DOI: 10.5061/dryad.dv41ns2cd
title: Data from: Annual survival in a dynamic species: Pronghorn survival patterns across their northern range

description: Quantifying variation in demographic patterns, such as survival and
 recruitment, is critical for understanding population dynamics and
 informing evidence-based and adaptive wildlife management. In
 this study, we leverage an extensive dataset from over 1,000 GPS collared
 pronghorn (Antilocapra americana) to provide the first large-scale
 evaluation of survival patterns for an iconic North American ungulate.
 This research spans multiple years, regions, and age and sex classes, and
 includes more than 3,000 animal-years of monitoring, representing the most
 spatially and temporally extensive survival dataset for the species to
 date. Using a hierarchical Bayesian analytical framework, we estimate the
 process distribution of survival, thereby capturing true biological
 variation in adult females, adult males, and juveniles. Our results reveal
 that, across pronghorn populations, mean pronghorn survival is variable
 yet statistically consistent across regions and through time. In
 contrast, adult female pronghorn show lower and more temporally variable
 survival than other sympatric ungulates across North America (e.g., deer
 and elk). Through this extensive survival analysis and robust dataset, we
 identify demographic mechanisms (e.g., temporally varying adult female
 survival rates) that contribute to dynamic population trends within the
 species. Our findings advance understanding of pronghorn demography and
 highlight species-specific vulnerability to stochastic events. More
 broadly, this work underscores the value of large-scale demographic
 monitoring for identifying sources of variation in population trends
 critical for informing adaptive management in the context of global
 environmental change.
description: # Data from: Annual survival in a dynamic species: Pronghorn survival
 patterns across their northern range Dataset DOI:
 [10.5061/dryad.dv41ns2cd](https://doi.org/10.5061/dryad.dv41ns2cd) ##
 Description of the data and file structure **Overview** This Dryad archive
 contains data supporting a Wildlife Biology manuscript examining pronghorn
 (*Antilocapra americana*) survival across multiple study areas and years.
 The dataset includes (1) study-area–level and annual environmental
 covariates and (2) individual-level encounter histories and metadata for
 pronghorn monitored over a 36‑month period. Data are organized to support
 time‑to‑event and capture–recapture / known‑fate survival analyses. The
 archive includes encounter history data for female pronghorn over three
 years (36 months), as well as corresponding datasets for male pronghorn
 and juvenile pronghorn (6‑month monitoring window). **File inventory**
 **1. covariates.csv** **Description:** Study area– and year‑specific
 environmental covariates used as predictors in survival models. **Rows:**
 One record per DAU (Data Analysis Unit) per year. **Columns:** * yr –
 Study year (integer; 1–3). * DAU – Data Analysis Unit name. * dau – Broad
 DAU grouping (e.g., Central MT, Mountain Valley). * pdsi – Palmer Drought
 Severity Index (mean annual or seasonal value). * ndvi – Normalized
 Difference Vegetation Index (scaled or summarized as used in analyses). *
 snow – Snow metric (e.g., cumulative snowfall or snow water equivalent). *
 cold – Cold exposure metric (e.g., number of cold days below a 10 degrees
 F). *Note:* Unnamed: 0 is an index column retained from data export and
 can be ignored. **2. ehmt.csv** **Description:** Montana monthly encounter
 histories and metadata for **female pronghorn**, monitored over 36 months.
 Female pronghorn monthly encounter histories for Montana Study Areas; NA =
 missing data for month (no observation) **Rows:** One record per
 individual. **Columns:** * animalID – Unique individual identifier. * 1–36
 – Monthly encounter history (1 = alive/observed, 0 = not observed or dead,
 depending on modeling framework). * Fate – Final fate of the individual
 (e.g., alive, mortality). * DateMort – Date of mortality, if applicable. *
 DAU – Data Analysis Unit where the individual was monitored. * Sex – Sex
 of individual (F). * agenow – Age class at start of study period and
 during monitoring (as defined in the associated manuscript). *Note:*
 Unnamed: 0 is an index column retained from data export and can be
 ignored. **3. ehsd.csv** **Description:** South Dakota monthly encounter
 histories and metadata for pronghorn monitored over 36 months, formatted
 for survival analysis with metadata fields preceding encounter histories.
 Female pronghorn monthly encounter histories for South Dakota Study Areas
 NA = missing data for month (no observation) **Rows:** One record per
 individual. **Columns:** * animalID – Unique individual identifier. * Sex
 – Sex of individual. * DAU – Data Analysis Unit. * agenow – Age class. *
 DateMort – Date of mortality, if applicable. * Fate – Final fate
 classification. * 1–36 – Monthly encounter history. *Note:* Unnamed: 0 is
 an index column retained from data export and can be ignored. **4.
 ehmale.csv** **Description:** South Dakota monthly encounter histories and
 metadata for male pronghorn monitored over 36 months, formatted for
 survival analysis with metadata fields preceding encounter histories. Male
 pronghorn monthly encounter histories for South Dakota Study Areas; NA =
 missing data for month (no observation) **Rows:** One record per
 individual. **Columns:** * animalID – Unique individual identifier. * Sex
 – Sex of individual. * DAU – Data Analysis Unit. * agenow – Age class. *
 DateMort – Date of mortality, if applicable. * Fate – Final fate
 classification. * 1–36 – Monthly encounter history. *Note:* Unnamed: 0 is
 an index column retained from data export and can be ignored. **5.
 ehjuv.csv** **Description:** South Dakota monthly encounter histories and
 metadata for juvenile male and female pronghorn monitored over three
 6-month periods (January – May), formatted for survival analysis with
 metadata fields preceding encounter histories. Juvenile pronghorn monthly
 encounter histories for South Dakota Study Areas; NA = missing data for
 month (no observation). Note that months from June - December will be all
 NA's as pronghorn became adults on June 1 and so we only recorded
 monthly survival for pronghorn from capture dates in January until May
 30th. We captured pronghorn in 3 subsequent years. **Rows:** One record
 per individual. **Columns:** * animalID – Unique individual identifier. *
 Sex – Sex of individual. * dau – Data Analysis Unit. * agenow – Age class
 at capture. * DateMort – Date of mortality, if applicable. * Fate – Final
 fate classification. * 1–29 – Monthly encounter history. *Note:* Unnamed:
 0 is an index column retained from data export and can be ignored. ##
 Code/software ### Software and workflow All data files in this archive are
 provided in **comma-separated values (.csv)** format and can be viewed
 using any standard spreadsheet software or text editor (e.g., Numbers,
 Microsoft Excel) or imported into statistical software. All data
 processing and analyses associated with this manuscript were conducted
 using **R**, an open-source statistical computing environment, with
 **RStudio** used as an integrated development environment. * **R
 version:** R ≥ 4.2.0 * **RStudio:** ≥ 2023.06 (optional) #### R packages
 used The following R packages were used for data processing and analysis:
 * `tidyverse` – data wrangling, formatting, and visualization * `r2jags` –
 Bayesian survival modeling implemented via JAGS These packages are freely
 available and open source. #### Workflow overview 1. Encounter history and
 metadata files were imported into R from `.csv` format. 2. Monthly
 encounter histories were formatted for survival analyses. 3.
 Individual-level encounter history data were linked to study area– and
 year-specific environmental covariates using Data Analysis Unit (DAU) and
 year identifiers. 4. Environmental covariates were processed prior to
 analysis, as described in the Methods section of the manuscript. 5.
 Survival models were fit using Bayesian methods implemented in JAGS via
 the `r2jags` package. No proprietary software is required to view,
 process, or analyze these data. Analytical methods and model structure are
 fully described in the associated manuscript.