DOI: 10.5061/dryad.pvmcvdnnt
title: Data and R code from: Spatiotemporal risk factors predict landscape-scale survivorship for a northern ungulate

description: 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.  
description: We combined relocations from GPS-collared adult female pronghorn
 (<em>n </em>= 170) with raster data that described potentially
 important spatiotemporal risk covariates. We first collated relocation and
 time-to-event data to remove individual pronghorn from the analysis that
 had no spatial data available. We then constructed seasonal risk periods
 based on the median migration dates determined from a previous analysis;
 thus, we defined 4 seasonal periods as winter (11 November–21 March),
 spring (22 March–10 April), summer (11 April–30 October), and autumn (31
 October–10 November). We used the package 'amt' in Program R to
 rarify relocation data to a common 4-hr interval using a 30-min tolerance.
 We used the package 'adehabitatHR' in Program R to estimate
 seasonal activity areas using 95% minimum convex polygon. We constructed
 annual- and seasonal-specific risk covariates by averaging values within
 individual activity areas. We specifically extracted values for linear
 features (road and fence densities), a proxy for snow depth (SWE), and a
 measure of forage productivity (NDVI). We resampled all raster data to a
 common resolution of 1 km<sup>2</sup>. Given that fence
 density models characterized regional-scale variation in fence density
 (i.e., 1.5 km<sup>2</sup>), this resolution seemed appropriate
 for our risk analysis.  We fit Bayesian proportional
 hazards (PH) models using a time-to-event approach to model the effects of
 spatiotemporal covariates on pronghorn mortality risk. We aimed to develop
 a model to understand the relative effects of risk covariates for
 pronghorn in the NSS. The effect of fence or road densities may depend on
 SWE such that the variables interact in affecting mortality risk. Thus,
 our full candidate model included four main effects and two interaction
 terms. We used reversible-jump Markov Chain Monte Carlo (RJMCMC) to
 determine relative support for a nested set of Bayesian PH models. This
 allowed us to conduct Bayesian model selection and averaging in one step
 by using two custom samplers provided for the R package
 'nimble'.  For brevity, we provide the final
 time-to-event dataset and analysis code rather than include all of the
 code, GIS, etc. used to estimate seasonal activity areas and extract and
 collate spatial risk covariates for each individual. Rather we provide the
 data and all code to reproduce the risk regression results presented in
 the manuscript.     
      
  
description: Please refer to ReadMe file.