Markov chain models for vegetation dynamics

A theoretical implementation of Markov chain models of vegetation dynamics is presented. An overview of 22 applications of Markov chain models is presented, using data from four sources examining different grassland communities with varying sampling techniques, data types and vegetation parameters. For microdata, individual transitions have been observed, and several statistical tests of model assumptions are performed. The goodness of fit of the model predictions is assessed both for micro- and macrodata using the mean square error, Spearman’s rank correlation coefficient and Wilcoxon’s signed-rank test. It is concluded that the performance of the model varies between data sets, microdata generate a lower mean square error than aggregated macrodata, and time steps of one year are preferable to three months. The rank order of dominant species is found to be the most reliable prediction achievable with the models proposed.