Chronic wasting disease alters the movement behavior and habitat use of mule deer during clinical stages of infection
Cite this dataset
Barrile, Gabriel et al. (2024). Chronic wasting disease alters the movement behavior and habitat use of mule deer during clinical stages of infection [Dataset]. Dryad. https://doi.org/10.5061/dryad.37pvmcvrp
Abstract
Integrating host movement and pathogen data is a central issue in wildlife disease ecology that will allow for a better understanding of disease transmission. We examined how adult female mule deer (Odocoileus hemionus) responded behaviorally to infection with chronic wasting disease (CWD). We compared movement and habitat use of CWD-infected deer (n = 18) to those that succumbed to starvation (and were CWD-negative by ELISA and IHC; n = 8) and others in which CWD was not detected (n = 111, including animals that survived the duration of the study) using GPS collar data from two distinct populations collared in central Wyoming, USA during 2018–2022. CWD and predation were the leading causes of mortality during our study (32 of 91 deaths attributed to CWD and 27 of 91 deaths attributed to predation). Deer infected with CWD moved slower and used lower elevation areas closer to rivers in the months preceding death compared with uninfected deer that did not succumb to starvation. Although CWD-infected deer and those that died of starvation moved at similar speeds during the final months of life, CWD-infected deer used areas closer to streams with less herbaceous biomass than deer that died of starvation. These behavioral differences may allow for the development of predictive models of disease status from movement data, which will be useful to supplement field and laboratory diagnostics or when mortalities cannot be quickly retrieved to assess cause-specific mortality. Furthermore, identifying individuals that are sick before predation events could help to assess the extent to which disease mortality is compensatory with predation. Finally, infected animals began to slow down around four months prior to death from CWD. Our approach for detecting the timing of infection-induced shifts in movement behavior may be useful in application to other disease systems to better understand the response of wildlife to infectious disease.
README: Chronic wasting disease alters the movement behavior and habitat use of mule deer during clinical stages of infection
GENERAL INFORMATION
Title of Dataset: CWD-HostBehavior-EcolEvol
Author Information
A. Principal Investigator Contact Information
Name: Gabriel Barrile
Institution: University of Wyoming
Address: 1000 E University Ave, Laramie, WY 82071
Email: gbarrile15@gmail.com
B. Associate or Co-investigator Contact Information
Name: Jerod Merkle
Institution: University of Wyoming
Address: 1000 E University Ave, Laramie, WY 82071
Email: jmerkle@uwyo.edu
C. Alternate Contact Information
Name: Gabriel Barrile
Email: gbarrile@uwyo.edu
Date of data collection:
December 2018 - October 2022
Geographic location of data collection:
Central Wyoming, USA. Data were collected on mule deer in the Bates Hole/Hat Six herd south of Casper, Wyoming and the Upper Powder River herd south of Buffalo, Wyoming.
Funding sources that supported the collection of the data:
Clear Creek Foundation, Knobloch Family Foundation, University of Wyoming School of Computing, the Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health (2P20GM103432), the Buffalo BLM Field Office, Wyoming Sportsmen’s Group, US Geological Survey NOROCK, Bow Hunters of Wyoming, Wyoming Governor’s Big Game License Coalition, and Wyoming Game and Fish Department’s Mule Deer Initiative
SHARING/ACCESS INFORMATION
Recommended citation for this dataset:
Barrile, G. M., Cross, P. C., Stewart, C., Malmberg, J., Jakopak, R. P., Binfet, J., Monteith, K. L., Werner, B., Jennings-Gaines, J., and Merkle, J. A. (2023). Data from: Chronic wasting disease alters the movement behavior and habitat use of mule deer during clinical stages of infection. Dryad Digital Repository.
METHODOLOGICAL INFORMATION
Description of methods used for collection/generation of data:
Captured mule deer were fit with GPS store-on-board collars, which obtained a fix every hour for deer in Bates Hole and every two hours for deer in Upper Powder River. We collated GPS data for adult (> 1.5 year old) females collared during 2021–2022 in Bates Hole (n = 64) and 2018–2021 in Upper Powder River (n = 115). GPS units were equipped with a mortality signal that activated after 10 hours of inactivity. Methods for determining cause of death (including from CWD and emaciation) are described in great detail in the manuscript. We calculated movement speed, turning angle, and displacement between consecutive relocations for each deer. To reduce spatiotemporal autocorrelation among telemetry locations, we summarized movement parameters as daily metrics (i.e., one value per day for each deer). We also intersected GPS relocations with land cover, topographic, hydrographic, and anthropogenic variables relevant to mule deer ecology. All environmental variables were derived from remotely sensed geospatial data layers at 30-m resolution.
Methods for processing the data:
Data are in the format described above. Data are GPS locations from collared mule deer (adult females) and their associated movement and habitat metrics. The exact coordinates of GPS fixes constitute sensitive information and were not needed to conduct analyses. GPS coordinates were thus removed from the dataset.
Instrument- or software-specific information needed to interpret the data:
We used Program R to format and analyze data. All R packages and functions needed to interpret the data are cited in the manuscript.
DATA-SPECIFIC INFORMATION:
Number of variables: 13
Number of cases/rows: 88,032 (including the top row of column names)
Variable Information:
id = the identification number for each individual mule deer. This column is used to uniquely identify each individual animal.
date = the date/time of the GPS fix in the format year-month-day hour:minute:second. For example, 2018-12-16 21:00:07 is December 16, 2018 in the seventh second of the first minute of hour 21 (9:00 PM).
group = grouping variable for analysis. Animals were grouped into "cwd", "emaciated", and "negative_control" for analysis. Please see main text for details.
population = whether a deer was in the Upper Powder River population (UPR) or the Bates Hole population (BH).
speed = mean speed between successive relocation in meters per second
dist2stream = distance to the nearest stream in meters
dist2road = distance to the nearest road in meters
Cover_Trees = percent tree cover
Cover_Trees = percent shrub cover
tri = terrain ruggedness index; higher values signify greater topographic heterogeneity
elevation = elevation in meters
herb_biomass = biomass of grasses and forbs in kilograms per hectare
radangle = relative turning angle in radians
*There should be no "NA" in the dataset
Funding
National Institute of General Medical Sciences