Perturbation, management and disease
Our work provides a valuable case study on how hunting can have unexpected consequences for pathogen transmission and evolution across scales. On the surface our results seem to contradict the perturbation effect hypothesis (Carter et al. 2007), as hunting in our treatment region reduced the number of putative transmission events as some theoretical models suggest (Lloyd-Smith et al. 2005; Potapovet al. 2012). However, in our case the cessation of hunting in a population (which was previously hunted) facilitated demographic change via increased male survivorship and abundance that perturbed the system to a different demographic state (Logan & Runge 2020a). Even though the ‘perturbation’ was different here, as reduced hunting pressure may have resulted in more interactions, likely via enhanced male-male competition, the underlying mechanism could be similar. An expansion of the perturbation effect to include any management action that leads to demographic or behavioral change may be warranted.
Our results also reveal potential shortcomings of relying on population estimates of prevalence to understand the impact of wildlife management actions on pathogen transmission. In our case, population estimates of FIVpco prevalence across time alone could not detect shifts in transmission associated with hunting and were not sensitive to changes in population size (Figs. S8/S9). The lack of signal from prevalence data may be a contributing factor behind the variability of the effects of culling on disease dynamics in empirical systems (Prentice et al. 2019). Prevalence data may be better able to detect shifts in population demography where the pathogen causes acute infections with shorter periods of immunity. The collection of pathogen molecular data from well-sampled wildlife populations across time is a logistical challenge, yet with ever cheaper and more mobile sequencing platforms, the potential to use approaches similar to ours is increasing, even for slowly evolving pathogens such as bacteria (Bieket al. 2015). This molecular and analytical approach can not only provide novel insights into the broader consequences of wildlife management on disease dynamics but can also help understand evolutionary relationships between hosts and pathogens in free-ranging species more broadly.