Raw Data from Uncertain links in host–parasite networks: lessons for parasite transmission in a multi-host system

For many parasites, the full set of hosts that are susceptible to infection is not known, and this could lead to a bias in estimates of transmission. We used counts of individual adult parasites from historical parasitology studies in southern Africa to map a bipartite network of the nematode parasites of herbivore hosts that occur in Botswana. Bipartite networks are used in community ecology to represent interactions across trophic levels. We used a Bayesian hierarchical model to predict the full set of host–parasite interactions from existing data on parasitic gastrointestinal nematodes of wild and domestic ungulates given assumptions about the distribution of parasite counts within hosts, while accounting for the relative uncertainty of less sampled species. We used network metrics to assess the difference between the observed and predicted networks, and to explore the connections between hosts via their shared parasites using a host–host unipartite network projected from the bipartite network. The model predicts a large number of missing links and identifies red hartebeest, giraffe and steenbok as the hosts that have the most uncertainty in parasite diversity. Further, the unipartite network reveals clusters of herbivores that have a high degree of parasite sharing, and these clusters correspond closely with phylogenetic distance rather than with the wild/domestic boundary. These results provide a basis for predicting the risk of cross-species transmission of nematode parasites in areas where livestock and wildlife share grazing land. This article is part of the themed issue ‘Opening the black box: re-examining the ecology and evolution of parasite transmission’.

对于诸多寄生虫而言，其全部易感宿主尚未被完全探明，这可能导致传播速率估算出现偏差。我们依托南非地区既往寄生虫学研究中记录的成年寄生虫个体计数数据，绘制了博茨瓦纳境内植食性宿主寄生线虫的二部网络（bipartite network）。二部网络在群落生态学中被用于表征不同营养级间的相互作用关系。我们基于宿主内寄生虫计数分布的相关假设，结合野生与家养有蹄类动物胃肠道寄生线虫的现有数据，构建贝叶斯分层模型（Bayesian hierarchical model）以预测完整的宿主-寄生虫相互作用集合，同时校正采样不足物种的相对不确定性。我们借助网络指标评估观测网络与预测网络之间的差异，并通过从二部网络投影得到的宿主-宿主单部网络（unipartite network），探究宿主间通过共享寄生虫形成的关联。该模型预测存在大量未被观测到的关联，并确定红麋羚、长颈鹿与石羚为寄生虫多样性不确定性最高的宿主类群。此外，单部网络还揭示了存在高比例寄生虫共享的植食性动物类群簇，且此类类群簇的划分与物种系统发育距离高度相关，而非与野生/家养的分类边界一致。上述研究结果为家畜与野生动物共享牧场区域内的线虫跨物种传播风险预测提供了理论支撑。本文属于专题刊‘打开黑箱：重新审视寄生虫传播的生态学与演化’的组成部分。