Species are shifting their distributions in response to climate change. This geographic reshuffling may result in novel co-occurrences among species, which could lead to unseen biotic interactions, including the exchange of parasites between previously isolated hosts. Identifying potential new host–parasite interactions would improve forecasting of disease emergence and inform proactive disease surveillance. However, accurate predictions of future cross-species disease transmission have been hampered by the lack of a generalized approach and data availability. Here, we propose a framework to predict novel host–parasite interactions based on a combination of niche modelling of future host distributions and parasite sharing models. Using the North American ungulates as a proof of concept, we show this approach has high cross-validation accuracy in over 85% of modelled parasites and find that more than 34% of the host–parasite associations forecasted by our models have already been recorded in the literature. We discuss potential sources of uncertainty and bias that may affect our results and similar forecasting approaches, and propose pathways to generate increasingly accurate predictions. Our results indicate that forecasting parasite sharing in response to shifts in host geographic distributions allow for the identification of regions and taxa most susceptible to emergent pathogens under climate change.

This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.

物种正在改变其分布以应对气候变化。这种地理上的重新洗牌可能会导致物种之间出现新的共现，这可能会导致看不见的生物相互作用，包括先前隔离的宿主之间的寄生虫交换。识别潜在的新宿主-寄生虫相互作用将改善对疾病出现的预测并为主动疾病监测提供信息。然而，由于缺乏通用方法和数据可用性，对未来跨物种疾病传播的准确预测受到了阻碍。在这里，我们提出了一个框架，基于未来宿主分布的利基模型和寄生虫共享模型的组合来预测新型宿主与寄生虫的相互作用。使用北美有蹄类动物作为概念证明，我们表明这种方法在超过 85% 的建模寄生虫中具有很高的交叉验证准确性，并发现我们的模型预测的宿主-寄生虫关联中超过 34% 已经记录在文学。我们讨论了可能影响我们的结果和类似预测方法的不确定性和偏差的潜在来源，并提出了生成越来越准确的预测的途径。我们的结果表明，预测寄生虫共享以应对宿主地理分布的变化，可以识别在气候变化下最容易受到新兴病原体影响的区域和类群。

本文是“传染病宏观生态学：全球寄生虫多样性和动态”主题的一部分。