1960. A new approach to linear filtering and prediction problems. Transactions of the ASM E-Journal of Basic Engineering, 82D: 34–45.

    Google Scholar 

  • Kavanagh P, Newlands N, Christensen V, Pauly D. 2004. Automated parameter optimization for Ecopath ecosystem models. Ecological Modelling, 172(2–4): 141–149.

    Article  Google Scholar 

  • Kennedy J N, Eberhart R C. 1995. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. IEEE, Perth, Australia. p. 1942–1948.

    Chapter  Google Scholar 

  • Kirkpatrick S, Gelatt C D, Vecchi M P. 1983. Optimization by simulated annealing. Science, 220(4598): 671–680.

    Article  Google Scholar 

  • Koza J R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge.

    Google Scholar 

  • Kuezera G, Parent E. 1998. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm. Journal of Hydrology, 211(14): 69–85.

    Article  Google Scholar 

  • Lawson L M, Spitz Y H, Hofmann E E, Long R B. 1995. A data assimilation technique applied to a predator-prey model. Bulletin of Mathematical Biology, 57(4): 593–617.

    Article  Google Scholar 

  • Matear R J. 1995. Parameter optimization and analysis of ecosystem models using simulated annealing: a case study at Station P. Journal of Marine Research, 53 (4): 571–607.

    Article  Google Scholar 

  • Miró A, Pozo C, Guillén-Gosálbez G, Egea J A, Jiménez L. 2012. Deterministic global optimization algorithm based on outer approximation for the parameter estimation of nonlinear dynamic biological systems. BMC Bioinformatics, 13(1): 90.

    Article  Google Scholar 

  • Moles C G, Mendes P, Banga J R. 2003. Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Research, 13(11): 2467–2474.

    Article  Google Scholar 

  • Morris D J, Speirs D C, Cameron A I, Heath M R. 2014. Global sensitivity analysis of an end-to-end marine ecosystem model of the North Sea: factors affecting the biomass of fish and benthos. Ecological Modelling, 273: 251–263.

    Article  Google Scholar 

  • Morris M D. 1991. Factorial sampling plans for preliminary computational experiments. Technometrics, 33 (2):161–174.

    Article  Google Scholar 

  • Oschlies A, Schartau M. 2005. Basin-scale performance of a locally optimized marine ecosystem model. Journal of Marine Research, 63(2): 335–358.

    Article  Google Scholar 

  • Pahlow M, Oschlies A. 2009. Chain model of phytoplankton P, N and light colimitation. Marine Ecology Progress Series, 376: 69–83.

    Article  Google Scholar 

  • Peng S T, Qin X B, Shi H H, Zhou R, Dai M X, Ding D W. 2012. Distribution and controlling factors of phytoplankton assemblages in a semi-enclosed bay during spring and summer. Marine Pollution Bulletin, 64(5): 941–948.

    Article  Google Scholar 

  • Rechenberg I. 1965. Cybernetic Solution Path of An Experimental Problem. Library Translation 1122, Royal Aircraft Establishment, Farnborough, Hants, U. K.

    Google Scholar 

  • Rose K A, Allen J I, Artioli Y, Barange M, Blackford J, Carlotti F, Cropp R, Daewel U, Edwards K, Flynn K, Hill S L, HilleRis Lambers R, Huse G, Mackinson S, Megrey B, Moll A, Rivkin R, Salihoglu B, Schrum C, Shannon L, Shin Y J, Smith S L, Smith C, Solidoro C, John M S, Zhou M. 2010. End-to-end models for the analysis of marine ecosystems: challenges, issues, and next steps. Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, 2(1): 115–130.

    Article  Google Scholar 

  • Rose K A, Roth B M, Smith E P. 2009. Skill assessment of spatial maps for oceanographic modeling. Journal of Marine Systems, 76(1–2): 34–48.

    Article  Google Scholar 

  • Rousseaux C S, Hirata T, Gregg W W. 2013. Satellite views of global phytoplankton community distributions using an empirical algorithm and a numerical model. Biogeosciences Discussions, 10(1): 1083–1109.

    Article  Google Scholar 

  • Saltelli A, Tarantola S, Chan K P S. 1999. A quantitative model-independent method for global sensitivity analysis of model output. Technometrics, 41(1): 39–56.

    Article  Google Scholar 

  • Sasaki Y. 1970. Some basic formalisms in numerical variational analysis. Monthly Weather Review, 98(12): 875–883.

    Article  Google Scholar 

  • Schartau M, Oschlies A. 2003. Simultaneous data-based optimization of a 1D-ecosystem model at three locations in the North Atlantic: Part I - method and parameter estimates. Journal of Marine Research, 61(6): 765–793.

    Article  Google Scholar 

  • Schwefel H P. 1965. Kybernetische Evolution als Strategie der experimentellen Forschung in der Strömungstechnik. Technical University, Berlin.

    Google Scholar 

  • Shi H H, Ding D W, Zheng W. 2012. The Key Technology and its Application of Evaluation, Simulation and Control of the Coastal Zone Ecosystem. China Ocean Press, Beijing, China. 289p. (in Chinese)

    Google Scholar 

  • Shi H H, Shen C C, Li F, Wang Y Z. 2014. Parameter sensitivity analysis of a coupled biological—physical model in Jiaozhou Bay. Acta Ecologica Sinica, 34(1): 41–49. (in Chinese with English abstract)

    Google Scholar 

  • Shi H H, Wang Z L, Fang G H, Zheng W, Hu L. 2011. A statistical dynamics model of the marine ecosystem and its application in Jiaozhou Bay. Chin. J. Oceanol. Limnol., 29(4): 905–911.

    Article  Google Scholar 

  • Sobol I M. 1993. Sensitivity estimates for nonlinear mathematical models. Mathematical Modeling and Computational Experiment, 1(4): 407–414.

    Google Scholar 

  • Solidoro C, Crise A, Crispi G, Pastres R. 2003. An a priori approach to assimilation of ecological data in marine ecosystem models. Journal of Marine Systems, 40–41:79–97.

    Article  Google Scholar 

  • Stow C A, Jolliff J, McGillicuddy Jr D J, Doney S C, Allen J I, Friedrichs M A M, Rose K A, Wallhead P. 2009. Skill assessment for coupled biological/physical models of marine systems. Journal of Marine Systems, 76(1–2): 4–15.

    Article  Google Scholar 

  • Stow C A, Scavia D. 2009. Modeling hypoxia in the Chesapeake Bay: ensemble estimation using a Bayesian hierarchical model. Journal of Marine Systems, 76(1–2): 244–250.

    Article  Google Scholar 

  • Tang Q S, Su J L, Sun S, Zhang J, Huang D J, Jin X S, Tong L. 2005. A study of marine ecosystem dynamics in the coastal ocean of China. Advances in Earth Science, 20(12): 1288–1299. (in Chinese with English abstract)

    Google Scholar 

  • Tashkova K, Šilc J, Atanasova N, Džeroski S. 2012. Parameter estimation in a nonlinear dynamic model of an aquatic ecosystem with meta-heuristic optimization. Ecological Modelling, 226(2): 36–61.

    Article  Google Scholar 

  • Wallhead P J, Martin A P, Srokosz M A, Franks P J S. 2009. Skill assessment via cross-validation and Monte Carlo simulation: an application to Georges Bank plankton models. Journal of Marine Systems, 76(1–2): 134–150.

    Article  Google Scholar 

  • Ward B A, Friedrichs M A M, Anderson T R, Oschlies A. 2010. Parameter optimisation techniques and the problem of underdetermination in marine biogeochemical models. Journal of Marine Systems, 81(1–2): 34–43.

    Article  Google Scholar 

  • Ward B A, Schartau M, Oschlies A, Martin A P, Follows M J, Anderson T R. 2013. When is a biogeochemical model too complex? Objective model reduction and selection for North Atlantic time-series sites. Progress in Oceanography, 116: 49–65.

    Article  Google Scholar 

  • Weber L, Völker C, Schartau M, Wolf-Gladrow D A. 2005. Modeling the speciation and biogeochemistry of iron at the Bermuda Atlantic Time-series Study site. Global Biogeochemical Cycles, 19(1): GB1019, http://dx.doi.org/10.1029/2004GB002340.

    Google Scholar 

  • Xiao Y J, Friedrichs M A M. 2014a. Using biogeochemical data assimilation to assess the relative skill of multiple ecosystem models in the Mid-Atlantic Bight: effects of increasing the complexity of the planktonic food web. Biogeosciences, 11(11): 3015–3030.

    Article  Google Scholar 

  • Xiao Y J, Friedrichs M A M. 2014b. The assimilation of satellite-derived data into a one-dimensional lower trophic level marine ecosystem model. Journal of Geophysical Research: Oceans, 119(4): 2691–2712.

    Google Scholar 

  • Zhang X S, Srinivasan R, Zhao K G, van Liew M. 2009. Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model. Hydrological Processes, 23 (3):430–441.

    Article  Google Scholar 

  • Zhao L, Wei H, Xu Y F, Feng S Z. 2005. An adjoint data assimilation approach for estimating parameters in a three-dimensional ecosystem model. Ecological Modelling, 186(2): 235–250.

    Article  Google Scholar 

  • Zheng W, Shi H H, Fang G H, Hu L, Peng S T, Zhu M Y. 2012. Global sensitivity analysis of a marine ecosystem dynamic model of the Sanggou Bay. Ecological Modelling, 247: 83–94.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Environmental Science and Engineering, Ocean University of China, Qingdao, 266100, China

    Chengcheng Shen  (沈程程)

  2. The First Institute of Oceanography, State Oceanic Administration, Qingdao, 266061, China

    Chengcheng Shen  (沈程程), Honghua Shi  (石洪华) & Dewen Ding  (丁德文)

  3. School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, China

    Yongzhi Liu  (刘永志) & Fen Li  (李芬)

Authors
  1. Chengcheng Shen  (沈程程)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Honghua Shi  (石洪华)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongzhi Liu  (刘永志)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fen Li  (李芬)
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dewen Ding  (丁德文)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Honghua Shi  (石洪华).

Additional information

Supported by the National Natural Science Foundation of China (Nos. 41206111, 41206112)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shen, C., Shi, H., Liu, Y. et al. Discussion of skill improvement in marine ecosystem dynamic models based on parameter optimization and skill assessment. Chin. J. Ocean. Limnol. 34, 683–696 (2016). https://doi.org/10.1007/s00343-016-5068-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00343-016-5068-3

Keywords

Search

Navigation