Cr. Lin et J. Buongiorno, TREE DIVERSITY, LANDSCAPE DIVERSITY, AND ECONOMICS OF MAPLE-BIRCH FORESTS - IMPLICATIONS OF MARKOVIAN MODELS, Management science, 44(10), 1998, pp. 1351-1366
Citations number
51
Categorie Soggetti
Management,"Operatione Research & Management Science","Operatione Research & Management Science
Markov decision process (MDP) models were effective in analyzing fores
t management policies. Even the simplest standard results gave useful
insights into forest ecology, such as how landscape diversity is shape
d by natural catastrophes, and how forests mature through successional
phases. The methods were also useful to predict the effects of differ
ent management policies on ecological and economic criteria. Optimizat
ion augmented the usefulness of the approach, suggesting that income f
rom 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 tree
s in shade-tolerance classes, would require some harvest. Instead, max
imum 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 keepin
g diversity above specified limits. The opportunity cost of increasing
tree size diversity was found to be much higher than for species dive
rsity. 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 ab
ility to model complex ecosystem processes with simple probability mat
rices, and in the rich MDP theory and algorithms. Limitations include
the difficulty of defining a space set large enough for accurate discr
etization, but small enough for practical application.