Estimating Riparian Understory Vegetation Cover with Beta Regression and Copula Models
Estimating Riparian Understory Vegetation Cover with Beta Regression and Copula Models
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DOI：
10.1093/forestscience/57.3.212
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发表时间：
2011-06
期刊：
影响因子：
1.4
通讯作者：
B. Eskelson;L. Madsen;J. Hagar;H. Temesgen
中科院分区：
文献类型：
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作者：
B. Eskelson;L. Madsen;J. Hagar;H. Temesgen
林下植被群落是森林生态系统的重要组成部分。因此，林下植被特征的建模在森林景观中的重要性变得更加明显。灌木覆盖率等绝对测量值介于0和1之间，表现出异方差误差方差，并且通常具有空间依赖性。这些分布特征往往被忽视时，灌木覆盖数据进行分析。贝塔分布已成功地用于描述植被覆盖的频率分布。β回归模型忽略空间依赖性（BR）和占空间依赖性（BRdep）被用来估计百分比灌木覆盖地形条件和上层植被结构的函数在河岸带在俄勒冈州西部。BR模型的解释力较差（伪R2 ≤ 0.34），但在均方预测误差和绝对偏倚方面优于普通最小二乘（OLS）和广义最小二乘（GLS）回归模型（具有对数转换响应）。我们引入了一个Copula（COP）模型，它是基于贝塔分布和帐户的空间依赖性。一个模拟研究的目的是说明不正确的假设正态性，等方差，空间独立性的影响。结果表明，BR、BRdep和COP模型提供了无偏参数估计值，而OLS和GLS模型对三个参数中的两个产生了轻微偏倚的估计值。在模拟研究的基础上，93-97%的GLS、BRdep和COP置信区间覆盖了真实参数，而OLS和BR仅导致84-88%的覆盖率，这证明了GLS、BRdep和COP在提供存在空间依赖性的参数估计的标准误差方面优于OLS和BR模型。
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 ≤ 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.