Probabilistic cellular automaton: model and application to vegetation dynamics
We offer a new framework for cellular automata modeling to describe and predict vegetation dynamics. The model can simulate community composition and spatial patterns by following a set of probabilistic rules generated from empirical data on plant neighborhood dynamics. Based on published data (Lippe et al. 1985), we apply the model to simulate Atlantic Heathland vegetation dynamics and compare the outcome with previous models described for the same site. Our results indicate reasonable agreement between simulated and real data and with previous models based on Markov chains or on mechanistic spatial simulation, and that spatial models may detect similar species dynamics given by non-spatial models. We found evidence that a directional vegetation dynamics may not correspond to a monotonic increase in community spatial organization. The model framework may as well be applied to other systems.
KeywordsAtlantic Heathland Chi-square statistics Markov chain Plant community Simulation Spatial pattern
Cellular automaton or cellular automata
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The research was supported by Conselho Nacional de Pesquisa Científica e Tecnológica (CNPq, Brazil) in the form of a grant and fellowship to V.P. and a doctoral scholarship to A.T.S.L. The paper benefited from comments and suggestions received during its presentation at the 45th IAVS Symposium held in Porto Alegre, Brazil.
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