Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/4683
Title: Markov chain models for vegetation dynamics
Authors: Balzter, Heiko
First Published: 28-Feb-2000
Publisher: Elsevier
Citation: Ecological Modelling, 2000, 126 (2-3), pp. 139-154
Abstract: 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.
DOI Link: 10.1016/S0304-3800(00)00262-3
ISSN: 0304-3800
Links: http://www.sciencedirect.com/science/article/pii/S0304380000002623
http://hdl.handle.net/2381/4683
Type: Article
Rights: This is the author's final draft of the paper published as Ecological Modelling, 2000, 126 (2-3), pp. 139-154. The final version is available from http://www.sciencedirect.com/science/journal/03043800. Doi: 10.1016/S0304-3800(00)00262-3
Appears in Collections:Published Articles, Dept. of Geography

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