A Rao-Blackwellized Particle Filter for Joint Parameter Estimation and Biomass Tracking in a Stochastic Predator-Prey System Articles uri icon

authors

  • MARTIN FERNANDEZ, LAURA
  • GILIOLI, GIANNI
  • LANZARONE, ETTORE
  • MIGUEZ ARENAS, JOAQUIN
  • PASQUALI, SARA
  • Ruggeri, Fabrizio
  • RUIZ, DIEGO P.

publication date

  • June 2014

start page

  • 573

end page

  • 597

issue

  • 3

volume

  • 11

International Standard Serial Number (ISSN)

  • 1547-1063

Electronic International Standard Serial Number (EISSN)

  • 1551-0018

abstract

  • Functional response estimation and population tracking in predator-prey systems are critical problems in ecology. In this paper we consider a stochastic predator-prey system with a Lotka-Volterra functional response and propose a particle filtering method for: (a) estimating the behavioral parameter representing the rate of effective search per predator in the functional response and (b) forecasting the population biomass using field data. In particular, the proposed technique combines a sequential Monte Carlo sampling scheme for tracking the time-varying biomass with the analytical integration of the unknown behavioral parameter. In order to assess the performance of the method, we show results for both synthetic and observed data collected in an acarine predator-prey system, namely the pest mite Tetranychus urticae and the predatory mite Phytoseiulus persimilis.

keywords

  • prey-predator system; parameter estimation; population tracking; state-space model; rao-blackwellized particle filter