Reductions in natural habitats urge that we better understand species' interconnection and how biological communities respond to environmental changes. However, ecological studies of species' interactions are limited by their geographic and taxonomic focus which can distort our understanding of interaction dynamics. We focus on bird-plant interactions that refer to situations of potential fruit consumption and seed dispersal. We develop an approach for predicting species' interactions that accounts for errors in the recorded interaction networks, addresses the geographic and taxonomic biases of existing studies, is based on latent factors to increase flexibility and borrow information across species, incorporates covariates in a flexible manner to inform the latent factors, and uses a meta-analysis data set from 85 individual studies. We focus on interactions among 232 birds and 511 plants in the Atlantic Forest, and identify 5% of pairs of species with an unrecorded interaction, but posterior probability that the interaction is possible over 80%. Finally, we develop a permutation-based variable importance procedure for latent factor network models and identify that a bird's body mass and a plant's fruit diameter are important in driving the presence of species interactions, with a multiplicative relationship that exhibits both a thresholding and a matching behavior.

翻译：自然栖息地减少促使我们必须更深入地理解物种间的相互联系以及生物群落如何响应环境变化。然而，针对物种相互作用的生态学研究受限于其地理与分类学焦点，这种局限性可能扭曲我们对相互作用动态的理解。本研究聚焦于鸟类-植物相互作用，具体涉及潜在果实消费与种子传播情境。我们开发了一种预测物种相互作用的方法，该方法能够校正已记录相互作用网络中的误差，应对现有研究的地理与分类偏差，基于隐因子以增强灵活性并跨物种共享信息，以灵活方式整合协变量以指导隐因子，并利用来自85项独立研究的元分析数据集。我们聚焦于大西洋森林中232种鸟类与511种植物之间的相互作用，识别出5%的物种对虽无记录相互作用，但其相互作用的可能后验概率超过80%。最后，我们开发了一种基于置换的隐因子网络模型变量重要性评估程序，发现鸟类体重与植物果实直径以呈现阈值效应与匹配行为的乘性关系，共同驱动物种相互作用的存在。