2.2 | Camera trap survey
We deployed remotely-triggered camera traps (Reconyx© PC 850, 850C, 900,
900C) throughout each site with camera placement and sampling design
proportional to study area size (Table 1). Our study uses data from four
surveys at DMP (2017, 2018, 2019, 2020), two surveys at SNWR (2016,
2018), two surveys at UMBS (2015, 2016), and four surveys at HMC (2016,
2017, 2018, and 2019). We captured the heterogeneity of habitat and
other environmental features to ensure ecological representation in the
micro-site selection of camera traps. Camera traps were affixed to trees
> 0.5m diameter and placed 0.5-1.0 m off the ground.
Site-specific placement of camera traps was determined by signs of
animal activity such as game trails and scat. Camera trap settings
included: high sensitivity, one-second lapse between three pictures in a
trigger, and a 15-second quiet period between triggers. Camera traps
were not baited.
Image identifications were initially crowd-sourced and filtered for
carnivores using a public-science program called Michigan ZoomINin combination with a consensus algorithm and expert validation (Gadsden
et al. 2021). Carnivore species identifications were later sorted and
confirmed by at least two independent researchers in the Applied
Wildlife Ecology Lab.
2.3 | Temporal activityTime stamps associated with the camera trap images were used to conduct
temporal analyses. Prior to all analyses, a 30-minute quiet period was
introduced for every species to account for pseudoreplication, given the
tendency of some animals to remain in front of the camera trap and
trigger it multiple times. Since surveys were conducted across different
times of the year, we scaled times to sunrise and sunset times using thesunTimes function in the ‘circular’ package in R (Ridout and
Linkie 2009). 2.31 | Variation between sites
We first compiled all raccoon triggers from each survey within a site to
have an aggregate across years of overall raccoon temporal activity at
each site. We then compared raccoon temporal activity between sites
using the Mardia-Watson-Wheeler
(MWW) test, which is a nonparametric test of differences in the angular
means between samples of circular data using the ‘circular’ package in R
(version 4.1.0). When the W value is high it results in a significantp value (p < 0.05), which we conclude to mean
that the compared temporal activities are unique.
2.3.2 | Seasonal and yearly variation
Our multi-site camera study allowed us to compare differences in raccoon
temporal activity based on landscape level differences along an
urban-rural gradient. Comparing between seasons can confound inferences
from the analyses, due to different seasons potentially resulting in
different detection rates (Marcus Rowcliffe et al. 2011). While we did
not have identical seasonal coverage for every site, the multiple
surveys at every site resulted in coverage for the entire year at every
site with the exception of UMBS (Figure S1). To determine if there was
consistency at sites regardless of season and year, we compared raccoon
activity between each survey within each site, and then looked for
broader patterns across sites.
2.3.3 | Coyotes on raccoon temporal activity
For each survey, we used a kernel density estimation for the independent
coyote triggers and designated the cameras that fell within the top
quantile of as ‘HIGH’ coyote intensity of use zones in ArcGIS Pro
(version 2.3.1). We used this rather than a fixed cutoff value of
expected detection rate because our sites spanned the entirety of the
urban-rural gradient and expected detection rates for coyote vary
depending on the composition of a site (Magle et al. 2014). Coyote
triggers were checked for spatial independence using Moran’s I prior to
kernel density estimation. We compared raccoon temporal activity between
the high coyote cameras and the rest of the site using the MWW test. For
additional evidence that temporal shifts by raccoons were due to
avoidance of coyotes, we then compared the overlap between coyote and
raccoon time use in the two raccoon test groups from the MWW test. To do
this, we calculated an overlap (Δ) coefficient of temporal activity for
coyotes and raccoons within each group (‘HIGH’ and ‘LOW’ coyote
intensity of use) along with 95% confidence intervals generated from
10,000 parametric bootstraps of the temporal distribution models. Δ
values range from 0 to 1, with 0 indicating completely distinct and
non-overlapping temporal activity between comparison groups, and 1
indicating complete overlap. Δ1 was used for comparisons
when one of the sample groups had less than 50 triggers; otherwise
Δ4 was used to estimate temporal overlap (Ridout and
Linkie 2009). Finally, the activity distributions were visually assessed
to determine qualitative characteristics of shifts (e.g. raccoons
shifting towards increased nocturnality in high coyote zones).