Identifying undocumented or potential future interactions among species is a challenge facing modern ecologists. Recent link prediction methods rely on trait data; however, large species interaction databases are typically sparse and covariates are limited to only a fraction of species. On the other hand, evolutionary relationships, encoded as phylogenetic trees, can act as proxies for underlying traits and historical patterns of parasite sharing among hosts. We show that, using a network-based conditional model, phylogenetic information provides strong predictive power in a recently published global database of host-parasite interactions. By scaling the phylogeny using an evolutionary model, our method allows for biological interpretation often missing from latent variable models. To further improve on the phylogeny-only model, we combine a hierarchical Bayesian latent score framework for bipartite graphs that accounts for the number of interactions per species with host dependence informed by phylogeny. Combining the two information sources yields significant improvement in predictive accuracy over each of the submodels alone. As many interaction networks are constructed from presence-only data, we extend the model by integrating a correction mechanism for missing interactions which proves valuable in reducing uncertainty in unobserved interactions.

中文翻译：

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使用缩放进化关系预测生态相互作用的分层贝叶斯模型

识别物种之间无证或潜在的未来相互作用是现代生态学家面临的挑战。最近的链接预测方法依赖于特征数据。但是，大型物种相互作用数据库通常是稀疏的，并且协变量仅限于一部分物种。另一方面，进化关系被编码为系统进化树，可以充当宿主之间寄生虫潜在特征和历史模式的代理。我们显示，使用基于网络的条件模型，系统发育信息在最近发布的全球宿主-寄生虫相互作用数据库中提供了强大的预测能力。通过使用进化模型缩放系统发育，我们的方法可以实现潜在变量模型中常常缺少的生物学解释。为了进一步改进纯系统发育模型，我们结合了用于二分图的分层贝叶斯潜在得分框架，该框架说明了每个物种之间相互作用的数量，并通过系统发育学告知了宿主依赖性。结合两个信息源，相对于每个子模型，预测准确性都得到了显着提高。由于许多互动网络是根据仅存在的数据构建的，因此我们通过集成针对缺失互动的校正机制来扩展模型，这对于减少未观察到的互动中的不确定性具有重要意义。