On the role of spatial stochastic models in understanding landscape indices in ecology

Abstract

Spatial stochastic models play an important role in understanding and predicting the behaviour of complex systems. Such models may be implemented with explicit knowledge of only a limited number of parameters relating to spatial relationships among locations. Consequently, they are often used instead of deterministic-mechanistic models, which may potentially require an unrealistically large number of parameters. Currently, in contrast to spatial stochastic models, the parameterization of the joint spatial distribution of objects in landscape models is more often implicit than explicit. Here, we investigate the similarities and differences between bona fide spatial stochastic models and landscape models by focusing mostly on the relationships between processes, their realizations (patterns), representation and measurement, and their use in exploratory as well as confirmatory data analysis. One of the most important outcomes of recognizing the importance of stochastic processes is the acknowledgement that the spatial pattern observed in a landscape is only one realization of that process. Hence, while ecologists have been using landscape pattern indices (LPIs) to characterize landscape heterogeneity and/or make inferences about processes shaping the landscape, no stochastic modelling framework has been developed for their proper statistical elucidation. Consequently, several (mis)uses of LPIs draw conclusions about landscapes which are suspect. We show that several reports about sensitivities of LPIs to measurements have common roots that can be made explicitly manageable by adopting stochastic models of spatial structure. The key parameters of these stochastic models are composition and configuration, which, in general, cannot be estimated independently from each other. We outline how to develop the stochastic framework to interpret observations and make some recommendations to practitioners about everyday usage. The conceptual linkages between patterns and processes are particularly important in light of recent efforts to bridge the static-structural and the dynamic-analytic traditions of ecology.

Usages des modèles spatiaux pour la prospective

“…L'évaluation de l'aptitude du modèle peut ainsi conduire à confirmer le choix d'un modèle ou, dans le cas contraire, à développer un modèle spécifique à l'aide de plateformes de modélisation par exemple. D'une façon générale, deux types de modèles de simulation se distinguent, et dont la dichotomie peut aider à l'analyse et au choix du modèle : process-based versus pattern-based modèles (Fortin et al, 2003 ;. Les premiers ont pour objectif de simuler les processus influençant l'évolution d'un paysage (modèles de croissance, de diffusion d'espèces, etc.).…”

Incertidumbre de los índices de paisaje en el análisis de la estructura espacial

“…Uno de los problemas de los índices de pai saje es que su distribución es desconocida, lo cual limita su comparación estadística (Turner et al 2005). Para so brellevar este problema una alternativa es la generación de intervalos de confianza para cada índice a través de la simulación de paisajes a partir de un paisaje neutral, con lo cual se puede obtener la distribución de los valores de un índice (Fortin et al 2003). La incertidumbre asociada con el análisis de datos provenientes de imágenes sateli tales es algo difícilmente evitable, sabiendo que no existe clasificación sin errores.…”

Modelling land use/cover change in Lake Mogan and surroundings using CA-Markov Chain Analysis

“…CA-Markov Chain analysis is the combination of CA and MARKOV methods and predicts the future state of a system based on its previous state (Baker, 1989;Weng, 2002;Fortin et al, 2003). The analysis describes the probability of land cover change from one period to another by developing a transition probability matrix between two discrete time periods.…”