DOI: 10.5061/dryad.sj3tx96br
title: Cougars, wolves, and humans drive a dynamic landscape of fear for elk

description: To manage predation risk, prey navigate a dynamic landscape of fear, or
 spatiotemporal variation in risk perception, reflecting predator
 distributions, traits, and activity cycles. Prey may seek to reduce risk
 across this landscape by using habitat at times and in places when
 predators are less active. In multi-predator landscapes, avoiding one
 predator could increase vulnerability to another, making the landscape of
 fear difficult to predict and navigate. Additionally, humans may shape
 interactions between predators and prey, and induce new sources of risk.
 Humans can function as a shield, providing a refuge for prey from
 human-averse carnivores, and as a predator, causing mortality through
 hunting and vehicle collisions and eliciting a fear response that can
 exceed that of carnivores. We used telemetry data collected between 2017
 and 2021 from 63 GPS-collared elk (Cervus canadensis), 42 cougars (Puma
 concolor) and 16 wolves (Canis lupus) to examine how elk habitat selection
 changed in relation to carnivores and humans in northeastern Washington,
 USA. Using step-selection functions, we evaluated elk habitat use in
 relation to cougars, wolves, and humans, diel period (daytime vs.
 nighttime), season (summer calving season vs. fall hunting season), and
 habitat structure (open vs. closed habitat). The diel cycle was critical
 to understanding elk movement, allowing elk to reduce encounters with
 predators where and when they would be the largest threat. Elk strongly
 avoided cougars at night but had a near neutral response to cougars in the
 day, whereas elk avoided wolves at all times of day. Elk generally used
 more open habitat where cougars and wolves were most active, rather than
 altering use of habitat structure depending on the predator species. Elk
 avoided humans during the day and ~ 80% of adult female mortality was
 human caused, suggesting that humans functioned as a “super predator” in
 this system. Simultaneously, elk leveraged the human shield against wolves
 but not cougars at night, and no elk were confirmed to have been killed by
 wolves. Our results add to the mounting evidence that humans profoundly
 affect predator-prey interactions, highlighting the importance of studying
 these dynamics in anthropogenic areas.
description: To evaluate elk movement we captured, collared, and monitored
 adult female and neonatal elk for 53 months (January 2017 to June 2021).
 Adult elk were fit with global positioning system (GPS) radio-collars
 (Model Survey, Vectronic Aerospace, Berlin, Germany) that recorded a fix
 every 4 hours and equipped with mortality sensors that sent emails and SMS
 notifications after 9 hours of inactivity. Neonatal elk 0-10-days old were
 fit with expandable GPS collars (Model Survey, Vectronic Aerospace,
 Berlin, Germany) that transmitted 1 fix daily or very high frequency (VHF)
 tracking collars (Models M2230B and M4210, Advance Telemetry Systems,
 Isanti, Michigan, USA). Calf collars signaled a mortality after 8-hours of
 inactivity, and calves were monitored remotely (if GPS collared) or with
 radio-telemetry (if VHF collared) daily from capture to the end of summer
 (31 August), twice per week through the fall (September –
 December). We captured cougars using trained dogs and
 baited cage traps and fit them with GPS collars (Model Vertex Lite,
 Vectronic Aerospace). Wolves were captured with padded leg-hold traps and
 by aerial darting, and fit with GPS collars (Models Vertex Lite and GPS
 Plus, Vectronic Aerospace, and Model TGW, Telonics Inc., Mesa, Arizona,
 USA). Cougar collars were programmed to record a fix every 4 hours and
 wolf collars were programmed to collect locations every 4-12 hours. Elk
 and cougar capture and handling followed protocols approved by the
 University of Washington Institutional Animal Care and Use Committee
 (IACUC Protocol #4226-01). Wolves were captured as part of existing
 management and conservation activities by the Washington Department of
 Fish and Wildlife and the Spokane Tribe of Indians in accordance with
 their agency-approved capture and handling protocols and the guidelines of
 the American Society of Mammologists. We used step
 selection functions to examine how elk navigated the landscape of fear. We
 created separate population-level models for elk in the summer (June –
 August) and in the primary hunting season in the fall, hereafter “fall”
 (September – December). We excluded locations associated with migration
 and removed individual elk with fewer than 200 fixes
 (<em>n</em> = 3) from all datasets (all others had &gt;
 700 fixes). To determine if selection reflected maternal behavior to
 mitigate calf predation risk, we also created and compared summer step
 selection functions for cow elk known to have a calf (collared calves that
 survived through summer) to those known not to have a calf (either the cow
 was not pregnant at capture the prior winter, or the calf died before
 reaching 10-days old). For the step selection functions, we generated 20
 random steps per taken step with the turning angle drawn from a von Mises
 distribution and step-length drawn from a gamma distribution using
 the <em>amt</em> package in program R version 4.2.3.
 Seasonal models contained the following covariates: cougar and
 wolf use indices, elevation, slope, percent open habitat, and the human
 footprint. We took different approaches to describe cougar and wolf use to
 account for differences in their density and distribution. The area is
 fully occupied by cougars, so we used resource selection functions (RSFs)
 as an index of cougar risk to elk. Cougar RSF values ranged from 0-1 and
 incorporated data from 42 cougars. Because we collared wolves from all
 wolf packs in the study area and movement from individual wolves within a
 pack tends to reliably describe pack level use, we effectively had a
 census of wolf packs. Additionally, track and camera surveys indicated
 that transient wolves outside of known wolf pack territories were rare.
 Therefore, to describe wolf pack presence while accounting for areas
 outside of pack territories, we created Localized Density Distributions
 (LDDs) for summer and fall to use as an index of wolf risk to elk. Wolf
 LDDs were scaled from 0-1 to align them with the cougar index, and they
 incorporated data from 16 wolves. We sourced elevation
 and slope layers from the Shuttle Radar Topography Mission (SRTM) Digital
 Elevation Model. We created a continuous covariate describing the
 proportion of open terrain within a 250 m moving window at 30 m
 resolution, selecting a 250 m buffer to reflect the median step-length
 taken by elk (summer: median = 243 m, fall: median = 255 m). We defined
 open terrain as areas classified agriculture, mesic grass, xeric grass,
 and xeric shrub in annual layers produced by the Cascadia Partner form
 (TerrAdapt:Cascadia; https://www.cascadiapartnerforum.org/terradapt). To describe human presence and associated landscape impacts, we used the TerrAdapt:Cascadia annual human footprint index, which represented the degree to which a landscape was used and altered by humans. Values ranged from 0 (unimpacted areas) to 1 (urban areas). Intermediate values described timber harvest (~ 0.3-0.5), agricultural and rural development (~ 0.5-0.7), and residential areas (&gt; ~ 0.8-0.9). We classified the end of each step as day (between sunrise and sunset) or night (after sunset and before sunrise) using the R package <em>suncalc</em>. All covariates were mapped at 30 m resolution. Elevation and slope were standardized such that the mean = 0 and standard deviation = 1, whereas cougar, wolf, human footprint, and % open habitat layers ranged from 0-1. 
description: # Cougars, wolves, and humans drive a dynamic landscape of fear for elk
 Data were collected by GPS
 collars affixed to elk (Cervus canadensis) in northeastern Washington, USA
 from January 2017 to June 2021. Collars were programmed to record a fix
 every 4 hours. Datasets are provided to conduct step selection functions
 for elk in the summer (Ganz-et-al-Ecology-SSF-summer.csv), fall
 (Ganz-et-al-Ecology-SSF-fall.csv), for adult female elk known to have a
 calf in the summer (Ganz-et-al-Ecology-SSF-calf.csv), and adult female elk
 known not to have a calf in the summer
 (Ganz-et-al-Ecology-SSF-NOcalf.csv). Twenty random locations were
 generated to pair with each used location based on the step length and
 turning angle of elk movement in each season. Due to the sensitive nature
 of the data, and as per the Washington Department of Fish and Wildlife
 Policy, the coordinates and time stamps of the used and random locations
 have been removed. Covariates are retained so that analyses can be
 replicated. ## Description of the data and file structure All four files
 contain the same 10 columns, described below: ID: The individual
 identifier for each adult female elk. step_id_: Each unique step ID allows
 for the used location (case\_ = TRUE) to be compared to the randomly
 generated location (case\_ = FALSE) available to the elk at the same time.
 case_: TRUE for a location that was used for an elk, FALSE for randomly
 generated available locations. night: 1 for telemetry location after
 sunset and before sunrise. 0 for telemetry location after sunrise or
 before sunset (i.e., in the daytime). elev.z: Elevation, scaled to have a
 mean of 0 and standard deviation of 1. slope.z: Slope, scaled to have a
 mean of 0 and standard deviation of 1. open: The proportion of habitat
 classified as open with a 250 meter radius area. We defined open terrain
 as areas classified agriculture, mesic grass, xeric grass, and xeric shrub
 in annual layers produced by the Cascadia Partner form
 (TerrAdapt:Cascadia;
 ). human_footprint:
 An index representing the degree to which a landscape was used and altered
 by humans. Data was sourced from the Cascadia Partner form
 (TerrAdapt:Cascadia;
 ). Values ranged
 from 0 (unimpacted areas) to 1 (urban areas). Intermediate values
 described timber harvest (~ 0.3-0.5), agricultural and rural development
 (~ 0.5-0.7), and residential areas (&gt; ~ 0.8-0.9). cougar: An index of
 cougar risk to elk, based on resource selection functions developed by
 Sarah Bassing. Cougar RSF values ranged from 0-1 and incorporated data
 from 42 cougars. wolfLDD: An index of wolf risk to elk, based on Localized
 Density Distrubutions. LDDs represent wolf pack level utilization
 distributions, weighted by the number of wolves in the pack. Wolf LDDs
 were scaled from 0-1 to align them with the cougar index, and they
 incorporated data from 16 wolves.