|
Activity Number:
|
368
|
|
Type:
|
Invited
|
|
Date/Time:
|
Wednesday, August 6, 2008 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Statistics and the Environment
|
| Abstract - #301925 |
|
Title:
|
Data-Model Integration for Forest Dynamics
|
|
Author(s):
|
Jarrett J. Barber*+ and Kiona Ogel and Darren E. Gemoets
|
|
Companies:
|
University of Wyoming and University of Wyoming and University of Wyoming
|
|
Address:
|
1000 E. University Ave, Laramie, WY, 82071,
|
|
Keywords:
|
Bayesian hierarchical model ; climate change ; data-model integration ; forest dynamics ; scaling
|
|
Abstract:
|
Does predicting forests' responses to climate change require an investigation of individual or species behavior, or is a coarser and computationally less burdensome approach adequate? Our tree growth model allows environmental factors to drive trees' species-specific physiological processes constrained by species specific allometries such as the relationship of height to diameter. We use a Bayesian hierarchical model to integrate a large forest inventory database and extensive literature information with the model to obtain posterior estimates of model parameters. Initial results suggest that understanding within- and between-species variability in model parameters is important to predicting forest responses. These results suggest that future work on scaling between individual trees and climate model grid cells should account for the variability within and between species.
|
