Abstract #300793

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JSM 2002 Abstract #300793
Activity Number: 250
Type: Contributed
Date/Time: Tuesday, August 13, 2002 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics & the Environment*
Abstract - #300793
Title: Assessing Sources of Uncertainty in a Dynamic Forest Model
Author(s): Catherine Calder*+ and Michael Lavine
Affiliation(s): Duke University and Duke University
Address: Box 90251, Duke University, Durham, North Carolina, 27708-0251, USA
Keywords: Bayesian statistics ; model selection ; computer model ; forest dynamics ; inverse problem
Abstract:

The dynamics of forest ecosystems are often studied using complex computer models. These models help ecologists predict how forest characteristics will change over time if the environment is modified. The Clark lab at Duke University has studied the complete demographic life history of the tree species in two forests in North Carolina and has collected large amounts of data on reproduction, dispersal, growth, and survival, as well as data on the local environment, such as understory light, soil moisture, and soil nutrients. Their goal is to estimate parameters of a forest stand simulator using the data. We address a statistical issue related to this forest model: How can the data be used to assess the amount of accuracy gained by adding complexity to the model? We restate this problem as a Bayesian inverse problem and use MCMC techniques to perform model selection for forest models of different levels of complexity.

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