title: A discrete model with density dependent fast migration.
abstract: The aim of this work is to develop an approximate aggregation method for certain non-linear discrete models. Approximate aggregation consists in describing the dynamics of a general system involving many coupled variables by means of the dynamics of a reduced system with a few global variables. We present discrete models with two different time scales, the slow one considered to be linear and the fast one non-linear because of its transition matrix depends on the global variables. In our discrete model the time unit is chosen to be the one associated to the slow dynamics, and then we approximate the effect of fast dynamics by using a sufficiently large power of its corresponding transition matrix. In a previous work the same system is treated in the case of fast dynamics considered to be linear, conservative in the global variables and inducing a stable frequency distribution of the state variables. A similar non-linear model has also been studied which uses as time unit the one associated to the fast dynamics and has the non-linearity in the slow part of the system. In the present work we transform the system to make the global variables explicit, and we justify the quick derivation of the aggregated system. The local asymptotic behaviour of the aggregated system entails that of the general system under certain conditions, for instance, if the aggregated system has a stable hyperbolic fixed point then the general system has one too. The method is applied to aggregate a multiregional Leslie model with density dependent migration rates.
authors: Departamento de Matemáticas, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain. mtbravo@alcala.es
authors: R
authors: R
authors: Bravo de la Parra
authors: E
authors: E
authors: Sánchez
authors: O
authors: O
authors: Arino
authors: P
authors: P
authors: Auger
mesh_terms: D004463:Ecology; D005843:Geography; D016014:Linear Models; D008954:Models, Biological; D011157:Population Dynamics; D013997:Time Factors