This paper describes a new SAS/STAT ® procedure for fitting models to non-normal or normal data with correlations or nonconstant variability. The GLIMMIX procedure is an add-on for the SAS/STAT product in SAS ® 9.1 on the Windows platform. PROC GLIMMIX extends the SAS mixed model tools in a number of ways. For example, it• models data from non-Gaussian distributions• implements low-rank smoothing based on mixed models
• provides new features for LS-means comparisons and display• enables you to use SAS programming statements to compute model effects, or to define link and variance functions• fits models to multivariate data in which observations do not all have the same distribution or linkApplications of the GLIMMIX procedure include estimating trends in disease rates, modeling counts or proportions over time in a clinical trial, predicting probability of occurrence in time series and spatial data, and joint modeling of correlated binary and continuous data.This paper describes generalized linear mixed models and how to use the GLIMMIX procedure for estimation, inference, and prediction.
To study the structure and growth of Estonian forest stands, a network of 501 permanent sample plots was established during 1995-2001. The plots were placed randomly using the grid of the European forest monitoring programme ICP FOREST. The network of permanent plots covers the main forest types and actual age interval of commercial forests. The plots are to be re-measured at 5-year intervals. The plots are circles holding a minimum of 100 trees each. On the plots, the polar coordinates and breast height diameters of all trees were measured. Additionally, the total height and crown length of selected sample trees were also measured. To date, the measurement data for 90 209 trees have been recorded in the database. The aim of the permanent plot network is to create and recalibrate Estonian forest-growth models. However, to achieve this, long-term measurement series on the permanent plots are still required. Nevertheless, on the basis of the existing material, some forest structure models have been tested and developed for Estonia, such as: (i) diameter distribution models; and (ii) height-diameter models. The Johnson's SB distribution was flexible enough to describe the diameter distributions of Estonian forests. Regression methods of parameter estimation represented a better fit than percentile methods. A diameter distribution model, the parameters of which were predicted by stand variables, has been developed for Estonian forests. A standardized height-diameter equation has been created for Estonian forests. Depending on the number of height-diameter measurements, the equation can be used as a one-parameter or two-parameter model. Model parameters can be estimated by solving a system of linear equations.
There is strong evidence that climate change alters tree growth in boreal forests. In Estonia, the analysis of ring measurements has been a common method to study growth changes. In this study, annual height growth data from dominant or co-dominant Scots pine (Pinus sylvestris L.) trees were used to develop a growth model for three forest generations. Stem analysis was applied and annual height growth was measured as the distance between whorls, as detected by branch knots of whorls on the split stem surface. Retrospective analysis of height growth produced comparative trends for three different age groups. Statistical analyses were used to estimate the impact of different factors on growth. Annual height growth was considered the best indicator for detecting possible trends in the growth potential of trees. Study results indicate that three generations (separated by time periods of 30-40 years) showed significant differences in growth patterns caused by shifts in climatic factors and management regimes (anthropogenic and natural disturbances).
Afforestation on reclaimed mining areas has high ecological and economic importance. However, ecosystems established on post-mining substrate can become vulnerable due to climate variability. We used tree-ring data and dendrochronological techniques to study the relationship between climate variables and annual growth of Scots pine (Pinus sylvestris L.) growing on reclaimed open cast oil shale mining areas in Northeast Estonia. Chronologies for trees of different age classes (50, 40, 30) were developed. Pearson's correlation analysis between radial growth indices and monthly climate variables revealed that precipitation in June-July and higher mean temperatures in spring season enhanced radial growth of pine plantations, while higher than average temperatures in summer months inhibited wood production. Sensitivity of radial increment to climatic factors on post-mining soils was not homogenous among the studied populations. Older trees growing on more developed soils were more sensitive to precipitation deficit in summer, while growth indices of two other stand groups (young and middle-aged) were highly correlated to temperature. High mean temperatures in August were negatively related to annual wood production in all trees, while trees in the youngest stands benefited from warmer temperatures in January. As a response to thinning, mean annual basal area increment increased up to 50 %. By managing tree competition in the closed-canopy stands, through the thinning activities, tree sensitivity and response to climate could be manipulated.
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