Title

Supplemental material for Cotterill et al. 2020: Parsing the effects of demography, climate, and management on recurrent brucellosis outbreaks in elk. Journal of Applied Ecology.

Description

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.

Document Type

Dataset

DCMI Type

Dataset

File Format

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

Viewing Instructions

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

Publication Date

11-26-2019

Funder

U.S. Geological Survey

Rocky Mountain Elk Foundation

Publisher

Utah State University

Award Number

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

Methodology

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

Location

Greater Yellowstone, WY

Language

eng

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.

Comments

***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'.

Disciplines

Natural Resources and Conservation

License

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

Identifier

https://doi.org/10.26078/f0fc-jw55

Files

ESM_README.txt (4 kB)
MD5: 916884AB4EDFD24C71239FE3DFDF897E

ESM.zip (94451 kB)
MD5: 6807745210AF27D0D310D8443A75166F

unif_births.R (7 kB)
MD5: 79d1188579466388ef860625f5ea04c5

covar.csv (5 kB)
MD5: 99c0b83d46ed75ae02504bc44b4edc1b

dat.csv (2 kB)
MD5: 563624767c8f1608df2d43853f4e3156

pomp_README.txt (1 kB)
MD5: 7fd8f0057e6232171c6080e82e69ecd7

dell_m0.R (9 kB)
MD5: 7b424c31ed5ba0b229cf4cd800353789

greys_m0.R (9 kB)
MD5: 5295bc771b45543bb6b51df18085a02c

muddy_m0_swe_04Aug.RData (7686 kB)
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muddy_m0_swe_global_04Aug.rda (386 kB)
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muddy_m0_swe_lik_local_04Aug.rda (1 kB)
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muddy_m0_swe_local_04Aug.rda (7293 kB)
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muddy_m0_swe_params_04Aug.csv (26 kB)
MD5: 94e198c37425e17bf1b0feeb733b5519

muddy_m0_interp.R (40 kB)
MD5: bcfc3165f2a1484f599ad977f39c2411

muddy_m0_run.R (10 kB)
MD5: c3fdae0734bafd95fb962ea0c90e6d34

muddy_m1_swe_04Aug.RData (10261 kB)
MD5: b4941473ec1397b191445f2e7711ef72

muddy_m1_swe_global_04Aug.rda (508 kB)
MD5: f0318052743084b0d05af93bc04e6044

muddy_m1_swe_lik_local_04Aug.rda (1 kB)
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muddy_m1_swe_local_04Aug.rda (9746 kB)
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muddy_m1_swe_params_04Aug.csv (28 kB)
MD5: d06cd85b39a0dd66f7e308297fd65ff2

muddy_m1_interp.R (10 kB)
MD5: 8e012937d955aa12900ea926017e358d

muddy_m1_run.R (10 kB)
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muddy_m2_swe_04Aug.RData (8031 kB)
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muddy_m2_swe_global_04Aug.rda (400 kB)
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muddy_m2_swe_lik_local_04Aug.rda (1 kB)
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muddy_m2_swe_local_04Aug.rda (7622 kB)
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muddy_m2_swe_params_04Aug.csv (26 kB)
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muddy_m2_interp.R (9 kB)
MD5: f5232fd54406fa49dc77124ed28db5df

muddy_m2_run.R (9 kB)
MD5: da71638b9d950750fbeb61e737a768c4

muddy_m0_04Aug_unc_SIRR_04Aug19.RData (11072 kB)
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muddy_m0_04Aug_unc50_TAS_comparisons_04Aug19.RData (21207 kB)
MD5: 22f0eb5eab5a4439f2423362506ee868

muddy_m0_04Aug_uncertain_04Aug19.RData (12547 kB)
MD5: 4e0ba47e315fb24ee4939eef2cc1ea7b

unc_SIRR_run.R (5 kB)
MD5: b52b1473b1c60d1d07d6aa2500956d14

unc_TAS_run.R (22 kB)
MD5: 2fc3079ff918e242021ccfde154dd150

uncertain_interp.R (11 kB)
MD5: ab195f15a0d94acb72d757f23ead0821

uncertain_run.R (5 kB)
MD5: f6629bb73e7945a907086cf383846082

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