DOI: 10.5061/dryad.44j0zpcj7
title: Data from: Large carnivores avoid humans while prioritizing prey acquisition in anthropogenic areas

description: 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. 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. 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. 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. 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. 
description: 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
 <em>suncalc</em> 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)