Multi-host wildlife pathogens are an increasing concern for both wildlife conservation and livestock husbandry. Here, we combined social network theory with microbial genetics to assess patterns of interspecific pathogen transmission among ten species of wild and domestic ungulates in Kenya. If two individuals shared the same genetic subtype of a genetically diverse microbe, Escherichia coli, then we inferred that these individuals were part of the same transmission chain. Individuals in the same transmission chain were interlinked to create a transmission network. Given interspecific variation in physiology and behavior, some species may function as "super-spreaders" if individuals of that species are consistently central in the transmission network. Pathogen management strategies targeted at key super-spreader species are theoretically more effective at limiting pathogen spread than conventional strategies, and our approach provides a means to identify candidate super-spreaders in wild populations. We found that Grant's gazelle (Gazella granti) typically occupied central network positions and were connected to a large number of other individuals in the network. Zebra (Equus burchelli), in contrast, seemed to function as bridges between regions of the network that would otherwise be poorly connected, and interventions targeted at zebra significantly increased the level of fragmentation in the network. Although not usually pathogenic, E. coli transmission pathways provide insight into transmission dynamics by demonstrating where contact between species is sufficient for transmission to occur and identifying species that are potential super-spreaders. © 2013 Elsevier Ltd.

Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in understanding transmission dynamics, even for environmentally transmitted microbes like E. coli. This study is the first to use microbial genetics to construct and analyse transmission networks in a wildlife population and highlights the potential utility of an approach integrating microbial genetics with network analysis.

Although pathogen transmission dynamics are profoundly affected by population social and spatial structure, few studies have empirically demonstrated the population-level implications of such structure in wildlife. In particular, epidemiological models predict that the extent to which contact patterns are clustered decreases a pathogen's ability to spread throughout an entire population, but this effect has yet to be demonstrated in a natural population. Here, we use network analysis to examine patterns of transmission of an environmentally transmitted parasite, Cryptosporidium spp., in Belding's ground squirrels (Spermophilus beldingi). We found that the prevalence of Cryptosporidium was negatively correlated with transitivity, a measure of network clustering, and positively correlated with the percentage of juvenile males. Additionally, network transitivity decreased when there were higher percentages of juvenile males; the exploratory behavior demonstrated by juvenile males may have altered the structure of the network by reducing clustering, and low clustering was associated with high prevalence. We suggest that juvenile males are critical in mediating the ability of Cryptosporidium to spread through colonies, and thus may function as "super-spreaders." Our results demonstrate the utility of a network approach in quantifying mechanistically how differences in contact patterns may lead to system-level differences in infection patterns. © 2013 Springer-Verlag Berlin Heidelberg.