Data and R code from: Spatiotemporal risk factors predict landscape-scale survivorship for a northern ungulate

These data and computer code (written in R, https://www.r-project.org)
were created to statistically evaluate a suite of spatiotemporal
covariates that could potentially explain pronghorn (Antilocapra
americana) mortality risk in the Northern Sagebrush Steppe (NSS) ecosystem
(50.0757o N, −108.7526o W). Known-fate data were collected from 170 adult
female pronghorn monitored with GPS collars from 2003-2011, which were
used to construct a time-to-event (TTE) dataset with a daily timescale and
an annual recurrent origin of 11 November. Seasonal risk periods (winter,
spring, summer, autumn) were defined by median migration dates of collared
pronghorn. We linked this TTE dataset with spatiotemporal covariates that
were extracted and collated from pronghorn seasonal activity areas
(estimated using 95% minimum convex polygons) to form a final dataset.
Specifically, average fence and road densities (km/km2), average snow
water equivalent (SWE; kg/m2), and maximum decadal normalized difference
vegetation index (NDVI) were considered as predictors. We tested for these
main effects of spatiotemporal risk covariates as well as the hypotheses
that pronghorn mortality risk from roads or fences could be intensified
during severe winter weather (i.e., interactions: SWE*road density and
SWE*fence density). We also compare an analogous frequentist
implementation to estimate model-averaged risk coefficients. Ultimately,
the study aimed to develop the first broad-scale, spatially explicit map
of predicted annual pronghorn survivorship based on anthropogenic features
and environmental gradients to identify areas for conservation and habitat
restoration efforts.