The relative impacts of invasive and persistence of a chronic pathogen on bighorn sheep demography, age structure and population growth rate

Background/Question/Methods

Theory from mathematical epidemiology predicts that invading pathogens whic impact host recruitment may have serious long-term consequences on host population growth. This prediction could apply to novel chronic pathogens that induce morbidity, since adult morbidity may lead to reduced recruitment. Here we explore whether this prediction holds for bighorn sheep populations subject to invasion by infectious pneumonia. Bighorn pneumonia has two phases, a pathogen introduction phase that often precipitates an all-age die-off, and a pathogen persistence phase characterized by poor recruitment. While bighorn management focuses on the introduction phase, data from our system suggest that pathogen persistence continues to impact recruitment for decades. Here we use statistical, mathematical, and simulation-based approaches to explore the relative impacts of pathogen introduction frequency and persistence period on bighorn population dynamics and demography. We first use an integrative population model (IPM) to estimate population vital rates in the presence and absence of disease. The IPM posteriors parameterize Leslie matrices in healthy and persistence years. We use these matrices to characterize the equilibrium population growth and age-structure under long-term pathogen persistence and and pathogen absence. We then build stochastic simulation models with year-to-year autocorrelations in disease status to explore the importance of two parameters, expected time to pathogen introduction and expected time of pathogen persistence, in shaping bighorn population growth.

Results/Conclusions

We find that pathogen introduction dominates disease dynamics when introduction rates are high. However, as pathogen introduction rates decline, persistence period becomes the dominant feature shaping population trajectories. In periods where introduction is relatively rare but persistence is long-term, the model predicts a shift toward older bighorn age structures, and this prediction is consistent with temporal changes in age-at-mortality data from our system. Our findings underscore the importance of the spillover mitigation tactics currently adopted by management agencies. However, our work also suggests that limiting introduction may be insufficient to spur rapid growth in already-infected bighorn populations.