DOI: 10.5061/dryad.51c59zwkg
title: Dynamic riskscapes for prey: Disentangling the impact of human and cougar presence on deer behavior using GPS smartphone locations

description: Dataset Summary This dataset includes high-resolution animal movement and
 environmental data used to evaluate prey behavioral responses to spatial
 and temporal risk from predators and humans. Data were collected between
 2015 and 2022 across two ecologically distinct study areas in the American
 Southwest (Book Cliffs, Colorado/Utah and Pine Valley, Utah). The dataset
 supports risk-sensitive habitat selection and movement analyses by
 integrating information on animal space use, human activity, and landscape
 features. Structure and Contents Wildlife GPS Data: Location data were
 collected from GPS-collared female mule deer (Odocoileus hemionus). Deer
 GPS fixes were recorded at 2-hour intervals. Each GPS location is
 associated with a phenological season (e.g., late gestation, rearing,
 hunting) and a diel period (day, night, crepuscular). To protect sensitive
 wildlife location information, all raw spatial coordinates have been
 removed or generalized. Derived environmental and behavioral metrics are
 retained for reuse. Full covariates are described in the README.
 Environmental Covariates: Spatially explicit raster data include
 vegetation indices (NDVI), terrain ruggedness, and reclassified land cover
 categories (e.g., open, forested, shrub). These covariates were extracted
 for each animal location and included with the 'Wildlife GPS
 Data.' Risk Surfaces: The dataset includes modeled probability
 surfaces representing the relative risk of human and cougar encounters,
 generated using resource selection functions. These covariates were
 extracted for each animal location and included with the 'Wildlife
 GPS Data.' Derived Movement Data: Step lengths and turn angles were
 calculated between successive deer GPS fixes. These data were used in
 integrated step selection analyses to evaluate behavioral responses to
 risk. These covariates were calculated for each animal location and
 included with the 'Wildlife GPS Data.' Reuse Potential Raw human
 mobility data are licensed under a data use agreement prohibiting
 redistribution or public sharing. With permission only. Reuse potential
 statements in the associated publication refer to the full dataset,
 including withheld raw human mobility data, and do not apply to this data
 package. Legal and Ethical Considerations Appropriate institutional animal
 care and use committees approved all animal capture and handling
 procedures and followed professional guidelines. The dataset also includes
 anonymized human mobility data derived from GPS-enabled smartphones and
 obtained via a third-party provider (Gravy Analytics). These data were
 collected from consenting users in compliance with the California Consumer
 Privacy Act (CCPA) and the European General Data Protection Regulation
 (GDPR). However, raw human mobility data are licensed under a data use
 agreement prohibiting redistribution or public sharing. Users wishing to
 replicate or extend human mobility data analyses must secure access
 independently.
description: # Dynamic riskscapes for prey: Disentangling the impact of human and
 cougar presence on deer behavior using GPS smartphone locations Dataset
 DOI: [10.5061/dryad.51c59zwkg](10.5061/dryad.51c59zwkg) ## Description of
 the data and file structure This dataset includes high-resolution animal
 movement and environmental data used to evaluate prey behavioral responses
 to spatial and temporal risk from predators and humans. Data were
 collected between 2015 and 2022 across two ecologically distinct study
 areas in the American Southwest (Book Cliffs, Colorado/Utah and Pine
 Valley, Utah). The dataset supports risk-sensitive habitat selection and
 movement analyses by integrating information on animal space use,
 landscape features, and modeled risk surfaces. **Important note:** Only
 the wildlife GPS data and associated environmental covariates are included
 in this Dryad data package. The raw human mobility data (e.g., smartphone
 detections) and aggregated products used in the original study are not
 included in this repository due to privacy and data-sharing
 restrictions. The data were obtained through a commercial license and are
 therefore proprietary, subject to data-sharing restrictions, and handled
 in compliance with applicable privacy laws. Reuse potential statements in
 the associated publication refer to the full dataset, including withheld
 raw human mobility data, and do not apply to this Dryad data package. ###
 Files and variables #### File: MD\_dataset.RDS **Description:** 
 **Wildlife GPS Data:** Location data were collected from GPS-collared
 female mule deer (*Odocoileus hemionus*). Deer GPS fixes were recorded at
 2-hour intervals. Each GPS location is associated with a phenological
 season (e.g., late gestation, rearing, hunting) and a diel period (day,
 night, crepuscular). To protect sensitive wildlife location information,
 all raw spatial coordinates have been removed. Derived environmental and
 behavioral metrics are retained for reuse. Full covariates are described
 in Table 1. **Environmental Covariates:** Spatially explicit raster data
 include vegetation indices (NDVI), terrain ruggedness, and reclassified
 land cover categories (e.g., open, forested, shrub). These covariates were
 extracted for each animal location and included with the 'Wildlife
 GPS Data.'  **Risk Surfaces:** The dataset includes modeled
 probability surfaces representing the relative risk of human and cougar
 encounters, generated using resource selection functions. These covariates
 were extracted for each animal location and included with the
 'Wildlife GPS Data.' **Derived Movement Data:** Step lengths and
 turn angles were calculated between successive deer GPS fixes. These data
 were used in integrated step selection analyses to evaluate behavioral
 responses to risk. These covariates were calculated for each animal
 location and included with the 'Wildlife GPS Data.' **Table 1.**
 Variable key for wildlife GPS data. | Variable | Description | |
 :----------------------- |
 :------------------------------------------------------------------------------------------- | | burst\_ | Unique movement burst identifier (each burst includes one used and multiple available steps) | | sl\_ | Step length (meters) between consecutive GPS locations | | ta\_ | Turn angle (radians) between consecutive steps | | t1\_ | Start time of movement step (POSIXct format) | | t2\_ | End time of movement step (POSIXct format) | | dt\_ | Duration between GPS fixes (e.g., "2 hours") | | tod\_end\_ | Time-of-day category at the end of the step (day, night, or crepuscular) | | case\_ | Logical indicator for whether the step was used (TRUE) or available (FALSE) | | step\_id\_ | Unique step identifier shared across used and available steps within a burst | | cos\_ta | Cosine of turn angle (used in movement modeling) | | log\_sl | Natural log of step length (used in modeling movement rate) | | NDVI | Normalized Difference Vegetation Index at the end of the step | | season | Biological season category (e.g., late\_gestation, birthing\_rearing, hunting) | | elev\_s2 | Elevation (meters) at the end of the step | | TRI\_s2 | Terrain Ruggedness Index at the end of the step | | nlcd\_s2 | Land cover classification at the end of the step (e.g., Forested, Shrub) | | dt\_rds | Distance (meters) to nearest improved road at the end location | | lionRSF\_s2 | Cougar encounter risk (relative probability) at the end of the step | | season.year | Combined biological season and year identifier (e.g., birthing\_rearing\_2019) | | hmd\_avg\_counts\_season | Average number of smartphone detections (human mobility) during the season | | hmd\_prob\_use\_season | Modeled human encounter risk (relative probability) during the season | | site | Study area identifier (BookCliffs or PineValley) | | animalID | Anonymized individual animal identifier | ## Code/software Program R (version 4.3.1) ## Human subjects data The data provider has anonymized the data. Human data has been scaled such that personal information is no longer identifiable.