A Rao-Blackwellized particle filter for joint parameter estimation and biomass tracking in a stochastic predator-prey system
Author(s) -
Laura Martín-Fernández,
Gianni Gilioli,
Ettore Lanzarone,
Joaquı́n Mı́guez,
Sara Pasquali,
Fabrizio Ruggeri,
Diego P. Ruíz
Publication year - 2014
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2014.11.573
Subject(s) - particle filter , predation , predator , biomass (ecology) , tracking (education) , particle (ecology) , kalman filter , control theory (sociology) , joint (building) , mathematics , environmental science , ecology , statistics , biology , computer science , engineering , artificial intelligence , psychology , control (management) , architectural engineering , pedagogy
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.
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