• dynamic programming;
  • implicit spatial models;
  • Markov models


1. A category count model consists of a fixed set of objects (often sites) each of which is classified as one of a set of mutually exclusive categories. Additionally, the category membership of each object evolves over time as a Markov process. The evolution of the objects can be affected by choosing the number of objects in each category that receive alternative management actions.

2. Category count models have been used for a variety of resource management applications, including conservation of endangered species, land management and the management of pest infestations.

3. This paper provides for the first time a general framework for such models, briefly reviews existing applications that fit the general framework, discusses the non-trivial problem of how transition probabilities can be computed as well as some of the challenges facing analysts using this framework.

4. The framework is applied to an existing application (the management of habitat for a threatened species), demonstrating the importance of modelling the stochasticity inherent in the problem.