DOI: 10.7291/d1mt27
title: Mesopredators retain their fear of humans across a development gradient

description: Anthropogenic impacts on wildlife behavior arise both from the immediate
 presence of people, which induces fear responses in many species, and the
 human footprint on the landscape (i.e., development), which affects animal
 movement and habitat use. Where both disturbance types co-occur,
 disentangling their impacts remains a challenge. Disturbance effects may
 interact such that species respond to increased human footprint by either
 reducing (habituation) or increasing (sensitization) avoidance of human
 presence. We experimentally manipulated perceived human presence, using
 playbacks of people talking, across a gradient of human footprint in
 California’s Santa Cruz Mountains and used camera traps to quantify the
 behavioral responses of bobcats (Lynx rufus), striped skunks (Mephitis
 mephitis), and Virginia opossums (Didelphis virginiana), mesopredators
 known to exhibit strong fear response to human presence, but which vary in
 their use of developed areas. Bobcats and skunks reduced activity in
 response to human playbacks but showed no change in responsiveness to
 playbacks across the development gradient, suggesting that these species
 are similarly fearful of humans at all development levels tested. Opossums
 exhibited a significant interaction between development and playback
 treatment such that reductions in activity level in response to human
 playbacks were strongest at higher levels of building density. Our results
 indicate that, rather than habituating to human presence some
 mesopredators retain a strong fear of humans or become more fearful when
 inhabiting more developed areas. We suggest that consistently high
 responsiveness to immediate human presence may benefit mesopredators
 living in human-dominated landscapes by mitigating the risk of
 anthropogenic mortality. Keywords: sensitization, habituation, ecology of
 fear, human impact, development gradient, playback experiment 
description: We ran a
 playback experiment at thirteen sites throughout the Santa Cruz Mountains
 between October 7th and December 4th of 2019. We chose playback sites of
 differing levels of human footprint ranging from remote protected areas to
 suburban development, whereby building density ranged from 0 to 391
 buildings within a 500 m radius of the site.
 We
 manipulated human presence following the protocol similar to that
 described by Suraci et al. (2019a). At each site, we deployed a single
 battery-powered speaker broadcasting either human or control
 vocalizations. Human playbacks consisted of a single female or male voice
 reading passages or responding to interview questions. As a control we
 used Pacific treefrog
 (<i>Pseudacris regilla)</i> vocalizations since this species
 is found throughout the Santa Cruz Mountains, does not pose a threat or
 represent a food source to any of our focal species, and can commonly be
 heard during day and night (Smith et al. 2017; Suraci et al.2019a,b). We
 used ten exemplars of each playback type, ranging in duration between 24
 and 229 s, which were played in random order. Playbacks were broadcast at
 a consistent volume of 75 dB. The speakers were set to be silent 60
 percent of the day and broadcast 40 percent of the day. Each experimental
 replicate ran simultaneously for eight weeks at each site. We utilized a
 repeated-measures design such that each site received either human or
 control playbacks for four consecutive weeks followed by the opposite
 treatment for a subsequent four weeks (Suraci et al. 2016, 2019a). We
 randomly selected half of the sites to begin with the control treatment
 and half to begin with the human treatment. Over the course of the
 experiment wildlife within hearing range of the speaker were exposed to
 either control or experimental playbacks for 9.6 hours per day for 56
 continuous days. Each site was checked once a week to ensure that playback
 equipment was functioning properly. 
 At each
 playback site we deployed a motion-sensitive wildlife camera (Bushnell
 Trophy Cam; Bushnell Corp., Overland Park, KS, USA). The cameras were
 programmed to take a burst of three photographs when triggered by motion
 with a one-minute delay between bursts. The cameras were active for the
 full eight weeks of the experiment. We placed a scent lure (perforated
 sardine tin) and food bait (boiled chicken egg) at each site (Fig. 1) to
 increase the chance that an individual of any species would investigate
 the site and be captured on camera (Suraci et al. 2019a). Lures and baits
 were replaced weekly. All playback images were scored for the presence
 of mesopredators by at least two trained individuals, with species
 assignment based on consensus between independent scorers. We defined an
 independent predator detection as an image or group of images of a
 particular species that was separated from another detection of the same
 species on the same camera by at least thirty
 minutes.   To test the effects of human
 presence and human footprint on mesopredator activity levels, we
 quantified the number of detections per week of each species at each
 camera site (Moll et al. 2018; Suraci et al. 2019a). Because experiments
 were run for eight weeks, we derived eight activity level estimates for
 each camera site (four during the human treatment and four during the
 control treatment), with the exception of a single site for which camera
 failure resulted in only seven weeks of data (four human and three
 control). A detection event is the result of two processes: (i) whether or
 not a species is present in the vicinity of a camera site and thus
 available to be sampled (a binary process); and (ii) the species activity
 level at a camera site if present, which determines the number of times
 the species is detected (a Poisson process) (Moll et al. 2018; Suraci et
 al. 2019a). We therefore fit zero-inflated Poisson (ZIP) (Zuur et al.
 2009) models to predator detection data, which can capture both processes
 through (i) a binomial component modeling whether or not each detection
 estimate is a zero and (ii) a Poisson component modeling the number of
 detections per week (a proxy for activity level). In all models, we fit
 random intercepts for camera site on both the binomial and
 Poisson submodels to account for repeated measurements of predator
 activity level at each site. For each focal species (striped skunks,
 bobcats, and opossums), we fit a full ZIP model consisting of (i) a
 building density covariate on the binomial (i.e., zero or nonzero)
 component to control for the potential influence of development on whether
 a given mesopredator was available to be sampled, and (ii) covariates for
 playback treatment, building density and their interaction on the Poisson
 component (i.e., number of detections per week). For each species, we also
 fit a reduced model with the playback treatment x building density
 interaction removed. For species for which the playback treatment x
 building density interaction term was not significant in the full model
 (i.e., 95% credible intervals crossed zero), we interpret the results from
 the no-interaction model. All models were fit in a Bayesian framework
 using the Stan programming language called through R via the rstan package
 (Stan Development Team, 2020). For each model, we ran 4000 iterations of
 three Hamiltonian Monte Carlo chains retaining 1000 samples from the
 posterior distribution of each chain. We used vague priors for all
 variables and random starting points for all chains. We checked model
 convergence by visually inspecting trace plots and confirming that the
 Gelman-Rubin statistic (“R-hat”) was &lt; 1 for all parameters. We
 tested model fit to the data using Bayesian P-values, which compare
 statistics calculated from model-generated data with those calculated from
 observed data. Here, we calculated Bayesian p-values for the mean and skew
 of model-generated and observed data (Hooten and
 Hobbs 2015). 
description: The attached dataset will support the replication of all analyses
 in the above paper. The attached ReadMe file defines the column headings
 of our data set. The number of detections of each species per week for
 site HPB13 during week 4 of the control (Pacific tree frog) is missing due
 to an SD card handling error.