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Smartphone GPS data pinpoints where people and wildlife cross paths

By Peter Fitzgibbon - 4th June 2025 - 12:41

A new study reveals how the smartphone can help scientists and conservationists understand and balance the needs of wildlife and recreationist

Researchers at Colorado State University and the USDA Forest Service have used anonymized, aggregated GPS data from smartphones to monitor how wildlife reacts to people.

This innovative research highlights the promise of human mobility data, measured via smartphones, as a transformative tool for tracking when and where people move across wild landscapes. Traditional methods like trail counters, trail cameras and satellite images of nighttime light often fall short in capturing human activity and its impacts, especially in remote areas. Human mobility data fills this gap.

The researchers stress that this smartphone data is fully anonymized from applications where users have opted to share their locations, ensuring personal privacy while delivering critical insights for conservation. They show how this technology can prevent and predict human-wildlife conflicts in the study published May 29 in the Journal of Applied Ecology.

The team combined cougar GPS collar data with human mobility data in California's Sierra Nevada Mountains to pinpoint where people and predators were most likely to cross paths—valuable knowledge for keeping people and big cats safer. In Wyoming's Teton Range, smartphone data revealed where people came closest to the wintering zones of bighorn sheep (pictured in our lead image), helping managers fine-tune seasonal closures or redirect human activity to minimize animal stress during harsh winters.

A conceptual framework illustrating how raw Human Mobility Data (HMD) represents spatiotemporal human activity. (a) HMD, derived from GPS smartphone apps, captures various user behaviours, such as slow movement in backcountry areas (e.g. hiking, hunting, skiing; yellow), high-speed highway travel (e.g. vehicular traffic; black) and intermediate speeds across mixed terrains (e.g. all-terrain vehicles, snowmobiles; blue). (b) HMD is processed for quality after extraction, and summary statistics (c) or modelled products (d) are generated to quantify human activity in space and time. (c) Representative examples of summary statistics or unmodelled products from processed HMD. For example, temporal products, such as time spent in an area (left) and the intensity of use within an area (right), can be quantified. (d) Representative examples of modelled data products from processed HMD. For example, the probability of human use surfaces (Abernathy, Ditmer, Stoner, et al., 2025) can be derived from resource selection models (left), and movement speeds fed into clustering algorithms can group users by activity type (e.g. hikers vs. ATV users; right). We generated the pictures of smartphone users in (a) using DALL-E (2024).

The researchers said using this data along major migration corridors can help with conservation planning. They envision real-time human activity data playing a key role in conservation and land management by helping to better predict how trails, traffic and recreation pressure might shift wildlife behaviour.

Snowshoers recreate in the Gifford Pinchot National Forest at Mount St. Helens National Volcanic Monument. U.S. Forest Service photo by Matthew Tharp. Credit: U.S. Forest Service/Matthew Tharp

"For a long time, we had no easy way to see where people go in wild places," said lead author Heather Abernathy, who conducted the study while she was a CSU postdoctoral fellow at the Rocky Mountain Research Station. "Smartphone data helps us see where human activity might fragment habitat, so we can work to keep landscapes connected—for both people and wildlife."

Human activity relative to bighorn sheep (BHS) winter zones in the Teton Range (a). Humans are displayed as points, where each detection indicates a unique smartphone relative to winter zones for BHS. The subset maps display a local ski resort (b, hashed red border) and Rendezvous Peak (c, solid purple border), highlighting areas where humans entered winter zones (multicolour) and those that stayed within 500 m of closure edges (grey points). Image: Journal of Applied Ecology

Recreation is one of the most popular uses of national forests and grasslands and helps build connections between people and nature. Scientists help ensure that people recreating on these lands have the best information to do it responsibly.

"Growing recreational activities are having adverse effects on some species and habitats," said co-author George Wittemyer, a CSU professor of fish, wildlife and conservation biology. "Human mobility data is critical for identifying threats to sensitive species and habitats and enacting management planning to reduce those threats."

The researchers said that this new resource can help to preserve the wild places people love, while ensuring they remain accessible and resilient.

Spatial proximity of GPS-collared cougars and smartphones in the eastern Sierra Nevada mountains. (a) Example of a smartphone user, likely a recreationist, near a recent cougar location. (b) Example of a cougar's multiple approaches to a busy highway (US-395) but returning to its original trajectory after encountering the road each time. Image: Journal of Applied Ecology

"Human mobility data provides a new lens to see how wild animals respond to differing levels and types of human presence," said co-author and CSU affiliate and alumnus Mark Ditmer from the U.S. Forest Service Rocky Mountain Research Station. "It gives land managers powerful new tools to protect animals and wild spaces while informing conservation and management."

More information: Heather N. Abernathy et al, A case for human mobility data applications in wildlife management, Journal of Applied Ecology (2025). DOI: 10.1111/1365-2664.70073

Journal information: Journal of Applied Ecology

Story Source: Colorado State University

 

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