Zoonotic pathogens can harm human health and wellbeing directly or by impacting livestock. Pathogens that spillover from wildlife can also impair conservation efforts if humans perceive wildlife as pests. Brucellosis, caused by the bacterium Brucella abortus, circulates in elk and bison herds of the Greater Yellowstone Ecosystem and poses a risk to cattle and humans. Our goal was to understand the relative effects of climatic drivers, host demography, and management control programs on disease dynamics.

Synthesis and applications: Positive serostatus is often weakly correlated with infectiousness but is nevertheless used to make management decisions including lethal removal in wildlife disease systems. We show how this can have adverse consequences whereas efforts that maintain herd immunity can have longer-term protective effects. Climatic drivers may not result in synchronous disease dynamics across populations unless vital rates are also similar because demographic factors have a large influence on disease patterns.

Author ORCID Identifier

Gavin G. Coterill

Johan T. du Toit

Paul C. Cross



Document Type




File Format

.cvs, .RData, .rda, .R, .txt

Viewing Instructions

.Rdata, .rda, and .R require software capable of reading R software.

Publication Date



U.S. Geological Survey

Rocky Mountain Elk Foundation


Utah State University

Award Number

U.S. Geological Survey G16AC00106; Rocky Mountain Elk Foundation UT140699


Using >20 years of serologic, demographic, and environmental data on brucellosis in elk, we built stochastic compartmental models to assess the influences of climate forcing, herd immunity, population turnover, and management interventions on pathogen transmission. Data were collected at feedgrounds visited in winter by free-ranging elk in Wyoming, USA.

Snowpack, hypothesized as a driver of elk aggregation and thus brucellosis transmission, was strongly correlated across feedgrounds. We expected this variable to drive synchronized disease dynamics across herds. Instead, we demonstrate asynchronous epizootics driven by variation in demographic rates.

We evaluated the effectiveness of test-and-slaughter of seropositive female elk at two feedgrounds. Test-and-slaughter temporarily reduced herd-level seroprevalence but likely reduced herd immunity while removing few infectious individuals, resulting in subsequent outbreaks once the intervention ceased. We simulated an alternative strategy of removing seronegative female elk and found it would increase herd immunity, yielding fewer infections. We evaluated a second experimental treatment wherein feeding density was reduced at one feedground, but we found no evidence for an effect despite a decade of implementation.

Referenced by

Cotterill, G.G., Cross, P.C., Merkle, J.A., Rogerson, J.D., Scurlock, B.M. and du Toit, J.T. (2019), "Parsing the Effects of Demography, Climate, and Management on Recurrent Brucellosis Outbreaks in Elk." J Appl Ecol. doi:10.1111/1365-2664.13553


Greater Yellowstone, WY



Code Lists

See ESM_README.txt and pomp_README.txt for variables.

file: 'covar.csv' definitions:

'Feedground' = subpopulation

'cyear' = capture year

'MJ' = March to June cumulative snowmelt water equivalent from nearest SNOTEL site, standardized by site and exponentiated

'std_springStart' = standardized and exponentiated spring start

'tot' = the number of serological tests in a given year

'pop' = the yearling and adult female elk count in a given year

'births' = the number of calves counted at the feedground during peak winter, divided by two under the assumption of equal sex ratios.

file: 'dat.csv' definitions:

"Feedground' = subpopulation

'dyear' = capture year

'pos' = the number of seropositive tests in a given year

'apparent' = the apparent seroprevalence (pos/tot)

'dpop' = the yearling and adult female elk count in a given year.


***The .zip file contains the data files in the original directory organization and is the version of the files that are recommended for use.***

'asynch sim' contains R code used to explore expectations of synchrony across subpopulations.

'data' contains all of the files required for the partially-observed Markov process (pomp) models:

The 'model code' folder contains example R code for the main pomp models and the separate pomp_README.txt file.

Directory: 'greys_dell':

'greys_m0.R' has example code for running the endogenous model at Greys River feedground.

'dell_m0.R' has example code for running the endogenous model at Dell Creek feedground.

Directory: 'muddy_swe'

Subdirectories: 'm0', 'm1', 'm2', 'uncertain'

For 'm0','m1','m2':

'[x]_run.R' scripts have the main pomp model and iterated filtering code. These could be modified (eg., remove the test-and-slaughter components) to recreate models for any of the other sites, or run NDVI models.

'out' is a directory with 5 output files from '[x]_run.R' so that users can run analysis code without devoting substantial computing time to likelihood calculations.

'[x]_interp.R' reads in the objects from 'out' and performs analyses, including figures.**

For 'uncertain':

'out': subdirectory containing 3 .RData files.

'uncertain_run.R' runs the endogenous Muddy Creek model with parameter uncertainty and saves results.

'unc_SIRR_run.R' runs the above model, and saves SIRR compartment information (with param. uncert.).

'unc_TAS_run.R' runs models needed for comparing test-and-slaughter regimes (with param. uncert.).

'uncertain_interp.R' reads in all files from 'out', performs analyses and creates figures.**

**The comparison of test and slaughter regimes is contained within 'muddy_m0_interp.R' and 'uncertain_interp_04Aug19.R'.


Natural Resources and Conservation


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.




Additional Files

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