ABSTRACT

Model-based learning of organism dynamics is challenging, particularly when modeling count correlated data. In this paper, we adapt the multivariate Poisson distribution to model nematode dynamics. This distribution relaxes the mean-equal-variance property of the univariate Poisson distribution and allows recovery of the correlation among nematode genera. An observational dataset with 68 soil samples, 11 nematode genera, and 12 soil parameters is analyzed. The Spike and Slab Variable Selection procedure is adapted to obtain parsimonious models for the nematode occurrence. Nematode genus to genus interaction is assessed through the correlation matrix of the model. A simulation study validated the model's implementation. As a result, the model determined the most important covariates for each nematode and classified pairs of nematodes as: sympathetic, antagonistic or neutral, based on their estimated correlations. The model is useful for researchers and practitioners interested in studying population dynamics. In particular, the current results are important inputs when planning strategies for improving or managing soil health regarding nematodes.

摘要

基于模型的生物动力学学习具有挑战性，尤其是在对相关数据进行建模时。在本文中，我们采用多元泊松分布来模拟线虫动力学。这种分布放宽了单变量泊松分布的均值等方差特性，并允许恢复线虫属之间的相关性。分析了包含 68 个土壤样本、11 个线虫属和 12 个土壤参数的观测数据集。Spike 和 Slab 变量选择程序适用于获得用于线虫发生的简约模型。通过模型的相关矩阵评估线虫属与属之间的相互作用。一项模拟研究验证了模型的实施。因此，该模型确定了每种线虫最重要的协变量，并将线虫对分类为：同情，对抗或中立，基于他们估计的相关性。该模型对有兴趣研究人口动态的研究人员和从业者很有用。特别是，当前的结果是规划改善或管理有关线虫的土壤健康的策略时的重要输入。