Quantifying and documenting succession has been a challenge to ecologists for many years. A variety of measures have been generated but do not seem to have been widely adopted. We propose the use of an intuitive and quantifiable measure that is amenable to both model building and hypothesis testing, and apply the method to a long-term, ongoing succession project in southeastern Ontario. We compare our measure with turnover rate (Diamond 1969) and lambda (Shugart and Hett 1973). We found that although these measures can determine when change within the community is occurring, the nature of this change and the resultant composition of the community is not readily gleaned from the measure. Our measure, by grouping plants as either 'early' or 'late', allows the relative composition of the community to be understood with a single number. The benefit of using an aggregate measure such as ours, is that a variety of questions can be examined, such as 'when will a community revert to its original composition following fire?' As an example, we utilized our measure on a post-fire succession data set from northern Montana. The results estimate that sites will take anywhere from 3 to 100 years to return to their pre-fire composition, based on current environmental conditions.There are several ways in which model quality can be assessed, and here I concentrate on the minimal message length principle which is a function of the prior probability of the model and its fit to the observed data, assuming the model to be correct. This principle has been shown to perform well when compared with other possibilities.