Estimating Riparian Understory Vegetation Cover with Beta Regression and Copula Models

• Published in: Forest science Vol. 57; no. 3; pp. 212 - 221
• Main Authors: Eskelson, Bianca N.I, Madsen, Lisa, Hagar, Joan C, Temesgen, Hailemariam
• Format: Journal Article
• Language: English
• Published: Bethesda Society of American Foresters 01-06-2011; Oxford University Press
• Subjects: Biodiversity; confidence interval; Estimates; forest ecosystems; Frequency distribution; Generalized linear models; Habitats; heteroskedasticity; landscapes; least squares; overstory; plant communities; prediction; Random variables; riparian areas; shrubs; Studies; understory; variance; vegetation cover; Oregon
• ISSN: 0015-749X; 1938-3738
• DOI: 10.1093/forestscience/57.3.212

Understory vegetation communities are critical components of forest ecosystems. As a result, the importance of modeling understory vegetation characteristics in forested landscapes has become more apparent. Abundance measures such as shrub cover are bounded between 0 and 1, exhibit heteroscedastic error variance, and are often subject to spatial dependence. These distributional features tend to be ignored when shrub cover data are analyzed. The beta distribution has been used successfully to describe the frequency distribution of vegetation cover. Beta regression models ignoring spatial dependence (BR) and accounting for spatial dependence (BRdep) were used to estimate percent shrub cover as a function of topographic conditions and overstory vegetation structure in riparian zones in western Oregon. The BR models showed poor explanatory power (pseudo-R2 less than 0.34) but outperformed ordinary least-squares (OLS) and generalized least-squares (GLS) regression models with logit-transformed response in terms of mean square prediction error and absolute bias. We introduce a copula (COP) model that is based on the beta distribution and accounts for spatial dependence. A simulation study was designed to illustrate the effects of incorrectly assuming normality, equal variance, and spatial independence. It showed that BR, BRdep, and COP models provide unbiased parameter estimates, whereas OLS and GLS models result in slightly biased estimates for two of the three parameters. On the basis of the simulation study, 93-97% of the GLS, BRdep, and COP confidence intervals covered the true parameters, whereas OLS and BR only resulted in 84-88% coverage, which demonstrated the superiority of GLS, BRdep, and COP over OLS and BR models in providing standard errors for the parameter estimates in the presence of spatial dependence.