Linking predator-prey interactions with exposure to a trophically transmitted parasite using PCR-based analyses

Molecular assessment of Hepatozoon (Apicomplexa: Adeleorina) infections in wild canids and rodents from north Africa, with implications for transmission dynamics across taxonomic groups

Parasites play a major role in ecosystems, and understanding of host-parasite interactions is important for predicting parasite transmission dynamics and epidemiology. However, there is still a lack of knowledge about the distribution, diversity, and impact of parasites in wildlife, especially from remote areas. Hepatozoon is a genus of apicomplexan parasites that is transmitted by ingestion of infected arthropod vectors. However, alternative modes of transmission have been identified such as trophic transmission. Using the 18S rRNA gene as a marker, we provide an assessment of Hepatozoon prevalence in six wild canid and two rodent species collected between 2003 and 2012 from remote areas in North Africa. By combining this with other predator-prey systems in a phylogenetic framework, we investigate Hepatozoon transmission dynamics in distinct host taxa. Prevalence was high overall among host species (African jerboa Jaculus jaculus [17/47, 36%], greater Egyptian jerboa Jaculus orientalis [5/7, 71%], side-striped jackal Canis adustus [1/2, 50%], golden jackal Canis aureus [6/32, 18%], pale fox Vulpes pallida [14/28, 50%], Rüppell's fox Vulpes rueppellii [6/11, 55%], red fox Vulpes vulpes [8/16, 50%], and fennec fox Vulpes zerda [7/11, 42%]). Phylogenetic analysis showed further evidence of occasional transmission of Hepatozoon lineages from prey to canid predators, which seems to occur less frequently than in other predator-prey systems such as between snakes and lizards. Due to the complex nature of the Hepatozoon lifecycle (heteroxenous and vector-borne), future studies on these wild host species need to clarify the dynamics of alternative modes of Hepatozoon transmission and identify reservoir and definitive hosts in natural populations. We also detected putative Babesia spp. (Apicomplexa: Piroplasmida) infections in two canid species from this region, V. pallida (1/28) and V. zerda (1/11).

Contrasting patterns of phenotype-dependent parasitism within and among populations of threespine stickleback

Variation in infection rate arises from variation in host exposure and resistance to parasites both within and among populations. All things being equal, phenotypes that increase exposure risk should covary positively with infection among individuals. It might therefore be expected that populations with mean phenotypes that increase exposure might also have higher rates of infection. However, such positive covariance between exposure and infection at the population level might be undermined by other factors such as geographic variation in parasite abundance or host resistance, negating or reversing in between-population comparisons. We studied rates of infection of two parasites among 18 populations of threespine stickleback (Gasterosteus aculeatus). As predicted, within populations, trophic morphology covaries with infection of two trophically transmitted parasites: individuals with benthic (or limnetic) phenotypes were more likely to be infected with a benthic (or limnetic) parasite. However, across populations, the relationship between morphology and infection rate was absent (limnetic parasite) or reversed (benthic parasite). Our results confirm the importance of phenotype-dependent exposure, but stress different factors or processes, such as the evolution of reduced susceptibility, might shape variation in infection at larger spatial scales.

Network transmission inference: host behavior and parasite life cycle make social networks meaningful in disease ecology

The process of disease transmission is determined by the interaction of host susceptibility and exposure to parasite infectious stages. Host behavior is an important determinant of the likelihood of exposure to infectious stages but is difficult to measure and often assumed to be homogenous in models of disease spread. We evaluated the importance of precisely defining host contact when using networks that estimate exposure and predict infection prevalence in a replicated, empirical system. In particular, we hypothesized that infection patterns would be predicted only by a contact network that is defined according to host behavior and parasite life cycle. Two competing host contact criteria were used to construct networks defined by parasite life cycle and social contacts. First, parasite-defined contacts were based on shared space with a time delay corresponding to the environmental development time of nematode parasites with a direct fecal-oral life cycle. Second, social contacts were defined by shared space in the same time period. To quantify the competing networks of exposure and infection, we sampled natural populations of the eastern chipmunk (Tamias striatus) and infection of their gastrointestinal helminth community using replicated longitudinal capture-mark-recapture techniques. We predicted that (1) infection with parasites with direct fecal-oral life cycles would be explained by the time delay contact network, but not the social contact network; (2) infection with parasites with trophic life cycles (via a mobile intermediate host; thus, spatially decoupling transmission from host contact) would not be explained by either contact network. The prevalence of fecal-oral life cycle nematode parasites was strongly correlated to the number and strength of network connections from the parasite-defined network (including the time delay), while the prevalence of trophic life cycle parasites was not correlated with any network metrics. We concluded that incorporating the parasite life cycle, relative to the way that exposure is measured, is key to inferring transmission and can be empirically quantified using network techniques. In addition, appropriately defining and measuring contacts according the life history of the parasite and relevant behaviors of the host is a crucial step in applying network analyses to empirical systems.