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Mathematical Biosciences and Engineering

2012, Volume 9, Issue 1: 75-96. doi: 10.3934/mbe.2012.9.75
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Nonlinear functional response parameter estimation in a stochastic predator-prey model

  • 1.
    Dipartimento di Scienze Biomediche e Biotecnologie, Università di Brescia, Viale Europa 11, 25125 Brescia
  • 2.
    CNR-IMATI, Via Bassini 15, 20133 Milano
  • Received: 01 May 2010 Accepted: 29 June 2018 Published: 01 December 2011

  • MSC : Primary: 62F15, 92D25, 65C05; Secondary: 65C30.

  • Parameter estimation for the functional response of predator-prey systems is a critical methodological problem in population ecology. In this paper we consider a stochastic predator-prey system with non-linear Ivlev functional response and propose a method for model parameter estimation based on time series of field data. We tackle the problem of parameter estimation using a Bayesian approach relying on a Markov Chain Monte Carlo algorithm. The efficiency of the method is tested on a set of simulated data. Then, the method is applied to a predator-prey system of importance for Integrated Pest Management and biological control, the pest mite Tetranychus urticae and the predatory mite Phytoseiulus persimilis. The model is estimated on a dataset obtained from a field survey. Finally, the estimated model is used to forecast predator-prey dynamics in similar fields, with slightly different initial conditions.

    Citation: Gianni Gilioli, Sara Pasquali, Fabrizio Ruggeri. Nonlinear functional response parameter estimation in a stochastic predator-prey model[J]. Mathematical Biosciences and Engineering, 2012, 9(1): 75-96. doi: 10.3934/mbe.2012.9.75

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  • Parameter estimation for the functional response of predator-prey systems is a critical methodological problem in population ecology. In this paper we consider a stochastic predator-prey system with non-linear Ivlev functional response and propose a method for model parameter estimation based on time series of field data. We tackle the problem of parameter estimation using a Bayesian approach relying on a Markov Chain Monte Carlo algorithm. The efficiency of the method is tested on a set of simulated data. Then, the method is applied to a predator-prey system of importance for Integrated Pest Management and biological control, the pest mite Tetranychus urticae and the predatory mite Phytoseiulus persimilis. The model is estimated on a dataset obtained from a field survey. Finally, the estimated model is used to forecast predator-prey dynamics in similar fields, with slightly different initial conditions.


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