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 how parasites are distributed within hosts, while accounting for the relative uncertainty of less sampled species. We used network metrics to assess the difference between theobserved and predicted networks, and to explore the connections between hosts via their shared parasites. 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, it 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 crossspecies transmission of nematode parasites in areas where livestock and wildlife share grazing land.
|Journal||Philosophical Transactions of the Royal Society of London B: Biological Sciences|
|Early online date||01 Mar 2017|
|Publication status||Published - 05 May 2017|
- Bayesian hierarchical model, ungulates, nematodes, Botswana, bipartite, negative binomial