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.