基于树型的物种相互作用网络推理
Tree-based Inference of Species Interaction Network from Abundance Data
摘要：
生态系统的行为主要依赖于它所涉及的物种之间的相互作用。我们考虑从丰度数据推断物种相互作用网络的问题。为了相关，任何网络推理方法都需要处理计数数据并考虑可能的环境影响。它还需要区分直接交互和间接关联，图形模型为此提供了一个方便的框架。我们介绍了一个通用的统计模型的网络推理的基础上丰富的数据。该模型包括用于解释环境协变量和采样努力的固定效应，以及用于编码物种相互作用的相关随机效应。通过以计算高效的方式对所有可能的树形（因此是稀疏的）网络进行平均来获得推断的网络。该过程的输出是每条边成为底层网络的一部分的概率。仿真研究表明，所提出的方法相比，以及与国家的最先进的方法，即使底层图强烈不同于树。两个数据集的分析突出了协变量对推断网络的影响。考虑协变量对于避免伪边缘至关重要。所提出的方法可以扩展到执行网络比较或寻找丢失的物种。
英文摘要：
The behavior of ecological systems mainly relies on the interactions between the species it involves. We consider the problem of inferring the species interaction network from abundance data. To be relevant, any network inference methodology needs to handle count data and to account for possible environmental effects. It also needs to distinguish between direct interactions and indirect associations and graphical models provide a convenient framework for this purpose. We introduce a generic statistical model for network inference based on abundance data. The model includes fixed effects to account for environmental covariates and sampling efforts, and correlated random effects to encode species interactions. The inferred network is obtained by averaging over all possible tree-shaped (and therefore sparse) networks, in a computationally efficient manner. An output of the procedure is the probability for each edge to be part of the underlying network. A simulation study shows that the proposed methodology compares well with state-of-the-art approaches, even when the underlying graph strongly differs from a tree. The analysis of two datasets highlights the influence of covariates on the inferred network. Accounting for covariates is critical to avoid spurious edges. The proposed approach could be extended to perform network comparison or to look for missing species.
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。
