Assessing global patterns in mammalian carnivore occupancy
and richness by integrating local camera trap surveys
Lindsey N. Rich
Courtney L. Davis
Zach J. Farris
David A. W. Miller
Jody M. Tucker
Mohammad S. Farhadinia
Mario S. Di Bitetti
Mamadou D. Kane
Nathaniel P. Robinson
Febri A. Widodo
Nigel G. Yoccoz
Bart J. Harmsen
Marcella J. Kelly
Department of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, Virginia
Department of Ecosystem Science and Management, Penn State University, Forest Resources Building, University Park, Pennsylvania
Department of Biological Sciences, Auburn University, Auburn, Alabama
Sequoia National Forest, U.S. Forest Service, Pacific Southwest Region, Porterville, California
Department of Arctic and Marine Biology, Faculty of Biosciences, Fisheries and Economics, UiT The Arctic University of Norway, Tromsø, Norway
Iranian Cheetah Society, Tehran, Iran
Wildlife Conservation Research Unit, Department of Zoology, University of Oxford, The Recanati-Kaplan Centre, Tubney, Abingdon, United Kingdom
Wildlife Biology Program, Department of Ecosystem and Conservation Science, University of Montana, Missoula, Montana
Instituto de Biología Subtropical (IBS) –nodo Iguaz
u, Universidad Nacional de Misiones and CONICET, Misiones, Argentina
on Civil Centro de Investigaciones del Bosque Atl
antico (CeIBA), Puerto Iguaz
u, Misiones, Argentina
Facultad de Ciencias Forestales, Universidad Nacional de Misiones, Eldorado, Misiones, Argentina
World Wildlife Fund, Conservation Science Unit, Baluwatar, Nepal
Senegalese National Parks Directorate, Dakar, Senegal
World Wildlife Fund, Jakarta, Indonesia
College of Forestry and Conservation, University of Montana, Missoula, Montana
The Cape Leopard Trust, Cape Town, South Africa
Audubon Canyon Ranch, Glen Ellen, California
Parks Canada, Banff National Park Resource Conservation, Banff, Alberta, Canada
Panthera, New York
American Museum of Natural History, Sackler Institute for Comparative Genomics, New York
University of Belize, Environmental Research Institute (ERI), Belmopan, Belize
Lindsey N. Rich, Department of Fish and
Wildlife Conservation, Virginia Tech,
Blacksburg, VA 24060, U.S.A.
Lindsey N. Rich, Department of Environ-
mental Science, Policy and Management,
University of California Berkeley, Berkeley,
CA 94720-3114, U.S.A
Aim: Biodiversity loss is a major driver of ecosystem change, yet the ecological data required to
detect and mitigate losses are often lacking. Recently, camera trap surveys have been suggested
as a method for sampling local wildlife communities, because these observations can be collated
into a global monitoring network. To demonstrate the potential of camera traps for global
monitoring, we assembled data from multiple local camera trap surveys to evaluate the inter-
change between fine- and broad-scale processes impacting mammalian carnivore communities.
Global Ecol Biogeogr.2017;1–12. wileyonlinelibrary.com/journal/geb V
C2017 John Wiley & Sons Ltd
Received: 26 July 2016
Revised: 8 March 2017
Accepted: 12 April 2017
Location: Argentina, Belize, Botswana, Canada, Indonesia, Iran, Madagascar, Nepal, Norway,
Senegal, South Africa, and the U.S.A.
Methods: We gathered camera trap data, totalling >100,000 trap nights, from across five conti-
nents. To analyse local and species-specific responses to anthropogenic and environmental
variables, we fitted multispecies occurrence models to each study area. To analyse global-level
responses, we then fitted a multispecies, multi-area occurrence model.
Results: We recorded 4,805 detections of 96 mammalian carnivore species photographed across
1,714 camera stations located in 12 countries. At the global level, our models revealed that carni-
vore richness and occupancy within study areas was positively associated with prey availability.
Occupancy within study areas also tended to increase with greater protection and greater distan-
ces to roads. The strength of these relationships, however, differed among countries.
Main conclusions: We developed a research framework for leveraging global camera trap data to
evaluate patterns of mammalian carnivore occurrence and richness across multiple spatial scales.
Our research highlights the importance of intact prey populations and protected areas in conserv-
ing carnivore communities. Our research also highlights the potential of camera traps for
monitoring wildlife communities and provides a case study for how this can be achieved on a
global scale. We encourage greater integration and standardization among camera trap studies
worldwide, which would help inform effective conservation planning for wildlife populations both
locally and globally.
big data analysis, camera trap, carnivore, global, hierarchical Bayesian models, multispecies model-
ling, species occurrence, species richness
Biodiversity loss, species-level extinction risks and anthropogenic pres-
sures on ecosystems are accelerating (Alkemade et al., 2009; Butchart
et al., 2010). Biodiversity loss, specifically, will rank among the major
drivers of ecosystem change in the 21st century, comparable to warm-
ing climate and increased nitrogen deposition (Hooper et al., 2012). To
address these alarming trends, the Convention on Biological Diversity
developed a strategic plan, signed by 193 nations, aimed at conserving,
restoring and wisely using biodiversity [Secretariat of the Convention
on Biological Diversity (SCBD), 2014]. However, the infrastructure
required to measure changes in biodiversity, such as trends in abun-
dance or changes in species’distributions, is often lacking, thereby lim-
iting our ability to monitor progress towards achieving Convention
goals (Ahumada, Hurtado, & Lizcano, 2013; Schmeller et al., 2015).
Additionally, collaborative research aimed at monitoring biodiversity
internationally is rare, which makes effective conservation planning
across geopolitical borders challenging (Feeley & Silman, 2011). The
increasingly urgent need to address major environmental challenges
and prioritize conservation actions has led to calls for coordinated
global monitoring networks (Ellison, 2010; Kelling et al., 2009;
Schmeller et al., 2015; Steenweg et al., 2017).
Leveraging the existing biodiversity data would also aid in the
development of effective, science-driven environmental policies across
multiple spatial scales (Hampton et al., 2013; Jones, Schildhauer,
Reichman, & Bowers, 2006; Kelling et al., 2009; O’Brien, Baillie,
Krueger, & Cuke, 2010; Schmeller et al., 2015). The challenge is not in
the availability of biodiversity data, an abundance of which is collected
annually. Instead, efforts are hampered by limited exchange and aggre-
gation of these data and the need for common data and analysis for-
mats (Ellison, 2010; Jones et al., 2006). Organizing and analysing large,
heterogeneous datasets is challenging. Data often suffer from observa-
tional biases, differences in sampling effort and differing levels of train-
ing (Ahumada et al., 2013; Hampton et al., 2013; Kelling et al., 2009).
Even when common protocols are used, researchers need to be willing
to work collaboratively and share data (Hampton et al., 2013; Kelling
et al., 2009). Successful implementation of global biodiversity monitor-
ing networks therefore requires international collaboration and well-
documented data collected in standardized formats with standardized
metadata (Hampton et al., 2013).
Camera traps offer a potential method for monitoring biodiversity,
because they provide standardized data that can be integrated across
multiple regions using an occupancy modelling framework (Rich, Miller,
Robinson, McNutt, & Kelly, 2016). Remote cameras detect animals
using motion- and heat-sensing infrared technology, providing records
of detections for a wide diversity of species, living in a broad range of
ecosystems, at any time of day or year. Each photographic detection
includes records of the time, date and location of the photograph.
These data allow researchers to account for species’detection proba-
bilities (i.e., the probability a species was present but went undetected)
using occupancy models (MacKenzie et al., 2002). The ability to
account for observation error allows results to be compared across
RICH ET AL.
species, sites and years. Consequently, this method has been used to
evaluate species’distributions, community richness, temporal activity
patterns, occupancy trends and intra- and interspecific interactions
(Ahumada et al., 2011; Burton et al., 2015; O’Brien et al., 2010; Steen-
weg et al., 2017). Recently, occupancy modelling has been extended to
a multi-region community model that accommodates data collected
across multiple regions of interest. This permits species richness and
species-specific occupancy to be modelled as a function of region-
specific covariates (Miller & Grant, 2015). Owing to this recent
advancement, camera traps may now serve as a tool for addressing
broad-scale ecological inquiries, such as monitoring understudied carni-
vore communities on a global scale.
Camera traps are especially useful for monitoring mammalian car-
nivores, because they are difficult to count directly owing to their gen-
erally low density and elusive nature. Nearly one-quarter of the world’s
245 carnivore species are threatened with extinction and many more
are experiencing population declines (Ripple et al., 2014; Schipper,
Hoffmann, Duckworth, & Conroy, 2008). Among the 31 largest carni-
vores, 19 are listed as threatened by the International Union for the
Conservation of Nature (IUCN) and 24 are decreasing in number. The
unremitting decline of many of our world’s carnivores is a threat not
only to these species, but also to the ecosystems in which they reside.
Carnivore declines have led to changes in plant diversity, biomass and
productivity, which are likely to impact nearly all other species and eco-
logical processes, such as nutrient cycling, frequency of wildfires, and
carbon sequestration (Ripple et al., 2014). To curtail the decline of our
world’s remaining carnivore species, a better understanding of the
global drivers of carnivore communities is vital.
A diverse range of anthropogenic and environmental variables
interact to determine the spatial distributions of carnivore commun-
ities. Among these variables, one of the most fundamental is the
availability of prey resources (Carbone & Gittleman, 2002; Farris
et al., 2015; Fuller & Sievert, 2001; Henden, Stien, Bårdsen, Yoccoz,
& Ims, 2014; Karanth et al., 2004). Carnivores are also known to be
positively associated with permanent water sources (Epps, Mutayoba,
Gwin, & Brashares, 2011; Schuette, Wagner, Wagner, & Creel, 2013)
and with vegetation cover and productivity, probably because of their
relationship with prey abundance (Pettorelli et al., 2005). These envi-
ronmental features are increasingly altered as a consequence of the
ever-growing human population and the corresponding demand for
land. Thus, land-use change, fragmentation and infrastructure devel-
opment continue to pose some of the greatest threats to biodiversity
worldwide (Alkemade et al., 2009) and negatively affect carnivore dis-
tributions and densities. Roads, for example, can alter animal move-
ments, fragment ecosystems and lead to increases in both legal and
illegal hunting pressure (Forman & Alexander, 1998). Threats posed
by human development are particularly acute for carnivores given
their protein-rich diet, large home range sizes and low densities, as
well as the real or perceived threats carnivores engender to humans
and their livelihoods (Woodroffe & Ginsberg, 1998). Understanding
how natural and anthropogenic drivers determine carnivore distribu-
tions is key to mitigating their declines.
We used a new approach for investigating patterns in mammalian
carnivore occupancy and richness at multiple spatial scales. Our
research was motivated by the recent call for coordinated global moni-
toring networks (Steenweg et al., 2017) and provides a case study for
how this can be achieved. We collated camera trap data from surveys
conducted in 12 countries, which include observations of 96 carnivore
species (c. 40% of the world’s extant carnivore species), ranging in size
from the long-tailed weasel (Mustela frenata;c. 0.27 kg) to the grizzly
bear (Ursus arctos;c. 340 kg). Over half of these species had decreasing
(n547) or unknown (n58) population trends according to the IUCN.
Our common framework allowed us to integrate data collected by mul-
tiple international organizations, including universities, federal agencies
and non-governmental organizations. Our objectives were to quantify
(a) mammalian carnivore occupancy and species richness at the local-
level, and (b) anthropogenic and environmental variables that affected
these patterns at global, local and species-specific levels. By examining
patterns at a global scale, we can evaluate the interchange between
fine- and broad-scale processes, which is vital in understanding ecosys-
tem dynamics (Peters et al., 2008) and in assessing global, regional and
local threats to carnivore communities (Ahumada et al., 2011; O’Brien
et al., 2010).
Our analysis integrated local monitoring efforts spanning five conti-
nents, with study area sizes ranging from 42 to 18,714 km
Figure 1). In North America, we included studies carried out in five
national parks in western Canada, ranging from Jasper National Park in
the north to Waterton Lakes National Park in the south (Steenweg
et al., 2016), the Sierra Nevada mountains of California in the U.S.A.
(Tucker, Schwartz, Truex, Wisely, & Allendorf, 2014) and the protected
Mayan forest in Belize (Wultsch, Waits, & Kelly, 2016). In South
America, we included studies from the Atlantic Forest of Misiones
Province in northeastern Argentina (Di Bitetti, Paviolo, & De Angelo,
2006) and the Yungas ecoregion in northwestern Argentina (Di Bitetti,
Albanesi, Foguet, Cuyckens, & Brown, 2011). We analysed the two
study areas in Argentina separately because they were independent
studies carried out in vastly different ecosystems. In Africa, studies
were located in the Cederberg mountains of Western Cape, South
Africa (Martins, 2010), Ngamiland District of northern Botswana (Rich
et al., 2016), Niokolo Koba National Park of Senegal (Kane, Morin, &
Kelly, 2015) and the Masoala-Makira protected landscape of Madagas-
car (Farris et al., 2015). In Asia, we included studies from the southern
Riau landscape of central Sumatra in Indonesia (Sunarto, Kelly,
Parakkasi, & Hutajulu, 2015), the Churia habitat in Chitwan National
Park in the south-central Terai Arc in Nepal (Thapa & Kelly, 2017) and
seven reserves across central Iran (Farhadinia et al., 2014). Lastly, in
Europe, we included a study carried out in four peninsulas along the
coast of Finnmark in northern Norway (Hamel, Killengreen, Henden,
Yoccoz, & Ims, 2013; Henden et al., 2014).
RICH ET AL.
TABLE 1 Details regarding camera trap surveys included in our analysis of carnivore occupancy and richness
detected Location Bait
Misiones 103 2 5,104 1,547 11 423 Road/trail No 1 25–50 FL Leaf River, TrailMAC Di Bitetti et al. (2006)
Yungas 46 1 1,258 376 11 80 Random Yes 1 40–50 FL Moultrie Di Bitetti et al. (2011)
Belize 213 2 12,437 1,030 13 630 Road/trail No 1–330–40 Both Reconyx, Moultrie Wultsch et al. (2016)
Botswana 179 2 5,345 1,154 22 1,033 Road No 1–330–60 Both Bushnell, Panthera Rich et al. (2016)
Canada 167 1 35,441 14,628 14 813 Road/trail No 5 c. 1,000 IR Reconyx Steenweg et al. (2016)
Indonesia 92 2 8,009 524 14 229 Trail No 1 30–60 FL DeerCam Sunarto et al. (2015)
Iran 220 1–2 12,768 4,749 10 327 Road/trail No 1 20–40 FL CamTrak, Panthera,
Farhadinia et al. (2014)
Madagascar 151 2 8,795 42 6 249 Trail No 1–310–30 Both Moultrie, Reconyx,
Farris et al. (2015)
Nepal 78 2 1,170 509 10 134 Trail No 3 30–60 FL Moultrie Thapa & Kelly (2016)
Norway 66 1 1,832 18,714 3 77 Random Yes Time lapse c. 1,000 IR Reconyx Hamel, Killengreen, Henden,
Yoccoz et al. (2013);
Henden et al. (2014)
Senegal 58 2 3,721 525 17 245 Road No 1–230–60 Both Moultrie, DeerCam Kane et al. (2015)
South Africa 22 5,077 2,476 14 110 Road/trail No 1 40 FL DeerCam Martins (2010)
U.S.A. 319 1 7,130 8,453 13 455 Random Yes 1 c. 1,000 IR Bushnell Tucker et al. (2014)
Note. We present study area sizes, numbers of camera stations, cameras per station, trap days, carnivore species detected and photographic detections of carnivore species (No. detected). We also describe
the location of camera stations (roads, trails or random), whether bait was used, the number of pictures recorded per trigger event (No. of pictures/trigger), whether cameras were infrared (IR) or white-
flash (FL), the camera brand and the corresponding literature reference.
RICH ET AL.
Camera trap surveys
A diversity of camera trap makes and models were used, including both
infrared and white-flash cameras (Table 1). All surveys were completed
between 2005 and 2015 (Table 1). Within each study area, 22–319
x5143; SD 585.5) camera stations were deployed across areas rang-
ing in size from 42 km
in Madagascar to 18,714 km
x54,210; SD 56,053; Table 1). Studies had a minimum of 1,000 trap
days, with the number of trap days ranging from 1,170 to 35,441
x59,007; SD 58,981; Table 1).
We hypothesized that the spatial distributions of mammalian carni-
vores may be influenced by both anthropogenic and environmental
variables. Our anthropogenic variables included the level of protection
and the distance to a major road. We used an ordinal variable ranging
from one to three indicating whether the camera station was located in
a fully protected, partly protected or unprotected area, respectively, to
represent level of protection. Fully protected areas included national
parks, reserves and sanctuaries; partly protected areas included
community-run wildlife areas and protected areas that permitted some
multiple use; and unprotected areas included logging, hunting, livestock
grazing and/or farming areas. Next, we used a global roads dataset
from the Center for International Earth Science Information Network
and Information Technology Outreach Services (CIESIN & ITOS, 2013)
to measure the distance from each camera station to the nearest major
road using ArcMap 10.3.1 (ESRI, CA). Mean and SD values for covari-
ates within each of the respective study areas are presented in Appen-
dix S1 in Supporting Information.
Our environmental variables included prey availability, distance to
water, and forest cover (Supporting Information Appendix S1). To rep-
resent relative prey availability, we used the detection rate of non-
carnivorous vertebrates at each camera station. The detection rate was
equal to the number of independent detections of a non-carnivorous
vertebrate standardized by sampling effort (i.e., number of days the
FIGURE 1 Relative locations of the local camera trap projects included in a global carnivore analysis. Examples of carnivores included in
the study are as follows: (a) grey wolf (Canis lupus;V
CR. Steenweg), (b) jaguar (Panthera onca;V
CM. Kelly), (c) lion (Panthera leo;V
(d) arctic fox (Vulpes lagopus;V
CS. Killengreen), (e) Asiatic cheetah (Acinonyx jubatus venaticus;V
CIranian Cheetah Society/CACP/DoE/
Panthera), (f) leopard (Panthera pardus fusca;V
CK. Thapa/WWF), (g) fisher (Pekania pennant;V
CJ. Tucker/U.S. Forest Service), (h) ocelot
CM. Di Bitetti), (i) African wild dog (Lycaon pictus;V
CL. Rich/Panthera), (j) wildcat (Felis silvestris;V
Leopard Trust), (k) fossa (Cryptoprocta ferox;V
CZ. Farris), and (l) Sumatran tiger (Panthera tigris sumatrae;V
RICH ET AL.
camera station was active). We defined independent detections of a
prey species as photo events separated by 30 min unless different
individuals could be distinguished (e.g., five individuals in a single pho-
tograph would be five events). We used this measure of relative prey
availability, rather than estimates of prey occupancy, because it pro-
vided more detailed information regarding the local activity levels of
prey species. However, we note that our measure did not account for
species-specific detection probabilities. To estimate distance to water,
we used the world water bodies layer from Esri Data and Maps (2011)
and calculated the distance (in kilometres) from each camera station to
the nearest body of water. The water layer did not account for smaller
bodies of water, such as streams, ponds and ephemeral water sources.
Consequently, the geographic information system (GIS) layer did not
include any bodies of water for Senegal. Lastly, to determine forest
cover, we used a 250 m resolution moderate-resolution imaging spec-
troradiometer (MODIS) dataset, based on data from 2000–2010, that
contained proportional estimates for forest, grassland and bare ground
cover (DiMiceli et al., 2011). We determined mean percentage forest
cover within a 1 km
buffered area surrounding each camera station.
All of our covariates were relatively coarse, but they allowed us to use
consistent and comparable values across all study areas.
We used multispecies occupancy models to estimate species-specific
occurrence probabilities in each study area, while correcting for
incomplete detection (Dorazio & Royle, 2005). Correcting for incom-
plete detection (i.e., instances when a species is present but not pho-
tographed) requires spatially or temporally replicated data. As is
common for camera trap studies, we treated each trap day as a repeat
survey at a particular camera station (Rich et al., 2016). In addition to
species’detection, our hierarchical model structure allowed us simul-
taneously to account for spatial variation in model parameters using a
mixed modelling approach with random effects to accommodate cam-
era station and study area-level influences on species occurrence
(Miller & Grant, 2015). We estimated the probability of observing
species iat camera station jon trap day kconditional on the site
being occupied as x
). The detection probability, p
, was the probability species iwas photographed at camera station j
during trap day k, given species iwas truly present at camera station
j. Occurrence, z
, was a latent binary variable where z
51 if camera
station jwas within the range occupied by species iand 0 otherwise,
and modelled as a Bernoulli random variable, z
), where w
is the probability that species ioccurred at camera station j
(MacKenzie et al., 2002).
We first fitted the occurrence model separately for each study
area. We incorporated site-level (i.e., camera station-specific) character-
istics affecting species-specific occurrence probabilities using a general-
ized linear mixed modelling approach (Dorazio & Royle, 2005). For
each study area, occurrence probability for species iat camera station j
was specified as follows:
5b0i1b1ilevel of protectionðÞ
j1b2idistance to roadsðÞ
1b3iprey detection ratesðÞ
1b5idistance to waterðÞ
We log transformed continuous covariates; then for each of the
study areas, we standardized all covariates to have a mean of 0 and SD
of 1. Therefore, the inverse logit of b0
was the occurrence probability
for species iat a camera station with average covariate values within
the respective study area. Remaining coefficients (b1
sent the effect of a one SD increase in the covariate value for species i.
This approach allows covariate relationships to be comparable across
study areas. We note, however, that the magnitude of the relationships
is study area specific because SD estimates varied across countries
(Supporting Information Appendix S1). Detection probability pwas
allowed to differ using a species-specific random effect, which was
drawn from a logit-normal distribution. In many cases, the density of a
species is related to both the overall occupancy probability and the
average site-specific detection probability, resulting in strong, positive
correlations between occupancy and detection among species (Royle &
Nichols, 2003). Therefore, we accounted for correlation (q)between
in the occupancy model and the species-specific detection parame-
, where logit(p)5a0
] by specifying the two parameters to be
jointly distributed (Dorazio & Royle, 2005).
For each of our occurrence models (n513 models representing
13 study areas), we linked species-specific responses using species-
specific random effects for intercept and slope parameters. Sharing
data across species leads to increased precision in estimates of species-
specific occupancy, particularly for rare and elusive species (Zipkin,
Royle, Dawson, & Bates, 2010), and provided a framework for estimat-
ing average effects and precision across the local carnivore community.
Specifically, for each occurrence model, the bcoefficients were mod-
elled as b
), where l
is the community-level mean
is the variance (Chandler et al., 2013). Thus, bcoefficients
were functions of both the community-level hyper-parameter and the
species-specific effect for the respective covariate.
We estimated posterior distributions of parameters using Markov
chain Monte Carlo (MCMC) implemented in JAGS (version 3.4.0),
which we called using R2Jags (Plummer, 2011) in R (R Core Develop-
ment Team 3.2.2). We generated three chains of 50,000 iterations
after a burn-in of 10,000 iterations and thinned by three. Priors for
each of the hyper-parameters included the following: a uniform distri-
bution of 0–1 on the real scale for b0
, a uniform distribution of 0–10
for rparameters, and a normal distribution with a mean of 0 and SD of
100 on the logit scale for the remaining covariate effects (b1
We assessed model convergence using the Gelman–Rubin statistic,
where values <1.1 indicated convergence (Gelman, Carlin, Stern, &
Finally, we were also interested in estimating average covariate
effects across all study areas (i.e., global-level effects) using a multi-
region, multispecies model (Miller & Grant, 2015). To do this, we
refitted data from all the study areas simultaneously, still including
region-specific hyper-parameters for species-specific effects. This
allowed us to estimate an average effect across all regions (and
RICH ET AL.
precision of this effect) by calculating the average of the l
for each of the regions. Our approach allowed us to propagate uncer-
tainty across the multiple hierarchical levels (site, species and region)
when estimating an average global effect. We also used this model to
generate estimates of species richness at the level of individual camera
station jby summing the number of estimated species at a site within
each region during each of the model iterations. This allowed us to
generate predicted mean species richness to facilitate comparisons of
total predicted richness with respect to region and covariate values
(Rich et al., 2016; Zipkin et al., 2010). Our model code is provided in
Supporting Information Appendix S2.
Our research included 4,805 detections of 96 mammalian species from
the Order Carnivora, collected during >100,000 trap nights at >1,700
camera stations (Table 1). The mean recorded number of carnivore spe-
cies per study area was 12 (Table 1), ranging from three species in Nor-
way to 22 species in Botswana. The mean estimated occupancy across
all species and countries was 0.31 (i.e., on average each species was
estimated to occur at 31% of the sites within a study area where the
species was known to occur; SD 50.235). Species-specific estimates of
occupancy ranged from 0.02 for striped skunk (Mephitis mephitis)in
Canada to 0.90 for spotted hyena (Crocuta crocuta) in Botswana. Given
that we treated each trap day as a repeat survey, our estimated detec-
tion probabilities were low (
x50.04, SD 50.007) but highly variable
among species, ranging from 0.002 for fisher (Pekania pennanti)in
Canada to 0.21 for Pampas fox (Lycalopex gymnocercus) in Argentina.
Species- and country-specific estimates of occupancy, detection and
covariate effects are presented in Supporting Information Appendix S3.
At the global level, prey detection rate had the largest impact on
carnivore occupancy probabilities, where carnivore occupancy increased
as prey detection rates increased (Table 2). Occupancy also tended to
increase in areas with greater protection and greater distance to major
roads [i.e., 95% credible intervals (CIs) overlapped 0.0, but values were
predominantly positive or negative], whereas no relationship was found
with forest cover and distance to water (Table 2). There was, however,
heterogeneity in the influences of covariates at the local level (Figure 2).
For instance, the positive relationship between occupancy and prey
detection rate was most evident in Nepal, Madagascar and Iran, but this
relationship was negative in Norway and the U.S.A. (Figure 2;
Supporting Information Appendix S4). The tendency for occupancy to
increase with greater distance to roads was most evident in Norway,
Madagascar and Indonesia, whereas carnivore occupancy tended to be
higher close to major roads in Nepal and Senegal (Figure 2; Supporting
Information Appendix S4). Among the local monitoring efforts that had
cameras deployed across multiple land designations, the tendency for
occupancy to increase in areas with greater protection was most evident
in Norway, Canada and Belize (Figure 2; Supporting Information
Appendix S4). The influence of forest cover was highly variable at the
local level, with occupancy generally increasing with increasing forest
cover in Indonesia, Canada and South Africa, and with decreasing forest
cover in Senegal, Nepal and Botswana (Figure 2; Supporting Information
Appendix S4). The influence of distance to a large body of water was
weak, with the exception of Madagascar and Indonesia, where carnivore
occupancy was greater in areas further from water bodies (Figure 2;
Supporting Information Appendix S4).
Among all countries, our camera station-specific estimates of
mammalian carnivore richness ranged from 0 (95% CI 50–1) to 13
(95% CI 511–15), with a mean of 4 (Supporting Information Appendix
S5). Mean carnivore richness (i.e., across all camera stations within the
respective country) was greatest in Botswana (
x57.9) and the
Misiones region of Argentina (
x55.9) and least in Norway (
and Madagascar (
x52.0). At the global level, relative carnivore richness
was mainly correlated with prey detection rates, with higher carnivore
richness occurring in areas with higher prey density (Figure 3). The
remaining covariates did not appear to influence camera station-
specific estimates of carnivore richness (Figure 3).
At both global and local scales, our research supported the founda-
tional ecological concept that prey availability is a fundamental deter-
minant of carnivore distributions (Carbone & Gittleman, 2002; Fuller &
Sievert, 2001). Detection rates of prey (i.e., non-carnivorous verte-
brates) had the strongest and consistently positive influence on mam-
malian carnivore occupancy and carnivore richness across the wide
diversity of ecosystems included in our study, with the exception of
only two study areas (Figure 2). Positive relationships between carni-
vores and their prey have been found in studies spanning the globe
and for species ranging from broad-striped vontsira (Galidictis fasciata;
Farris et al., 2015) to wolverines (Gulo gulo; Henden et al., 2014) and
tigers (Panthera tigris; Karanth et al., 2004). Our measure of prey avail-
ability was camera station specific to reflect spatial variation in prey
resources (Fuller & Sievert, 2001) and to help ensure that it was com-
parable across local monitoring efforts.
Although the relationship between observed prey and carnivore
richness was clear, there were some limitations to how we measured
TABLE 2 Global-level mean (
xÞand 95% credible interval (95% CI)
estimates for the covariates hypothesized to influence the occur-
rence probabilities of carnivores in study areas spanning 12
Level of protection
20.36 22.753 0.585
Distance to roads 0.11 20.072 0.293
Prey detection rates 0.99 0.576 1.410
Forest cover 0.13 20.500 0.759
Distance to water 0.29 21.236 1.821
Note. Within each study area, we standardized all covariates to have a
mean of 0 and SD of 1.
Ordinal variable ranging from 1, fully protected,
to 3, unprotected. Only includes study areas that had cameras deployed
across multiple land designations (excludes Yungas Argentina, Indonesia,
Iran, Nepal and Senegal).
RICH ET AL.
prey availability. We did not account for prey detectability, treated all
prey species as equivalent, and did not include important components
of many carnivore diets (e.g., small mammals, invertebrates, fish and
fruit). In the U.S.A., for example, many carnivores consume a wide vari-
ety of food items; thus, the prey species photographed most often
(chipmunks and squirrels) represented only a fraction of their diets (Zie-
linski & Duncan, 2004). This incongruence is particularly true for the
most commonly detected carnivore species in the region, black bears
(Ursus americanus), and helps to explain the negative relationship we
found between carnivore occupancy and prey availability in the U.S.A.
The negative relationship in Norway may be design related. The Nor-
way study baited cameras with reindeer (Rangifer tarandus) slaughter
remains to increase carnivore detection probabilities (Henden et al.,
2014). This might have resulted in lower prey detection rates that were
unrepresentative of regional prey abundance. Indeed, Henden et al.
(2014) found that reindeer density, estimated from aerial surveys, was
generally positively associated with carnivore distributions in Norway.
Despite these limitations, our research still supported the vital role
prey populations play in maintaining carnivore communities across a
broad range of ecosystems (Carbone & Gittleman, 2002; Fuller &
At the global level and for several of the study areas, mammalian
carnivore occupancy also tended to be greater in areas with higher lev-
els of protection (e.g., national parks and reserves) that were further
from major roads (Figure 2). Our results are consistent with studies
showing that human development negatively affects biodiversity
worldwide (Alkemade et al., 2009; Butchart et al., 2010). Additionally,
our finding that carnivore occupancy generally increased as the level of
FIGURE 2 Standardized bcoefficients, and 95% credible intervals, for the influence of (a) level of protection as represented by an ordinal
variable ranging from one (fully protected) to three (unprotected), (b) distance to major roads, (c) prey detection rates (mean number of
detections of non-carnivorous vertebrates per trap night), (d) percentage forest cover, and (e) distance to a major water source, on the
probability a carnivore species used an area during camera trap surveys carried out in 13 study areas and the mean effect on carnivore
occupancy across all study areas (global)
RICH ET AL.
protection increased supports studies highlighting the important role
protected areas play in conserving wildlife populations (Bertzky et al.,
2012; Watson, Dudley, Segan, & Hockings, 2014). Maintaining,
expanding and effectively managing these areas is crucial to the con-
servation of carnivore diversity as well as to safeguarding landscapes
and protecting essential ecosystem services (Bertzky et al., 2012;
Watson et al., 2014). Our measure of protection was broad and did not
fully encompass the effects that differences in habitat quality, enforce-
ment of protected area boundaries or types of human activity (e.g., log-
ging versus agriculture) might have on carnivore populations. Likewise,
our measure of distance to roads was coarse; it did not include smaller,
four-wheel drive roads, and we treated all roads as equal. With better
information on the level of protection and road characteristics, impor-
tant nuances and greater power to detect relationships might emerge.
Regardless, our results generally point to the role of areas that are
protected and far from major roads in supporting carnivore diversity
(Table 2), which adheres to the over-arching negative effect of human
activity on carnivore species (Alkemade et al., 2009; Butchart et al.,
Distance to water and forest cover were highly variable in their
effects both within and among study systems. Several species, such as
servals (Leptailurus serval), African civets (Civittictis civetta) and lions
(Panthera leo) in Botswana, were more likely to occupy areas close to
major water sources (Supporting Information Appendix S3). Generally,
however, water was related only weakly to carnivore occupancy. This
was probably a consequence of the ability of carnivores to travel long
distances and the diversity of ecosystems included in our analysis.
Additionally, we may have been limited by our need to relate patterns
to a global water layer, which did not account for smaller bodies of
water, such as streams, ponds and ephemeral water sources. These
smaller sources of water are common in many of the areas and poten-
tially easier to access. The highly variable influence of forest cover was
FIGURE 3 Relative carnivore richness (estimated richness at camera station/number of carnivore species photographed in respective study
area) at 1,714 camera stations located across 13 study areas in relationship to (a) level of protection (ordinal variable where 1 5fully
protected and 3 5unprotected), (b) distance from camera station to a major road, (c) prey detection rate (mean number of detections of
non-carnivorous vertebrates per trap night), (d) percentage forest cover, and (e) distance from the camera station to a major water source.
Covariate values were standardized within each study area to have a mean of 0 and SD of 1
RICH ET AL.
also a consequence of the diversity of ecosystems included in our
research, ranging from tropical forests to the arctic tundra. The varia-
tion in the effects of forest cover on carnivore occupancy highlights
the importance of tailoring conservation strategies to the particular
ecosystem of interest.
Our approach allowed us to determine factors related to carnivore
richness that were consistent across regions (e.g., prey availability and,
to a lesser extent, protection status and distance to roads) but also
when relationships varied among regions. In general, we expect that
global-scale results will not be reliable proxies for local-scale processes
when (a) heterogeneity is large and not well described by a statistical
distribution, (b) the way relationships scale with respect to the same
covariate differs among locations, and (c) the processes that affect dis-
tributions differ among study areas. In the present study, we have
taken a first large step to reduce heterogeneity by accounting for
observation bias and by including random effects to help accommodate
the diversity of study designs. However, our analysis was limited in
that all of the covariates we included were relatively coarse. As tech-
nology continues to improve, the resolution of available data is becom-
ing finer and and the spatial coverage broader. We encourage future
studies to build upon and improve our global assessment by using this
anthropogenic and environmental data to capture heterogeneity better
at the local level. Our global assessment could also be improved by
accounting for differences among areas in the structure of the respec-
tive carnivore guilds (i.e., size and type of carnivores). This might be
important, because covariates such as the level of protection, for exam-
ple, could affect generalist species in a different way from specialists.
Lastly, to gain a full understanding of the influence of covariates, we
encourage future studies to continue presenting both global and local
estimates. This is important because the mean, global estimate might
be unrepresentative if the influence of covariates is not linear or the
strength of these effects differs across studies (e.g., opposing effects of
forest cover on forest versus savanna carnivore assemblages).
Collaborative and integrative biodiversity monitoring is necessary to
develop effective conservation planning and to mitigate biodiversity loss
(Ellison, 2010; Jones et al., 2006; Schmeller et al., 2015). Our research
demonstrates that when researchers are willing to work collaboratively
and share data, broad-scale assessments are possible. Our research also
highlights the potential of camera traps as a tool for monitoring global
biodiversity in accordance with the Convention on Biological Diversity
(Ahumada et al., 2013; Burton et al., 2015; O’Brien et al., 2010; Steen-
weg et al., 2017). With the exponential increase in the use of camera
traps over the last decade, there is further opportunity to standardize
methods in ways that would increase their utility for continental and
global assessments. Given that the design of a camera trap study can
greatly influence results (Hamel, Killengreen, Henden, Eide, et al., 2013),
monitoring should aim to increase consistency among study areas. This
should include standardization of sampling design, field methods (e.g.,
placement of cameras), and minimal standards for spatial and temporal
extent. By developing a standardized design that targets all species or a
specified subset, we can improve inferences on species diversity and
richness at the global scale. Lastly, by avoiding heterogeneity in data col-
lection across time, we will be better equipped to assess and quantify
temporal trends. Greater integration and standardization among camera
trap studies worldwide is key in developing a global biodiversity monitor-
ing network (Steenweg et al., 2017). This would allow policy-makers and
managers to track, improve and adapt policies and management actions
aimed at addressing the loss of wildlife populations at both local and
global scales (Butchart et al., 2010; Schmeller et al., 2015). Our research
framework provides a starting point and blueprint for how this can be
achieved on an international scale for carnivores.
We thank the Ministry of the Environment, Wildlife and Tourism, the
Department of Wildlife and National Parks and the Botswana Preda-
tor Conservation Trust in Botswana; the Ministry of Environment,
Water, Forest and Tourism and Wildlife Conservation Society in
Madagascar; the Department of National Parks and United States
Agency for International Development/Wula Nafaa Project in Senegal;
and The Cederberg Conservancy and CapeNature in South Africa for
permission and/or supporting the research in Africa. In North America,
we thank Parks Canada staff for collecting data in Canada and the
National Science Foundation for funding part of this project (Long
Term Research In Environmental Biology Grant 1556248); the Pacific
Southwest Region of the U.S. Forest Service and the Sierra and
Sequoia National Forests for supporting research in the U.S.A.; and in
Belize, we thank the Forest Department, Programme for Belize, Las
Cuevas Research Station, Bull Run Farm, Belize Audubon Society,
Wildtracks, Gallon Jug Estate and Yalbac Ranch. In South America, we
thank the Ministry of Ecology and Natural Resources of Misiones, the
National Parks Administration of Argentina, Fundaci
Ledesma S.A. and ARAUCO Argentina S.A. for permissions and sup-
port to conduct camera trap surveys. In Asia, we thank the Iran
Department of Environment for permission to work within the
reserves in Iran, the World Wildlife Fund, the National Trust for
Nature Conservation, Chitwan National Park and Department of
National Parks and Wildlife Conservation for permission and support
to survey in Nepal; and in Indonesia, World Wildlife Fund Networks,
U.S. Fish & Wildlife Service and the Hurvis Family for financially sup-
porting the research, the Indonesian Ministry of Forestry for permis-
sion to conduct the study, and the World Wildlife Fund Team for all
their support. Lastly, in Europe we thank the Directorate for Nature
Management and The Norwegian Research Council for financing the
camera trap data collected in Norway. Thank you to H. S. Robinson
for help during the planning stages of the manuscript.
Ahumada, J. A., Hurtado, J., & Lizcano, D. (2013). Monitoring the status
and trends of tropical forest terrestrial vertebrate communities from
camera trap data: A tool for conservation. PLoS One,8, e73707.
Ahumada, J. A., Silva, C. E. F., Gapjapersad, K., Hallam, C., Hurtado, J.,
Martin, E., ... Andelman, S. J. (2011). Community structure and
diversity of tropical forest mammals: Data from a global camera trap
network. Philosophical Transactions of the Royal Society B: Biological
RICH ET AL.
Alkemade, R., van Oorschot, M., Miles, L., Nellemann, C., Bakkenes, M.,
& Brink, B. (2009). GLOBIO3: A framework to investigate options for
reducing global terrestrial biodiversity loss. Ecosystems,12, 374–390.
Bertzky, B., Corrigan, C., Kemsey, J., Kenney, S., Ravilious, C., Besancon,
C., & Burgess, N. D. (2012). Protected planet report: Tracking progress
towards global targets for protected areas. Cambridge, U.K.: Interna-
tional Union for Conservation of Nature and United Nations Environ-
ment Programme-World Conservation Monitoring Centre.
Burton, A. C., Neilson, E., Moreira, D., Ladle, A., Steenweg, R., Fisher, J.
T., ... Boutin, S. (2015). Wildlife camera trapping: A review and rec-
ommendations for linking surveys to ecological processes. Journal of
Applied Ecology,52, 675–685.
Butchart, S. H. M., Walpole, M., Collen, B., van Strien, A., Scharlemann, J.
P., Almond, R. E., ... Watson, R. (2010). Global biodiversity: Indica-
tors of recent declines. Science,328, 1164–1168.
Carbone, C., & Gittleman, J. L. (2002). A common rule for the scaling of
carnivore density. Science,295, 2273–2276.
Center for International Earth Science Information Network (CIESIN),
Columbia University, and Information Technology Outreach Services
(ITOS), University of Georgia. (2013). Global roads open access data
set, version 1. Retrieved from https://doi.org/10.7927/H4VD6WCT
Chandler, R. B., King, D. I., Raudales, R., Trubey, R., Chandler, C., &
avez, V. J. A. (2013). A small-scale land-sparing approach to con-
serving biological diversity in tropical agricultural landscapes. Conser-
vation Biology,27, 785–795.
Di Bitetti, M. S., Albanesi, S., Foguet, M. J., Cuyckens, G. A. E., & Brown,
A. (2011). The Yungas Biosphere Reserve of Argentina: A hot spot of
South American wild cats. CAT News,54,25–29.
Di Bitetti, M. S., Paviolo, A., & De Angelo, C. (2006). Density, habitat use
and activity patterns of ocelots (Leopardus pardalis) in the Atlantic
Forest of Misiones, Argentina. Journal of Zoology,270, 153–163.
DiMiceli, C. M., Carroll, M. L., Sohlberg, R. A., Huang, C., Hansen, M. C.,
& Townshend, J. R. G. (2011). MODIS vegetation continuous fields.
Retrieved from http://glcf.umd.edu/data/vcf/
Dorazio, R. M., & Royle, J. A. (2005). Estimating size and composition of
biological communities by modeling the occurrence of species. Jour-
nal of American Statistical Association,100, 389–398.
Ellison, A. M. (2010). Repeatability and transparency in ecological
research. Ecology,91, 2536–2539.
Epps, C. W., Mutayoba, B. M., Gwin, L., & Brashares, J. S. (2011). An empir-
ical evaluation of the African elephant as a focal species for connectivity
planning in East Africa. Diversity and Distributions,17, 603–612.
Esri Data and Maps. (2011). World water bodies. Retrieved from www.arc-
Farhadinia, M. S., Eslami, M., Hobeali, K., Hosseini-Zavarei, F., Gholikhani,
N., & Taktehrani, A. (2014). Status of Asiatic cheetah in Iran: A
country-scale assessment (Project Final Report). Tehran: Iranian Chee-
tah Society (ICS).
Farris, Z. J., Golden, C. D., Karpanty, S., Murphy, A., Stauffer, D., Ratelo-
lahy, F., ... Kelly, M. J. (2015). Hunting, exotic carnivores, and habitat
loss: Anthropogenic effects on a native carnivore community, Mada-
gascar. PLoS One,10, e0136456.
Feeley, K. J., & Silman, M. R. (2011). The data void in modeling current
and future distributions of tropical species. Global Change Biology,17,
Forman, R. T. T., & Alexander, L. E. (1998). Roads and their major ecolog-
ical effects. Annual Review of Ecology and Systematics,29, 207–231.
Fuller, T. K., & Sievert, P. R. (2001). Carnivore demography and the con-
sequences of changes in prey availability. In J. L. Gittleman, S. M.
Funk, D. W. Macdonald, & R. K. Wayne (Eds.), Carnivore conservation
(pp. 163–178). Cambridge, U.K.: Cambridge University Press and The
Zoological Society of London.
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian data
analysis. Boca Raton, FL: Chapman and Hall.
Hamel, S., Killengreen, S. T., Henden, J.-A., Eide, N. E., Roed-Eriksen, L.,
Ims, R. A., & Yoccoz, N. G. (2013). Towards good practice guidance
in using camera-traps in ecology: Influence of sampling design on
validity of ecological inferences. Methods in Ecology and Evolution,4,
Hamel, S., Killengreen, S. T., Henden, J.-A., Yoccoz, N. G., & Ims, R. A.
(2013). Disentangling the importance of interspecific competition,
food availability, and habitat in species occupancy: Recolonization of
the endangered Fennoscandian arctic fox. Biological Conservation,
Hampton, S. E., Strasser, C. A., Tewksbury, J. J., Gram, W. K., Budden, A.
E., Batcheller, A. L., ... Porter, J. H. (2013). Big data and the future
of ecology. Frontiers in Ecology and Environment,11, 156–162.
Henden, J. A., Stien, A., Bårdsen, B. J., Yoccoz, N. G., & Ims, R. A. (2014).
Community-wide mesocarnivore response to partial ungulate migra-
tion. Journal of Applied Ecology,51, 1525–1533.
Hooper, D. U., Adair, E. C., Cardinale, B. J., Byrnes, J. E. K., Hungate, B.
A., Matulich, K. L., ... O’Connor, M. I. (2012). A global synthesis
reveals biodiversity loss as a major driver of ecosystem change.
Jones, M. B., Schildhauer, M. P., Reichman, O. J., & Bowers, S. (2006).
The new bioinformatics: Integrating ecological data from the gene to
the biosphere. Annual Review of Ecology, Evolution, and Systematics,
Kane, M. D., Morin, D. J., & Kelly, M. J. (2015). Potential for camera-
traps and spatial mark-resight models to improve monitoring of the
critically endangered West African lion (Panthera leo). Biodiversity and
Karanth, K. L., Nichols, J. D., Samba Kumar, N., Link, W. A., & Hines, J. E.
(2004). Tigers and their prey: Predicting carnivore densities from
prey abundance. PNAS,101, 4854–4858.
Kelling, S., Hochachka, W. M., Fink, D., Riedewald, M., Caruana, R., Bal-
lard, G., & Hooker, G. (2009). Data-intensive science: A new para-
digm for biodiversity studies. BioScience,59, 613–620.
MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Royle, J. A.,
& Langtimm, C. A. (2002). Estimating site occupancy rates when
detection probabilities are less than one. Ecology,83, 2248–2255.
Martins, Q. E. (2010). The ecology of the leopard Panthera pardus in the
Cederberg Mountains (Unpublished Doctoral Dissertation). University
Miller, D. A. W., & Grant, E. H. C. (2015). Estimating occupancy dynam-
ics for large-scale monitoring networks: Amphibian breeding occu-
pancy across protected areas in the northeast United States. Ecology
and Evolution,5, 4735–4746.
O’Brien, T. G., Baillie, J. E. M., Krueger, L., & Cuke, M. (2010). The Wild-
life Picture Index: Monitoring top trophic levels. Animal Conservation,
lins, S. L., Michener, W. K., & Huston, M. A. (2008). Living in an
increasingly connected world: A framework for continental-scale
environmental science. Frontiers in Ecology and Environment,6,
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker, C. J., &
Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess
ecological responses to environmental change. Trends in Ecology and
RICH ET AL.
Plummer, M. (2011). JAGS: A program for the statistical analysis of
Bayesian hierarchical models by Markov Chain Monte Carlo.
Retrieved from http://sourceforge.net/projects/mcmc-jags/
Rich, L. N., Miller, D. A. W., Robinson, H. S., McNutt, J. W., & Kelly, M.
J. (2016). Using camera trapping and hierarchical occupancy
modeling to evaluate the spatial ecology of an African mammal and
bird community. Journal of Applied Ecology,53, 1225–1235.
Ripple, W. J., Estes, J. A., Beschta, R. L., Wilmers, C. C., Ritchie, E. G.,
Hebblewhite, M., ... Wirsing, A. J. (2014). Status and ecological
effects of the world’s largest carnivores. Science,343, 151–163.
Royle, J. A., & Nichols, J. D. (2003). Estimating abundance from repeated
presence–absence data or point counts. Ecology,84, 777–790.
Schipper, J., Hoffmann, M., Duckworth, J. W., & Conroy, J. (2008). The
2008 IUCN red listing of the world’s small carnivores. Small Carnivore
Schmeller, D. S., Julliard, R., Bellingham, P. J., B€
ohm, M., Brummitt, N.,
Chiarucci, A., ... Belnap, J. (2015). Towards a global terrestrial spe-
cies monitoring program. Journal for Nature Conservation,25,51–57.
Schuette, P., Wagner, A. P., Wagner, M. E., & Creel, S. (2013). Occu-
pancy patterns and niche partitioning within a diverse carnivore com-
munity exposed to anthropogenic pressures. Biological Conservation,
Secretariat of the Convention on Biological Diversity (SCBD). (2014).
Strategic plan for biodiversity 2011–2020. Retrieved from www.cbd.
Steenweg, R., Hebblewhite, M., Kays, R., Ahumada, J., Fisher, J. T., Bur-
ton, C., ... Rich, L. N. (2017). Scaling-up camera traps: Monitoring
the planet’s biodiversity with networks of remote sensors. Frontiers
in Ecology and Environment,15,26–34.
Steenweg, R., Whittington, J., Hebblewhite, M., Forshner, A., Johnston,
B., Petersen, D., ... Lukacs, P. (2016). Remote-camera-based occu-
pancy monitoring at large scales: Power to detect trends in grizzly
bears across the Canadian Rockies. Biological Conservation,201,
Sunarto, S., Kelly, M. J., Parakkasi, K., & Hutajulu, M. B. (2015). Cat coex-
istence in central Sumatra: Ecological characteristics, spatial and tem-
poral overlap, and implications for management. Journal of Zoology,
Thapa, K., & Kelly, M. J. (2017). Density and carrying capacity in the for-
gotten tigerland: Tigers in understudied Nepalese Churia. Integrative
Tucker, J. M., Schwartz, M. K., Truex, R. L., Wisely, S. M., & Allendorf, F.
W. (2014). Sampling affects the detection of genetic subdivision and
conservation implications for fisher in the Sierra Nevada. Conserva-
tion Genetics,15, 123–136.
Watson, J. E. M., Dudley, N., Segan, D. B., & Hockings, M. (2014). The
performance and potential of protected areas. Nature,515,67–73.
Woodroffe, R., & Ginsberg, J. R. (1998). Edge effects and the extinction
of populations inside protected areas. Science,280, 2126–2128.
Wultsch, C., Waits, L. P., & Kelly, M. J. (2016). A comparative analysis of
genetic diversity and structure in jaguars (Panthera onca), pumas (Puma
concolor), and ocelots (Leopardus pardalis) in fragmented landscapes of
a critical Mesoamerican linkage zone. PLoS One,11, e0151043.
Zielinski, W. J., & Duncan, N. P. (2004). Diets of sympatric populations
of American martens (Martes americana) and fishers (Martes pennanti)
in California. Journal of Mammalogy,85, 470–477.
Zipkin, E. F., Royle, J. A., Dawson, D. K., & Bates, S. (2010). Multi-species
occurrence models to evaluate the effects of conservation and man-
agement actions. Biological Conservation,143, 479–484.
LINDSEY N. RICH’Sresearch focuses on population dynamics and commu-
nity ecology. She uses new, quantitative approaches to assess wildlife
data collected using various field techniques (e.g., camera trap surveys,
radiotelemetry and public questionnaires) in order to address conserva-
tion and management challenges.
Additional Supporting Information may be found online in the sup-
porting information tab for this article.
How to cite this article: RichLN,DavisCL,FarrisZJ,etal.
Assessing global patterns in mammalian carnivore occupancy and
richness by integrating local camera trap surveys. Global Ecol
Biogeogr. 2017;00:1–12. https://doi.org/10.1111/geb.12600
RICH ET AL.