DOI: 10.5061/dryad.sqv9s4nfb
title: A case for human mobility data applications in wildlife management

description: Human activities have significantly altered terrestrial ecosystems,
 leading to biodiversity loss and habitat fragmentation. Traditional
 methods for measuring human impacts often lack the precision required for
 localized assessments, fail to capture temporal dynamics, or are
 scale-limited. Human mobility data (HMD) from GPS-enabled smartphone
 applications offers a valuable approach to understanding human movement
 patterns, overcoming many of these limitations.  We present case
 studies that demonstrate the use of HMD in assessing human activity within
 ecologically sensitive habitats (e.g., winter ranges and breeding grounds)
 and in evaluating where human-wildlife interactions are likely to occur to
 inform proactive conflict management. We also discuss how HMD can improve
 inference from habitat connectivity analyses and provide detailed, timely
 insights on how HMD can broadly support conservation and wildlife
 management goals.  HMD revealed patterns of recreational use in
 bighorn sheep's (Ovis canadensis) winter range and helped inform the
 timing and scope of protective measures for sheep. HMD also allowed us to
 quantify the frequency and timing of potential wildlife-human
 interactions, such as cougar (Puma concolor) proximity to human activity,
 identifying high human-wildlife conflict areas and opportunities to
 mitigate mortality risks (e.g., road crossings).  We discussed
 how future uses of HMD can improve conservation and connectivity, allowing
 managers to assess barriers to movement, identify critical thresholds of
 human disturbance, and refine strategies for mitigating impacts on species
 sensitive to human disturbance. We provided a simple example by
 highlighting differences in human use of a migration corridor for
 pronghorn (Antilocapra americana).  Synthesis and applications:
 HMD provides a transformative tool for wildlife management by offering
 scalable, dynamic insights into human activity that traditional methods
 cannot fully capture. These data enable managers to prioritize
 intervention areas, improve compliance with management zones, mitigate
 conflict risks, and enhance connectivity for sensitive species, supporting
 effective conservation strategies in a human-dominated world.
description: # A case for human mobility data applications in wildlife management
 Dataset DOI: [10.5061/dryad.sqv9s4nfb](10.5061/dryad.sqv9s4nfb) ##
 Description of the data and file structure This dataset includes the
 anonymized cougar data used in the cougar–human interaction analysis. To
 assess spatial and temporal patterns of human–cougar interactions, GPS
 collar data from eight mountain lions were collected at 3-hour intervals
 in California’s eastern Sierra Nevada mountains as part of an ongoing
 California Department of Fish and Wildlife study. These data were paired
 with smartphone-based Human Mobility Data (HMD) from anonymized users to
 identify spatiotemporal proximity events between cougars and people.
 Potential interactions were classified into high, medium, and low
 probability categories based on distance and time thresholds (≤75m/30min,
 ≤150m/60min, ≤300m/120min, respectively). This design allowed researchers
 to evaluate where, when, and how frequently cougars came into proximity
 with humans, to identify patterns of human recreation that may increase
 interaction risk and inform proactive conflict mitigation strategies. All
 animal handling followed approved welfare protocols. **NOTE**: Due to the
 sensitive nature of wildlife GPS data, particularly for large carnivores
 like cougars in California, location information has been modified to
 protect individual animals and conservation efforts. Specifically, we
 subtracted the mean X-coordinate of the cougar locations involved with an
 interaction from all cougar and HMD X-coordinates and then repeated the
 process with the Y-coordinates. HMD used in the analysis are proprietary
 and cannot be shared due to legally binding data-sharing agreements with a
 commercial provider (Supporting Information 1). As such, we cannot release
 the raw HMD underlying the analysis. However, we provide derived variables
 from the HMD, including spatial and temporal differences between cougar
 and human locations, and the distance from the nearest anthropogenic
 features (e.g., buildings, roads) as used in the interaction modelling in
 case study two. Other raw datasets, including winter closure zone data
 provided by Grand Teton National Park, are similarly restricted from
 public release due to their sensitive nature. ### Files and variables ####
 File: SierraInteractions\_Centered.csv **Description:**  ##### Variables *
 cougarID: Unique cougar ID number * distance_m: Distance (m) between the
 cougar and HMD locations * month: Month of the potential interaction *
 hour: Hour of the potential interaction * DiffTime_s: Difference (in
 seconds) between the cougar and HMD locations * dist_road: Distance (m) of
 HMD location to the nearest roadway * dist_build: Distance (m) of HMD
 location to the nearest building * X_cougar: Centered X-location of the
 cougar * Y_cougar: Centered Y-location of the cougar ## Code/software All
 scripts were run in the RStudio environment (v2022.12.0+353), which
 facilitates project organization and reproducibility. The workflow
 included: Importing and preprocessing: Cougar GPS collar data and Human
 Mobility Data (HMD) were imported as .rds and spatial .shp files.
 Timestamps were standardized using lubridate, and spatial layers were
 projected using sf. Interaction classification: Distances and time
 differences between cougar and smartphone user locations were calculated.
 Based on predefined spatiotemporal thresholds, proximity events were
 classified into high, medium, or low probability categories using dplyr
 and custom R functions. Summarization and visualization: Summary
 statistics and monthly or hourly interaction trends were computed with
 data.table, and plots were generated using ggplot2. sf (v1.0-14) – for
 spatial data manipulation and projection dplyr (v1.1.2) – for data
 wrangling lubridate (v1.9.2) – for date-time processing ggplot2 (v3.4.2) –
 for visualization data.table (v1.14.8) – for efficient data handling of
 large datasets ## Access information Other publicly accessible locations
 of the data: * None Data was derived from the following sources: * Humans:
 Unacast * Cougars: California Department of Fish and Wildlife