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Data from: Large carnivores avoid humans while prioritizing prey acquisition in anthropogenic areas

Cite this dataset

Barker, Kristin et al. (2023). Data from: Large carnivores avoid humans while prioritizing prey acquisition in anthropogenic areas [Dataset]. Dryad.


  1. Large carnivores are recovering in many landscapes where the human footprint is simultaneously growing. When carnivores encounter humans, the way they behave often changes, which may subsequently influence how they affect their prey. However, little research investigates the behavioral mechanisms underpinning carnivore response to humans. As a result, it is not clear how predator-prey interactions and their associated ecosystem processes will play out in the human-dominated areas into which carnivore populations are increasingly expanding.
  2. We hypothesized that humans would reduce predation risk for prey by disturbing carnivores or threatening their survival. Alternatively, or additionally, we hypothesized that humans would increase predation risk by providing forage resources that congregate herbivorous prey in predictable places and times.
  3. Using gray wolves (Canis lupus) in Jackson Hole, Wyoming, USA as a study species, we investigated 170 kill sites across a spectrum of human influences ranging from heavily restricted human activities on protected federal lands to largely unregulated activities on private lands. Then, we used conditional logistic regression to quantify how the probability of predation changed across varied types and amounts of human influences, while controlling for environmental characteristics and prey availability.
  4. Wolves primarily made kills in environmental terrain traps and where prey availability was high, but predation risk was significantly better explained with the inclusion of human influences than by environmental characteristics alone. Different human influences had different, and even converse, effects on the risk of wolf predation. For example, where prey were readily available, wolves preferentially killed animals far from motorized roads but close to unpaved trails. However, wolves responded less strongly to humans, if at all, where prey were scarce, suggesting they prioritized acquiring prey over avoiding human interactions.
  5. Overall, our work reveals that the effects of large carnivores on prey populations can vary considerably among different types of human influences, yet carnivores may not appreciably alter predatory behavior in response to humans if prey are difficult to obtain. These results shed new light on the drivers of large carnivore behavior in anthropogenic areas while improving understanding of predator-prey dynamics in and around the wildland-urban interface. 


Data to support analysis of the relationship between human influences and the likelihood of a wolf kill occurring in a given location. 

Kill site (i.e., used) locations were identified in the field using the cluster searching method. Matched non-kill (i.e., available) locations were drawn from a 10km radius centered around the wolf GPS collar location immediately preceding the kill (i.e., the last location prior to the formation of the cluster). 

Continuous covariates were standardized prior to inclusion in models; here we provide the raw values prior to standardization.

Column names refer to the following:

  • siteID (identifier for each kill site [i.e., used] location and its matched non-kill [i.e., available] locations)
  • used (binary indicator of whether the location represents a kill site [1] or non-kill site [0])
  • datetimeLocal (date and time associated with kill site formation, in Mountain Standard Time)
  • x_nad83z12 and y_nad83z12 (X and Y UTM coordinates of location, respectively. In NAD 1983 Zone 12N [EPSG 3742])
  • day (binary; whether the kill occurred during daylight hours, determined using the suncalc package in program R)
  • can (percent canopy cover of the location, from the National Landcover Database)
  • prey (relative prey availability index, from a utilization distribution calculated based on spatially-explicit yearly aerial elk counts)
  • tpi (terrain position index, calculated from USGS digital elevation model using the raster package in program R)
  • northness (cosine of aspect, calculated from USGS digital elevation model using the raster package in program R)
  • snowCm (snow depth in centimeters, based on local SNOTEL and National Weather Station records)
  • distMoto (Euclidean distance to the closest paved plowed road, based on road data from local cities and counties with additional manual digitization where appropriate)
  • distNonmoto (Euclidean distance to the closest unpaved oversnow travel route, based on US Forest Service shapefiles and additional manual digitization based on ground-truthing)
  • distFeed (distance to the nearest active ungulate feed ground)
  • hunt (binary indicator of whether the wolf had [1] or had not [0] been exposed to hunting by humans)


Berkeley Fellowship