A linear mixed-effects model that accounts for the covariances among repeated measurements and for random plot effects is developed. A continuous-time autocorrelation error structure permits the model to be applied to irregularly spaced, unbalanced data. A strategy for fitting this model and diagnostic tools for assessing its goodness of fit are presented and demonstrated. The model is fitted to two permanent-plot data bases and shows marked improvement compared with models that do not account for the error structure.
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Grégoire et al. (1995) studied this question.