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Within guild co‐infections influence parasite community membership: a longitudinal study in African Buffalo
- Henrichs, Brian, Oosthuizen, Marinda C., Troskie, Milana, Gorsich, Erin, Gondhalekar, Carmen, Beechler, Brianna R., Ezenwa, Vanessa O., Jolles, Anna E.
- The journal of animal ecology 2016 v.85 no.4 pp. 1025-1034
- Syncerus caffer, buffaloes, diagnostic techniques, hosts, longitudinal studies, mixed infection, models, parasites, polymerase chain reaction
- Experimental studies in laboratory settings have demonstrated a critical role of parasite interactions in shaping parasite communities. The sum of these interactions can produce diverse effects on individual hosts as well as influence disease emergence and persistence at the population level. A predictive framework for the effects of parasite interactions in the wild remains elusive, largely because of limited longitudinal or experimental data on parasite communities of free‐ranging hosts. This 4‐year study followed a community of haemoparasites in free‐ranging African buffalo (Syncerus caffer). We detected infection by 11 haemoparasite species using PCR‐based diagnostic techniques, and analyzed drivers of infection patterns using generalized linear mixed models to understand the role of host characteristics and season on infection likelihood. We tested for (i) effects of co‐infection by other haemoparasites (within guild) and (ii) effects of parasites infecting different tissue types (across guild). We found that within guild co‐infections were the strongest predictors of haemoparasite infections in the buffalo; but that seasonal and host characteristics also had important effects. In contrast, the evidence for across‐guild effects of parasites utilizing different tissue on haemoparasite infection was weak. These results provide a nuanced view of the role of co‐infections in determining haemoparasite infection patterns in free living mammalian hosts. Our findings suggest a role for interactions among parasites infecting a single tissue type in determining infection patterns.