Co‐infection best predicts respiratory viral infection in a wild host

The dynamics of directly transmitted pathogens in natural populations are likely to result from the combined effects of host traits, pathogen biology, and interactions among pathogens within a host. Discovering how these factors work in concert to shape variation in pathogen dynamics in natural host–multi‐pathogen systems is fundamental to understanding population health. Here, we describe temporal variation in incidence and then elucidate the effect of hosts trait, season and pathogen co‐occurrence on host infection risk using one of the most comprehensive studies of co‐infection in a wild population: a suite of seven directly transmitted viral and bacterial respiratory infections from a 4‐year study of 200 free‐ranging African buffalo Syncerus caffer. Incidence of upper respiratory infections was common throughout the study—five out of the seven pathogens appeared to be consistently circulating throughout our study population. One pathogen exhibited clear outbreak dynamics in our final study year and another was rarely detected. Co‐infection was also common in this system: The strongest indicator of pathogen occurrence for respiratory viruses was in fact the presence of other viral respiratory infections. Host traits had minimal effects on odds of pathogen occurrence but did modify pathogen–pathogen associations. In contrast, only season predicted bacterial pathogen occurrence. Though a combination of environmental, behavioural, and physiological factors work together to shape disease dynamics, we found pathogen associations best determined infection risk. Our study demonstrates that, in the absence of very fine‐scale data, the intricate changes among these factors are best represented by co‐infection. The dynamics of directly transmitted pathogens in natural populations are likely to result from the combined effects of host traits, pathogen biology and pathogen interaction within a host. The authors study demonstrates that, in the absence of very fine‐scale data, the intricate relationships among these factors are best represented by pathogen–pathogen associations.