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• W2300593175 abstract "We propose and develop a general approach based on reaction-diffusion equations for modelling a species dynamics in a realistic two-dimensional (2D) landscape crossed by linear one-dimensional (1D) corridors, such as roads, hedgerows or rivers. Our approach is based on a hybrid “2D/1D model”, i.e, a system of 2D and 1D reaction-diffusion equations with homogeneous coefficients, in which each equation describes the population dynamics in a given 2D or 1D element of the landscape. Using the example of the range expansion of the tiger mosquito Aedes albopictus in France and its main highways as 1D corridors, we show that the model can be fitted to realistic observation data. We develop a mechanistic-statistical approach, based on the coupling between a model of population dynamics and a probabilistic model of the observation process. This allows us to bridge the gap between the data (3 levels of infestation, at the scale of a French department) and the output of the model (population densities at each point of the landscape), and to estimate the model parameter values using a maximum-likelihood approach. Using classical model comparison criteria, we obtain a better fit and a better predictive power with the 2D/1D model than with a standard homogeneous reaction-diffusion model. This shows the potential importance of taking into account the effect of the corridors (highways in the present case) on species dynamics. With regard to the particular case of A. albopictus, the conclusion that highways played an important role in species range expansion in mainland France is consistent with recent findings from the literature." @default.
• W2300593175 created "2016-06-24" @default.
• W2300593175 creator A5050921613 @default.
• W2300593175 creator A5091552228 @default.
• W2300593175 date "2016-03-17" @default.
• W2300593175 modified "2023-09-25" @default.
• W2300593175 title "Modelling Population Dynamics in Realistic Landscapes with Linear Elements: A Mechanistic-Statistical Reaction-Diffusion Approach" @default.
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• W2300593175 doi "https://doi.org/10.1371/journal.pone.0151217" @default.
• W2300593175 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4795701" @default.
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• W2300593175 hasPublicationYear "2016" @default.
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