DOI: 10.25338/b87s5g
title: Understanding Behavioral Responses of Wildlife to Traffic to Improve Mitigation Planning

description: Creating and maintaining sustainable transportation systems depends in
 part on understanding and mitigating ecological impacts. Wildlife crossing
 structures (WCS) are often used to mitigate impacts on wildlife
 populations. WCS and existing structures may provide passage for multiple
 species, depending on their sensitivity to traffic disturbance and
 perception of the roadway. In a previous project, we found that traffic
 conditions and traffic noise could reduce WCS effectiveness in
 facilitating passage of diverse and sensitive species. In the current
 project, we expanded the geographic scope to 26 sites throughout
 California, including detailed measurements of vehicle noise and lighting
 impacts on wildlife use of structures. We investigated individual animal
 behavior as they approached structures as a possible mechanism for
 reducing species diversity due to traffic disturbance. In order to inform
 future WCS planning, placement and construction, we studied traffic noise
 and light impacts on wildlife in the vicinity of the proposed Liberty
 Canyon wildlife over-crossing (over US 101), the first and largest of its
 kind in California. We improved a preliminary statistical model of the
 effects of traffic on WCS use of existing structures. We recommend
 strategies for transportation agencies to use in developing and modifying
 WCS to improve wildlife passage.
description: <strong>Traffic noise and light
 measurements</strong> Sound pressure levels were recorded in
 A-weighted decibels (dbA) and C-weighted decibels (dbC) using digital
 sound level meter devices (TENMA 72-947 and PCE-322; 30-130 range, set to
 slow). To correspond to timing of crepuscular and night-time activities,
 we sampled sound levels for one evening (11pm – 2am) at 1-second intervals
 within a) the crossing structure entrance and b) the closest camera
 station in the background area. To characterize overall sound conditions
 at the structures and following the camera trapping period, we also
 collected dbA and dbC sound pressure levels at the crossing structure
 entrance for 1 week at 59-second intervals. Low-level light intensity as
 total lumine scence was
 measured along a 50m transect away from each of the 26 crossing structures
 (0m, 10m, 30m, 50m). We used a novel approach employing a camera with a
 very wide-angle lens to capture low light levels in collaboration with the
 Longcore lab at the University of California Los Angeles (Jechow et al.,
 2017). <strong>Habitat classification in the surrounding
 landscape</strong> We characterized habitat
 surrounding each structure using 16-class land cover data from the 2016
 National Land Cover Dataset (NLCD, US Geological Survey), which has a
 spatial resolution of 30 m<sup>2</sup> For each underpass we
 classified land cover within a 100 m<sup>2</sup> radius and 1
 km<sup>2</sup> radius buffer in ArcGIS. This will determine
 whether habitat in the approach zone and background area respectively
 influences species-specific movement on a small and/or large scale and
 noise attenuation. <strong>Species detection at WCS in relation to
 background</strong> We compared species detections at
 WCS with detections at quiet ‘background’ camera stations at all 26
 sites. We used the same model of camera
 traps across all sites (Bushnell Aggressor Trophy Camera). We set each of
 the cameras to capture still images and have a minimum of 3 seconds
 between trigger events, and one trigger event at a time. To avoid
 capturing the same individual multiple times, we classed a unique capture
 event of the same species as one image &gt; 15 minutes apart. Four
 camera traps were positioned at the WCS 0.5 m to 1.0 m above the ground
 facing into or at an angle across the opening of structures (Figure
 4). In order to measure background
 species detections and further examine the impact of noise on WCS use, we
 measured the distance to background noise levels from the nearest study
 WCS (~800 m) and established 4 bait stations with associated cameras, at
 &gt;100 m intervals, for each of the sites. We used salt blocks,
 peanut butter, dried corn, grain, canned cat food, and chicken parts in an
 attempt to attract a wide range of species. We also included four
 non-baited cameras in these quieter areas, &gt;200 m apart from the
 baited cameras. Cameras were positioned adjacent to areas with visible
 animal tracks.  Cameras were set to have a 10-second delay between trigger
 events due to the high occurrence of false triggers caused by
 vegetation. <strong>Wildlife
 Activity</strong> To assess wildlife activity
 (hereafter referred to as “behavior”), Browning Dark Ops Pro cameras were
 set to video mode and deployed at and near to highway crossing structures
 (<i>n</i> = 2; “Mesa 2” site, SR 74; “PM24” site, I-80) and
 adjacent to the site of the proposed Liberty Canyon wildlife crossing
 structure. Based on preliminary data collection for deer and coyote, 20
 types of behavior were extracted from videos as point events or state
 events (table 1). The activities were grouped into two categories of
 behavior (table 2).  Species identification and behavior time budgets was
 extracted from all videos using the Behavioural Observation Research
 Interactive Software (BORIS; Friard and Gamba, 2016; figure 6). The number
 of humans and domestic dogs present were recorded for each video. In
 addition, for videos deployed at the highway crossing structures, we
 classified traffic within a video recording into one of three categories:
 1) continuous traffic, 2) occasional, distinguishable traffic,
 representing between 1-5 clearly audible vehicles passing at random
 intervals, and 3) zero traffic.