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BACKGROUND Bemisia tabaci is a serious pest of agricultural and horticultural crops in greenhouses and fields around the world. This paper deals with the distribution of the pest under field conditions. In Europe, the insect is currently found in coastal regions of Mediterranean countries where it is subject to quarantine regulations. To assess the risk presented by B. tabaci to Europe, the area of potential establishment of this insect, in light of the climate change scenario, was assessed by a temperature‐dependent physiologically based demographic model (PBDM). RESULTS The simulated potential distribution under current climate conditions has been successfully validated with the available field records of B. tabaci in Europe. Considering climate change scenarios of +1 and +2 °C, range expansion by B. tabaci is predicted, particularly in Spain, France, Italy, Greece and along the Adriatic coast of the Balkans. Nonetheless, even under the scenario of +2 °C, northern European countries are not likely to be at risk of B. tabaci establishment because of climatic limitations. CONCLUSION Model validation with field observations and evaluation of uncertainties associated with model parameter variability support the reliability of model results. The PBDM developed here can be applied to other organisms and offers significant advantages for assessing the potential distribution of invasive species. © 2014 Society of Chemical Industry

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A nonlinear stochastic model for the dynamics of a population with either a continuous size structure or a discontinuous stage structure is formulated in the Eulerian formalism. It takes into account dispersion effects due to stochastic variability of the development process of the individuals. The discrete equations of the numerical approximation are derived, and an analysis of the existence and stability of the equilibrium states is performed. An application to a copepod population is illustrated; numerical results of Eulerian and Lagrangian models are compared.

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International standards propose several methods for measuring thermal conductivity; however, they often require expensive experimental layouts and marginally consider the uncertainty in the estimation procedure. In this article, we propose a temperature transient method and a Bayesian procedure to estimate the thermal conductivity of a homogeneous mass, and to reproduce the entire temperature profile evolution over time by means of latent temperature data. To validate the approach, experiments are conducted giving heating and cooling cycles to a polymethyl methacrylate (PMMA) specimen. Results show the applicability of the proposed approach and good estimations.

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.