Access to this full-text is provided by Wiley.
Content available from Conservation Letters
This content is subject to copyright. Terms and conditions apply.
Received: March Revised: November Accepted: December
DOI: ./conl.
LETTER
Global camera trap synthesis highlights the importance of
protected areas in maintaining mammal diversity
Cheng Chen1,2Jedediah F. Brodie3Roland Kays4,5T. Jonathan Davies2,6,7
Runzhe Liu1,8Jason T. Fisher9Jorge Ahumada10 William McShea11
Douglas Sheil12,13 Bernard Agwanda14 Mahandry H. Andrianarisoa15
Robyn D. Appleton7,16 Robert Bitariho17 Santiago Espinosa18,19
Melissa M. Grigione20 Kristofer M. Helgen21 Andy Hubbard22
Cindy M. Hurtado1Patrick A. Jansen23,24 Xuelong Jiang25 Alex Jones26
Elizabeth L. Kalies27 Cisquet Kiebou-Opepa28 Xueyou Li25
Marcela Guimarães Moreira Lima29 Erik Meyer30 Anna B. Miller31
Thomas Murphy32 Renzo Piana16 Rui-Chang Quan33 Christopher T. Rota34
Francesco Rovero35,36 Fernanda Santos37 Stephanie Schuttler4
Aisha Uduman1Joanna Klees van Bommel1Hilary Young38
A. Cole Burton1,2
Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia, Canada
Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
Division of Biological Sciences and Wildlife Biology Program, University of Montana, Missoula, Montana, USA
North Carolina Museum of Natural Sciences, Raleigh, North Carolina, USA
Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina, USA
Department of Botany, University of British Columbia, Vancouver, British Columbia, Canada
Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada
Biology Department, Lund University, Lund, Sweden
School of Environmental Studies, University of Victoria, Victoria, British Columbia, Canada
Moore Center for Science, Conservation International, Arlington, Virginia, USA
Smithsonian Conservation Biology Institute, Front Royal, Virginia, USA
Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway
Forest Ecology and Forest Management Group, Wageningen University & Research, Wageningen, The Netherlands
Mammal Section, National Museums of Kenya, Nairobi, Kenya
Centre ValBio, Ifanadiana, Madagascar
Spectacled Bear Conservation Society Peru, La Quinta Batan Grande, Lambayeque, Peru
Institute of Tropical Forest Conservation, Mbarara University of Science and Technology, Mbarara, Uganda
Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México
Escuela de Ciencias Biológicas, Pontificia Universidad Católica del Ecuador, Quito, Ecuador
Department of Biology, Pace University, Pleasantville, New York, USA
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the
original work is properly cited.
© The Authors. Conservation Letters published by Wiley Periodicals LLC
Conservation Letters. ;:e. wileyonlinelibrary.com/journal/conl 1of14
https://doi.org/./conl.
2of14 CHEN .
Australian Museum Research Institute, Australian Museum, Sydney, Australia
National Park Service, Sonoran Desert Network, Tucson, Arizona, USA
Wildlife Ecology and Conservation Group, Wageningen University & Research, Wageningen, The Netherlands
Smithsonian Tropical Research Institute, Panama, the Republic of Panama
State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
Campus Natural Reserves, University of California, Santa Cruz, Santa Cruz, California, USA
The Nature Conservancy, Durham, North Carolina, USA
Wildlife Conservation Society - Congo Program, Brazzaville, Congo
Laboratory of Conservation Biogeography and Macroecology, Universidade Federal do Pará, Belém, Brazil
Sequoia & Kings Canyon National Parks, Three Rivers, California, USA
Institute of Outdoor Recreation and Tourism, Utah State University, Logan, Utah, USA
Department of Anthropology, Edmonds College, Lynwood, Washington, USA
Center for Integrative Conservation, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, China
Division of Forestry and Natural Resources, West Virginia University, Morgantown, West Virginia, USA
Department of Biology, University of Florence, Sesto Fiorentino, Italy
Tropical Biodiversity Section, MUSE – Museo delle Scienze, Trento, Italy
Departamento de Mastozoologia, Museu Paraense Emílio Goeldi, Belém, Pará, Brazil
Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, Santa Barbara, California, USA
Correspondence
Cheng Chen and A. Cole Burton, Forest
Sciences Centre , Main Mall,
Vancouver, BC VT Z, Canada.
Email: chengchen@gmail.com;
cole.burton@ubc.ca
Abstract
The establishment of protected areas (PAs) is a central strategy for global biodi-
versity conservation. While the role of PAs in protecting habitat has been high-
lighted, their effectiveness at protecting mammal communities remains unclear.
We analyzed a global dataset from over camera traps in countries on four
continents that detected medium- to large-bodied mammal species. We found
a strong positive correlation between mammal taxonomic diversity and the pro-
portion of a surveyed area covered by PAs at a global scale (β=., % confi-
dence interval [CI] =.–.) and in Indomalaya (β=., % CI =.–.),
as well as between functional diversity and PA coverage in the Nearctic (β=.,
% CI =.–.), after controlling for human disturbances and environmental
variation. Functional diversity was only weakly (and insignificantly) correlated
with PA coverage at the global scale (β=., % CI =−.–.), pointing to
a need to better understand the functional response of mammal communities to
protection. Our study provides important evidence of the global effectiveness of
PAs in conserving terrestrial mammals and emphasizes the critical role of area-
based conservation in a post- biodiversity framework.
KEYWORDS
camera trap, functional diversity, human accessibility, human footprint, mammal diversity,
protected area, species richness, taxonomic diversity
1 INTRODUCTION
Humans are a dominant geographical and environmen-
tal force on the planet (Díaz et al., ). The current era
has been termed the “Anthropocene” (Lewis & Maslin,
), with anthropogenic disturbances such as overex-
ploitation, habitat destruction, and invasive species driving
extensive loss of biodiversity and ecosystem services (Dirzo
et al., ). The formal establishment of protected areas
(PAs) is one of the most prominent conservation actions
CHEN . 3of14
for mitigating these losses. Globally, terrestrial protected
area coverage has increased from .% to .% in the past
decade (Maxwell et al., ), and this trend is expected
to continue under international policy commitments to
conservation. For example, the parties to the Convention
on Biological Diversity (CBD) are expected to agree on a
new global biodiversity framework with targets that may
include protecting % of the planet by and % by
(Dinerstein et al., ). Given the global focus on
increasing PAs as a primary conservation strategy, it is crit-
ical to evaluate their effectiveness at conserving biodiver-
sity (Bhola et al., ).
The effectiveness of PAs has been questioned because
relatively few have sufficient management practices in
place (Geldmann et al., ). While many PAs appear
to be effective at protecting habitat, there is limited evi-
dence about whether they also protected animal popula-
tions (Geldmann et al., ). For instance, declines in
large mammals and other taxa have been documented in
several PAs (e.g., Laurance et al., ), especially those
that are inadequately funded (Waldron et al., ). Even
within some relatively well-protected areas, wildlife habi-
tats have been significantly degraded (Geldmann et al.,
); wildlife can also be negatively affected by noncon-
sumptive activities such as recreation (Naidoo & Burton,
), and by human-altered fire activity (Mansuy et al.,
).
In many respects, pressures on wildlife within PAs
reflect the broader impacts of human activities across the
planet (Geldmann et al., ;K.R.Jonesetal.,;
Wittemyer et al., ). The “human footprint” (here-
after footprint) is often used to characterize cumulative
human disturbances across terrestrial landscapes, which
now extend across % of the planet’s land surface and have
been linked to changes in the behavior, distribution, and
diversity of medium- and large-bodied mammals (Belote
et al., ; Tucker et al., ;Venteretal.,b). PAs
may be an effective tool to reduce the impacts of land-use
disturbances, but recent evidence indicates that many PAs
still experience considerable human impact (K. R. Jones
et al., ). Moreover, protected and unprotected areas
vary in their accessibility to people, which can be a strong
measure of potential human impacts on wildlife from over-
exploitation and other disturbances (Deith & Brodie, ;
Weiss et al., ). Before enhancing international commit-
ments to PAs as a main conservation strategy, it is impor-
tant to know whether existing PAs are counteracting pres-
sures from increasing human footprint and accessibility,
and thereby effectively helping achieve global biodiversity
targets.
A key challenge to understanding contemporary drivers
of biodiversity loss, and the effectiveness of conservation
actions, is the relatively slow pace of biodiversity assess-
ments. For example, IUCN Red List assessments of a given
species or group may be separated by years or more
(Mace et al., ). Clearly, more timely assessments of
biodiversity status from standardized global observation
systems are needed (Pereira et al., ). Recent advances
in remote camera (camera trap) technology enable rapid
monitoring of changes in the abundance and distribu-
tion of terrestrial vertebrate communities, especially for
medium- and large-bodied terrestrial species. Despite the
rapidly growing number of camera trap studies (Burton
et al., ;Steenwegetal.,), relatively few have
pooled data across a large number of sites to collabo-
ratively address conservation questions at regional and
global scales. Notable exceptions include regional evalu-
ation of trends in occupancy (Beaudrot et al., )and
functional composition of mammal communities (Rovero
et al., ), and similar large-scale assessments of car-
nivore assemblages and threatened species (Davis et al.,
). These studies highlight the potential to further
scale up camera trap data to inform global-scale analyses
(Steenweg et al., ).
Here, we build on these efforts with a global evalua-
tion of the relationships between mammal diversity and
three key indicators of anthropogenic pressure: human
footprint, human accessibility, and PA coverage (i.e., the
proportion of an area under formal protected status).
We assembled a dataset of mammal species occurrences
and relative abundances from camera trap surveys—
covering areas inside and outside of PAs— across coun-
tries on four continents (Figure ) to evaluate the effective-
ness of PAs at conserving mammal diversity, while control-
ling for other environmental factors. We evaluated two key
dimensions of mammal diversity: taxonomic richness and
functional diversity. While there has been much focus on
the former, in terms of species extirpations and population
declines (Ceballos et al., ), far less is known about how
such declines affect the functional diversity of assemblages
and the functioning of ecosystems (Gagic et al., ).
2 MATERIALS AND METHODS
2.1 Camera trap dataset and target
species
We synthesized a global dataset of mammal detections
from camera trap surveys spanning four continents
(Figure ), including data from more than camera trap
stations distributed inside and outside of PAs, collectively
sampled for , camera trap-days (Table S). These
surveys include projects run by study authors and collab-
orators, previously published studies (Lima et al., ;
Swanson et al., ), and surveys that were available
4of14 CHEN .
FIGURE 1 Locations of camera trap study areas from which mammal diversity was estimated, spanning countries and four
continents in different zoogeographic realms (inset; background richness from Jenkins et al., ). Three example camera trap surveys
illustrate the gradient of camera trap sampling of protected area coverage (PA_cov), from entirely outside (e.g., (a) Project ITBD,.%
PA_cov.), to partly within (e.g., (b) Project EMML_UCSC, .% PA_cov), to entirely within protected areas (e.g., (c) Project TEAM_NAK,
% PA_cov)
through public databases (e.g., eMammal, Kays et al.,
), implemented between January and August
. Surveys targeted medium- and large-bodied mam-
mals (> g) with unbaited sampling and reported the
number of total detections of each species. We used
camera days as a minimum per-survey sampling effort
(Tobleretal.,). We also removed species that were
unlikely to be consistently detected by cameras so as to
reduce the potential influence of detection bias (see Sup-
porting Information Methods for details).
2.2 Biodiversity metrics
We calculated metrics of taxonomic diversity and func-
tional diversity for each project. First, we calculated a rela-
tive abundance index (RAI) for each species in each study
area as the number of independent detections per
camera trap-days. Consecutive detections were considered
independent if they contained different species or were of
the same species but separated by more than a threshold
time interval. Most of the studies used a threshold of –
min for independence (O’Brien et al., ); where stud-
ies used a shorter threshold, we adjusted it to min. We
assumed that RAI reflected differences in relative abun-
dances across species and surveys although we note that
RAI may also be influenced by animal movement and
detectability (Broadley et al., ), as are other metrics
derived from camera trap detections (e.g., occupancy, Neil-
son et al., ). We calculated abundance-weighted diver-
sity metrics as well as metrics based on presence–absence
and examined the correlation between them.
For taxonomic diversity, we calculated the Shannon
index (Magurran & McGill, ) using RAI for each
species at each site, and also species richness (SR). For
functional diversity, we calculated both Petchey and Gas-
ton’s dendrogram index for multiple traits (Petchey &
Gaston, ), functional richness (Villéger & Mouillot,
), and Petchey and Gaston’s dendrogram index based
on presence–absence data (FDPA) in the fundiv pack-
age (Gagic et al., ) in R statistical software (R Core
Team, ), using four ecologically relevant traits for
each species obtained from the PanTHERIA database (K.
E. Jones et al., ) or other literature. The selected
traits were adult body mass (g), trophic level (omnivore,
herbivore, carnivore), activity cycle (diurnal, nocturnal,
CHEN . 5of14
both), and diet breadth (includes over categories).
For traits that were unavailable for a given species, we
used values from the most closely related species (Brodie
et al., ). We tested correlations among all biodiver-
sity metrics; functional richness was correlated with taxo-
nomic diversity (SR from presence–absence data, Pearson’s
r=.), and was therefore not considered in further anal-
ysis. Numerically, our biodiversity metrics were continu-
ous variables bounded between −and.
2.3 Environmental and anthropogenic
variables
We collected spatial data for each project site to generate
the predictor variables that we hypothesized would affect
mammal diversity. To delineate the survey area for each
project, we used one of three types of spatial information.
First, we used the spatial extent (e.g., shapefile) of a project
if it was provided by the data source. Otherwise, we created
a minimal convex hull polygon around the set of camera
points with a m buffer (Figure ). Multiple convex hull
polygons were used when a study was conducted in differ-
ent areas or there were distinct clusters of cameras. If we
did not have shapefiles or camera locations, we used a cir-
cular polygon approximately covering the study area based
on the longitude, latitude, and area reported by the project
(for of projects; Supporting Information Methods).
We quantified three predictor variables to assess the rela-
tionships between mammal diversity and the degree of
protection or pressure (Table ; Figure Sa). For protec-
tion, we calculated the total percentage (%) of each study
area polygon that overlapped with one or more PAs (of any
IUCN category) in the World Database on Protected Areas
(Table ) (IUCN & UNEP-WCMC, ). We used this mea-
sure of PA coverage since projects spanned a range from
having all cameras inside a PA, to some cameras inside
and some outside, to all cameras outside (Figures and ;
relatively fewer projects were partially inside and outside
a PA; therefore, the variable had a bimodal distribution).
To test the accuracy of this measure, we compared it with
finer-scale measures for the subset of projects that reported
camera locations, specifically with the percentage of PA
overlap within km and m buffers around each cam-
era trap. We found strong correlations between PA cover-
age based on the project-level convex hull polygons and
the camera trap-level buffers (see Supporting Information
Methods). Of all the PAs that overlapped with camera trap
study areas, .% were areas of stricter nature protection
in IUCN categories Ia (.%), Ib (.%), and II (.%), while
the other .% were in categories III (.%), IV (.%), V
(.%), and VI (.%), which allow more human activity
(Day et al., ).
To estimate human impacts independently from PA cov-
erage, we calculated the mean human footprint index
within each survey area using the global human footprint
map of (Venter et al., a). The human footprint
index provides a single metric accounting for the extent of
built environments, croplands, pasture lands, human pop-
ulation density, electric infrastructure roads, railways, and
waterways (Venter et al., a). We also calculated the
mean human accessibility score for each survey area using
the global accessibility map (Weiss et al., ), which esti-
mates travel time from a location to the nearest major city
via surface transportation, including minor roads such as
unpaved rural roads and exurban residential streets. Since
the human accessibility index reflects travel time, a high
value indicates low accessibility (i.e., higher remoteness).
We calculated additional covariates to control for
environmental differences across camera trap survey
areas (Table ), specifically: annual average temperature
(TEMP), average elevation (ELEV), zoogeographic realm
(REALM), forest canopy height (CH), actual evapotran-
spiration (AET), and cumulative Dynamic Habitat Index
(DHI) measured as the fraction of absorbed photosynthet-
ically active radiation (fPAR), which is an indicator of
vegetation productivity (details in Supporting Information
Methods).
2.4 Statistical analyses
We used linear and mixed-effects linear regression mod-
els to examine relationships between mammal diversity
and human influence. Explanatory variables were assessed
for collinearity using Pearson’s correlation coefficient and
were excluded if r>. (AET was dropped due to high
collinearity with other variables, see Supporting Informa-
tion Methods; Table S). We first built a core model with
the environmental (nonanthropogenic) variables (Table )
and compared it to other candidate models with these
variables and all combinations of the human influence
variables (Table S). All continuous variables were stan-
dardized (mean =, variance =) to allow the direct com-
parison of effect sizes. We ran mixed-effects models with
the global dataset where zoogeographic realm was treated
as a random intercept for both abundance-weighted taxo-
nomic diversity and functional diversity. To test whether
there were different regional responses to human influ-
ence, we ran separate linear models (without a random
effect) for data subsets from the four different zoogeo-
graphic realms: Nearctic, Neotropical, Afrotropical, and
Indomalaya.
We used model selection to evaluate statistical support
across candidate models based on Akaike’s Information
Criterion (AICc) adjusted for small sample sizes (Burn-
6of14 CHEN .
TABLE 1 Predictor variables included in mixed effects linear models to explain global patterns of mammal taxonomic and functional
diversity measured at camera trap survey sites. Spatial resolution, year of data collection, and data source are provided for each variable
Varia ble
Resolution
(meter at
equator)
Yea r o f data
collection Data source
Human Influences
Human footprint (HF) km (Venter et al., ;
https://doi.org/./dryad.q)
Human accessibility (HA) km (Weiss et al., ;https://www.map.ox.ac.
uk/accessibility_to_cities)
Percentage of survey area
within protected areas
(PAcov)
Continuous
variable
February World database on protected areas
(https://www.protectedplanet.net)
Environment
Annual average temperature
(TEMP)
km World-clim
(http://www.worldclim.com/version)
Elevation (ELEV) m March ASTER GDEM
(https://asterweb.jpl.nasa.gov/gdem.asp)
Biogeographic
Zoogeographic realm
(REALM)
Shapefile (Holt et al., ;https://macroecology.ku.
dk/resources/wallace)
Habitat structure
Fraction of absorbed
photosynthetically active
radiation based dynamic
habitat indices (fPAR-based
DHI)
km – MODIS
(http://silvis.forest.wisc.edu/data/dhis/)
Canopy height (CH) km March (Roll et al., ; Simard et al., ;
https://webmap.ornl.gov/ogc/dataset.
jsp?ds_id =)
ham & Anderson, ). Models with the lowest AICc,
or within two AICc units of the best-fit model, were con-
sidered to have the most support. We used standardized
regression coefficients and their % confidence intervals
(CI) from the best (lowest AICc) model to assess the direc-
tion, magnitude, and statistical significance of estimated
effect sizes. For variables not included in the best model,
we used coefficients from the model with the next lowest
AICc or from the FULL model if the variable in question
was not included in any of the models of best-fit. To com-
pare mixed-effects models with different fixed effects, we
fitted, ranked, and weighted our models using maximum
likelihood (ML) but then we estimated the variance com-
ponent parameters using restricted maximum likelihood
(REML; Luke, ; Zuur et al., ). We checked for nor-
mality and homogeneity of variance by visual inspection of
residuals (Figure S). We assessed goodness-of-fit by calcu-
lating marginal and conditional Rfor mixed-effects mod-
els using the rsquared.GLMM function (Barton, & Barton,
), or Rfor linear models using summary function. The
mean Racross all best-fit models was . (range: .–
.; Table ,TablesS and S). All statistical analyses were
performed using the lme4 and MuMIn packages (Barton &
Barton, ; Bates et al., ) in R statistical software ver-
sion .. (R Core Team, ).
3RESULTS
Globally, we found that mammal taxonomic diversity
was positively associated with PA coverage (mixed-effects
model: β=., % CI =.–.), but not with
human footprint or accessibility (Figures and ,Table
S). By contrast, mammal functional diversity was not
significantly related to PA coverage at the global scale
(β=., % CI =−. to .), nor to footprint or
accessibility (Figure ,TableS). Taxonomic diversity was
positively associated with elevation across the surveyed
areas (β=., % CI =.–., Table S), while
functional diversity was negatively related to forest CH (β
CHEN . 7of14
TABLE 2 Model selection results and Rfor mixed effects linear models testing human influences on global taxonomic and functional
diversity of mammals
Model names AICc ΔAICc AICc weight Marginal R2Conditional R2
Taxonomic diversity (Shannon index)
PAcov 227.09 00.48 0.31 0.38
HA_PAcov 228.48 1.40 0.24 0.31 0.38
HF_PAcov 228.88 1.79 0.20 0.33 0.38
FULL . . . . .
HA . . . .
CORE . . . .
HF . . . .
HF_HA . . . .
Abundance weighted functional diversity (Petchey and Gaston)
PAcov 257.62 0 0.31 0.14 0.25
HA_PAcov 258.88 1.26 0.16 0.15 0.27
CORE 258.93 1.31 0.16 0.12 0.28
HA 259.33 1.71 0.13 0.13 0.3
HF_PAcov . . . . .
HF . . . . .
FULL . . . . .
HF_HA . . . . .
Note: Bold values indicate which human influence variables best explained variation in mammal diversity (i.e., within two ∆AICc of top-ranked models. Model
names are specified in Table S.
Abbreviations: HA, human accessibility; HF, human footprint; PAcov, protected area coverage.
FIGURE 2 Regression coefficients (β) for anthropogenic factors related to (a) taxonomic diversity (Shannon index) and (b) functional
diversity, for mammal assemblages sampled from camera trap study areas. For each estimate, circles denote the mean, thicker vertical lines
show the % confidence interval, and thinner vertical lines extend to the % confidence interval. Estimates are from either the global model
or submodels from the different zoogeographic realms
8of14 CHEN .
FIGURE 3 Model-estimated relationships between the
taxonomic diversity (Shannon diversity index, dark brown solid
line) and functional diversity (dark green dash line) of mammals
and protected area (PA) coverage. The colored line shows the mean
prediction and the shaded area shows the % confident interval.
Taxonomic diversity and functional diversity were predicted using
parameters from the top global model for PA coverage (with other
variables held at mean). Points represent project-specific values
across the camera trap surveys
=−., % CI =−. to −.). The random effect of
biogeographic realm explained little variance in the global
model (., compared to . residual variance). For both
taxonomic and functional diversity, models with one or
more indicators of human influence (PA coverage, human
footprint, or human accessibility) had lower AICc and
explained a similar or greater amount of variation (R,
Table ) as models including only environmental, habi-
tat, and biogeographic covariates (CORE model, Table ).
Although, global SR and functional diversity based on
presence–absence (FDPA) data were correlated (r=.),
they responded differently to human influence. Species
SR was significantly related to PA coverage (β=.,
% CI =.–.) but not to human footprint or acces-
sibility, while functional diversity was not significantly
related to any human influences (Table S). They were
both positively associated with elevation (SR: β=., %
CI =.–., FDPA: β=., % CI =.–.) and
temperature (SR: β=., % CI =.–., FDPA: β
=., % CI =.–.).
In the regional models, PA coverage was significantly
related to mammal taxonomic diversity in Indomalaya (β
=., % CI =.–.) and functional diversity in
Nearctic (β=., % CI =.–.). Taxonomic diver-
sity in the Nearctic was also positively related to the human
accessibility index (i.e., to higher remoteness; β=., %
CI =.–.; Figure ,TableS). For the environmental
factors, functional diversity in the Nearctic was negatively
associated with temperature (β=−., % CI =−. to
.), while taxonomic diversity in the Neotropical realm
was positively associated with elevation (β=., %
CI =. to .).
4DISCUSSION
The taxonomic diversity of terrestrial mammals is posi-
tively associated with coverage by PAs at a global scale, and
this association is stronger than those between diversity
and environmental factors or disturbances from human
footprint and accessibility. This suggests that global efforts
to create PAs have been worthwhile investments in biodi-
versity conservation, at least at current levels of human
disturbances. While PAs are a cornerstone of conserva-
tion strategies, their effectiveness at conserving taxonomic
and functional diversity has been questioned (Brum et al.,
). In contrast to smaller-scale studies showing that
variation in mammal communities was not associated with
PA coverage (Brashares et al., ; Stewart et al., ), our
results confirm the importance of PAs for global patterns of
mammal diversity.
While we infer the positive association between mam-
mal diversity and PAs to be indicative of conservation effec-
tiveness, we note that the observed pattern could result if
PAs were created in areas of higher biodiversity. However,
previous studies have shown PAs to be disproportionately
placed in economically marginal lands to reduce opportu-
nity costs, rather than in the most biodiverse areas (Ven-
teretal.,). A positive association between mammal
diversity and PA coverage could be observed even if diver-
sity is declining in PAs (Chen et al., ), necessitating an
assessment of trends in diversity inside and outside of PAs,
and ideally before and after their establishment, to more
rigorously evaluate effectiveness. While previous work has
demonstrated effective protection by PAs of forest cover
(Naughton-Treves et al., ) and habitat structure (Geld-
mann et al., ; Joppa et al., ), our study indicates
that those benefits extend to mammal communities. Long-
term data to support such evaluation at a global scale are
generally lacking, but previous studies do indicate that PAs
facilitate vertebrate population persistence (Barnes et al.,
) and prevent systematic declines in mammal diversity
over the short term (Beaudrot et al., ). Future efforts
must continue to build the evidence base from which the
effectiveness of PAs for mammals and other components
of biodiversity can be reliably monitored.
While our study represents the largest synthesis of cam-
era trap surveys to date, there are many regional gaps in
sampling coverage that may have influenced our results
and remain to be filled by future research. For example,
most of our surveys in the Afrotropical and Neotropical
regions, where mammal diversity is naturally higher, were
CHEN . 9of14
in PAs, whereas half of the surveys in the less diverse
Nearctic were outside PAs (Figure Sb). The Indoma-
layan surveys were relatively evenly distributed across a
gradient of protected area coverage, but in this and the
Afrotropical regions, sampling underrepresented areas of
higher human footprint and accessibility (Figure Sb).
More complete sampling coverage may thus improve on
our results, and we echo previous calls and emerging
efforts for more collaboration and coordination of cam-
era trap research to help fill such gaps (Ahumada et al.,
; Steenweg et al., ). More generally, we recom-
mend coordinated sampling networks using biodiversity-
sensing technologies, such as camera traps and acoustic
recorders, that more evenly and comprehensively mea-
sure diversity across biomes and anthropogenic gradients,
including direct experimental contrasts across conserva-
tion interventions like habitat protection.
We found mammal taxonomic diversity to be more
strongly related to PA coverage than was functional diver-
sity, suggesting that species-rich mammal communities
associated with PAs also tend to have high functional
redundancy (Cooke et al., ; Flynn et al., ;May-
field et al., ). This may also be reflected in the negative
association between functional diversity and CH, as high
forest canopy promotes the packing of functionally similar
species in biodiverse regions (Cooke et al., ;Feng
et al., ). Such redundancy may mean that ecosystem
functions provided by mammals could to some degree be
resilient to species loss (Naeem, ), as has been doc-
umented for some other taxonomic groups (e.g., aquatic
invertebrates; Schmera et al., ). However, the degree
to which mammal assemblages can maintain their eco-
logical functions in the face of increasing human impacts
remains uncertain (Laliberté et al., ). Furthermore, an
alternative explanation for the lack of an effect of PA cov-
erage on functional diversity could be that while PAs have
lost few species (i.e., maintain high taxonomic diversity),
they may have lost functionally unique species that have
also been lost outside PAs. For example, frugivores and
herbivores can be disproportionately affected by hunting
(Brodie et al., ), which still occurs in many PAs
(Harrison, ) and would lead to declines in functional
diversity that exceed those in taxonomic diversity. PAs
in the Nearctic could be an exception: we found func-
tional diversity in this region to be positively correlated
with PA coverage, suggesting that North American PAs
are effective at protecting functionally distinct species
such as large mammals (Barnes et al., ;Loiseau
et al., ).
It is notable that incorporating information on species
abundances informed our inferences about mammal
responses, as SR was not significantly correlated with PA
coverage in our models based only on presence–absence
data. One explanation for this difference could be that
species with high abundance contribute more to diversity,
and thus the observed effects of PA coverage on mam-
mal diversity could be driven by abundant species. Our
analyses demonstrate the value of camera trap surveys in
generating data on multispecies abundances within mam-
mal communities, especially in the context of widespread
declines in abundance (“biological annihilation”; Ceballos
et al., ), although we acknowledge that further work is
needed to develop and test robust estimators of abundance
accounting for variation in detectability (Burgar et al., ;
Gilbert et al., ).
Further work is also needed to better understand rela-
tionships between mammal diversity and human influ-
ences at regional scales, including the effects of human.
Unlike some previous studies (e.g., Torres-Romero &
Olalla-Tárraga, ), we only found a strong relationship
between accessibility and diversity in the Nearctic region,
which may be because the index we used (travel time from
the nearest city) is a relatively coarse metric for many parts
of the world. The scaling-up of more accurate and precise
metrics of accessibility—such as from human movement
models that incorporate population density, transporta-
tion networks, and landscape features (Deith & Brodie,
)—would facilitate more robust assessments of mam-
mal responses to human access.
Overall, our results suggest that PAs are effective in con-
serving components of global terrestrial biodiversity. How-
ever, as human populations and consumption rates grow,
so too do pressures in and around PAs. Encouragingly,
about % of PAs in this study are categorized as IUCN type
V, which permits more human use. This is consistent with
previous findings that PAs focused on sustainable interac-
tions between people and nature can retain more biodiver-
sity than most unprotected areas (Gray et al., ), and is
also aligned with our finding that diversity was not nega-
tively related to human footprint and accessibility. It there-
fore remains critical to improve understanding of factors
underlying current and predicted variation in the effective-
ness of PAs and other area-based conservation measures
(OECMs), such as community-managed forests (Nepstad
et al., ) and Indigenous Peoples’ lands (O’Bryan et al.,
). Research is particularly needed on the effectiveness
of bottom-up and top-down governance structures, fund-
ing, management actions, and connectivity on PAs and
OECMs (Maxwell et al., ;Packeretal.,).
With international attention moving beyond the Aichi
targets to a post- biodiversity framework (Visconti
et al., ), there is an urgent need for reliable indicators
of biodiversity change and rigorous assessments of conser-
vation effectiveness. Our study highlights how camera trap
surveys can generate standardized data on multispecies
abundances within mammal communities across varied
10 of 14 CHEN .
ecosystems, thereby facilitating rapid assessments of global
terrestrial vertebrate diversity (Kissling et al., ;Vis-
conti et al., ), and ultimately supporting more effective
conservation.
ACKNOWLEDGMENTS
Cheng Chen was supported by China Scholarships Coun-
cil (No. ). A. Cole Burton was supported by
the Canada Research Chairs program. Christopher T. Rota.
was supported by McIntire Stennis project WVA. We
gratefully acknowledge funding by The Research Coun-
cil of Norway (project NFR) to Douglas Sheil. We
are grateful to all additional data collectors and providers,
including (but not limited to): E. Akampurira, T. Brn-
cic, K. Boekee, J. Burgar, A. Campos-Arceiz, C. Fletcher,
K. Gajapersad, C. Kayijamahe, D. Kenfack, O. Madrigal,
W. Marthy, E. Martin, B. Mugerwa, A. Mtui, A. Nkwa-
sibwe, L. Nolan, W. Spironello, B. Swanepoel, J. Salvador, L.
Tumugabirwe, R. Vasquez, and Uganda Wildlife Authority
rangers. We thank S. Jing, H. Qiao, and P. Wang for help
with data organization. We thank A. Granados, J. Chen,
handling editor X. Giam, and three anonymous reviewers
for helpful comments on earlier drafts of the manuscript.
CONFLICT OF INTEREST
The authors declare no conflict of interest interests.
AUTHOR CONTRIBUTIONS
Cheng Chen and A. Cole Burton conceived the study and
drafted and revised the manuscript. Cheng Chen and Run-
zhe Liu compiled and analyzed the data. Jedediah F. Brodie
and T. Jonathan Davies contributed to conceptual develop-
ment and drafting the manuscript. A. Cole Burton, Roland
Kays, Jason T. Fisher, Jorge Ahumada, William McShea,
and Douglas Sheil organized data collection and revised
the manuscript. Bernard Agwanda, Mahandry H. Andri-
anarisoa, Robyn D. Appleton, Robert Bitariho, Santiago
Espinosa, Melissa M. Grigione, Kristofer M. Helgen, Andy
Hubbard, Cindy M. Hurtado, Patrick A. Jansen, Xuelong
Jiang, Alex Jones, Elizabeth L. Kalies, Cisquet Kiebou-
Opepa, Xueyou Li, Marcela Guimarães Moreira Lima, Erik
Meyer, Anna B. Miller, Thomas Murphy, Renzo Piana, Rui-
Chang Quan, Christopher T. Rota, Francesco Rovero, Fer-
nanda Santos, Stephanie Schuttler, Aisha Uduman, Joanna
Klees van Bommel, and Hilary Young, collected data and
commented on the manuscript.
DATA AVAILABILITY STATEMENT
Camera trap data obtained from eMammal (https://
emammal.si.edu), TEAM (https://www.conservation.org/
projects/team-network), now Wildlife Insights (https://
www.wildlifeinsights.org/), and other sources listed in
Table . The replication data and code for mixed-effects lin-
ear regression model can be obtained from (https://https:
//doi.org/./dryad.qfttdzg).
ORCID
Cheng Chen https://orcid.org/---
Roland Kays https://orcid.org/---
Marcela Guimarães Moreira Lima https://orcid.org/
---
Aisha Uduman https://orcid.org/---X
Joanna Klees van Bommel https://orcid.org/--
-
A. Cole Burton https://orcid.org/---
REFERENCES
Ahumada, J. A., Fegraus, E., Birch, T., Flores, N., Kays, R., O’Brien,
T. G., Palmer, J., Schuttler, S., Zhao, J. Y., Jetz, W., Kinnaird, M.,
Kulkarni, S., Lyet, A., Thau, D., Duong, M., Oliver, R., & Dancer,
A. (). Wildlife insights: A platform to maximize the poten-
tial of camera trap and other passive sensor Wildlife data for the
Planet. Environmental Conservation,47(), –. https://doi.org/.
/S
Barnes, M. D., Craigie, I. D., Harrison, L. B., Geldmann, J., Collen,
B., Whitmee, S., Balmford, A., Burgess, N. D., Brooks, T., Hock-
ings, M., & Woodley, S. (). Wildlife population trends in pro-
tected areas predicted by national socio-economic metrics and
body size. Nature Communications,7,–.https://doi.org/./
ncomms
Barton, K., & Barton, M. K. (). Package ‘mumin.’. Vers ion ,1(),
.
Bates, D., Mächler, M., Bolker, B., & Walker, S. (). Fitting linear
mixed-effects models using lme. Journal of Statistical Software,
67(), –. https://doi.org/./jss.v.i
Beaudrot, L., Ahumada, J. A., O’Brien, T., Alvarez-Loayza, P., Boe-
kee, K., Campos-Arceiz, A., Eichberg, D., Espinosa, S., Fegraus,
E., Fletcher, C., Gajapersad, K., Hallam, C., Hurtado, J., Jansen, P.
A., Kumar, A., Larney, E., Lima, M. G. M., Mahony, C., Martin, E.
H.,..., &Andelman, S. J. (). Standardized assessment of bio-
diversity trends in tropical forest protected areas: The end is not
in sight. PLoS Biology,14(), –. https://doi.org/./journal.
pbio.
Belote,R.T.,Faurby,S.,Brennan,A.,Carter,N.H.,Dietz,M.S.,Hahn,
B., McShea, W. J., & Gage, J. (). Mammal species composi-
tion reveals new insights into Earth’s remaining wilderness. Fron-
tiers in Ecology and the Environment,18,–.https://doi.org/
./fee.
Bhola, N., Klimmek, H., Kingston, N., Burgess, N. D., Soesbergen,
A., Corrigan, C., Harrison, J., & Kok, M. T. J. (). Perspectives
on area-based conservation and its meaning for future biodiver-
sity policy. Conservation Biology,35,–.https://doi.org/.
/cobi.
Brashares, J. S., Arcese, P., & Sam, M. K. (). Human demogra-
phy and reserve size predict wildlife extinction in West Africa. Pro-
ceedings of the Royal Society B: Biological Sciences,268(), –
. https://doi.org/./rspb..
Broadley, K., Burton, A. C., Avgar, T., & Boutin, S. ().
Density-dependent space use affects interpretation of camera trap
CHEN . 11 of 14
detection rates. Ecology and Evolution,9, –. https://doi.
org/./ece.
Brodie, J. F., Williams, S., & Garner, B. (). The decline of mam-
mal functional and evolutionary diversity worldwide. Proceedings
of the National Academy of Sciences,118(), e. https://
doi.org/./pnas.
Brum, F. T., Graham, C. H., Costa, G. C., Hedges, S. B., Penone, C.,
Radeloff, V. C., Rondinini, C., Loyola, R., & Davidson, A. D. ().
Global priorities for conservation across multiple dimensions
of mammalian diversity. Proceedings of the National Academy
of Sciences of the United States of America,114(), –.
https://doi.org/./pnas.
Burgar, J. M., Stewart, F. E. C., Volpe, J. P., Fisher, J. T., & Burton,
A. C. (). Estimating density for species conservation: Compar-
ing camera trap spatial count models to genetic spatial capture-
recapture models. Global Ecology and Conservation,15, e.
https://doi.org/./j.gecco..e
Burnham, K. K. P., & Anderson, D. R. D. (). Model selec-
tion and multimodel inference: A practical information-theoretic
approach. In Ecological Modelling,172,–.https://doi.org/.
/j.ecolmodel...
Burton, A. C., Neilson, E., Moreira, D.,Ladle, A., Steenweg, R., Fisher,
J. T., Bayne, E., & Boutin, S. (). Wildlife camera trapping: A
review and recommendations for linking surveys to ecological pro-
cesses. Journal of Applied Ecology,52(), –. https://doi.org/
./- .
Ceballos, G., Ehrlich, P. R., & Dirzo, R. (). Biological annihilation
via the ongoing sixth mass extinction signaled by vertebrate pop-
ulation losses and declines. Proceedings of the National Academy
of Sciences of the United States of America,114(), E–E.
https://doi.org/./pnas.
Chen, C., Quan, R. C., Cao, G., Yang, H., Burton, A. C., Meitner, M.,
& Brodie, J. F. (). Effects of law enforcement and community
outreach on mammal diversity in a biodiversity hotspot. Conser-
vation Biology,33(), –. https://doi.org/./cobi.
Cooke, R. S. C., Bates, A. E., & Eigenbrod, F. (). Global trade-
offs of functional redundancy and functional dispersion for birds
and mammals. Global Ecology and Biogeography,28(), –.
https://doi.org/./geb.
Davis, C. L., Rich, L. N., Farris, Z. J., Kelly, M. J., Di Bitetti, M.
S., Blanco, Y. D., Albanesi, S., Farhadinia, M. S., Gholikhani, N.,
Hamel, S., Harmsen, B. J., Wultsch, C., Kane, M. D., Martins, Q.,
Murphy, A. J., Steenweg, R., Sunarto, S., Taktehrani, A., Thapa, K.,
... Miller, D. A. W. (). Ecological correlates of the spatial co-
occurrence of sympatric mammalian carnivores worldwide. Ecol-
ogy Letters,21, –. https://doi.org/./ele.
Day, J., Dudley, N., Hockings, M., Holmes, G., Laffoley, D. d’A,
Stolton, S., & Wells, S. M. (). Guidelines for applying the IUCN
protected area management categories to marine protected areas.
IUCN.
Deith, M. C. M., & Brodie, J. F. (). Predicting defaunation: Accu-
rately mapping bushmeat hunting pressure over large areas. Pro-
ceedings of the Royal Society B: Biological Sciences,287,.
https://doi.org/./rspb..
Díaz, S., Settele, J., Brondízio, E. S., Ngo, H. T., Agard, J., Arneth, A.,
Balvanera, P., Brauman, K. A., Butchart, S. H. M., Chan, K. M. A.,
Garibaldi, L. A., Ichii, K., Liu, J., Subramanian, S. M., Midgley, G.
F., Miloslavich, P.,Molnár,Z., Obura,D.,Pfaff,A.,...,Zayas,C.N.
(). Pervasive human-driven decline of life on Earth points to
the need for transformative change. Science,366(), eaax.
https://doi.org/./science.aax
Dinerstein, E., Vynne, C., Sala, E., Joshi, A. R., Fernando, S., Love-
joy, T. E., Mayorga, J., Olson, D., Asner, G. P., Baillie, J. E. M.,
Burgess, N. D., Burkart, K., Noss, R. F., Zhang, Y. P., Baccini, A.,
Birch, T., Hahn, N., Joppa, L. N., & Wikramanayake, E. ().
A global deal for nature: Guiding principles, milestones, and tar-
gets. Science Advances,5(), –. https://doi.org/./sciadv.
aaw
Dirzo, R., Young, H. S., Galetti, M., Ceballos, G., Isaac, N. J. B.,
& Collen, B. (). Defaunation in the anthropocene. Science,
345(), –. https://doi.org/./science.
Feng, G., Zhang, J., Girardello, M., Pellissier, V., & Svenning, J. C.
(). Forest canopy height co-determines taxonomic and func-
tional richness, but not functional dispersion of mammals and
birds globally. Global Ecology and Biogeography,29(), –.
https://doi.org/./geb.
Flynn, D. F. B., Gogol-Prokurat, M., Nogeire, T., Molinari, N., Rich-
ers, B. T., Lin, B. B., Simpson, N., Mayfield, M. M., & DeClerck, F.
(). Loss of functional diversity under land use intensification
across multiple taxa. Ecology Letters,12(), –. https://doi.org/
./j.- ...x
Gagic, V., Bartomeus, I., Jonsson, T., Taylor, A., Winqvist, C., Fis-
cher, C., Slade, E. M., Steffan-Dewenter, I., Emmerson, M.,
Potts, S. G., Tscharntke, T., Weisser, W., & Bommarco, R. ().
Functional identity and diversity of animals predict ecosystem
functioning better than species-based indices. Proceedings of the
Royal Society B: Biological Sciences,282(), –.
https://doi.org/./rspb..
Geldmann, J., Barnes, M., Coad, L., Craigie, I. D., Hockings, M., &
Burgess, N. D. (). Effectiveness of terrestrial protected areas in
reducing habitat loss and population declines. Biological Conser-
vation,161, –. https://doi.org/./j.biocon...
Geldmann, J., Coad, L., Barnes, M., Craigie, I. D., Hockings, M.,
Knights, K., Leverington, F., Cuadros, I. C., Zamora, C., Woodley,
S., & Burgess, N. D. (). Changes in protectedarea management
effectiveness over time: A global analysis. Biological Conservation,
191, –. https://doi.org/./j.biocon...
Geldmann, J., Joppa, L. N., & Burgess, N. D. (). Mapping
change in human pressure globally on land and within protected
Areas. Conservation Biology,28(), –. https://doi.org/.
/cobi.
Geldmann, J., Manica, A., Burgess, N. D., Coad, L., & Balmford,
A. (). A global-level assessment of the effectiveness of pro-
tected areas at resisting anthropogenic pressures. Proceedings of
the National Academy of Sciences of the United States of America,
116(), –. https://doi.org/./pnas.
Gilbert, N. A., Clare, J. D. J., Stenglein, J. L., & Zuckerberg, B. ().
Abundance estimation of unmarked animals based on camera-
trap data. Conservation Biology,–.https://doi.org/./cobi.
Gray, C. L., Hill, S. L. L., Newbold, T., Hudson, L. N., Börger, L.,
Contu, S., Hoskins, A. J., Ferrier, S., Purvis, A., & Scharlemann,
J. P. W. (). Local biodiversity is higher inside than outside ter-
restrial protected areas worldwide. Nature Communications,7(),
. https://doi.org/./ncomms
Harrison, R. D. (). Emptying the forest: Hunting and the extir-
pation of wildlife from tropical nature reserves. Bioscience,61(),
–. https://doi.org/./bio....
12 of 14 CHEN .
Holt, B. G., Lessard, J. P., Borregaard, M. K., Fritz, S. A., Araújo, M.
B., Dimitrov, D., Fabre, P. H., Graham, C. H., Graves, G. R., Jøns-
son, K. A., Nogués-Bravo, D., Wang, Z., Whittaker, R. J., Fjeldså,
J., & Rahbek, C. (). An update of Wallace’s zoogeographic
regions of the world. Science,339(), –. https://doi.org/.
/science.
IUCN, & UNEP-WCMC. (). The World Database on Protected
Areas (WDPA). www.protectedplanet.net
Jenkins C. N., Pimm S. L., Joppa L. N. (). Global patterns of ter-
restrial vertebrate diversity and conservation. Proceedings of the
National Academy of Sciences,110(), E–E. https://doi.
org/./pnas.
Jones, K. E., Bielby, J., Cardillo, M., Fritz, S. A., O’Dell, J., Orme, C.
D. L., Safi, K., Sechrest, W., Boakes, E. H., Carbone, C., Connolly,
C., Cutts, M. J., Foster, J. K., Grenyer, R., Habib, M., Plaster, C. A.,
Price, S. A., Rigby, E. A., Rist, J., .. . Purvis, A. (). PanTHERIA:
A species-level database of life history, ecology, and geography of
extant and recently extinct mammals. Ecology,90(), –.
https://doi.org/./-.
Jones, K. R., Venter, O., Fuller, R. A., Allan, J. R., Maxwell, S. L.,
Negret, P. J., & Watson, J. E. M. (). One-third of global pro-
tected land is under intense human pressure. Science,360(),
–. https://doi.org/./science.aap
Joppa, L. N., Loarie, S. R., & Pimm, S. L. (). On the protection of
“protected areas. Proceedings of the National Academy of Sciences
of the United States of America,105(), –. https://doi.org/
./pnas.
Kays, R., McShea, W. J., & Wikelski, M. (). Born-digital biodiver-
sity data: Millions and billions. Diversity and Distributions,26(),
–. https://doi.org/./ddi.
Kissling, W. D., Ahumada, J. A., Bowser, A., Fernandez, M., Fernán-
dez, N., García, E. A., Guralnick, R. P., Isaac, N. J. B., Kelling,
S., Los, W., McRae, L., Mihoub, J. B., Obst, M., Santamaria, M.,
Skidmore, A. K., Williams, K. J., Agosti, D., Amariles, D., Arvan-
itidis, C., ..., Hardisty, A. R. (). Building essential biodiver-
sity variables (EBVs) of species distribution and abundance at a
global scale. Biological Reviews,93(), –. https://doi.org/.
/brv.
Laliberté, E., Wells, J. A., Declerck, F., Metcalfe, D. J., Catterall, C.
P., Queiroz, C., Aubin, I., Bonser, S. P., Ding, Y., Fraterrigo, J. M.,
McNamara, S., Morgan, J. W., Merlos, D. S., Vesk, P.A., & Mayfield,
M. M. (). Land-use intensification reduces functional redun-
dancy and response diversity in plant communities. Ecology Let-
ters,13(), –. https://doi.org/./j.- ...x
Laurance, W. F., Carolina Useche, D., Rendeiro, J., Kalka, M., Brad-
shaw, C. J., Sloan, S. P., Laurance, S. G., Campbell, M., Aber-
nethy, K., Alvarez, P., Arroyo-Rodriguez, V., Ashton, P., Benítez-
Malvido, J., Blom, A., Bobo, K. S., Cannon, C. H., Cao, M., Car-
roll, R., Chapman, C., .. . Zamzani, F. (). Averting biodiversity
collapse in tropical forest protected areas. Nature,489, –.
https://doi.org/./nature
Lewis, S. L., & Maslin, M. A. (). Defining the Anthropocene.
Nature,519(), –. https://doi.org/./nature
Lima, F., Beca, G., Muylaert, R. L., Jenkins, C. N., Perilli, M. L. L.,
Paschoal, A. M. O., Massara, R. L., Paglia, A. P., Chiarello, A. G.,
Graipel, M. E., Cherem, J. J., Regolin, A. L., Oliveira Santos, L.
G. R., Brocardo, C. R., Paviolo, A., Di Bitetti, M. S., Scoss, L. M.,
Rocha, F. L., Fusco-Costa, R., .. . Galetti, M. (). ATLANTIC-
CAMTRAPS: A dataset of medium and large terrestrial mammal
communities in the Atlantic Forest of South America. Ecology,
98(), . https://doi.org/./ecy.
Loiseau, N., Mouquet, N., Casajus, N., Grenié,M., Guéguen, M., Mait-
ner, B., Mouillot, D., Ostling, A., Renaud, J., Tucker, C., Velez, L.,
Thuiller, W., & Violle, C. (). Global distribution and conserva-
tion status of ecologically rare mammal and bird species. Nature
Communications,11(), . https://doi.org/./s--
-w
Luke, S. G. (). Evaluating significance in linear mixed-effects
models in R. Behavior Research Methods,49(), –. https:
//doi.org/./s---y
Mace, G. M., Collar, N. J., Gaston, K. J., Hilton-Taylor, C., Akçakaya,
H. R., Leader-Williams, N., Milner-Gulland, E. J., & Stuart, S. N.
(). Quantification of extinction risk: IUCN’s system for clas-
sifying threatened species. Conservation Biology,22(), –.
https://doi.org/./j.-...x
Magurran, A. E., & McGill, B. J. (). Biological diversity: Frontiers
in measurement and assessment. Oxford University Press.
Mansuy, N., Miller, C., Parisien, M.-A., Parks, S. A., Batllori, E., &
Moritz, M. A. (). Contrasting human influences and macro-
environmental factors on fire activity inside and outside protected
areas of North America. Environmental Research Letters,14(),
. https://doi.org/./-/abbc
Maxwell, S. L., Cazalis, V., Dudley, N., Hoffmann, M., Rodrigues, A.
S. L., Stolton, S., Visconti, P., Woodley, S., Kingston, N., Lewis, E.,
Maron, M., Strassburg, B. B. N., Wenger, A., Jonas, H. D., Ven-
ter, O., & Watson, J. E. M. (). Area-based conservation in the
twenty-first century. Nature,586(), –. https://doi.org/
./s---z
Mayfield, M. M., Bonser, S. P., Morgan, J. W., Aubin, I., McNamara,
S., & Vesk, P. A. (). What does species richness tell us about
functional trait diversity? Predictions and evidence for responses
of species and functional trait diversity to land-use change. Global
Ecology and Biogeography,19(), –. https://doi.org/./j.
-...x
Naeem, S. (). Species redundancy and ecosystem reliability. Con-
servation Biology,12(), –. https://doi.org/./j.-.
..x
Naidoo, R., & Burton, A. C. (). Relative effects of recreational
activities on a temperate terrestrial wildlife assemblage. Conserva-
tion Science and Practice,2(), e. https://doi.org/./csp.
Naughton-Treves, L., Holland, M. B., & Brandon, K. (). The role
of protected areas in conserving biodiversity and sustaining local
livelihoods. Annual Review of Environment and Resources,30,–
. https://doi.org/./annurev.energy...
Neilson, E. W., Avgar, T., Cole Burton, A., Broadley, K., & Boutin,
S. (). Animal movement affects interpretation of occupancy
models from camera-trap surveys of unmarked animals. Eco-
sphere,9(), e. https://doi.org/./ecs.
Nepstad, D., Schwartzman, S., Bamberger, B., Santilli, M., Ray, D.,
Schlesinger, P., Lefebvre, P., Alencar, A., Prinz, E., Fiske, G., &
Rolla, A. (). Inhibition of Amazon deforestation and fire by
parks and indigenous lands. Conservation Biology,20(), –.
https://doi.org/./j.-...x
O’Brien, T. G., Kinnaird, M. F., & Wibisono, H. T. (). Crouch-
ing tigers, hidden prey: Sumatran tiger and prey populations in
a tropical forest landscape. Animal Conservation,6(), –.
https://doi.org/./S
CHEN . 13 of 14
O’Bryan, C. J., Garnett, S. T., Fa, J. E., Leiper, I., Rehbein, J. A.,
Fernández-Llamazares, Á., Jackson, M. V., Jonas, H. D., Brondizio,
E. S., Burgess, N. D., Robinson,C. J., Zander, K. K., Molnár,Z., Ven-
ter, O., & Watson, J. E. M. (). The importance of Indigenous
Peoples’ lands for the conservation of terrestrial mammals. Con-
servation Biology,35(), –. https://doi.org/./cobi.
Packer, C., Loveridge, A., Canney, S., Caro, T., Garnett, S. T., Pfeifer,
M., Zander, K. K., Swanson, A., MacNulty, D., Balme, G., Bauer,
H., Begg, C. M., Begg, K. S., Bhalla, S., Bissett, C., Bodasing, T.,
Brink, H., Burger, A., Burton, A. C., . .. Polasky, S. (). Conserv-
ing large carnivores: Dollars and fence. Ecology Letters,16,–.
https://doi.org/./ele.
Pereira, H. M., Ferrier, S., Walters, M., Geller, G. N., Jongman, R. H.
G., Scholes, R. J., Bruford, M. W., Brummitt, N., Butchart, S. H.
M., Cardoso, A. C., Coops, N. C., Dulloo, E., Faith, D. P., Freyhof, J.,
Gregory, R. D., Heip, C., Hoft, R., Hurtt, G., Jetz, W., . . . Wegmann,
M. (). Essential biodiversity variables. Science,339(), –
. https://doi.org/./science.
Petchey, O. L., & Gaston, K. J. (). Functional diversity (FD),
species richness and community composition. Ecology Letters,
5(), –. https://doi.org/./j.- ...x
R. Core Team. (). R: A language and environment for statistical
computing. R Foundation for Statistical Computing. https://www.
r-project.org
Rovero, F., Ahumada, J., Jansen, P. A., Sheil, D., Alvarez, P., Boekee,
K., Espinosa, S., Lima, M. G. M., Martin, E. H., O’Brien, T. G., Sal-
vador, J., Santos, F., Rosa, M., Zvoleff, A., Sutherland, C., & Tenan,
S. (). A standardized assessment of forest mammal commu-
nities reveals consistent functional composition and vulnerability
across the tropics. Ecography,43(), –. https://doi.org/./
ecog.
Schmera, D., Baur, B., & Erős, T. (). Does functional redundancy
of communities provide insurance against human disturbances?
An analysis using regional-scale stream invertebrate data. Hydro-
biologia,693(), –. https://doi.org/./s---z
Steenweg, R., Hebblewhite, M., Kays, R., Ahumada, J., Fisher, J. T.,
Burton, C., Townsend, S. E., Carbone, C., Rowcliffe, J. M., Whit-
tington, J., Brodie, J., Royle, J. A., Switalski, A., Clevenger, A. P.,
Heim, N., & Rich, L. N. (). Scaling-up camera traps: moni-
toring the planet’s biodiversity with networks of remote sensors.
Frontiers in Ecology and the Environment,15(), –. https://doi.
org/./fee.
Stewart, F. E. C., Volpe, J. P., Eaton, B. R., Hood, G. A., Vujnovic, D.,
& Fisher, J. T. (). Protected areas alone rarely predict mam-
malian biodiversity across spatial scales in an Albertan working
landscape. Biological Conservation,240(May), . https://doi.
org/./j.biocon..
Swanson, A., Kosmala, M., Lintott, C., Simpson, R., Smith, A., &
Packer, C. (). Snapshot Serengeti, high-frequency annotated
camera trap images of mammalian species in an African
savanna. Scientific Data,2,–.https://doi.org/./sdata.
.
Tobler, M. W., Carrillo-Percastegui, S. E., Leite Pitman, R., Mares,
R., & Powell, G. (). An evaluation of camera traps for inven-
torying large- and medium-sized terrestrial rainforest mammals.
Animal Conservation,11(), –. https://doi.org/./j.-
...x
Torres-Romero, E. J., & Olalla-Tárraga, M. A. (). Untangling
human and environmental effects on geographical gradients of
mammal species richness: A global and regional evaluation. Jour-
nal of Animal Ecology,84(), –. https://doi.org/./-
.
Tucker, M. A., Böhning-Gaese, K., Fagan, W. F., Fryxell, J. M., Van
Moorter, B., Alberts, S. C., Ali, A. H., Allen, A. M., Attias, N., Avgar,
T., Bartlam-Brooks, H., Bayarbaatar, B., Belant, J. L., Bertassoni,
A., Beyer, D., Bidner, L., van Beest, F. M., Blake, S., Blaum, N., .. .
Mueller, T. (). Moving in the Anthropocene: Global reductions
in terrestrial mammalian movements. Science,359(), –.
https://doi.org/./science.aam
Venter, O., Magrach, A., Outram, N., Klein, C. J., Possingham, H. P.,
Di Marco, M., & Watson, J. E. M. (). Bias in protected-area
location and its effects on long-term aspirations of biodiversity
conventions. Conservation Biology,32(), –. https://doi.org/
./cobi.
Venter, O., Sanderson, E. W., Magrach, A., Allan, J. R., Beher, J.,
Jones, K. R., Possingham, H. P., Laurance, W. F., Wood, P., Fekete,
B. M., Levy, M. A., & Watson, J. E. M. (a). Global terrestrial
human footprint maps for and . Scientific Data,3,–
. https://doi.org/./SDATA..
Venter, O., Sanderson, E. W., Magrach, A., Allan, J. R., Beher, J.,
Jones, K. R., Possingham, H. P., Laurance, W. F., Wood, P., Fekete,
B. M., Levy, M. A., & Watson, J. E. M. (b). Sixteen years of
change in the global terrestrial human footprint and implications
for biodiversity conservation. Nature Communications,7,.
https://doi.org/./ncomms
Villéger, M., & Mouillot. (). New multidimensional functional
diversity indices for a multifaceted framework in functional ecol-
ogy. Ecology,89(), –. https://doi.org/./-.
Visconti, P., Butchart, S. H. M., Brooks, T. M., Langhammer, P. F.,
Marnewick, D., Vergara, S., Yanosky, A., & Watson, J. E. M. ().
Protected area targets post-. Science,364(), eaav.
https://doi.org/./science.aav
Waldron, A., Miller, D. C., Redding, D., Mooers, A., Kuhn, T. S.,
Nibbelink, N., Roberts, J. T., Tobias, J. A., & Gittleman, J. L. ().
Reductions in global biodiversity loss predicted from conserva-
tion spending. Nature,551(), –. https://doi.org/./
nature
Weiss, D. J., Nelson, A., Gibson, H. S., Temperley, W., Peedell, S.,
Lieber, A., Hancher, M., Poyart, E., Belchior, S., Fullman, N., Map-
pin, B., Dalrymple, U., Rozier, J., Lucas, T. C. D., Howes, R. E.,
Tusting, L. S., Kang, S. Y., Cameron, E., Bisanzio, D., . . . Geth-
ing, P. W. (). A global map of travel time to cities to assess
inequalities in accessibility in . Nature,553(), –.
https://doi.org/./nature
Wittemyer, G., Elsen, P., Bean, W. T., Burton, A. C. O., & Brashares, J.
S. (). Accelerated human population growth at protected area
edges. Science,321(), –. https://doi.org/./science.
Roll, U., Geffen, E., & Yom-Tov, Y. (). Linking vertebrate species
richness to tree canopy height on a global scale. Global Ecology and
Biogeography,24(), –. https://doi.org/./geb.
14 of 14 CHEN .
Simard, M., Pinto, N., Fisher, J. B., & Baccini, A. (). Mapping
forest canopy height globally with spaceborne lidar. Journal of
Geophysical Research,116(G), G. https://doi.org/./
JG
Venter, O., Sanderson, E. W., Magrach, A., Allan, J. R., Beher, J.,
Jones, K. R., Possingham, H. P., Laurance, W. F., Wood, P., Fekete,
B. M., Levy, M. A., & Watson, J. E. M. (). Global terrestrial
Human Footprint maps for and . Scientific Data,3,–
. https://doi.org/./SDATA..
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A., & Smith, G.
M. (). Mixed effects models and extensions in ecology with R.
Springer.
SUPPORTING INFORMATION
Additional supporting information may be found in the
online version of the article at the publisher’s website.
How to cite this article: Chen, C., Brodie, J. F.,
Kays, R., Davies, T. J., Liu, R., Fisher, J. T.,
Ahumada, J., McShea, W., Sheil, D., Agwanda, B.,
Andrianarisoa, M. H., Appleton, R. D., Bitariho, R.,
Espinosa, S., Grigione, M. M., Helgen, K. M.,
Hubbard, A., Hurtado, C. M., Jansen, P. A., . ..
Burton, A. C. (). Global camera trap synthesis
highlights the importance of protected areas in
maintaining mammal diversity. Conservation
Letters,15, e. https://doi.org/./conl.
Content uploaded by Fernanda da Silva Santos
Author content
All content in this area was uploaded by Fernanda da Silva Santos on Jan 27, 2022
Content may be subject to copyright.
Content uploaded by Renzo P. Piana
Author content
All content in this area was uploaded by Renzo P. Piana on Jan 26, 2022
Content may be subject to copyright.
Content uploaded by Robert Bitariho
Author content
All content in this area was uploaded by Robert Bitariho on Jan 26, 2022
Content may be subject to copyright.