Computer Modeling the Forest...and the Trees
By Sean Henahan, Access Excellence
San Diego, CA (9/19/97)- A powerful new computer modeling system
is allowing researchers to better understand the dynamics of forest
ecosystems based on the behavior of individual trees. The research,
appearing in Science, is the first peer-reviewed article written
exclusively as a publication for the World Wide Web.
"The Web
site includes scores of still images and a dozen color animations,"
noted Douglas Deutschman, San Diego State University. "The 3D results are
critical to the readers' understanding the complexity of the model. This
is impossible to accomplish in conventional publishing." A built-in feedback
feature, moreover, allows visitors to the site to share their thoughts
about its success in communicating science.
The computer system, called SORTIE, can be programmed to simulate a
wide range of forest conditions. The behaviors of the trees in the simulations
are based on studies conducted in the Great Mountain Forest in Connecticut.
The researchers conducted several experiments, including simulations of
large clear-cuts in the forest, increase of individual-tree mortality,
removal of spatially explicit interactions, and approximation of the functional
responses of species.
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Base-line forest
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Dying forest
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SORTIE was designed to evaluate local competition among nine
species of trees. Light (the limiting resource) is measured for each tree
on the landscape by means of an approach based on
fish-eye photography of the forest canopy. The light available to each
tree is then used to calculate species-specific growth rate and risk of
mortality. Surviving trees produce seedlings as an increasing function
of tree size, and the seedlings are dispersed away from the parent tree.
The larger picture of overall forest dynamics emerge as the collective
result of these localized interactions among trees.
Deutschman ran hundreds of simulations examining different scenarios
as the forests evolved over 1,000 years in 5-year time-steps.
"Insofar as this mechanistic model mirrors nature, it provides insight
into the critical detail controlling the emergence of forest patterns from
the interactions of trees. This quasi-experimental approach with a detailed,
mechanistic model derived from data is a powerful method for
addressing questions of relevant detail, emergent properties, and scale,"
the researchers note.
"This work is part of our broader program to understand how much detail
at the level of individuals is needed to understand the macroscopic dynamics
of ecosystems," says Simon Levin, Director of the Princeton Environmental
Institute. "The forest growth work, which builds on a model developed by
Steve Pacala and others, represents the most important advances to date."
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