Markovian model of succession

Paul Colinvaux

Horn (1974, 1976) has applied the mathematics of Markov chains to forest data to predict future states. The initial data set is an estimate of the probability that any one tree in a forest will be replaced by one of its own kind or one of another kind. These data most easily could be derived by mapping every tree in a forest and than remapping the same forest at some future year, say, 50 years off. A more practicable way to get usable data is from estimates of replacement probabilities made from measures of saplings actually under trees in the forest, supplemented by measures of relative shade tolerance.

When a matrix of probabilities of individual replacement for all species of a forest is known, fairly straightforward calculations allow extrapolations to the future composition of a forest. The only data going into these calculations, therefore, are present composition of a forest and measured probabilities of individual replacements. The method has been applied to early succession data in New Jersey forests and predicts the composition of future climax communities to be closely similar to that existing in a virgin New Jersey stand now (Horn, 1975).

Fonte: Colinvaux, P. 1986. Ecology. NY, Wiley.