Climate change and reductions in natural habitats necessitate that we better understand species' interactivity and how biological communities respond to environmental changes. However, ecological studies of species' interactions are limited by geographic and taxonomic bias which can lead to severe under-representation of certain species and distort our understanding of inter-species interactions. We illustrate that ignoring these biases can result in poor performance. We develop a model for predicting species' interactions that (a) accounts for errors in the recorded interaction networks, (b) addresses the geographic and taxonomic bias of existing studies, (c) is based on latent factors to increase flexibility and borrow information across species, (d) incorporates covariates in a flexible manner to inform the latent factors, and (e) uses a meta-analysis data set from 166 individual studies. We focus on interactions among 242 birds and 511 plants in the Brazilian Atlantic Forest, and identify 5% of pairs of species with an unrecorded interaction, but posterior probability of existing that is over 80%. Finally, we develop a permutation-based variable importance procedure and identify that a bird's body mass and a plant's fruit diameter are most important in driving the presence and detection of species interactions, with a multiplicative relationship.

翻译：气候变化和自然生境的减少要求我们更好地了解物种的相互作用和生物群落如何应对环境变化。然而,物种相互作用的生态研究受到地理和分类偏见的限制,这可能导致某些物种严重代表不足,并扭曲我们对物种间相互作用的理解。我们指出,忽视这些偏见可能导致不良表现。我们开发了一个预测物种相互作用的模型,(a) 说明记录的互动网络中的错误,(b) 解决现有研究的地理和分类偏差,(c) 以潜在因素为基础,增加物种间的灵活性并借取信息,(d) 以灵活的方式纳入共变体,为潜在因素提供信息,(e) 使用166项单独研究的元分析数据集。我们侧重于巴西大西洋森林242只鸟和511只植物之间的互动,并查明5%的物种配对互动没有记录,但现有互动的后继概率超过80%。最后,我们开发了一个基于变异重要性的程序,并查明鸟类的体质量和植物的水果直径是驱动物种存在和多种互动关系的最重要因素。