Abstract
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’.
Highlights
Management strategies for parasites could potentially be improved by incorporating additional realism into models of transmission, such as multi-host interactions and environmental effects [1]
By projecting a host – parasite bipartite network into a host–host unipartite network, a ‘potential transmission network’ is constructed in which hosts are connected through shared parasites [14]
To determine how the predicted host– parasite network differs in network structure from the observed network, the median and 95% credible interval of occurrence from the final fitted model were compared with the data-only host– parasite occurrence network by calculating network-level indices connectance, links per species, cluster coefficient and nestedness using the bipartite package v. 2.05 in R [63,64]
Summary
Management strategies for parasites could potentially be improved by incorporating additional realism into models of transmission, such as multi-host interactions and environmental effects [1]. We applied a Bayesian hierarchical modelling 2 method to existing data from extensive post-mortem studies in southern Africa where nematode parasites were counted and identified These studies, conducted over the past century, provide an excellent resource as many report estimated or exact counts for each nematode species found in each individual host from a wide range of wild and domestic ungulate species [25]. To determine how the predicted host– parasite network differs in network structure from the observed network, the median and 95% credible interval of occurrence (uij) from the final fitted model were compared with the data-only (unweighted) host– parasite occurrence network by calculating network-level indices connectance, links per species, cluster coefficient and nestedness using the bipartite package v. Data on the phylogenetic relationship between the host species of interest was extracted from the TimeTree database [70,71] using the R package ape v. 3.4 [72] and visualised using the iTOL website [73]
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