An innovative multistage, physiologically structured, population model to understand the European grapevine moth dynamics

DOI10.1016/J.JMAA.2011.04.021zbMath1223.35217OpenAlexW1964146298MaRDI QIDQ549732

Delphine Picart, Denis Thiéry, Bedr'Erddine Ainseba

Publication date: 18 July 2011

Published in: Journal of Mathematical Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmaa.2011.04.021

zbMATH Keywords

existenceuniquenessage-structured population modelgrape variety

Mathematics Subject Classification ID

Population dynamics (general) (92D25) First-order nonlinear hyperbolic equations (35L60) Initial-boundary value problems for first-order hyperbolic systems (35L50) Integro-partial differential equations (35R09)

Related Items (11)

The contribution of age structure to cell population responses to targeted therapeutics ⋮ Optimal control in a multistage physiologically structured insect population model ⋮ Exact null controllability of a stage and age-structured population dynamics system ⋮ Parameter identification in multistage population dynamics model ⋮ An adaptative model for a multistage structured population under fluctuating environment ⋮ On the existence of solution of a four-stage and age-structured population dynamics model ⋮ Optimal control problem on insect pest populations ⋮ Exact null controllability of the Lobesia botrana model with diffusion ⋮ Null controllability of a four stage and age-structured population dynamics model ⋮ A general nonlinear model for the interaction of a size-structured population and its environment: Well-posedness and approximation ⋮ A physiologically-structured fish population model with size-dependent foraging

Cites Work

• Parameter identification in multistage population dynamics model
• A model of physiologically structured population dynamics with a nonlinear individual growth rate
• Mathematical biology. Vol. 1: An introduction.
• Non-linear age-dependent population dynamics
• Probabilistic methods for addressing uncertainty and variability in biological models: application to a toxicokinetic model

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