利用多元广义线性混合模型研究根系发育--以微电子观测为例
Using Multivariate Generalised Linear Mixed Models for Studying Roots Development: An Example Based on Minirhizotron Observations
摘要：
在耕地中的根系的空间和时间分布的表征取决于根系所占据的土壤体积（分散），以及在该领域中的根定殖的局部强度（强度）。我们引入了一个多变量广义线性混合模型，用于同时描述散射和强度，使用用微型电子管获得的数据（即，带观察窗的管子，插入土壤中，能够直接观察根部）。所提出的模型允许研究复杂的空间和时间的依赖模式，使用图形模型来表示潜在的随机组件的依赖结构。用二项式混合模型（观察窗中存在根）描述散射。通过一个专门定义的泊松混合模型，利用微型电子管观测窗口中穿过参考线的根数来估计强度。我们探索的事实是，它是可能的广义线性混合模型，允许同时表示模式的依赖性的散射和强度，随着时间和空间构建多变量扩展。我们提出了一个例子，其中的强度和散射同时确定在三个不同的时间点。发现在每个时间点的强度和分散之间的正相关，这表明植物不补偿减少占用的土壤，通过增加单位体积的土壤的根的数量。利用图形模型的一般性质，我们确定了一个一阶马尔可夫连续观察到的散射和强度之间的依赖模式。这种记忆的缺乏表明，没有持久的时间因果效应影响根的发展。上述两种依赖模式无法用单变量模型检测。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
