Biological Conservation 241 (2020) 108383

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Biological Conservation
journal homepage: www.elsevier.com/locate/biocon

Human presence and human footprint have non-equivalent eﬀects on
wildlife spatiotemporal habitat use

T⁎Barry A. Nickela,1, Justin P. Suracia, ,1, Maximilian L. Allenb, Christopher C. WilmersaabCenter for Integrated Spatial Research, Environmental Studies Department, University of California - Santa Cruz, Santa Cruz, CA 95064, USA
Illinois Natural History Survey, Prairie Research Institute, University of Illinois, Champaign, IL 61820, USA

A R T I C LE I N FO

A B S T R A C T

Keywords:
Camera trap
Community ecology
Occupancy model
Wildlife nocturnality
Anthropogenic disturbance
Recreation ecology

Human impacts on wildlife stem from both our footprint on the landscape and the presence of people in wildlife
habitat. Each may inﬂuence wildlife at very diﬀerent spatial and temporal scales, yet eﬀorts to disentangle thesetwo classes of anthropogenic disturbance in their eﬀects on wildlife have remained limited, as have eﬀorts topredict the spatial extent of human presence and its impacts independently of human footprint. We used camera
trap data from a 1400-km2 grid spanning wildlands and residential development in central California to compare
the eﬀects of human presence (human detections on camera) and footprint (building density) on mammalianpredators. We then developed a model predicting the spatial extent of human presence and its impacts across thebroader landscape. Occupancy modeling and temporal activity analyses showed that human presence andfootprint had non-equivalent and often opposing eﬀects on wildlife. Larger predators (pumas Puma concolor,bobcats Lynx rufus, coyotes Canis latrans) were less active where human footprint was high but avoided highhuman presence temporally rather than spatially. Smaller predators (striped skunks Mephitis mephitis, Virginia
opossums Didelphis virginiana) preferred developed areas but exhibited reduced activity where human presence
was high. A spatial model, based on readily available landscape covariates (parking lots, trails, topography),
performed well in predicting human activity outside of developed areas, and revealed high human presence evenin remote protected areas that provide otherwise intact wildlife habitat. This work highlights the need to integrate multiple disturbance types when evaluating the impacts of anthropogenic activity on wildlife.

1. Introduction
The expanding inﬂuence of humans has greatly impacted wildlife by
disrupting the distribution and activity patterns of animals globally(Dirzo et al., 2014; Gaynor et al., 2018; Tucker et al., 2018). The increasing human footprint on the landscape (i.e., urbanization, land usechange) is a key threat to wildlife across virtually all taxonomic groups,
not only through habitat loss and fragmentation (Hansen et al., 2005;
Fischer and Lindenmayer, 2007), but also because urbanized areas represent concentrations of anthropogenic “disturbance” (i.e., real or
perceived threats that elicit antipredator responses (Frid and Dill,
2002)), which may be actively avoided by wildlife. However, humanimpacts are not restricted to developed areas only, as the mere presenceof humans has been shown to impact wildlife behavior and activity
even in wildland areas (Suraci et al., 2019a). The latter is particularly
salient given the rapid expansion of outdoor recreation into previously
undisturbed landscapes (Cordell et al., 2008) and its potential negativeeﬀect on many wildlife species (Larson et al., 2016).
Wildlife species respond to human activities in complex ways, ranging from acute behavioral changes to chronic distributional eﬀects,
which may depend on the type, intensity, and frequency of disturbance(Larson et al., 2016; Tablado and Jenni, 2017; Gaynor et al., 2018;
Tucker et al., 2018). Humans are a major source of mortality for many
wildlife species, particularly mammalian predators (Darimont et al.,2015), and recent experimental work conﬁrms that many species
therefore exhibit strong fear responses to human presence (Clinchy
et al., 2016; Smith et al., 2017; Suraci et al., 2019a). The fear inducedby human presence has correspondingly been shown to aﬀect behavior
and activity patterns of wildlife at the landscape scale (Suraci et al.,2019a), and fear may therefore mediate many of the impacts associatedwith recreational activity in wildland areas (Larson et al., 2016;
Tablado and Jenni, 2017). When compared to the relatively transient
presence (and associated fear) of humans in wildlife habitat (e.g.,during recreation), sustained and high-intensity disturbance associated⁎Corresponding author.
E-mail address: justin.suraci@gmail.com (J.P. Suraci).
1
These authors contributed equally.
https://doi.org/10.1016/j.biocon.2019.108383
Received 16 August 2019; Received in revised form 2 December 2019; Accepted 6 December 20190006-3207/ © 2019 Elsevier Ltd. All rights reserved.

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B.A. Nickel, et al.

predators, which experience the highest per capita risk of humancaused mortality (Darimont et al., 2015) and are correspondingly
known to exhibit strong behavioral responses to the immediate presence of people (Clinchy et al., 2016; Smith et al., 2017; Suraci et al.,2019a), but which also represent a range of responses to human development, from reclusive large carnivores to synanthropic mesopredators. This work was conducted in areas of the Santa Cruz Mountains
ranging from undeveloped tracts of forest to moderately developed
rural and exurban areas, thus typifying the mosaic of wildlife habitatand human development characteristic of the wildland-urban interface(WUI) (Radeloﬀ et al., 2005, 2010).

with long-term land use changes (e.g., housing developments) may be
expected to exert even greater impacts on wildlife habitat use. Yet many
synanthropic species (e.g., mesopredators like striped skunks, Mephitis
mephitis, and Virginia opossums, Didelphis virginiana) appear to beneﬁt
from increased human footprint on the landscape, taking advantage ofresource subsidies such as food waste (Ordeñana et al., 2010; Wanget al., 2015) and/or decreased risk from other predators where humanactivity is high (Muhly et al., 2011). Indeed, multiple anthropogenic
inﬂuences may simultaneously aﬀect wildlife, potentially in opposition,
if for instance some species avoid risky interactions with people buttake advantage of human infrastructure or resources (Beckmann andBerger, 2003; Bateman and Fleming, 2012; Suraci et al., 2019a).
An animal's response to a particular anthropogenic disturbance may
additionally depend upon the relative constancy or regularity of the
disturbance type in space and time and thus the animal's ability to
predict when and where potential threats from humans are likely tooccur. Predator-prey theory suggests that long-term, consistent spatial
variation in risk should lead to outright avoidance and thus changes inprey space use (e.g., the “risky places hypothesis”) (Creel et al., 2008;
Dröge et al., 2017). Alternatively, predation risk that is more spatiallyvariable but exhibits regular temporal ﬂuctuations (e.g., due to thepredator's daily activity cycle; Kohl et al., 2018) may lead to temporal
partitioning, where prey avoid predators in time by increasing activity
at times of day when the predator is less active (Suraci et al., 2019b).
Thus, it is possible that human development as a long-term, spatiallyconstant source of risk may be more likely to induce spatial displacement and altered habitat use for wildlife species (i.e., avoidance of risky
places) (Frid and Dill, 2002; Tucker et al., 2018), while human presence
in wildlife habitat, which is less constant and largely restricted todiurnal periods, may prompt shifts in temporal activity (Gaynor et al.,2018).
Despite ample reason to expect that human footprint and humanpresence will diﬀer in their impacts on wildlife behavior and habitat
use, ambiguity exists in how wildlife species respond to these two categories of anthropogenic disturbance. One reason for such ambiguity isthat human footprint is often used as a proxy for multiple forms ofanthropogenic disturbance, due in part to the ease of acquiring landscape level data on, e.g., land cover, human population density, andbuilt infrastructure (Venter et al., 2016). However, such variables may
be poor predictors of human presence across the landscape, particularly
in wildland areas where outdoor recreation is growing (Cordell et al.,2008; Balmford et al., 2015). Using the human footprint as a proxy forhuman presence may therefore conﬂate the eﬀects of diﬀerent types ofhuman disturbance on wildlife (Tablado and Jenni, 2017). However,measuring the spatial extent of human presence outside of developed
areas, and thus the area over which human activity is likely to impact
wildlife, remains a challenge. Studies of human presence in wildlandareas typically rely on the localized deployment of sensors (e.g., camera
traps) in the environment, a site-speciﬁc approach that may not be
representative of landscape-scale patterns of human presence (Larson
et al., 2016; Gutzwiller et al., 2017) and may therefore overlook humandisturbance and its impacts in parts of the landscape not directly covered by camera trapping surveys (Monz et al., 2013). Thus, there is aneed to predict human presence in wildland areas from readily available landscape-level variables, allowing estimation of broad-scale spatial patterns of human activity and associated impacts on wildlife beyond sites at which on-the-ground surveys have been conducted (Ladle
et al., 2017).
Here we use a network of camera-traps deployed across a gradientof human recreational use and development in the Santa Cruz
Mountains of California to quantify the eﬀects of both human footprint(building density) and actual human presence (occurrence of people on
camera traps) on wildlife behavior and habitat use. We then model
where and when the observed impacts of human presence are likely to
be greatest on the landscape using a suite of spatial predictors of humanactivity. We focus our analyses on large and medium-sized mammalian

2. Methods
2.1. Study area
The Santa Cruz Mountains (37° 10′ N, 122° 3′ W) encompass a diverse landscape comprised of large tracts of relatively undisturbed native vegetation intermixed with low- and intermediate-density development that are surrounded by heavily developed areas along thefringe. One-third of the landscape falls within the wildland-urban interface (Martinuzzi et al., 2015) with a substantial portion of more
remote public lands being available for recreational activities (e.g.,biking, hiking, dog walking). The study area is crisscrossed by highways
and numerous smaller roads providing access to rural houses and developments. The region thus provides marked gradients of development
and human recreational pressures.

2.2. Camera trap study design
We deployed a grid of camera traps (Bushnell Trophy Cameras)
across approximately 1400 km2 of the Santa Cruz Mountains, CA, USA,focusing on undeveloped and moderately developed areas that still
provide substantial wildlife habitat (Fig. 1). We created a grid for 100
camera traps, with 4 km between the center of each grid cell. Thissystematic sampling approach was chosen to facilitate accurate estimates of habitat use by a range of species that diﬀer in average homerange size and movement patterns (Eﬀord and Dawson, 2012; Neilson
et al., 2018) We placed camera traps within 400 m of the center of eachgrid cell along landscape features likely to maximize detection of
mammalian predators. We were unable to place 12 cameras due to land
access or safety issues, leading to a total deployment of 88 cameras.
Cameras were deployed using standardized procedures (O'Connell
et al., 2011; Burton et al., 2015), being placed at a height of 30–90 cm
and oriented along hiking trails, forest roads, or game trails used bywildlife to increase the probability of detection. We programmed
cameras to take 3 photos per detection event, with at least a 1 min delay
between subsequent detections.
Camera traps were deployed for ﬁve to seven weeks during three
separate study periods: Spring 2015 (10 March to 13 April), Autumn
2015 (23 August to 10 October), and Spring 2016 (4 March to 21 April).
During each study period, we deployed and retrieved camera traps on arolling basis, and the date ranges noted above correspond to the weeks
when at least 80% of the 88 camera traps were active. We scored allcamera trap images for the presence of mammalian predator species
including the sole large carnivore in the Santa Cruz Mountains, thepuma (Puma concolor), and a suite of mammalian mesopredators(coyote Canis latrans, bobcat Lynx rufus, gray fox Urocyon cinereoargenteus, northern raccoon Procyon lotor, striped skunk, and Virginia
opossum). We also scored all detections of humans on camera traps. All
images of the same species (including humans) on the same camera
were considered independent occurrences if separated by at least
30 min (Wang et al., 2015).

2

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Fig. 1. The study area in the Santa Cruz Mountains, California, USA, displaying the locations of camera traps (black dots), protected areas (dark green shading),
undeveloped areas (light green), and developed areas (light gray) across the region. (For interpretation of the references to color in this ﬁgure legend, the reader isreferred to the web version of this article.)(i.e., separated by 30 min) human groups detected per day across the 88
camera sites ranged from 0 to 6 (mean ± SD = 1.2 ± 1.8 humangroups per day), with 12 of 88 sites experiencing an average of 0
visitors per day. The largest total number of independent human groups
detected in single day was 13. Building density ranged from 0 to 568
buildings within 1 km of the camera site (mean ± SD = 60.5 ± 100.2
buildings). Importantly, these two forms of human disturbance were
not strongly correlated across camera sites (Pearson's correlation coefﬁcient = −0.22, p = 0.058).

2.3. Anthropogenic and habitat covariates
For each camera site, we measured several anthropogenic and habitat covariates that could potentially aﬀect both human and wildlife
activity, including building density (BUILDING), road density (ROAD),
trail density (TRAIL), distance to urban area (URBAN), distance topublic open space (i.e., state and local parks, outdoor recreation areas;
OPEN), distance to parking lot (a proxy for accessibility; ACCESS),
proportion of forest cover (FOREST), elevation gradient (ELEV), proportion of visible landscape (i.e., vista opportunities; VIEW), ruggedness (RUGGED), topographic position index (TPI), and amount of
wildland-urban interface within 1 km (WUI). All covariates are presented in Table 1 and described in detail in the Supplementary
methods. All GIS analyses were performed using the Python programming language (v. 2.7.9; Python Software Foundation, Wilmington, DE,
USA) and ArcGIS for Desktop (v. 10.6.1; ESRI Inc., Redlands, CA, USA).
Our camera grid spanned a substantial gradient of both humanpresence and human footprint. The average number of independent

2.4. Comparing the eﬀects of human presence and human footprint on
wildlife habitat use
For each camera trap, we derived estimates of human presence atboth the daily and study period (i.e., Spring 2015, Autumn 2015, orSpring 2016) levels from images of humans recorded on camera traps.
Daily estimates were simply the total number of independent humandetections during each calendar day on a given camera trap (humans

Table 1
Environmental variables measured for each camera site and data sources from which variables were derived.
Variable
Anthropogenic
BUILDING
WUI
URBAN
OPEN
ROAD
TRAIL
ACCESSWEEKEND
Habitat
FOREST
VIEW
ELEV
RUGGED
TPI

Description

Data source# of buildings within a 1 km radius# of wildland-urban interface parcels within a 1 km radius
Euclidean distance to the nearest medium to high density urban areas
Euclidean distance to the nearest public open space
Length of road network within a 1 km radius
Length of trail network within a 1 km radius
Euclidean distance to the nearest parking lot or campgroundDay of the week, coded as weekend (Saturday, Sunday) or weekday

Municipal, county, and state agencies; Microsoft Building Footprint, 2018
(Martinuzzi et al., 2015)(Martinuzzi et al., 2015)
California Protected Areas Database; GreenInfo Network, 2017
Municipal, county, and state agencies
Municipal, county, and state agencies; OpenStreetMap, 2017
Municipal, county, and state agencies; OpenStreetMap, 2017–

% of forested landscape within a 1 km radius% of visible landscape within a 1 km radius
Elevation range within a 1 km radius
Surface roughness within a 1 km radius
Topographic position relative to ridges and valleys within 1 km

National Land Cover, v2 (30m); US Geological Survey 2011
Digital Elevation Model (30 m); US Geological Survey 2011
Digital Elevation Model (30 m); US Geological Survey 2011
Digital Elevation Model (30 m); US Geological Survey 2011
Digital Elevation Model (30 m); US Geological Survey 20113

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B.A. Nickel, et al.

‘posterior probability’).

per day, or HPD). Study period-level estimates were the number ofhumans detected per day on a given camera averaged across each study
period (meanHPD). We used building density (i.e., BUILDING, described above) as our estimate of human footprint at each camera site.
To estimate the eﬀects of the two forms of human disturbance on
wildlife habitat use and behavior, we ﬁt multi-species occupancy
models (Burton et al., 2012; Broms et al., 2016) to camera trap data on
detections of the seven mammalian predators described above. Occupancy models estimate two parameters, both of which may be aﬀectedby human disturbance: site occupancy (ψ) and detection probability (p).
As originally formulated (e.g., MacKenzie et al., 2003), occupancy anddetection probability depend on an assumption of closure, i.e., that anindividual that uses a given camera site will always be present andavailable for sampling. This assumption may be violated in camera trap
studies when the actual area sampled by a camera is substantiallysmaller than the home ranges of the animals surveyed (Eﬀord andDawson, 2012; Burton et al., 2015; Neilson et al., 2018). We therefore
treated the occupancy (ψ) and detection probability (p) parameters
estimated from our models as proxies for the probability of occurrence(i.e., whether a given species occurred at a site during sampling) andintensity of use (i.e., the overall activity of a species at a given site)
respectively. Species occurrence is likely to be lower where high humandisturbance reduces habitat suitability. Intensity of use is a function ofboth animal behavior and the density of individuals using the areasampled by the camera trap. Thus, if fewer individuals occur in disturbed habitats and/or if individuals reduce their activity levels or increased cryptic behavior where disturbance is high, this will lead to
fewer detections on camera and lower estimates of intensity of use.
As all of our target wildlife species are at least partially nocturnal,
particularly where humans are present (Wang et al., 2015), we deﬁned
each night that a camera was active as a survey (Burton et al., 2012),
and recorded whether a given species was detected (1) or not (0) over a
24-hour period spanning each night (from noon to noon). Our estimatesof wildlife species detection were therefore oﬀset (by 12 h) from ourdaily estimates of human activity on camera such that detection of a
wildlife species during a given nocturnal period is modeled as a function of the number of humans present during the immediately preceding diurnal period.
We formulated our occupancy models as hierarchical zero-inﬂatedbinomial models with separate binomial submodels describing ψ and p(MacKenzie et al., 2002; Royle and Dorazio, 2008). We ﬁt two occupancy models varying only in the type of human disturbance used tomodel ψ and p, i.e., human footprint (building density) or human presence (meanHPD and HPD for ψ and p respectively). Each model also
included environmental covariates that could aﬀect occupancy anddetection probability at camera sites (i.e., FOREST, RUGGED, and TPI),
as well as an interaction between FOREST and human disturbance(presence or footprint) to account for potential changes in wildlife responsiveness to human disturbance with varying levels of protective
cover. All covariates were normalized (mean centered and scaled by
one standard deviation) prior to model ﬁtting. Full details of the multispecies occupancy model are provided in the Supplementary methods.
We analyzed the occupancy models in a Bayesian framework using
the JAGS language called through the package R2jags in R (v. 3.4.2; R
Core Team, 2019). For each model, we ran three Markov Chain Monte
Carlo (MCMC) chains of 20,000 iterations each and made inference
from 1000 samples from the posterior distribution of each chain after aburn in of 15,000 and a thinning rate of 5. We chose vague priors for all
variables and random starting values for all chains. We conﬁrmed theconvergence of MCMC chains by visually inspecting trace plots, and via
 ) (Hobbs and Hooten, 2015). We assessed
the Gelman-Rubin statistic (Rmodel ﬁt using Bayesian p-values (Hobbs and Hooten, 2015) calculated
from the Freeman-Tukey and chi-squared statistics (Royle and Dorazio,2008). In describing the eﬀect of model covariates on ψ and p below, wereport the posterior probability that the coeﬃcient estimate for a given
covariate is more extreme (greater or less) than zero (hereafter,

2.5. Comparing the eﬀects of human presence and human footprint on
wildlife nocturnality
We estimated the degree to which wildlife activity was concentrated
during nocturnal hours by calculating the time diﬀerence in hours between noon and each detection of a wildlife species on camera. We used
the absolute value of this “hours to noon” estimate as a measure of
nocturnality such that we considered detections occurring farther fromnoon (i.e., closer to the middle of the night) to exhibit greater nocturnality. We then calculated the average nocturnality value for eachspecies at each camera site, as well as the total number of independent
detections of that species at each site. We used linear regression tocompare the eﬀects of human presence and human footprint on species
nocturnality. We estimated camera site-level human presence as humandetections per week (HPW), averaged across all three study periods (seeabove) at a site, and estimated site-level human footprint from building
density (BUILDING) as described above. Both HPW and BUILDING werenormalized to permit direct comparison of coeﬃcient estimates, and logtransformed to account for heterogeneity of variances. For each wildlife
species, we ﬁt a single, weighted linear regression model including both
HPW and BUILDING as covariates and total detections of the species asa weighting term. We conﬁrmed model ﬁt by inspecting residual vs.
ﬁtted value and quantile-quantile plots.
2.6. Predicting the intensity of human presence across the landscapeWe evaluated landscape scale predictors of human activity to determine where and when human presence would be concentrated on
the landscape beyond developed areas. We estimated camera site-level
human presence based on daily counts of humans detected at eachcamera, as described above. We expected temporal variation in humanactivity due to a “day-of-the-week eﬀect” (WEEKEND), given that
outdoor activity is typically greater on weekends when recreational
opportunities are greatest (Nix et al., 2018). We therefore summed
human counts for each camera site based on day of the week, creating
two estimates per site corresponding to expected low and high humanactivity, i.e., counts during the 5 days in the middle of the week
(Monday–Friday; low activity) and those during the 2 days on theweekend (Saturday and Sunday; high activity). To account for unevensampling eﬀort between level of activity (weekday versus weekend), we
included the total number of weekday or weekend days a camera wasactive as an oﬀset in models described below, resulting in a response ofhumans per day (HPD). We modeled HPD as a function of multiple
anthropogenic covariates related to human activity and accessibility(ROAD, BUILD, URBAN, WUI, TRAIL, OPEN, and ACCESS), as well as
natural features that may drive recreational use (FOREST, RUGGED,
ELEV and VIEW). To evaluate whether any landscape characteristics
associated with human activity varied between weekends versus
weekdays, we included a 2-way interaction between WEEKEND and allother covariates. We normalized all covariates to improve model convergence and to facilitate comparison of model coeﬃcients amongcovariates (Gelman, 2008).
All pairs of covariates were tested for collinearity by examining
Pearson correlation coeﬃcients (rp) and three pairs were found to berelatively strongly correlated (rp > 0.7). ELEV and RUGGED showed
substantial collinearity (rp = 0.94) and, because RUGGED provided ameaningful measure of terrain complexity assumed to inﬂuence recreational activity such as hiking, ELEV was dropped from the analysis.
ROAD and BUILD were also found to be strongly correlated with WUI(rp = 0.78 and rp = 0.87, respectively) and given that WUI is determined, in part, by both BUILD and ROAD (see Supplementary
methods), we only retained WUI in the model. All other pairs of covariates exhibited limited correlation (rp ≤ 0.4).
Based on Vuong closeness tests (Vuong, 1989), we formulated our4Biological Conservation 241 (2020) 108383

B.A. Nickel, et al.

probability = 1; Fig. 2, Table S2). Several well-known synanthropic
species (coyotes, skunks, and opossums) exhibited relatively strong
positive associations between occurrence probability and footprint(posterior probability: coyotes = 0.99, skunks = 0.96, opossums = 0.92).
Human presence and human footprint had similarly non-equivalenteﬀects on the intensity with which wildlife used the camera sites (i.e.,
overall activity level, p). The number of human detections per day had astrong negative eﬀect on the intensity of use by skunks and opossums(posterior probability = 0.99 and 0.97, respectively), with bobcats andfoxes showing the opposite pattern (posterior probability = 1 for both
species; Fig. 2, Table S1). Pumas (0.97), bobcats (0.99), and foxes (1.0)
were all less active with increasing human footprint, while common
synanthropic species showed increasing intensity of use with increasing
footprint (skunks and raccoons, posterior probability = 1.0 for both
species; Fig. 2, Table S2).
For several species, the eﬀects of human presence on habitat use
were mediated by the availability of forest cover. Both foxes andopossums were increasingly likely to occur at sites with high humanpresence as the availability of forest cover increased (Fig. 3a,b; Table
S1). Coyotes similarly exhibited increased occurrence probability athigh building density sites where high forest cover was available, whilebobcats showed the opposite pattern, though with substantial variability (Fig. 3c,d; Table S2). Full results of the human presence andhuman footprint occupancy models are presented in Tables S1 and S2.
Estimates of average use intensity (p) and probability of occurrence (ψ)
for each species are presented in Table S3.

model as a zero-inﬂated Poisson (ZIP) model, as data were skewed towards counts of zero humans per camera day. ZIP model and Vuong
closeness tests were ﬁt using the R package pscl. Beginning with a fullmodel that included all covariates just described (except those excluded
for collinearity), we determined the best supported ZIP model via
backward stepwise elimination of variables based on AICc (calculatedusing the R package MuMIn). Model quality and goodness of ﬁt wasassessed using adjusted pseudo-R2 as proposed by Martin and Hall(2016). We performed all analyses in R (v. 3.4.2; R Development Core
Team, 2010).
Finally, to illustrate the relative intensity of human presence acrossthe broader landscape, we used the suite of spatial covariates retainedin the best supported zero-inﬂated Poisson model to project model
predictions across the Santa Cruz Mountains study area. Spatial predictions were only made for areas characterized by relatively low development (≤120 buildings km−2, constituting wildland to exurban
areas, see Fig. S1), as human presence in heavily developed areas (i.e.,the cities of Santa Cruz and San Jose and major suburbs) is assumed tobe consistently high. We limited our prediction area to a 2-km buﬀer(half the distance between neighboring camera traps) surrounding theminimum convex polygon of all camera trap locations such that predictions were restricted to the area actually sampled by camera traps(Fig. S1).
3. Results
3.1. Eﬀects of human presence and human footprint on wildlife habitat use
Our multi-species occupancy models revealed that human presence
and human footprint are not equivalent in their eﬀects on wildlife habitat use (Fig. 2, Tables S1 and S2), with the magnitude and sign of theeﬀect of each human disturbance type varying substantially between
species. Both human presence and human footprint models exhibited < 1.1 for all model terms) and excellent ﬁt
successful convergence (R(Bayesian p-values: 0.445 ≤ p ≤ 0.499). Several species exhibited apositive relationship between the probability of occurring at a camera
site (ψ) and human presence (average human detections per day) at thatsite (pumas, bobcats, and foxes; posterior probability for all species≥0.99; Table S1). By contrast human footprint (building density) had astrong negative eﬀect on fox occurrence probability (posterior

3.2. Eﬀects of human presence and human footprint on wildlife nocturnality
Several wildlife species exhibited signiﬁcant temporal shifts in theirdiel activity patterns across the gradients of human presence and/or
footprint. Pumas (weighted linear regression: F1,67 = 17.22,
p < 0.001), bobcats (F1,67 = 3.81, p = 0.05), and coyotes(F1,67 = 8.47, p = 0.007) exhibited increased nocturnality as humanpresence at a site increased. Bobcats also exhibited increased nocturnality with increasing building density (F1,67 = 9.51, p = 0.003) whilefoxes were moderately less nocturnal at increasing levels of building
density (F1,67 = 8.38, p = 0.005; Fig. 4, Table S4).

Fig. 2. Coeﬃcient estimates from the multispecies occupancy models showing the eﬀect of human presence (detections on camera; red) and human footprint(building density; yellow) on occurrence probability (ψ) and intensity of use (p) for each wildlife species. Symbols to the right of zero (vertical line) indicate a positiveeﬀect of the human disturbance metric on occurrence or intensity of use, and symbols to the left of zero indicate a negative eﬀect. Thick horizontal lines are 90%
Bayesian credible intervals, and thin horizontal lines are 95% credible intervals. (For interpretation of the references to color in this ﬁgure legend, the reader isreferred to the web version of this article.)5Biological Conservation 241 (2020) 108383

B.A. Nickel, et al.

Fig. 3. Human disturbance interacts with
forest cover to aﬀect wildlife species occurrence probability. The eﬀect of human presence (detections on camera; a,b) and humanfootprint (building density; c,d) on occurrence probability are shown for (a) foxes, (b)opossums, (c) bobcats, and (d) coyotes atboth low (yellow) and high (green) levels offorest cover. Low and high forest cover are
here deﬁned as the 20% and 80% quantiles,
respectively, of forest cover across all camera
sites. Lines and shaded areas are predictions
and 95% Bayesian credible intervals from themulti-species occupancy model. (For interpretation of the references to color in thisﬁgure legend, the reader is referred to the
web version of this article.)use and activity patterns. These eﬀects are likely widespread, as modeling results indicated that human presence outside of developed areas
was extensive, particularly in protected areas with high recreational
potential. Moreover, we were able to predict the intensity of humanpresence from landscape-level variables, demonstrating a methodologythat can be used by other researchers to estimate human impacts on
wildlife outside of developed areas. Together, these results reﬁne ourunderstanding of how both human activity and development drivechanges in wildlife behavior and demonstrate that solely focusing on
one type of anthropogenic disturbance may lead to erroneous conclusions regarding the way human-induced risk aﬀects wildlife.

3.3. Predicting the intensity of human presence across the landscape
The ZIP model revealed a strong association between several landscape-level variables and the intensity of human activity outside of
developed areas (χ11,176 = 1163, p < 0.001; Pseudo R2 = 0.63;
Table 2; Fig. 5a). As expected, human activity (i.e., humans per day,HPD) varied depending on the time of the week with increased activity
on weekends (βWEEKEND = 0.29, p < 0.001) compared to weekday use.
HPD also increased signiﬁcantly in areas with greater opportunities forrecreational activities, including proximity to public open space(βOPEN = −0.77, p < 0.001), higher trail density (βTRAIL = 0.99,
p < 0.001), greater proportion of vista opportunities (βVIEW = 0.10,
p < 0.001), and adjacency to recreational access points(βACCESS = −0.89, p < 0.001). Additionally, HPD was greater inproximity to highly developed urban areas (βURBAN = −0.16,
p = 0.04) as well as those dominated by exurban expansion into rurallandscapes (βWUI = 0.11, p < 0.001). However, an interaction withweekend (βURBAN:WEEKEND = 0.46, p < 0.001) suggests activity is moreprevalent in areas farther away from developed areas on the weekend,
with use localized to areas near development during weekdays. The
surrounding natural characteristics of a site also inﬂuenced HPD withdecreased activity in more rugged landscapes (βRUGGED = −0.14,
p < 0.001), though human use of rugged areas increased on weekends(βRUGGED:WEEKEND = 0.2, p = 0.004). We did not ﬁnd signiﬁcant correlations for the remainder of the variables. The Vuong test suggested
that the ZIP model was a signiﬁcant improvement over a standard
Poisson regression model (p < 0.0001).

4.1. Contrasting eﬀects of human presence and human footprint on wildlife
habitat use and behavior
Our results demonstrate that human presence and human footprint
are not equivalent in their impacts on wildlife habitat use and behavior.
Building density and other forms of development represent long-termand spatially constant sources of disturbance, and several sensitive
wildlife species have been shown to avoid these risky places (Riley,2006; Ordeñana et al., 2010; Wilmers et al., 2013). We correspondingly
found negative eﬀects of building density on the habitat use of several
mammalian predators. Pumas and bobcats exhibited strong decreases inintensity of use with increasing building density, though occurrence
probability was unaﬀected (Fig. 2), suggesting that these species reduced their overall activity levels in areas of high human footprint, but
still occasionally use habitat with moderate levels of development. Grayfoxes appeared to be particularly sensitive to human footprint, withbuilding density having a strong negative eﬀect on fox occurrence
probability and intensity of use (Fig. 2). Thus, foxes tended to avoid
high human footprint and exhibited lower activity levels when present
in these areas.
By contrast, these same three predators (and to a lesser extent,
coyotes) exhibited increased occurrence and/or activity with increasing
human presence, despite growing experimental evidence that several
large and medium-sized predator species (including pumas and bobcats) exhibit strong fear responses to humans (Clinchy et al., 2016;

4. Discussion
Although a growing body of research demonstrates that both humanfootprint and human presence (including recreation) can have negative
impacts on wildlife (Larson et al., 2016; Gaynor et al., 2018; Tucker
et al., 2018), studies aimed at disentangling the concurrent eﬀect ofboth forms of disturbance on wildlife behavior are surprisingly rare.
Our results demonstrate that human presence and human footprint
have diﬀering, and in some cases opposite, eﬀects on wildlife habitat
6

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B.A. Nickel, et al.

Fig. 4. The eﬀect of human disturbance on wildlife nocturnality. Nocturnality estimates (i.e., average time of detections on camera expressed as hours from noon) at a given
camera site are plotted against human detections per week
and building density at that camera site for (a) pumas, (b)bobcats, (c) coyotes, and (d) foxes. Fitted lines and shaded
areas represent the predicted eﬀect, ± 95% conﬁdence intervals, of human disturbance on nocturnality. Absence of aﬁtted line indicates no signiﬁcant eﬀect of disturbance on
nocturnality. Only those wildlife species for which at least
one human disturbance type signiﬁcantly aﬀected nocturnality are shown.

such as pumas, bobcats, and coyotes, may be positively associated withareas of high human presence because, like humans, these species are
attracted to trails through wooded areas (Kays et al., 2017), which
provide energetically eﬃcient movement pathways across relatively
large home ranges (Dickie et al., 2017). Our results indicate that, instead of avoiding humans in space, these species take advantage of thehigh temporal predictability of human activity to avoid humans in time,
becoming increasingly nocturnal as human presence increases (Fig. 4).
Increased wildlife nocturnality as a response to increasing human disturbance has been demonstrated for many wildlife species globally(Gaynor et al., 2018) and has been associated with both human footprint (Beckmann and Berger, 2003; Díaz-Ruiz et al., 2016) and highSmith et al., 2017; Suraci et al., 2019a). Previous observational studies
examining the impacts of recreationalists on mammalian predators
have found mixed results, ranging from a negative eﬀect of humanpresence on predator detections to neutral or positive eﬀects on predator habitat use (Reed and Merenlender, 2008; Reilly et al., 2017;
Kays et al., 2017). We suggest that these diﬀerences may be due in partto the spatial distribution of human presence on the landscape. Our
spatial modeling results (Fig. 5) revealed that human presence is relatively high throughout many of the protected areas in the Santa Cruz
Mountains, and thus likely overlaps with high-quality habitat for many
species, potentially making the outright avoidance of human presence
in wildland areas overly costly. Additionally, highly mobile species
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B.A. Nickel, et al.

in the Santa Cruz Mountains is expected to experience low levels ofboth human presence and human footprint. Completely “undisturbed”
habitat – areas predicted to experience zero human presence and to beat least 1 km from the nearest building – accounts for only 1.14% of thestudy area.
Human presence beyond heavily developed areas varied widely
across the region with the most important drivers of use related tovariation in accessibility and demand for recreation. In particular,
human presence was higher in conserved lands with infrastructure
providing access to recreational opportunities. Proximity to public open
space, vehicle access, and trail density were the strongest predictors ofhuman presence at a site, consistent with previous ﬁndings suggestingthat access to dense trail networks is correlated with signiﬁcantly
higher visitation rates (Reed and Merenlender, 2008, 2011; Larson
et al., 2018). Similarly, ruggedness and the scenic potential of a sitewere positively associated with human use. As such, human presence
was predicted to be regular and widespread in protected areas withhigh recreation potential and where recreation infrastructure is welldeveloped. Given that protected lands often overlap with high-quality
habitat for many species, elevated human activity in these areas likely
leads to substantial, yet unintended, impacts on wildlife from disturbance.
We found higher levels of human presence at sites adjacent to urban
development as well as those immediately surrounded by the wildlandurban interface (Patten and Burger, 2018; Larson et al., 2018). Thispattern is particularly signiﬁcant for urban-adjacent reserves as thepotential for human-wildlife interactions and thus negative impacts islikely greatest given the already elevated presence of humans in protected areas of the region. However, we detected a signiﬁcant interaction between distance to urban center and weekend (Table 1), indicating that human presence in protected areas shifted depending on
the day of the week with greater concentrations of activity at sites
further away from developed areas on weekends (Rossi et al., 2015;
Larson et al., 2018). This shift in human activity depending on day of
the week meant that even relatively remote areas of the Santa Cruz
Mountains were exposed to substantial human presence during sometime periods.

Table 2
Fixed eﬀects terms from the best-supported model for predicting human presence on the landscape.
Model parameter

β

SE

t

p

Pseudo R2 = 0.63
TRAIL
ACCESSOPENWEEKENDRUGGED
WUI
VIEW
URBAN
URBAN:WEEKEND
RUGGED:WEEKEND

0.99
−0.89
−0.77
0.29
−0.14
0.11
0.10
−0.16
0.46
0.20

0.004
0.006
0.004
0.004
0.004
0.005
0.004
0.004
0.004
0.004

7.53
5.73
14.64
12.92
4.89
−4.68
−2.21
−2.12
−2.43
−2.21

< 0.001< 0.001< 0.001< 0.001< 0.001< 0.001
0.05
0.03
0.004
0.02

human presence (e.g., recreation) in wildland areas (Wang et al., 2015;
Reilly et al., 2017). Restricting activity to nocturnal hours may involve
substantial costs for some wildlife species, e.g., by interfering withforaging behavior or increasing overlap with predators or competitors(Gaynor et al., 2018; Smith et al., 2018), but may nonetheless promotecoexistence by allowing humans and wildlife to use the same habitat(Carter et al., 2012; Suraci et al., 2019b).
Human presence and human footprint also had opposing eﬀects onhabitat use by skunks and opossums, synanthropic species which
commonly take advantage of human resource subsidies (e.g., foodwaste) in developed areas (Bateman and Fleming, 2012). Recent experimental work demonstrates that, despite their strong association
with human development, these species are nonetheless fearful of immediate human presence, reducing activity and foraging behavior whenperceived human presence was experimentally increased (Suraci et al.,2019a). The present study conﬁrms that these experimental results are
relevant at the regional scale. Despite exhibiting increased occupancy(skunks and opossums) and detection probability (skunks) with increasing building density, both skunks and opossums were substantiallyless detectable in areas with high human presence, consistent with reduced activity levels in the presence of people. Taken together, theseﬁndings suggest that, even for synanthropic species, human presence ispotentially costly, and that the beneﬁts of exploiting anthropogenic
environments (e.g., resources subsidies) must be traded oﬀ against therisks of a potentially dangerous direct encounter with humans.
For several predator species, occupancy of habitats with either highhuman presence (foxes and opossums) or high human footprint (coyotes) increased with increasing forest cover, indicating that the availability of protective cover reduces the risk that some wildlife species
perceive from humans. Previous studies have similarly demonstrated
increased use of human-dominated landscapes by mammalian predators when the availability of protective cover is high (Boydston et al.,
2003; Ordiz et al., 2011; Suraci et al., 2019b), suggesting that coveravailability plays a key role in mediating coexistence between humans
and predators by reducing the impacts of anthropogenic disturbance on
predator behavior.

4.3. Conservation and management implications
Our approach to modeling human presence and evaluating species
response to human activity can be used to guide future research andinform guidelines for the management of non-consumptive recreationin open space lands. For example, landscape level measures of thehuman footprint, such as land cover, population density, and built infrastructure, are well developed and readily available (Venter et al.,2016), however spatially explicit data on human presence outside of
developed areas are notably absent (but see Gutzwiller et al., 2017).
Our approach helps address this gap and demonstrates that the intensity of human presence on the landscape (including in wildlandareas) can be predicted from landscape-level variables, allowing estimation of human impacts on wildlife even outside of developed areas.
Further, we showed that modeling human presence is possible using
increasingly available sources of spatial data (e.g., parking lots, elevation, trails) allowing for projecting or forecasting the intensity ofhuman activity at the landscape scale (Fig. 5). Predictive models of thisnature could be used to improve landscape-wide management of nonconsumptive forms of human disturbance. For example, when planningfor future recreation uses of an area, land-use managers could predict
human activity, and thus disturbance potential, based on where theyplace trails and parking lots.
Taken together, our research demonstrates that in many cases thehuman footprint on the landscape is unlikely to be an adequate predictor of total anthropogenic impacts on wildlife communities giventhat (i) human presence and development diﬀer in their eﬀects on
wildlife behavior and habitat use (Fig. 2), and (ii) the potential impacts

4.2. Predicting the intensity of human presence across the landscape
Projecting the predictions of our human presence model across thebroader landscape (Fig. 5A) reveals that human activity outside of developed areas is pervasive throughout the Santa Cruz Mountains region,
with several major protected areas (composed of otherwise high-quality
wildlife habitat) predicted to have relatively high levels of human use
intensity. Comparing the spatial distribution of human presence withthat of human footprint (Fig. 5B) highlights the fact that human presence (and its associated impacts on wildlife habitat use and temporal
activity) is not necessarily spatially linked to development, with areasof high human presence intensity extending well beyond the heavily
developed cities and suburbs (Fig. 5). Indeed, relatively little land area8

Biological Conservation 241 (2020) 108383

B.A. Nickel, et al.

Fig. 5. Comparing human presence and human footprint across the broader landscape. (A) The relative intensity of human presence, ranging from low (light pink) tohigh (dark red), is derived from our modeling results (Table 2) using publicly available sources of spatial data. Human presence is projected across areas of the Santa
Cruz Mountains (SCM) characterized by relatively low development (≤120 buildings km−2; see Methods and Fig. S1 for details). (B) Human footprint (low = light
blue, high = dark blue) is derived from the density of all building points, estimated using a Gaussian kernel with 1-km bandwidth, and is plotted for all areas of SCM
with building density ≥ 1 building km−2. Human presence and human footprint surfaces are truncated to within 2 km of the extent of our camera trap grid. (Forinterpretation of the references to color in this ﬁgure legend, the reader is referred to the web version of this article.)conservation. This is especially true in and around protected areas
where recreation activity is often greatest but not always compatible
with conservation objectives (Larson et al., 2016). Conservation planning should thus take into account not only wildlife distributions but
also the spatial extent of human activities, including recreational demand, and its compatibility with conservation goals and other landmanagement objectives.

of human presence extend well beyond the spatial footprint of developed areas (Fig. 5). Occupancy modeling results revealed that several
wildlife species avoided human development overall, meaning thatprojected increases in development (Smith et al., 2019) will only increase the importance of protected areas in providing wildlife habitat.
However, as development intensiﬁes so will human activity and theassociated costs to wildlife in terms of changes in behavior and activity
patterns, posing serious challenges for landscape management and9Biological Conservation 241 (2020) 108383

B.A. Nickel, et al.

CRediT authorship contribution statement

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Barry A. Nickel: Conceptualization, Methodology, Formal analysis,
Writing - original draft, Visualization. Justin P. Suraci:
Conceptualization, Methodology, Formal analysis, Writing - originaldraft, Visualization. Maximilian L. Allen: Conceptualization,
Methodology, Investigation, Writing - review & editing. Christopher C.
Wilmers: Conceptualization, Methodology, Writing - review & editing,
Project administration, Funding acquisition.
Acknowledgements
We thank P. Houghtaling, R. King, and K. Briner for help in the ﬁeld,
A. Nisi for data management assistances, and multiple undergraduate
volunteers for help scoring camera trap images. We are grateful to themany Santa Cruz Mountains property owners who provided access totheir land. Funding was provided by the Gordon and Betty Moore
Foundation, National Science Foundation (grants 1255913 and0963022 to CCW), the Blue Foundation, Peninsula Open Space Trustand Resources Legacy Fund.
Role of funding sources
None of the funding sources played any role in study design; in thecollection, analysis, and interpretation of data; in the writing of thereport; or in the decision to submit the paper for publication.
Declaration of competing interest
The authors have no conﬂicts of interest to declare. None of thefunding sources played any role in study design; in the collection,
analysis, and interpretation of data; in the writing of the report; or in
the decision to submit the paper for publication.
Appendix A. Supplementary material
Supplementary methods, tables and ﬁgures for this article can befound online at https://doi.org/10.1016/j.biocon.2019.108383.
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