  A Bayesian approach is developed to analyze change points in multivariate
time series and space-time data. The methodology is used to assess the impact
of extended inundation on the ecosystem of the Gulf Plains bioregion in
northern Australia. The proposed approach can be implemented for dynamic
mixture models that have a conditionally Gaussian state space representation.
Details are given on how to efficiently implement the algorithm for a general
class of multivariate time series and space-time models. This efficient
implementation makes it feasible to analyze high dimensional, but of realistic
size, space-time data sets because our approach can be appreciably faster,
possibly millions of times, than a standard implementation in such cases.
