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Modelling Population Dynamics in Realistic Landscapes with Linear Elements: A Mechanistic-Statistical Reaction-Diffusion Approach

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  • Lionel Roques
  • Olivier Bonnefon

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.

Suggested Citation

  • Lionel Roques & Olivier Bonnefon, 2016. "Modelling Population Dynamics in Realistic Landscapes with Linear Elements: A Mechanistic-Statistical Reaction-Diffusion Approach," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-20, March.
  • Handle: RePEc:plo:pone00:0151217
    DOI: 10.1371/journal.pone.0151217
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    References listed on IDEAS

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    1. Christopher K. Wikle, 2003. "Hierarchical Models in Environmental Science," International Statistical Review, International Statistical Institute, vol. 71(2), pages 181-199, August.
    2. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
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    1. Chantal Gascuel & Michèle Tixier-Boichard & Benoit Dedieu & Cécile Détang-Dessendre & Pierre Dupraz & Philippe Faverdin & Laurent Hazard & Philippe Hinsinger & Isabelle Litrico-Chiarelli & Françoise M, 2019. "Réflexion prospective interdisciplinaire pour l’agroécologie. Rapport de synthèse," Post-Print hal-02154433, HAL.
    2. Venecia Chávez-Medina & José A González & Francisco S Guzmán, 2019. "Location of sources in reaction-diffusion equations using support vector machines," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-12, December.

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