论文标题
使用多元广义线性混合模型来研究根源：基于微小观测的示例
Using Multivariate Generalised Linear Mixed Models for Studying Roots Development: An Example Based on Minirhizotron Observations
论文作者
论文摘要
根系在培养场中的空间和时间分布的表征取决于根系（散射）占据的土壤体积，以及该田间根定植的局部强度（强度）。我们引入了一个多元广义线性混合模型，用于同时描述使用minirhizotrons获得的数据（即带有观察窗的管，将其插入土壤中，可以直接观察根）。提出的模型允许使用图形模型研究复杂的空间和时间依赖模式，以表示潜在随机组件的依赖性结构。 散射是通过二项式混合模型（观测窗中的根）描述的。 Minirhizotron观察窗口中参考线的根数用于通过特殊定义的泊松混合模型来估计强度。我们探讨了这样一个事实，即可以构建广义线性混合模型的多元扩展，这些模型允许同时代表散点的依赖性模式以及强度以及时间和空间。 我们提出一个示例，其中强度和散射在三个不同的时间点同时确定。发现每个时间点的强度与散射之间的正相关，表明植物没有通过增加每卷土壤的根数来补偿土壤的占用减少。使用图形模型的一般属性，我们确定了持续观察到的散射和强度之间的一阶马尔可夫依赖模式。缺乏记忆表明，没有长期的暂时因果影响会影响根源的发展。上述两个依赖模式无法使用单变量模型检测到。
The characterisation of the spatial and temporal distribution of the root system in a cultivated field depends on the soil volume occupied by the root systems (the scatter), and the local intensity of the root colonisation in the field (the intensity). We introduce a multivariate generalised linear mixed model for simultaneously describing the scatter and the intensity using data obtained with minirhizotrons (i.e., tubes with observation windows, which are inserted in the soil, enabling to observe the roots directly). The models presented allow studying intricate spatial and temporal dependence patterns using a graphical model to represent the dependence structure of latent random components. The scatter is described by a binomial mixed model (presence of roots in observation windows). The number of roots crossing the reference lines in the observational windows of the minirhizotron is used to estimate the intensity through a specially defined Poisson mixed model. We explore the fact that it is possible to construct multivariate extensions of generalised linear mixed models that allow to simultaneously represent patterns of dependency of the scatter and the intensity along with time and space. We present an example where the intensity and scatter are simultaneously determined at three different time points. A positive association between the intensity and scatter at each time point was found, suggesting that the plants are not compensating a reduced occupation of the soil by increasing the number of roots per volume of soil. Using the general properties of graphical models, we identify a first-order Markovian dependence pattern between successively observed scatters and intensities. This lack of memory indicates that no long-lasting temporal causal effects are affecting the roots' development. The two dependence patterns described above cannot be detected with univariate models.
