title: A Bayesian estimation approach for the mortality in a stage-structured demographic model.
abstract: Control interventions in sustainable pest management schemes are set according to the phenology and the population abundance of the pests. This information can be obtained using suitable mathematical models that describe the population dynamics based on individual life history responses to environmental conditions and resource availability. These responses are described by development, fecundity and survival rate functions, which can be estimated from laboratory experiments. If experimental data are not available, data on field population dynamics can be used for their estimation. This is the case of the extrinsic mortality term that appears in the mortality rate function due to biotic factors. We propose a Bayesian approach to estimate the probability density functions of the parameters in the extrinsic mortality rate function, starting from data on population abundance. The method investigates the time variability in the mortality parameters by comparing simulated and observed trajectories. The grape berry moth, a pest of great importance in European vineyards, has been considered as a case study. Simulated data have been considered to evaluate the convergence of the algorithm, while field data have been used to obtain estimates of the mortality for the grape berry moth.
authors.affiliation: CNR-IMATI, Via A. Corti 12, 20133, Milan, Italy.
authors.forename: E
authors.initials: E
authors.lastname: Lanzarone
authors.affiliation: CNR-IMATI, Via A. Corti 12, 20133, Milan, Italy. sara.pasquali@mi.imati.cnr.it.
authors.forename: S
authors.identifier: 0000-0001-6185-8148
authors.initials: S
authors.lastname: Pasquali
authors.affiliation: Department of Molecular and Translational Medicine, University of Brescia, Viale Europa 11, 25123, Brescia, Italy.
authors.forename: G
authors.initials: G
authors.lastname: Gilioli
authors.affiliation: AGREA S.r.l. Centro Studi, Via Garibaldi 5/16, 37057, S. Giovanni Lupatoto (VR), Italy.
authors.forename: E
authors.initials: E
authors.lastname: Marchesini
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D003198:Computer Simulation; D007300:Insect Control; D016013:Likelihood Functions; D008954:Models, Biological; D009026:Mortality; D009036:Moths; D011157:Population Dynamics
keywords: Bayesian inference; Fokker–Plank equations; Lobesia botrana; MCMC algorithms; Pest; Population dynamics