Data from: State-space modeling to support management of brucellosis in the Yellowstone bison population

The Yellowstone bison (Bison bison) exemplifies the challenge of
conserving large mammals that migrate across the boundaries of
conservation areas. Bison in Yellowstone are infected with brucellosis
(Brucella abortus). Their seasonal movements can expose livestock to
infection. We developed a Bayesian state-space model to reveal the
influence of brucellosis on the dynamics of the Yellowstone bison
population and to inform decisions on bison management. A model of
frequency dependent transmission was superior to a density dependent model
in its ability to predict out-of-sample observations of the probability of
horizontal transmission (mean square prediction error frequency model =
0.78, density dependent model = 0.91). Conditional on the frequency
dependent model, the median transmission rate of brucellosis was 1.86
year-1 (95% equal-tailed credible interval, BCI = 1.5, 2.2). The median of
the posterior distribution of the basic reproductive ratio (R0) was 1.76
(BCI = 1.47, 2.36). Seroprevalence of adult females varied around 60%
during the last two decades; however only 13 of 100 adult females were
infectious (BCI = 0.1, 0.15). Estimation of population growth rate (_) in
the presence of brucellosis reflected the depressing effect of the disease
on recruitment; _ for an infected population averaged 1.07 (BCI = 1.03,
1.11) and for a healthy population _ = 1.12 (BCI = 1.07, 1.16). We used
forecasting with a five year horizon to evaluate the ability of different
actions to meet goals for management relative to a no action alternative.
Annually removing 200 seropositive female bison increased the probability
of reducing seroprevalence below 40% by 30-fold relative to no action and
increased the probability of achieving a 50% reduction in transmission
probability by a factor of 110 relative to no action. Annually vaccinating
200 seronegative animals increased the probability of achieving a 50%
reduction transmission probability by five fold over no action. Forecasts
of the future state of the population became increasingly uncertain with
increases in the forecast horizon. Our findings emphasize the necessity of
iterative, adaptive management with a relatively short term commitment to
action, a commitment that must be reevaluated frequently in response to
new data and model forecasts.