Tree diversity, landscape diversity, and economics of maple–birch forests: Implications of Markovian models

Markov decision process (MDP) models were effective in analyzing forest management policies. Even the simplest standard results gave useful insights into forest ecology, such as how landscape diversity is shaped by natural catastrophes, and how forests mature through successional phases. The methods were also useful to predict the effects of different management policies on ecological and economic criteria. Optimization augmented the usefulness of the approach, suggesting that income from Wisconsin's maple–birch forests could be increased without ruining their diversity of landscape, tree size, and tree species. It showed that maximizing species diversity, defined by the distribution of trees in shade-tolerance classes, would require some harvest. Instead, maximum tree size diversity occurred in unmanaged forests, but this gave a less diverse landscape and no income. The MDP method allowed for the design of compromise policies that would maximize income while keeping diversity above specified limits. The opportunity cost of increasing tree size diversity was found to be much higher than for species diversity. Comparing the maximum timber income owners could have got with what they actually cut suggested that the amenity value of forests was four times that of timber. Advantages of the methods reside in the ability to model complex ecosystem processes with simple probability matrices, and in the rich MDP theory and algorithms. Limitations include the difficulty of defining a space set large enough for accurate discretization, but small enough for practical application.