Calibrating a Generalized Diameter Distribution Model with Mixed Effects
This article proposes a mixed model approach as an alternative to the traditional parameter prediction method in diameter distribution modeling. Unthinned and thinned plots established in mixed stands were used for calibrating a generalized diameter distribution model. The model was based on a two-parameter Weibull cumulative density function (cdf). This cdf was linearized through a complementary log–log link function. Plot random effects were included to account for autocorrelation and the dependent variable, i.e., cumulative stem frequency, was assumed to follow a binomial distribution. With respect to the parameter prediction method, this mixed model approach may generate a consistent estimator with consistent and unbiased variance for the vector of parameters when the variance-covariance matrix of the error terms is properly parameterized. Moreover, the approach enables a better assessment of the different variance components. For the whole group of plots, it provides a predicted average diameter distribution. At the plot level, the random effects can be considered as a departure from this average distribution. As long as the diameter distributions of the individual plots do not exhibit major departures from unimodality, the method proposed in this study should be used to calibrate generalized diameter distribution models.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
Publication date: 2006-12-01
More about this publication?
- Important Notice: SAF's journals are now published through partnership with the Oxford University Press. Access to archived material will be available here on the Ingenta website until March 31, 2018. For new material, please access the journals via OUP's website. Note that access via Ingenta will be permanently discontinued after March 31, 2018. Members requiring support to access SAF's journals via OUP's site should contact SAF's membership department for assistance.
Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
Forest Science is published bimonthly in February, April, June, August, October, and December.
2016 Impact Factor: 1.782 (Rank 17/64 in forestry)
Average time from submission to first decision: 62.5 days*
June 1, 2016 to Feb. 28, 2017
Also published by SAF:
Journal of Forestry
Other SAF Publications
- Submit a Paper
- Membership Information
- Author Guidelines
- Ingenta Connect is not responsible for the content or availability of external websites