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Selecting parameters for calibration via sensitivity analysis: An individual-based model of mosquitofish population dynamics

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  • Beaudouin, Rémy
  • Monod, Gilles
  • Ginot, Vincent

Abstract

A stochastic individual-based model (IBM) of mosquitofish population dynamics in experimental ponds was constructed in order to increase, virtually, the number of replicates of control populations in an ecotoxicology trial, and thus to increase the statistical power of the experiments. In this context, great importance had to be paid to model calibration as this conditions the use of the model as a reference for statistical comparisons. Accordingly, model calibration required that both mean behaviour and variability behaviour of the model were in accordance with real data. Currently, identifying parameter values from observed data is still an open issue for IBMs, especially when the parameter space is large. Our model included 41 parameters: 30 driving the model expectancy and 11 driving the model variability. Under these conditions, the use of “Latin hypercube” sampling would most probably have “missed” some important combinations of parameter values. Therefore, complete factorial design was preferred. Unfortunately, due to the constraints of the computational capacity, cost-acceptable “complete designs” were limited to no more than nine parameters, the calibration question becoming a parameter selection question. In this study, successive “complete designs” were conducted with different sets of parameters and different parameter values, in order to progressively narrow the parameter space. For each “complete design”, the selection of a maximum of nine parameters and their respective n values was carefully guided by sensitivity analysis. Sensitivity analysis was decisive in selecting parameters that were both influential and likely to have strong interactions. According to this strategy, the model of mosquitofish population dynamics was calibrated on real data from two different years of experiments, and validated on real data from another independent year. This model includes two categories of agents; fish and their living environment. Fish agents have four main processes: growth, survival, puberty and reproduction. The outputs of the model are the length frequency distribution of the population and the 16 scalar variables describing the fish populations. In this study, the length frequency distribution was parameterized by 10 scalars in order to be able to perform calibration. The recently suggested notion of “probabilistic distribution of the distributions” was also applied to our case study, and was shown to be very promising for comparing length frequency distributions (as such).

Suggested Citation

  • Beaudouin, Rémy & Monod, Gilles & Ginot, Vincent, 2008. "Selecting parameters for calibration via sensitivity analysis: An individual-based model of mosquitofish population dynamics," Ecological Modelling, Elsevier, vol. 218(1), pages 29-48.
  • Handle: RePEc:eee:ecomod:v:218:y:2008:i:1:p:29-48
    DOI: 10.1016/j.ecolmodel.2008.06.033
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    References listed on IDEAS

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    1. Charlotte K. Hemelrijk & Hanspeter Kunz, 2005. "Density distribution and size sorting in fish schools: an individual-based model," Behavioral Ecology, International Society for Behavioral Ecology, vol. 16(1), pages 178-187, January.
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    Cited by:

    1. Halsey, Samniqueka J. & Miller, James R., 2018. "A spatial agent-based model of the disease vector Ixodes scapularis to explore host-tick associations," Ecological Modelling, Elsevier, vol. 387(C), pages 96-106.
    2. Smajgl, Alex & House, Alan P.N. & Butler, James R.A., 2011. "Implications of ecological data constraints for integrated policy and livelihoods modelling: An example from East Kalimantan, Indonesia," Ecological Modelling, Elsevier, vol. 222(3), pages 888-896.
    3. Portell, Xavier & Gras, Anna & Ginovart, Marta, 2014. "INDISIM-Saccha, an individual-based model to tackle Saccharomyces cerevisiae fermentations," Ecological Modelling, Elsevier, vol. 279(C), pages 12-23.
    4. Haythorne, Sean & Skabar, Andrew, 2013. "An improved pattern-guided evolution approach for the development of adaptive individual-based ecological models," Ecological Modelling, Elsevier, vol. 252(C), pages 72-82.
    5. Dur, Gaël & Jiménez-Melero, Raquel & Beyrend-Dur, Delphine & Hwang, Jiang-Shiou & Souissi, Sami, 2013. "Individual-based model of the phenology of egg-bearing copepods: Application to Eurytemora affinis from the Seine estuary, France," Ecological Modelling, Elsevier, vol. 269(C), pages 21-36.
    6. David, Viviane & Joachim, Sandrine & Tebby, Cleo & Porcher, Jean-Marc & Beaudouin, Rémy, 2019. "Modelling population dynamics in mesocosms using an individual-based model coupled to a bioenergetics model," Ecological Modelling, Elsevier, vol. 398(C), pages 55-66.

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