Using Markov models to incorporate serial dependence in studies of vegetation change.
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In this paper, we re-examine data from an intertidal salt-marsh, recording changes over 14 years. We use a method that explicitly accounts for possible temporal dependency between successive observations. This method employs first-order Markov models. We compare this with other methods used to explore these data without regard for serial dependence, and also investigate whether a higher-order Markov process is desirable. Although a salt-marsh has strong gradients, which might suggest continuous change, the results suggest that, in terms of major processes, two distinct communities are present with relatively weak linkages between them.
Acta Oecologica - International Journal of Ecology
Copyright 2002 Elsevier Masson SAS. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.