DOI: 10.5061/dryad.98sf7m0mg
title: Behavioral “bycatch” from camera trap surveys yields insights on prey responses to human-mediated predation risk

description: Human disturbance directly affects animal populations but indirect effects
 of disturbance on species behaviors are less well understood. Camera traps
 provide an opportunity to investigate variation in animal behaviors across
 gradients of disturbance. We used camera trap data to test predictions
 about predator-sensitive behavior in three ungulate species (caribou
 Rangifer tarandus; white-tailed deer, Odocoileus virginianus; moose, Alces
 alces) across two boreal forest landscapes varying in disturbance. We
 quantified behavior as the number of camera trap photos per detection
 event and tested its relationship to predation risk between a landscape
 with greater industrial disturbance and predator abundance (Algar) and a
 “control” landscape with lower human and predator activity (Richardson).
 We also assessed the influence of predation risk and habitat on behavior
 across camera sites within the disturbed Algar landscape. We predicted
 that animals in areas with greater predation risk (more wolf activity,
 less cover) would travel faster and generate fewer photos per event, while
 animals in areas with less predation risk would linger (rest, forage),
 generating more photos per event. Consistent with predictions, caribou and
 moose had more photos per event in the landscape where predation risk was
 reduced. Within the disturbed landscape, no prey species showed a
 significant behavioral response to wolf activity, but the number of photos
 per event decreased for white-tailed deer with increasing line of sight
 (m) along seismic lines (i.e. decreasing visual cover), consistent with a
 predator-sensitive response. The presence of juveniles was associated with
 shorter behavioral events for caribou and moose, suggesting greater
 predator sensitivity for females with calves. Only moose demonstrated a
 positive association with vegetation productivity (NDVI), suggesting that
 for other species influences of forage availability were generally weaker
 than those from predation risk. Behavioral insights can be gleaned from
 camera trap surveys and provide information about animal responses to
 predation risk and the indirect impacts of human disturbances.
description: We deployed a single camera trap
 (CT) (Reconyx HyperFire PC900, Holman, WI) at each sampling site across
 the Algar and Richardson study areas in stratified random designs. The
 primary objectives of the surveys were to assess the distribution and
 relative abundance of medium- and large-bodied mammals in relation to
 landscape characteristics, particularly industrial disturbances like
 seismic lines. In the Algar study area, 73 CTs were deployed between
 November 2015 and November 2017, with year-round sampling continuing until
 November 2019. Sixty CT sites were on seismic lines and 13 were off of
 seismic lines. On-line sites were further stratified by restoration
 category (treated, regenerating, unrestored control, human use). In the
 Richardson study area, CTs were deployed in November 2017 and 2018 at 57
 sites stratified by in (27) vs. out (30) of burned area and on (18) vs.
 off (39) of a seismic line, with year-round sampling continuing until
 November 2019. In both study areas, cameras were left in place and sampled
 continuously once deployed (mean sampling days per station was 1024 in
 Algar and 508 in Richardson). At all sampling sites, CTs were placed on a tree 2
 to 5 m from the edge of a seismic line or game trail, facing across the
 line (i.e., perpendicular to expected direction of animal travel), at a
 height of approximately 0.8 m above the ground (range = 0.7 – 1.1 m),
 targeting medium- to large-bodied mammals without bait or lure. One
 picture was taken per motion trigger with no delay between subsequent
 triggers and sensitivity was set to maximum with a fast shutter speed. CTs
 were active for 24 hours per day with no quiet period. One timelapse
 picture was taken each day at noon to ensure camera function (any
 camera-days with snow occluding the lens were excluded, but this rarely
 occurred). We processed CT
 images by identifying the focal ungulate and predator species (woodland
 caribou, white-tailed deer, moose, wolf). For ungulate species, we counted
 the number of unique individuals (i.e., group size) and classified images
 by the sex (male, female) and age class (adult or juvenile, i.e. young of
 the year) of visible individuals. We characterized observed behavior for
 ungulate prey species in each image as either Secure (resting; foraging
 with food visible in its mouth or its head down with open mouth;
 inspecting camera with the face covering &gt; 20% the image), or
 Travelling (animal seen walking past camera). Species identifications were
 made from images within Camelot software; any uncertain identifications
 were excluded from analysis. For each species, we defined a detection
 event at a given site as a sequence of images separated by no more than 15
 minutes between consecutive images. This 15-minute threshold was based on
 identifying a consistent gap time between subsequent images across all
 focal species. We evaluated three
 ways of distinguishing “at risk” from “secure” behaviors for each
 detection event of ungulate prey: classification, event duration, and
 number of photos per event. We first considered whether the majority of
 photos in an event demonstrated Secure or Travelling behaviors (classified
 following above definitions), but determined that behavioral classes could
 be difficult to discern over short event durations or when multiple
 behaviors were exhibited within a single event. We next inferred that
 risk-averse behaviors, such as travelling through an area without
 foraging, would result in detection events of shorter duration with fewer
 photos, whereas more secure behaviors (e.g., foraging, resting, inspecting
 camera) would have longer detection events and more photos, as the animal
 lingered in front of the camera. We found that large time intervals
 between detections could inflate event durations and skew the observed
 distribution (resulting in poor model fit). Therefore, while the three
 different response variables were correlated, we focused our analysis on
 the number of photos per detection event as the primary response of
 interest. 
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