Modelos ocultos de Markov para el análisis de patrones espaciales

Resum

Hidden Markov models (HMM) constitute a flexible modelling tool, originally used in the field of automated speech recognition, that have found wide application in the last years in many scientific and technical problems, although their use in ecology is still scarce. In this review, the essential elements of HMM are described, the basic algorithms that facilitate their estimation are presented and some recent applications are pointed out, with emphasis on the possibilities that HMM offer in analysing complex spatial patterns, as they allow incorporating a priori information about the system into the modelling process. An example of application is presented where HMM are used to model vegetation transects with presence-absence data, aimed at analysing disturbances in the spatial distribution of the vegetation after a wildfire in a semiarid zone.