Received: 25 February 2021

| Accepted: 25 March 2021

DOI: 10.1111/gcb.15650

PRIMARY RESEARCH ARTICLE

Disturbance type and species life history predict mammal
responses to humans
Justin P. Suraci1| Kaitlyn M. Gaynor2 | Maximilian L. Allen3,4 | Peter Alexander5 |Justin S. Brashares6 | Sara Cendejas-­Zarelli7 | Kevin Crooks8 | L. Mark Elbroch9 |Tavis Forrester10 | Austin M. Green11 | Jeffrey Haight12| Nyeema C. Harris13 |Mark Hebblewhite14 | Forest Isbell15 | Barbara Johnston16 | Roland Kays17,18 | Patrick
E. Lendrum19 | Jesse S. Lewis20 | Alex McInturff21 | William McShea22 | Thomas
W. Murphy23 | Meredith S. Palmer24 | Arielle Parsons18 | Mitchell A. Parsons25 | Mary
E. Pendergast26 | Charles Pekins27 | Laura R. Prugh28 | Kimberly A. Sager-­Fradkin7 |Stephanie Schuttler17 | Çağan H. Şekercioğlu11,29 | Brenda Shepherd30 |Laura Whipple4 | Jesse Whittington16 | George Wittemyer8 | Christopher C. Wilmers1
1

Center for Integrated Spatial Research, University of California, Santa Cruz, CA, USA

2

National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, CA, USA

3

Illinois Natural History Survey, University of Illinois, Champaign, IL, USA

4

Department of Natural Resources and Environmental Sciences, University of Illinois, Urbana, IL, USA

5

Craighead Beringia South, Kelly, WY, USA

6

Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA

7

Lower Elwha Klallam Tribe, Port Angeles, WA, USA

8

Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO, USA

9Panthera, New York, NY, USA

10

Oregon Department of Fish and Wildlife, La Grande, OR, USA

11

School of Biological Sciences, University of Utah, Salt Lake City, UT, USA

12

School of Life Sciences, Arizona State University, Tempe, AZ, USA

13

Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA

14

Department of Ecosystem and Conservation Science, University of Montana, Missoula, MT, USA

15

Department of Ecology, Evolution and Behavior, University of Minnesota, St. Paul, MN, USA

16

Parks Canada Agency, Banff, AB, Canada

17

North Carolina Museum of Natural Sciences, Raleigh, NC, USA

18
19

Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA

World Wildlife Fund, Northern Great Plains Program, Bozeman, MT, USA

20

College of Integrative Sciences and Arts, Arizona State University, Mesa, AZ, USA

21

University of California, Santa Barbara, CA, USA

22

Smithsonian Conservation Biology Institute, Front Royal, VA, USA

23

Edmonds College, Lynnwood, WA, USA

24

Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA

25

Wildland Resources Department, Utah State University, Logan, UT, USA

26

Wild Utah Project, Salt Lake City, UT, USA

27

Fort Hood Natural Resources Management Branch, United States Army Garrison, Fort Hood, TX, USA

28

School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA

29

Department of Molecular Biology and Genetics, Koç University, Istanbul, Turkey

30

Parks Canada Agency, Jasper, AB, Canada

Glob Change Biol. 2021;00:1–14.

wileyonlinelibrary.com/journal/gcb
© 2021 John Wiley & Sons Ltd.

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SURACI et al.

Correspondence and present address
Justin P. Suraci, Conservation Science
Partners, Inc., 11050 Pioneer Trail, Suite
202, Truckee, CA 96161, USA.
Email: justin.suraci@gmail.com
Funding information
NSF LTREB, Grant/Award Number:
1556248; Alberta Conservation
Association; Rocky Mountain Elk
Foundation; Safari Club International
Foundation; Natural Sciences andEngineering Research Council of
Canada; Alberta Environment andParks; NSF, Grant/Award Number:
DEB-­1652420, DEB-­1832016, EF-­0723676, EF-­1413925, PRFB #1810586,
1232442 and 1319293; NOAA, Grant/Award Number: NA10NOS4290149;
Climate Program Office, Grant/Award
Number: NA09SEC4690036; Minnesota
Environment and Natural Resources
Trust Fund; Summerlee Foundation;
Paisley Foundation; VWR Foundation;
USDA National Wildlife ResearchCenter; Administration for Native
Americans (Department of Health andHuman Services)-­ANA Environmental
Regulatory Grant, Grant/Award Number:
90NR0302; Schmidt Science Fellows;
American Alliance of Museums; California
Department of Fish and Wildlife, Grant/Award Number: P1680002; Parks Canada;
US Department of State; Global Change
and Sustainability Center; Terracon
Foundation; ACES Undergraduate
Research Scholarship Program; National
Geographic Society; The Wildlife Society;
National Institute of Food and Agriculture
McIntire-­Stennis Capacity Grant; Panthera

Abstract
Human activity and land use change impact every landscape on Earth, driving declines in many animal species while benefiting others. Species ecological and life history traits may predict success in human-­dominated landscapes such that only species
with “winning” combinations of traits will persist in disturbed environments. However,this link between species traits and successful coexistence with humans remains obscured by the complexity of anthropogenic disturbances and variability among study
systems. We compiled detection data for 24 mammal species from 61 populations
across North America to quantify the effects of (1) the direct presence of people and(2) the human footprint (landscape modification) on mammal occurrence and activity levels. Thirty-­three percent of mammal species exhibited a net negative response(i.e., reduced occurrence or activity) to increasing human presence and/or footprint
across populations, whereas 58% of species were positively associated with increasing disturbance. However, apparent benefits of human presence and footprint tended
to decrease or disappear at higher disturbance levels, indicative of thresholds in mammal species’ capacity to tolerate disturbance or exploit human-­dominated landscapes.
Species ecological and life history traits were strong predictors of their responses tohuman footprint, with increasing footprint favoring smaller, less carnivorous, faster-­
reproducing species. The positive and negative effects of human presence were distributed more randomly with respect to species trait values, with apparent winners
and losers across a range of body sizes and dietary guilds. Differential responses bysome species to human presence and human footprint highlight the importance ofconsidering these two forms of human disturbance separately when estimating anthropogenic impacts on wildlife. Our approach provides insights into the complex
mechanisms through which human activities shape mammal communities globally,
revealing the drivers of the loss of larger predators in human-­modified landscapes.
KEYWORDS

anthropogenic disturbance, carnivore, conservation, environmental filter, human footprintindex, human-­wildlife coexistence, occupancy, traits, ungulate, wildlife

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I NTRO D U C TI O N

differential responses of species to human disturbance may havea filtering effect (Aronson et al., 2016; Santini et al., 2019), such

As the spatial extent and intensity of human activity expandsthat only species with “winning” combinations of ecological andworldwide (Larson et al., 2016; Venter et al., 2016), it is increas-

life history traits (i.e., those suited to coexistence with humans)

ingly critical to understand how animal communities respond towill persist in disturbed environments (Pineda-­Munoz et al., 2021).

anthropogenic disturbance (Gallo et al., 2017; Magle et al., 2016;

Human disturbance may, therefore, reshape mammal commu-

Parsons et al., 2018). Disturbance effects on animal distribution

nities in ways that are predictable from suites of species traits,

and activity are typically assumed to be negative (Belote et al.,with implications for both single-­species conservation efforts and2020; Dirzo et al., 2014), yet for some species, human activities

broader patterns of ecosystem functioning (Estes et al., 2011;confer benefits as well as costs. These trade-­offs are particularly

Schmitz et al., 2018).

common for mammals, as greater resource availability and re-

Anthropogenic activity involves multiple distinct stressors,

duced competition or predation in human-­d ominated landscapes

which may interact with species traits to determine the net effect(Bateman & Fleming, 2012; Moll et al., 2018) may offset the im-of human influence on mammal behavior and habitat use. Recent

pacts of habitat loss and exposure to anthropogenic mortality (Hillwork (Doherty et al., 2021; Nickel et al., 2020) demonstrates thatet al., 2020; Sévêque et al., 2020). At the community level, thetwo broad types of human disturbance—­direct human presence

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SURACI et al.

(e.g., recreation, hunting; Kays et al., 2017; Naidoo & Burton, 2020)2015; Ripple et al., 2014) and may experience higher rates of an-

and human footprint on the landscape (e.g., habitat fragmentation,

thropogenic mortality (Darimont et al., 2015; Hill et al., 2020). To

development; Smith et al., 2019; Suraci et al., 2020; Venter et al.,test our hypotheses, we compiled camera trap data for 24 medium-­2016)—­have different and often opposing effects on mammals,

to-­large ungulate and carnivore species from 61 study areas acrosslikely because these two disturbance types represent distinct sets

North America (Figure 1a), which collectively represent a substantialof filters that interact differently with species traits. For instance,

proportion of the North American range for all mammal species inmammal body size and trophic position may determine whether the

our analysis. Each camera trapping project deployed cameras acrossimmediate presence of humans induces fear responses that result

gradients of both human presence (Figure 1b) and human footprint

in reduced habitat use and suppressed activity (Clinchy et al., 2016;(Figure 1c), covering a broad range of both disturbance types, from

Ordiz et al., 2019; Suraci, Clinchy, et al., 2019) or whether human

undeveloped, remote landscapes to well used parks and urban cen-presence leads to indirect benefits through relaxed predation/

ters. Our analysis addresses two objectives. We first quantify mam-competition (Berger, 2007; Muhly et al., 2011). Species traits may

mal species responses to human disturbance across North America,

similarly determine mammal responses to human footprint. Species

incorporating variation among populations of the same species towith large space requirements may be more negatively impacted

determine the net effect of human presence and human footprintby habitat loss and fragmentation (Crooks et al., 2017; Ripple et al.,on habitat use and activity levels for each species. We then model2014), whereas those with higher dietary flexibility may benefit frommammal responses to anthropogenic disturbance as a function ofincreased resource availability in modified landscapes (Bateman &species ecological and life history traits to discern the mechanistic

Fleming, 2012; Newsome & Van Eeden, 2017). Across disturbancedrivers of human influence on mammal communities.

types, suites of traits may be strongly related to both the likelihoodthat a species will occur in areas of high human influence (Aronson
et al., 2016; Evans et al., 2011; Santini et al., 2019), as well as theintensity with which a species uses such areas when present (e.g.,
the number of individuals present and/or the frequency with which

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M ATE R I A L S A N D M E TH O DS

2.1 | Camera trapping projects and species

a site is visited; Lewis et al., 2015; Moll et al., 2018; Suraci, Clinchy,
et al., 2019), potentially allowing ecologists to predict shifts in mam-

We compiled data from 61 camera trapping studies (here after,

mal community structure and species interactions with increasing“projects”) from across the continental United States, Canada, anddisturbance intensity.

Mexico, representing 3212 unique camera locations sampled for a

However, variation among populations may obscure the link be-

total of 454,252 trap days. Details of each camera trapping project

tween species-­level traits and measured responses to human distur-are presented in Table S1. Projects were conducted between 2007

bance. Within a given mammal species, populations frequently varyand 2019, ranged in spatial extent between 0.4 and 61,506 km2

in the intensity or directionality of their response to a given distur-(x ± SD = 3473.1 ± 9834.9), deployed camera traps at three to 487

bance type depending on local conditions, including habitat produc-

unique camera sites (x ± SD = 52.6 ± 87.6) and operated for between

tivity and exposure to anthropogenic mortality (Belote et al., 2020;

63 and 106,480 trap days (x ± SD = 7446.7 ± 17,488.5). Although

Kays et al., 2017; Moreno-­Rueda & Pizarro, 2009; Sévêque et al.,the specific locations across North America sampled in this study2020). Indeed, studies of recreation impacts in protected areas com-were driven by the availability of existing camera trap data sets,

monly report contrasting responses to human presence by different

we endeavored to cover a large and representative proportion of

populations of the same species (Bateman & Fleming, 2017; Patten

the continent and to focus on areas with overlapping mammal spe-

& Burger, 2018; Reed & Merenlender, 2008; Reilly et al., 2017), andcies composition. We focused our analyses on 24 medium-­to-­large

use of developed areas may also vary among populations based on

mammal species in the orders Artiodactyla and Carnivora that weretrade-­offs between anthropogenic threat and resource availability

reliably identifiable from camera trap images and which repre-(Bateman & Fleming, 2012; Carlos et al., 2009). Therefore, elucidat-

sented three trophic guilds: herbivores, omnivores, and carnivores

ing general patterns in mammal responses to human disturbance re-(Table S2). We only included those species that were detected byquires explicitly accounting for variation among populations as wellat least three camera trapping projects and with a total of at leastas across species.

100 independent detections to ensure convergence of occupancy

Here, we examine the link between mammal species traits andmodels (see below). Due to data limitations, we treated eastern andresponses to human disturbance at the continental scale, hypoth-western spotted skunks (Spilogale putorius and Spilogale gracilis) as

esizing that species with particular combinations of trait values

a single species. We considered different camera trapping projects

are more negatively impacted by human influence. Specifically, weto approximate separate populations of each focal species, while

hypothesized that larger, more carnivorous species and those withacknowledging that there may be some overlap among adjacentslower life history strategies (i.e., longer maturation periods, slower

projects.

reproductive rates) are more negatively affected by both human

We used the geographic location of each camera site to stan-presence and human footprint, given that these species are typicallydardize the spacing between sites by (i) treating groups of camera

more likely to come into conflict with humans (Oriol-­Cotterill et al.,sites within 10 m of each other as a single site and (ii) subsampling

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SURACI et al.

F I G U R E 1 Continental-­scaleassessment of mammal responses tohuman disturbance. (a) Locations of
61 camera trap projects across North
America are shown. Projects ranged fromundeveloped but heavily used protectedareas (i) through mosaics of developed
and undeveloped land (ii) to urban centers(iii). Within each project, camera sites
spanned a gradient of both (b) humanpresence and (c) human footprint. The
color of each camera site (point) in b andc represents human detections per day
and the Human Footprint Index value,
respectively, at that location (both color
gradients on the log scale). Mean ± SD
disturbance levels are shown for the three
example projects

camera sites such that each site was at least 500 m from its nearest

camera provide a fine-­scale estimate of hotspots of human presenceneighbor. For the latter step, when two or more camera sites wereacross a landscape (e.g., where recreational activity is highest).

within 500 m of each other, we retained the site with the longest

Human footprint at each camera site was estimated from thesampling duration. Because data on camera activity and camera fail-

Human Footprint Index (HFI), which ranges between 0 and 50 andures were inconsistently recorded across projects, we used detec-

integrates multiple anthropogenic pressures on the landscape in-tion (i.e., photograph) time stamps to algorithmically identify breaks

cluding development, agriculture, and transportation infrastructure

in camera activity of greater than 4 weeks (28 days). We considered(Venter et al., 2016). We used the most recent (i.e., 2009) HFI map

these to be likely camera failures, and the durations of these activity

and extracted the average HFI value within a 1-­km buffer aroundbreaks were therefore subtracted from the total trap days for theeach camera site. This buffer size matches the resolution of the HFIcorresponding camera site. Averaged across all camera sites, theselayer itself and is comparable to the buffer size used in previous

breaks in activity accounted for 4.6% (±12.3% SD) of the total time astudies examining the effects of landscape context on occupancy

camera was deployed across the study.

model parameters across multiple study areas and for species witha range of movement capacities (e.g., Nickel et al., 2020; Rich et al.,2.2 | Estimating human presence andhuman footprint2017). We note that the spatial resolution of the HFI (i.e., 1 km)
may not match the optimal scale of effect of human footprint for
all species considered here (Moll et al., 2020). However, this layer
is among the few available at the necessary geographic extent (i.e.,

We estimated human presence as the detection rate (i.e., detections

all of North America) and has been found to correlate strongly with

per trap day) of humans at each camera site. Human detections in-wildlife behavioral responses in previous large-­scale studies consid-

cluded all people on foot, bicycles, and motorized vehicles but did

ering a range of mammal species (e.g., Tucker et al., 2018).

not include detections of domestic animals unless a person was

Across all projects, human presence ranged from zero people/

also present in the photograph. Because the number of individu-

groups per day in remote areas to more than 10 per day (max = 12.5)

als in each photograph was inconsistently reported between pro-in heavily used protected areas and suburban neighborhoods (Figure

jects, each detection event could include one or multiple humans,

S1a). The human footprint ranged from an HFI of zero, represent-

and thus, detection rates should be interpreted as groups of humans

ing undeveloped landscapes in or near protected areas, to more

detected per trap day (Nickel et al., 2020). Detections of people onthan 40 (max = 46.1) in urban centers such as Detroit, Michigan,

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SURACI et al.

and Albany, New York (Figure S1b). Areas of high human footprint

we elected to use forest cover and NPP as continuous proxies foroften also have many people present (although this is not always

habitat and ecosystem type rather than expending model degrees

the case, e.g., in heavily modified agricultural landscapes). However,of freedom on several discrete habitat categories. Principal investi-

given that camera trap detections represent a fine-­scale estimate ofgators for each camera trapping project provided data on whether

human presence (i.e., in the immediate vicinity of the camera) andtheir project baited camera sites (with either scent lures or food re-

because cameras in suburban and urban landscapes were often set

wards) as well as the prevalence of legal hunting within the projectin locations frequented by wildlife but not by people (e.g., riparianarea for both carnivores and ungulates. Typically, hunting data were

corridors, woodlots, or private property), our measures of humannot available for each individual camera site within a project. We,

presence and footprint are uncorrelated at the level of individual

therefore, assigned each camera site to one of two hunting preva-

cameras (Pearson's r = 0.003, p = 0.77) and only weakly correlated atlence categories depending on hunting activity across the project

the project level (project-­level means; r = 0.22, p = 0.09).

area: 0 = no or only limited hunting (of carnivores and/or ungulates)
within the project area, where limited hunting implies that only a

2.3 | Quantifying mammal responses to humandisturbance via occupancy models

subset of camera sites was located within areas where hunting was
permitted/occurring; 1 = hunting occurred across the project area
(i.e., at most or all camera sites, as reported by data contributors).
Finally, we included the geographic location of each camera site

We fit a series of single-­species occupancy models to detection data

(latitude and longitude) in our occupancy models to account for po-

for each of the 24 focal species. Occupancy models estimate two

tential similarities between sites in occupancy stemming from geo-

linked parameters, occupancy probability, ψ, the probability that at

graphic proximity (Rota et al., 2016). Several other aspects of camera

least one individual of a focal species “occupies” a given site, anddeployment (e.g., camera height, whether cameras were set on trails)

intensity of use, p, the probability of detecting the species at that

may affect the probability of detecting mammal species, but data on

site, given that the site is occupied (Burton et al., 2015; MacKenzie

these variables were only available for a subset of camera trapping

et al., 2002). When studying wide-­ranging wildlife species, individu-

projects. We, therefore, included a random effect for each project inals may use multiple camera sites and may be absent from any given

the occupancy models described below to accommodate variation

camera site for periods longer than the survey interval (Burton et al.,between projects not explicitly modeled by detection covariates.

2015; Efford & Dawson, 2012; Neilson et al., 2018). We, therefore,

Several camera trapping projects spanned multiple seasons andinterpret ψ as “site use” rather than occupancy per se (Kays et al.,years (Table S1). To satisfy the occupancy model assumption of clo-2020). We refer to p as “intensity of use” (rather than simply “de-sure to changes in site-­level occupancy status during a given sam-tection probability” [sensu MacKenzie et al., 2002]) to reflect thepling period (Burton et al., 2015), data for each camera site werefact that the likelihood of detecting a species at a used site depends

divided into seasonal sampling periods of at most 6 months: summer

strongly on the local abundance of the species at that site (Royle,(March to August) and winter (September to February). We deter-

2004; Royle & Nichols, 2003) and likely also varies with changes inmined the total duration that each camera was active within eachindividual behavior (e.g., increased crypsis or reduced activity levels

sampling period using the timestamp of the first and last detection

where perceived mortality risk is high; Suraci, Clinchy, et al., 2019).

event within that period. We treated each week of a given samplingWe, therefore, use covariates on p to investigate how anthropogenic

period as a separate survey and modeled the number of weeks indisturbance affects the frequency or intensity with which an occu-

which the focal species was detected during sampling period i at

pied site is used (Lewis et al., 2015).

camera site j in project k as

In addition to human presence and footprint, we estimated several covariates for each camera site with the potential to affect occupancy and/or intensity of use (Table S1). We calculated percent
forest cover around each camera site using the 2010 Global Tree()
yijk ∼ BetaBinom pijk ∗ zijk , Sijk , 𝜌( )zijk ∼ Bernoulli Ψijk

Cover database (30-­m resolution; Hansen et al., 2013) and estimated net primary productivity (NPP) around each site using NASA’s

where Sijk is the total number of weeks that camera site j (nested in

MODIS system annual NPP layer (500-­m resolution; Running &project k) was active during sampling period i and thus available for

Zhao, 2019) for 2016, the median year of all detection events in thissampling, and ρ is the overdispersion parameter of the beta-­binomial

study. Forest cover and NPP were estimated within a 1-­km buffer

distribution. We modeled y as beta-­binomially distributed becausearound each camera site, matching the spatial scale at which thepreliminary analyses indicated that variation between projects led

human footprint was estimated and capturing the landscape context

to overdispersion in detection data relative to the variability accom-

experienced by animals using that camera site (Nickel et al., 2020;

modated by the more standard binomial distribution, a situation that

Rich et al., 2017). We also estimated forest cover in the immediate

is well handled by a beta-­binomial model (Gelman & Hill, 2007). The

vicinity of each site (i.e., 100-­m buffer) to capture the effects of local

mean of the beta-­binomial distribution for camera site j during sam-vegetation cover on species detection probability. Given the large

pling period i is given by the intensity of use, p, multiplied by z, thenumber of ecoregions represented by our continental-­scale data set,latent occupancy state of the focal species at that camera site (Royle &6|

SURACI et al.

Dorazio, 2008). zijk is in turn drawn from a Bernoulli distribution withcoefficients were comparable across species. We therefore fit identi-

probability ψ, that is, the probability that the focal species occupies

cal occupancy models to each species’ data (using all model terms justcamera site j during sampling period i.

described) rather than attempting to identify the best model for each

Occupancy probability and intensity of use were modeled as

individual species through model comparison. All model covariates
were mean-­centered, and continuous covariates were scaled by two( )logit Ψijk = 𝛼 0jk + 𝛼 1k Presenceij + 𝛼 2k Footprintj + 𝛼 3k Presence2ij+𝛼 4k Footprint2j + 𝛼 5 Huntj + 𝛼 6 Forest_1 kmj + 𝛼 7 NPPj

+𝛼 8 Seasoni + 𝛼 9 Latitudej + 𝛼 10 Longitudej

standard deviations. We centered and scaled disturbance covariates
prior to subsetting the data for each single-­species model such that agiven value of human presence/footprint received the same standardized value in all models to ensure comparability across species. Pearson
correlation coefficients (r) between all pairs of model covariates were( )logit pijk = 𝛽 0jk + 𝛽 1k Presenceij + 𝛽 2k Footprintj + 𝛽 3k Presence2ij+𝛽 4k Footprint2j + 𝛽 5 Huntj + 𝛽 6 Forest_100 mj+𝛽 7 Baitj + 𝛽 8 Seasoni<0.6, with the exception of latitude and longitude (r = −0.62). For eachfocal species, occupancy models only included data from projects that
detected the species at least once.
To determine whether each human disturbance type had a net

We modeled occupancy as a function of percent forest coverpositive or negative effect on site occupancy and intensity of use forin a 1-­km radius around the camera site to estimate the effect ofa given species, we calculated the probability that each human dis-

local habitat type on the probability of site use, whereas detection

turbance linear effect (i.e., α1, α2, β1, and β2 above) was more extreme

probability was modeled as a function of forest cover in the immedi-(greater or less) than zero using the Bayesian posterior distribution ofate vicinity of the camera (100-­m radius), as sight lines and thus thethe coefficient estimate (referred to as the “posterior probability” ofability to detect species that are present may be reduced in more

the disturbance effect). We quantified the number of species exhib-

heavily forested habitats relative to open areas. For both humaniting an effect of each disturbance type by counting the species forpresence and human footprint, we fit linear and quadratic terms towhich the posterior probability of a positive or negative effect was

test for potential nonlinear effects of increasing human disturbance>0.9. This value corresponds to 80% Bayesian credible intervals aroundon species responsiveness. Because all covariates were mean cen-the disturbance coefficient that do not include zero and was chosen

tered for direct comparison (see below for details), linear terms areto capture species with limited sample sizes and/or high variability be-interpretable independently of quadratic terms (Schielzeth, 2010).

tween populations that nonetheless exhibited a strong trend toward a

Different populations of a given mammal species may not necessar-

positive or negative association with anthropogenic influence.

ily exhibit consistent responses to human disturbance. For both ψand p, we, therefore, allowed the intercept (α0, β0) and the coefficientestimates for human presence (α1, β1) and human footprint (α2, β2), as
well as their quadratic terms (α3, α4, β3, β4), to vary by camera trap-

2.4 | Modeling mammal responses to disturbanceas a function of species traits

ping project k. All project-­level covariates were modeled as being
drawn from a common distribution, with hyperparameters μ and σ2

A primary objective of this study was to examine how a species’ traits

describing the mean and variance across all projects in the data set.

affect its responsiveness to human disturbance while explicitly ac-

For instance, α0k was modeled ascounting for variation between populations of the same species. We
therefore extracted project-­level estimates for the (linear) effect of()
𝛼 0k ∼ N 𝜇𝛼0 , 𝜎 2𝛼0 .

human presence and human footprint on both occupancy and detection probability (i.e., α1k, α2k, β1k, and β2k above) from each single-­species

To account for the fact that many camera sites were sampledoccupancy model and modeled these values as a function of species

repeatedly across multiple sampling periods, the intercepts weretraits. We modeled each of the four disturbance–­response combina-

further modeled as camera site-­level random effects, with camera

tions (i.e., presence and footprint effects on ψ and p) separately.

site j nested in project k, that is,

For each focal species, we used the PanTHERIA database (Joneset al., 2009) to extract a suite of species traits that may affect re-)(
𝛼 0jk ∼ N 𝛼 0k , 𝜎 2𝛼ksponsiveness to human disturbance. This included several traits related to size and space use requirements, namely adult body mass,home range size, and longevity (i.e., maximum lifespan). We also)(
𝛽 0jk ∼ N 𝛽 0k , 𝜎 2𝛽extracted traits related to species reproductive strategy, including

where 𝜎 2𝛼 is an error term describing the variance between camera

come independent of their mother for nutritional needs), and age

sites for a given project (and likewise for 𝜎 2𝛽 ).
kat sexual maturity (Jones et al., 2009). We used the EltonTraits

Because a primary objective of this study was to model mammal

database (Wilman et al., 2014) to extract data on the proportion

responses to human disturbance (estimated from occupancy modelof each of 10 diet categories in the diet of each species and usedcoefficients) as a function of species traits, it was essential that model

these data to calculate two dietary indices. Following Santini et al.

kklitter size, weaning age (i.e., the average age at which young be-

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SURACI et al.

(2019), we used the Shannon Index to estimate dietary diversity for

Hamiltonian Monte Carlo (HMC) chains of either 2000 or 4000

each species based on all 10 diet categories. We also calculated theiterations each (depending on ease of model convergence) and re-proportion of the diet consisting of vertebrate prey as an estimate

tained 1000 samples from the posterior distribution of each chainof each species’ degree of carnivory (EltonTraits diet categories

for inference. Following Gelman et al. (2008) and Gabry et al. (2019),“vertebrate-­endoderm,” “vertebrate-­ectoderm,” “vertebrate-­fish,”

we chose vague or weakly informative priors for all random variables

and “vertebrate-­unknown”). The proportion of vertebrate prey andand random starting values for each HMC chain. We confirmed con-scavenging in the diet was used to classify each species into threevergence of HMC chains by visual inspection of trace plots and via

trophic guilds: herbivore (0%), omnivore (1%–­50%), and carnivore

the Gelman–­Rubin statistic (“R-­hat” values <1.1 for all parameters;(>50% vertebrate prey and scavenging). All species trait values are

Gelman, 2006) and verified the reliability of model inference follow-

presented in Table S2. We conducted a principal components anal-

ing the procedure outlined by Gabry et al. (2019). We tested model

ysis (PCA) on all trait and dietary data and extracted the first two

fit using Bayesian p-­values, which compare test statistics calculated

principal components (PC1 and PC2), which collectively accounted

from observed and expected (i.e., model-­generated) data (Hobbs &for 72.3% of the variation in trait values between species. We thenHooten, 2015). P-­values were calculated using test statistics appro-

used PC1 and PC2 as covariates in modeling species responses topriate for the model distributions—­for occupancy models we calculated Freeman–­Tukey (Conn et al., 2018) and chi-­squared statistics

human disturbance.
Our response variables in these models, that is, the effects(Royle & Dorazio, 2008), and for the trait models we used the meanof presence or footprint on species occupancy or detection, areand coefficient of variation (Hobbs & Hooten, 2015). We detected

themselves model estimates and are thus not perfectly observed.

satisfactory to excellent fit between model and data for all occu-

We, therefore, determined the posterior variance around eachpancy models (0.41 ≤ p ≤ 0.92; all p-­values shown in Table S5) andproject-­level disturbance effect size, as estimated by single-­species

trait models (0.35 ≤ p ≤ 0.81; all p-­values shown in Table S4).

occupancy models, and propagated this error through the multi-­
species trait models. We treated the “true” disturbance effect on
occupancy or detection, wis, as an unobserved latent variable for3|

R E S U LT S

which we have an observed value, vis, with an associated “observation” error value, 𝜎 2o,isStandardized occupancy model coefficients, summarizing the average response to disturbance across all populations of a given()
vis ∼ N wis , 𝜎 2o,isspecies, revealed that 17% of the 24 mammal species in our study

where i is a project-­level disturbance effect size observation forhuman presence (i.e., the posterior probability of a negative ef-

species s. We then modeled the true, unobserved disturbance ef-

fect of human presence on occupancy and/or intensity of use was(three herbivores and one carnivore) were negatively affected byfect, wis, as a function of PC1, PC2, and their interaction using a>0.9, see Section 2). Elk (Cervus elaphus) exhibited reduced site oc-

Gaussian linear model. We also included the geographic location of

cupancy with increasing human presence, whereas moose (Alceseach project (project-­level mean latitude and longitude, Table S1) inalces), mountain goats (Oreamnos americanus), and wolverines (Gulo

all models to account for potential similarities between projects ingulo) exhibited reduced intensity of use (Figure 2a,b). Thirty-­threemammal responses stemming from geographic proximity. The linear

percent of species were positively associated with human presence

models included a random intercept for species to account for the(two herbivores, two omnivores, four carnivores). Bighorn sheep

fact that each species’ response to disturbance was estimated for(Ovis canadensis), black bears (Ursus americanus), and wolverines ex-

several camera trapping projects. The species-­level random effecthibited increased site occupancy with increasing human presence,

was nested within family to account for the influence of taxonomic

whereas mule deer (Odocoileus hemionus), bobcats (Lynx rufus), grayrelatedness in driving similar responses between species. Other

foxes (Urocyon cinereoargenteus), pumas (Puma concolor), and wolves

taxonomic levels were not included because there were either too(Canis lupus) tended to increase intensity of use in areas of higher

few categories (only two orders, Artiodactyla and Carnivora, were

human activity (Figure 2a,b).

represented) or too few observations per category (most genera

Human footprint had a negative effect on site occupancy and/were only represented by a single species) to estimate the random

or intensity of use for 25% of mammal species (one herbivore, one

effects, inhibiting model convergence.

omnivore, and four carnivores), and a positive effect for 38% of species (three herbivores, two omnivores, and four carnivores). Species

2.5 | Model fitting and checking

negatively associated with human footprint included grizzly bears(Ursus arctos), lynx (Lynx canadensis), wolves, and wolverines, all ofwhich were less likely to occupy sites with increasing footprint andWe analyzed all occupancy models (24, one for each focal spe-

exhibited decreased intensity of use when present, whereas moosecies) and species trait models (4) in a Bayesian framework using theand martens (Martes americanus) exhibited decreased intensity of

Stan programming language called through the Rstan package in

use only. Raccoons (Procyon lotor) and white-­t ailed deer (Odocoileus

R (Stan Development Team, 2020). For each model, we ran threevirginianus) were positively associated with human footprint in terms

8|

SURACI et al.

F I G U R E 2 Human presence andhuman footprint have contrasting impacts
within and among mammal species.
Occupancy model coefficient estimates
for the effects of human presence (square)and human footprint (diamond) on (a) site
occupancy and (b) intensity of site use.
Error bars are 95% (thin) and 80% (thick)
Bayesian credible intervals. Coefficientestimates are grouped by trophic guild(based on percent of vertebrate prey andscavenging in the diet, see Table S2 andSection 2)

F I G U R E 3 Nonlinear effects ofhuman disturbance. Occupancy model
predictions for the change in intensityof site use with increasing (a) humanpresence and (b) human footprint. Results
are shown for species for which models
estimated a >0.9 probability of a quadratic
effect. Lines are color-­coded by trophic
guild, as shown in Figure 2of both occupancy and intensity of use, while elk, mule deer, striped

disturbance levels (Figure 3a). Full results for all occupancy models

skunks (Mephitis mephitis), red foxes (Vulpes vulpes), bobcats, coyotes

are shown in Figure S2.

(Canis latrans), and pumas all tended to use occupied sites more intensively at higher levels of human footprint (Figure 2a,b).

The first two components of the PCA performed on species trait
values (accounting for 72.3% of trait variation) describe axes of in-

For several species exhibiting positive responses to disturbance,creasing body size with decreasing life history speed (i.e., smaller lit-

intensity of use peaked at low to intermediate values of human pres-ters, slower maturation; PC1), and increasing space use with greater

ence (six species, Figure 3a) or footprint (eight species, Figure 3b),

carnivory (PC2; Figure 4a and Table S3). Bayesian regression models

indicative of thresholds in these species’ capacity to tolerate dis-revealed that the effects of human footprint on both site occupancy

turbance or exploit human-­dominated landscapes. Red foxes were

(Figure 4b; β = −0.22 [95% CI = −0.48,0.01]) and intensity of use

the single exception, being the only species to exhibit an increas-(Figure 4c; β = −0.23 [−0.57,0.04]) were increasingly negative at

ingly positive association with human presence at medium to highhigher values of PC1 (posterior probability of a negative slope = 0.97

| 9

SURACI et al.

F I G U R E 4 Species traits predict responses to human footprint. (a) Biplot of the principal components analysis performed on species
trait data, with each mammal species plotted based on its values of the first two principal components (PC1—­larger body size and slower
life history and PC2—­greater space use and more carnivorous). Factor loadings for each trait are shown in gray (see also Table S3). Mammal
responses to human footprint were increasingly negative with increasing values of PC1 for both (b) site occupancy and (c) intensity of use.
Occupancy responses to footprint (d) were also negatively associated with PC2. Data points in b–­d are population-­level estimates of eachspecies’ response to human footprint, where values above zero (dashed line) indicate a positive response (i.e., increased occupancy orintensity of use with increasing footprint) and those below zero​indicate a negative response. Solid lines and shaded areas are the estimatedslope and 95% Credible Intervals from hierarchical linear models (see also Table S4). Data in all plots are color-­coded by trophic guild, as
shown in Figure 2

and 0.95, respectively; Table S4), indicating that larger, longer-­lived

comprehensive understanding of mammal responses to human dis-mammal species and those with slower maturation and reproduc-turbance. We found that species’ ecological and life history traits

tive rates are less likely to occur in modified landscapes and are lesswere strong predictors of their responses to human footprint on theactive when present. Human footprint also had an increasingly neg-landscape, confirming our hypothesis that larger, more carnivorous,

ative effect on occupancy (but not intensity of use; Table S4) forand slower reproducing species are more negatively affected by

species with higher PC2 values (Figure 4d; β = −0.40 [−0.76, −0.04];

human landscape modification. Contrary to our expectations, how-probability of a negative slope = 0.99), indicating that species with

ever, species traits were not strongly related to their responses tomore carnivorous diets and larger home ranges are less likely to use

human presence, highlighting the importance of considering thesea given site as landscape modification increases. We did not detect

two forms of human disturbance separately when estimating an-a relationship between human presence and species traits in theirthropogenic impacts on wildlife (Nickel et al., 2020; Sévêque et al.,effects on site occupancy or intensity of use (Bayesian regression;2020).

posterior probability of a negative slope ranged between 0.33 and0.85 for all relationships; Table S4).

Our occupancy model estimates revealed relatively few overall “winners” and “losers”, that is, species that tended to respondpositively or negatively across disturbance types. Instead, most4|

DISCUSSION

species exhibited differing, and frequently opposing, responses tohuman presence and footprint (Figure 2a,b). This pattern was previously reported for mammals in a single study area (Nickel et al.,By bringing together data on activity and habitat use from mam-2020), and our results suggest this is a common feature of human–­mal populations across North America, our study provides amammal interactions across North America. Negative effects of10|

SURACI et al.

human presence likely stem from fear of humans causing mammals

space requirements were less likely to occur in modified landscapes.

to suppress their activity levels (Suraci, Clinchy, et al., 2019) or avoid

Wide-­ranging carnivores frequently come into conflict with humans

areas of high human influence entirely (Oriol-­Cotterill et al., 2015),

over perceived threats to human life and livelihood (Carter & Linnell,

while positive associations may reflect the shield that human pres-2016; Chapron & López-­Bao, 2016; Treves & Karanth, 2003), andence provides for some species against predators or competitors

their large home range sizes may make them more susceptible to the(Berger, 2007; Hebblewhite et al., 2005; Muhly et al., 2011). Positive

impacts of habitat fragmentation in modified landscapes (Crooks,

responses to human presence by larger bodied species with substan-

2002; Ripple et al., 2014). Although our analyses were focused ontial space requirements (e.g., pumas, wolves, wolverines; Figure 2b)

North American mammal communities, we suggest that the patterns

may also reflect the growing intensity of recreation and ecotourism

observed here are likely applicable to medium-­to-­large mammal spe-in otherwise relatively undisturbed areas of wildlife habitat (Antoncies globally. Recent work focused on urban environments has simi-

et al., 2020; Nickel et al., 2020), leaving such species with little

larly shown that both high reproductive output (large litter size) andchoice but to share space with people.

high dietary diversity are frequently associated with mammal use

Despite the loss of natural habitat associated with increasing humanof urban areas worldwide (Santini et al., 2019), highlighting the im-

footprint, agricultural lands and developed areas nonetheless present

portance of these traits in allowing mammals to successfully exploit

opportunities for some species through resource subsidies or relaxed

modified landscapes. Additionally, the exclusion of larger predatory

predation/competition (Prugh & Sivy, 2020; Sévêque et al., 2020).

mammals from areas of high human footprint is a common phenom-

Increased human footprint is also associated with decreased move-enon in systems around the world (Ordiz et al., 2013; Oriol-­Cotterill

ment and smaller home ranges for many mammal species (Doherty

et al., 2015).

et al., 2021; Tucker et al., 2018), including large predators such as

Our study provides a framework for predicting the filtering ef-

pumas (Nickel et al., 2021). Thus, higher intensity of use in more heavily

fect of human land use change on mammal communities, helping

modified habitats by species that otherwise tend to avoid human foot-to clarify the often ambiguous relationship between human foot-print (e.g., pumas and bobcats, Serieys et al., 2021; Suraci et al., 2020)

print and mammal community composition (Belote et al., 2020; Hill

may reflect restricted movement options and thus intensified use of

et al., 2020). Our results indicate that as human footprint increases,

remaining habitat fragments in moderately developed areas.

mammal community composition will shift toward smaller herbiv-

Importantly, for several mammal species that exhibited positive

orous and omnivorous species with faster reproductive strategies

associations with human presence or footprint, the apparent ben-and smaller space requirements. The speed and extent of shifts inefits of human activity were diminished or reversed at higher dis-

community composition following land conversion to agriculture

turbance levels (Figure 3). For instance, black bears, elk, and wolves

or development will likely depend on local legal protections (e.g.,

were most active at sites visited by approximately one person/

hunting regulations; Chapron & Treves, 2016); and environmentalgroup every 8–­12 days (Figure 3a), and several species exhibited

conditions (e.g., ecosystem productivity; Belote et al., 2020). Our

peak intensity of use in partially cleared habitats with low density

analysis accounted for population-­level variation in these and addi-

development (i.e., intermediate HFI values, Figure 3b). These results

tional factors, showing that, whereas the effects of hunting and en-

indicate that several mammal species exhibit thresholds in their tol-vironmental covariates varied substantially between species (Figure

erance for human disturbance beyond which habitat may no longer

S2), trends toward reduced occurrence and activity of large-­bodied,be viable. We suggest that such thresholds are critical to consider

slow-­reproducing mammals in more developed areas were robust towhen attempting to promote “landscapes of coexistence” (i.e., eco-variation in local conditions.

logical conditions that allow the long-­term persistence of sensitive

Our estimates of human footprint (i.e., contemporary landscapemammal species in human-­dominated landscapes; Gehr et al., 2017;

modification) represent only a snapshot in time and do not explicitly

Oriol-­Cotterill et al., 2015) and functional connectivity between

incorporate legacies of human land use (e.g., historical management

populations, particularly as several large mammal species continue

regimes; Jonason et al., 2014; Moreira & Russo, 2007) or other forms

to recolonizing modified landscapes in North American and glob-of ecological disturbance such as fire (Geary et al., 2020; Pastro et al.,ally (Gantchoff & Belant, 2017; Gilbert et al., 2016; Hemmingmoore2014), both of which are known to play important roles in shaping

et al., 2020; Rio-­Maior et al., 2019).

observed community structure by affecting vegetation composition

Larger mammal species and those with slower life histories wereand species interactions. Our objective in the present study was toboth less likely to occur in areas of high human footprint and ex-

detect general patterns in human disturbance impacts on mammals

hibited lower intensity of use when present. The human footprint

across a range of ecological conditions. However, we note that un-

is associated with a multitude of threats including vehicle strikes

derstanding the interaction between current human land use and(Grilo et al., 2020), sensory pollution (Dominoni et al., 2020), andother drivers of landscape pattern, both historical and contempo-

invasive species (Shochat et al., 2010). Our results suggest thatrary, will be critical in accurately predicting mammal community re-smaller mammals and those with faster life histories are better ablesponses to future landscape modification in any particular location.

to avoid these threats (Hill et al., 2020) or can offset heightened an-

Although species traits predicted responses to human footprint,

thropogenic mortality through high reproductive rates (Santini et al.,they were not predictive of responses to human presence, suggest-2019). Additionally, species with more carnivorous diets and larger

ing that the benefits and costs of using habitats frequently visited by

| 11

SURACI et al.

humans (e.g., recreationists in protected areas or urban green spaces)

All code necessary to replicate the analyses is available on GitHub atare distributed somewhat randomly across body sizes, trophic guilds,

https://github.com/jsura​ci/Surac​i-­etal-­Mamma​lLife​History.

and reproductive strategies. This finding likely reflects the fact thatmany species are able to mitigate the impacts of human presence

ORCID

on space use through increased nocturnality (Gaynor et al., 2018).

Justin P. Suraci

https://orcid.org/0000-0001-5952-2186

Whereas landscape modification is relatively constant in space andJeffrey Haighthttps://orcid.org/0000-0002-3773-1566

time, human presence is largely diurnal, meaning that even species
that are relatively sensitive to the immediate presence of people

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AC K N OW L E D G M E N T S
We thank the many volunteers and field technicians who helped tocollect and process the large amount of camera trap data used here,
as well as all of the property owners who allowed data collection on
their land. Please see the supplementary material for full acknowledgements from all authors.
AU T H O R C O N T R I B U T I O N S
JPS, CCW, MLA, and KMG conceived of the study. All authors contributed data to the analysis. JPS led the analysis and writing, with
substantial feedback on analysis from CCW, MLA, and KMG, andwith valuable input on writing from all authors.
DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are openly available
on Figshare at https://doi.org/10.6084/m9.figsh​are.14444​600.v1.

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S U P P O R T I N G I N FO R M AT I O N
Additional supporting information may be found online in the
Supporting Information section.

How to cite this article: Suraci JP, Gaynor KM, Allen ML, et
al. Disturbance type and species life history predict mammal
responses to humans. Glob Change Biol. 2021;00:1–14.
https://doi.org/10.1111/gcb.15650