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The Wildlife Picture Index: monitoring Mongolian biodiversity with camera trapping

The Wildlife Picture Index: monitoring
Mongolian biodiversity
with camera trapping
Susan E. Townsend, Batbayar Galtbalt, Munkhjargal Myagmar, Jonathan Baillie
and Timothy G. O’Brien
The Wildlife Picture Index (WPI) was developed to
measure trends in biodiversity at the landscape
level using camera trap sampling. As a case study
from 2009 to 2011, we tested the use of the WPI to
assess how well protected areas were functioning
to conserve wildlife (biodiversity) in Mongolia.
Mongolia supports a rich ungulate and carnivore
fauna, has low human population density and has
established protected areas, presenting ideal condi-
tions to test this new conservation tool for assessing
trends in biodiversity. Our effort resulted in 26 816
trap-nights producing 416 300 photographs from
five study areas. In this paper, we present species-
specific occupancy estimates for one study area and
the WPI for the study area for which we had three
seasons of data. The WPI effectively measured bio-
diversity and the status of individual species at a
landscape level within the Protected Areas. We
detected a total of 22 (possibly 24) mammalian spe-
cies ranging from six to 13 species at each site. In the
least protected area of a study site, the WPI rose
14.5% after the first year and declined 38% in the
third year, and in the more protected area, it rose
12.6% after the first year and then declined back to
baseline in the third year. This approach could
prove to be very useful and cost effective for long-
term monitoring and adaptive management.
With recent advances in camera trapping meth-
ods, it is now possible to monitor trends in the
diversity, abundance and distribution of a broad
range of terrestrial mammals and birds in a vari-
ety of habitats ranging from savannah to deserts
to tropical ecosystems. Camera trapping is a par-
ticularly attractive approach for monitoring
because it is non-obtrusive, has low observer
error, is comparable across sites, data can be
aggregated for various indices, and photographs
allow for verifiability. Setting and maintaining
camera traps does not require highly skilled staff.
It is lower in cost when compared to other
approaches of equal rigor (O’Brien and Kinnaird
2013; Silveira et al. 2003).
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AN: 921133 ; Meek, Paul, Fleming, Peter, Ballard, Guy, Banks, Peter, Claridge, Andrew W., Sanderson, James, Swann, Don.; Camera Trapping : Wildlife Management
and Research
Account: deakin.main.eds
PArt 1 – cAmErA trAPPIng for AnImAL monItorIng: cAsE studIEs
Landscape level wildlife monitoring with
camera traps has generally been implemented in
forested tropical ecosystems (O’Brien et al. 2010;
Ahumada et al. 2011 [see Tropical Ecosystem
Assessment & Monitoring Network; <http://www.>]), often targeting a single spe-
cies (tiger: Karanth et al. 2011; pygmy hippopoto-
mus: Collen et al. 2011) and certain trophic levels
(such as carnivores, see Pettorelli et al. 2010; Burton
et al. 2011).
The Wildlife Picture Index (hereafter, ‘WPI’) is a
metric for biodiversity using camera trapping
(O’Brien 2010; O’Brien et al. 2010). The WPI uses
species-specific occupancy statistics scaled to the
first year of camera trapping data; these data are
then used to produce the index using the geometric
mean for all species in a given year as per Buckland
et al. (2005). Because we were interested in species
occurrence and distribution, camera trapping is a
logical choice for documenting occupancy over
large areas. Because a species may go undetected
in a sample unit even if that species is actually pre-
sent, this ‘false absence’ leads to underestimation
of true occupancy. Unless the probability of detec-
tion is determined, measures of occupancy over
time are invalid (MacKenzie et al. 2005). Occupancy
modelling allows for estimation of true occupancy
and detection probabilities, and provides the basis
for the Wildlife Picture Index (O’Brien et al. 2010).
Mongolia has seen rapid social, economic and
environmental change over the past two decades
due to a transition to the market economy. This
transition resulted in major shifts in the human
population and, to some extent, a breakdown of
regulatory mechanisms. This, coupled with high
unemployment, led to a flourishing illegal and
wildlife trade, now estimated to be worth more
than US$100 million annually (Wingard and
Zahler 2006). It has been difficult to define the
impact that this has had on Mongolian wildlife, as
few scientifically robust studies on population
trends have been carried out over the past two dec-
ades. The Mongolian Red List for Mammals, initi-
ated by the World Bank, indicates that massive
declines have taken place; 79% of large herbivores
and 12% of carnivores are listed as threatened with
extinction (Clark et al. 2006). While the Red List is
an excellent first step, the certainty of many of the
status assessments is low due to poor data quality
and the reliance on anecdotal information and
expert opinion.
As a case study from 2009 to 2011, we tested the
WPI as a tool to assess how well protected areas
were functioning to conserve wildlife (biodiver-
sity) in Mongolia with the intent of having the park
administration staff implement long-term monitor-
ing. Mongolia supports a rich ungulate and carni-
vore fauna, has low human population density and
has established protected areas, presenting ideal
conditions to test this new conservation tool for
assessing trends in biodiversity. Mongolia supports
both temperate grassland and forested ecosystems
and is part of the Holarctic ecozone.
We hypothesised that biodiversity and species-
specific occupancy estimates would be higher in
the more restricted management zones in the pro-
tected areas than in the less restricted management
zones. We expected there would be less anthropo-
genic influence in the more restrictive zone. In
order to test this, we set up camera-trapping grids
in two different zones in each study area.
In this paper, we present representative find-
ings from one of our study areas to illustrate base-
line information that can be generated from one
season and present the WPI from another site,
Myangan Ugalzat (MU), from which we have three
seasons of data. Establishing baseline occupancy
estimates and diversity and being able to detect
trends over time are two useful outcomes of this
approach towards effective management.
Five study areas representing four ecoregions that
were sampled over 3 years (Plate 7). These study
areas included: (1) Myangan Ugalzat (MU) located
in Tsetseg Soum, Hovd Aimag in western Mongo-
lia in the south-western portion of the Altai Sayan
region; (2) Khonin Nuga (KN) located in a valley in
the western Khentii region of Northern Mongolia;
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5 – tHE WILdLIfE PIcturE IndEx: monItorIng mongoLIAn bIodIvErsIty WItH cAmErA trAPPIng
(3) Onon Balj (OB) located in the Eastern Khentii in
the Amur River Basin ecoregion; (4) Mongol
Daguur (MD) located in north-eastern Dornod
Aimag in the Eastern Steppe of Mongolia; and (5)
Numrug (NU) located in the farthest east of Mon-
golia in Dornod Aimag. In this paper, we present
representative findings from KN and MU.
Our field work was conducted during the
summer season between June and August in 2009,
2010 and 2011. In MU during the summer season of
2009, we deployed 40 SG550 camera traps in
2 × 20 km2 grids with 1 km spacing; one in a zone
that allowed grazing (Buffer Zone, Grid I) and one
that did not (Core Area, Grid II). In 2010 and 2011,
we deployed 100 SG550 camera traps at stations at
MU, KN and OB and at MU, MD and NU, respec-
tively, in 2 × 50 km2 grids (1 km spacing) in two
different management zones at each study area; a
less restricted zone (Grid I) and a more restricted
zone (Grid II). The first camera station location for
each grid was randomly selected. We predicted our
occupancy estimates, terrestrial mammalian diver-
sity and the presence of expected red-listed species
would be higher in Grid II due to less human influ-
ence and more protection for wildlife than Grid I,
characterised by more human influence and less
protection for wildlife.
Data analysis
Protocols were standardised and followed TEAM
published guidelines (<http://www.teamnetwork.
org/data/manage>). All photographs were cata-
logued by attaching location coordinates to EXIF
data and extracting EXIF data into excel spread-
sheets using Picture Information Extractor (PIE)
software. All photographs were viewed for animal
identification and then entered into the excel
spreadsheet. Occupancy estimates were deter-
mined for all species that were reliably detected.
We used single-season occupancy models to esti-
mate occupancy (ψ) and detection probabilities (ρ)
for each species (MacKenzie et al. 2003) using the
program PRESENCE (v3.2, Hines 2006). Occupancy
models account for imperfect detection and pro-
vide unbiased estimates of occupancy. To apply
these models, detection histories were compiled for
each species at each site (Grids I and II) for each
camera station in a series of ones (detection) and
zeros (non-detection). Each day the camera was set
and functioning was considered a replicate sample.
Each day the camera station was ‘down’ or not
functioning was treated as a missing value.
We modelled each of our grids separately. We
ran two predefined models and used the model
with lowest delta AIC (Akaike’s Information Crite-
rion) to estimate probability of detection and occu-
pancy as calculated by the software PRESENCE
(Hines 2006). The first model estimated the same
occupancy probability for all camera station loca-
tions and that detection probability (ρ) was con-
stant across both camera station location and
survey occasions (i.e. two parameters). The second
model assumes that all camera station locations
have the same probability of occupancy (ψ), but
that ρ varies between the surveys although at each
survey occasion, ρ is the same at each camera sta-
tion location. Several species were detected too
infrequently to generate occupancy estimates and,
in those cases, we report the observed occupancy.
The Wildlife Picture Index (WPI)
Methods to calculate the WPI including the R script
are discussed in detail in O’Brien et al. (2010) and
O’Brien (2010). The most essential details are pre-
sented below. To develop the WPI, species-specific
occupancy estimates were generated from the
camera trap grid data for each season. Each survey
season had a series of repeated surveys represented
by each camera trap day. The index used each spe-
cies-specific occupancy estimate for species i at site
j in year k. In this case, 3 years of seasonal data pro-
vided three periods to generate occupancy esti-
mates and an index. Occupancy in year k was
divided by the estimated occupancy at the initial
season, Oijk = ψijk/ψij1. Each species-specific index
measured change in occupancy from initial condi-
tions. The estimate for k = 1 is always 1. The WPI
for year k and site j and n species in geometric mean
of scaled occupancy statistics for n species was cal-
culated as follows:
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jk ijk
The WPI was calculated for MU, the only site for
which we had 3 years of occupancy estimates.
Camera trapping effort resulted in 26 816 camera
trap-nights for all sites for all years. This effort
resulted in 416 294 images and 133 341 events (Table
5.1). We detected a total of 22 (possibly 24) mam-
malian species ranging from 6–13 species at each
site. Red Listed species were detected at all of our
study sites (Table 5.2).
For all sites, we established a list of detected
species and baseline occupancy estimates for each
management zone in year 1. An example of base-
line results from year 1 at KN showed a higher
diversity of carnivores (five versus two species)
Table 5.1. Camera trapping effort for Wildlife Picture Index (WPI) study sites in Mongolia protected areas, 2009–11.
Protected area Year Trap- nights Images Events
Myangan Ugalzat 2009 561 14 410 4 803 40
Myangan Ugalzat 2010 5 889 35 141 9 752 100 *
Myangan Ugalzat 2 011 3 065 18 033 6 011 100
Onon Balj 2010 3 649 114 790 34 271 100
Khonin Nuga 2010 7 250 105 218 35 073 100
Mongol Daguur 2011 3 16 4 67 145 22 912 100
Numrug 2011 3 238 61 556 20 519 10 0
Total 26 816 416 293 133 341 500
* includes area used in 2009
† same area as 2010
Table 5.2. IUCN Red List of Threatened Species-listed mammalian species detected at WPI study sites in Mongolia (from
Clark et al. 2006) ranked in descending threatened status order.
KN = Khonin Nuga, OB = Onon Balj, MU = Myangan Ugalzat, MD = Mongol Daguur, NU = Numrug. D = detected.
Species Common Name IUCN Status* KN OB MU MD NU
Cervus elaphus Red deer Critically Endangered D D D
Marmota sibirica Siberian marmot Endangered DDD
Moschus moschiferus Musk deer Endangered D
Ovis ammon Argali Endangered D
Alces alces Elk (moose) Endangered D D
Procapra gutturosa Mongolian gazelle Endangered D
Martes zibellina Sable Vulnerable D
Lynx lynx Eurasian lynx Near Threatened D D
Otocolobus manul Pallas’ cat Near Threatened D
Canis lupus lupus Grey wolf Near Threatened DDDDD
Vulpes corsac Corsac Near Threatened D D
Vulpes vulpes Red fox Near Threatened DDDD
Sus scrofa Wild boar Near Threatened D D
Capra sibirica Siberian ibex Near Threatened D
* IUCN Red List online <>, accessed September 2013.
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and higher occupancy for the brown bear (Ursus
arctos) in Grid I (Fig. 5.1). In contrast for ungulates
(and a small herbivore, the mountain hare, Lepus
timidus), diversity and occupancy were similar for
both grids (Figs 5.1 [for L. timidus] and 5.2). In the
case of KN, we had anticipated that Grid I would
be subject to more human influence than Grid II.
However, as was the case for most of our study
Lepus timidusMeles
Lynx lynx Canis lupusUrsus arctos
Grid 1
Grid 2
Fig. 5.1. Occupancy estimates for mountain hare (Lepus timidus) and carnivores (Meles leucurus = Asian badger, Martes
zibellina = sable, Lynx lynx = Eurasian lynx, Canis lupus = grey wolf and Ursus arctos = brown bear) at Khonin Nuga
Protected Area for Grids I and II, 2010 (error whiskers = ± standard error; columns without error bars are naïve estimates).
Sus scrofaAlces alcesCapreolus
Cervus elaphusMoschus
Grid 1
Grid 2
Fig. 5.2. Occupancy estimates for the ungulates (Sus scrofa = wild boar, Alces alces = moose, Capreolus pygargus = roe
de er, Cervus elaphus = red deer and Moschus moschiferus = musk deer) for Grid I and II at Khonin Nuga 2010 (error
whiskers = ± standard error; columns without error bars are naïve estimates).
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areas except for NU, Grid II had more human influ-
ence than what was expected. For KN, unantici-
pated tree extraction was taking place, which was
documented on our camera traps.
The WPI was calculated for MU using occu-
pancy estimates for those species (Table 5.3) for
which we could obtain occupancy estimates for
the three seasons. The Siberian ibex and argali
were detected in the Grid I but not Grid II; signifi-
cant in so far as these species were expected in the
Core Area (Grid II) of the park where there was
better habitat. Over the 3 years, while not allowed
for ‘core areas’ of the National Park, there were
consistently high levels of livestock and nomadic
herder families. In Grid I, the WPI rose 14.5% after
the first year and declined by 38% after the second
year (Fig. 5.3). In Grid II, the WPI rose 12.6% after
the first year and then declined back to the 2009
level (Fig. 5.4).
Over 3 years, the WPI was implemented at five
study sites in Mongolia resulting in a baseline for
four protected areas and the WPI for one protected
area (MU). This paper presents an example of year
1 occupancy estimates from one site and the WPI
from another. The method developed for the WPI
also produces baseline occupancy estimates and
species-specific information on the presence and
status of mammalian species (> 1 kg) in and around
Mongolian protected areas.
For all five study areas, we saw no consistent
relationship between number of species (diversity),
occupancy and its level of protection. We hypothe-
Table 5.3. Mammalian species detected in camera trap grids for the Buffer Zone (Grid I) and Core Area (Grid II) in Myangan
Ugalzat National Park, Mongolia.
Trapping effort is shown in Table 5.1. D = detected
Order Species Common Name Buffer Zone Core Area
Lagomorpha Ochotona sp. Pika D
Lepus tolai Tolai h ar e D D
Rodentia Marmota sibirica Siberian marmot D D
Spermophilus undulatus Souslik D
Carnivora Canis lupus lupus Grey wolf D
Gulo gulo Wolverine D
Mustela eversmanni Siberian ferret D
Otocolobus manul Manul D
Vulpes vulpes Red fox D D
Vulpes corsac Corsac D
Artiodact yla Capra sibirica Siberian ibex D
Ovis ammon Argali D
2008 2009 2010 2011 2012
Fig. 5.3. The WPI plotted from 2009–11 for Grid I
(Buffer Zone), Myangan Ugalzat National Park, Mongolia
(mean = geometric mean, LCL = lower confidence limit,
UCL = upper confidence limit)
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sised that diversity and occupancy would be lower
in management zones that were subject to greater
human influence. However, we did not anticipate
that the zones subject to the greatest amount of
human influence were in fact the more restricted
zones (contrary to their management objectives);
we found that the areas that were subject to less
human influence did support higher diversity and
occupancy, supporting the hypothesis that human
activity had a negative influence on biodiversity.
Evaluating the effectiveness of each management
zone was confounded by the fact that humans were
more prevalent in areas that had the most restric-
tive zoning. For example, humans and livestock
were far more abundant in the Core Area (Grid II)
of MU than in the Buffer Zone (Grid I) even though
no families or their livestock were allowed to use
the Core Area.
Additionally, for KN, tree extraction was found
to be prevalent in an area thought to be lacking vir-
tually any human influence due to its remoteness
and difficulty to access. In this case, and it is
unclear why, the carnivores were more prevalent
and diverse in the area without tree extraction
despite the presence of a variety of ungulates
(prey). These baseline occupancy estimates indi-
cated that trophic levels (or guilds) may be dispro-
portionately affected by differences in land use,
anthropogenic influence and other variables (see
2008 2009201020112012
Fig. 5.4. The WPI plotted for 2009–2011 for Grid II
(Core Area), Myangan Ugalzat National Park, Mongolia
(mean = geometric mean, LCL = lower confidence limit,
UCL = upper confidence limit).
also Ahumada et al. 2011). For protected area man-
agement, these baseline estimates can be used to
set quantitative goals and measure the influence of
land use or management changes over time. Addi-
tionally, these baseline estimates and documenta-
tion of presence or absence can provide essential
information on how well certain areas are meeting
conservation objectives or goals.
The WPI was calculated for the MU study area.
The WPI (relative to the first year) showed a down-
ward trend in Grid I and relatively steady trend
(little change) in Grid II over the 3 years. Both
grids had a slight increase in biodiversity from
2009 to 2010. However, in 2011 Grid I had a 38%
decrease relative to 2009. By contrast, Grid II had
no change from 2009 to 2011 indicating that the
park was conserving biodiversity at baseline rates.
However, the numerous livestock and humans
present within the Core Area (Grid II) are likely
adversely affecting biodiversity. The WPI could
prove to be a very practical, quantitative tool to
evaluate the functioning of protected areas in
Mongolia and towards setting and obtaining con-
servation and management goals.
Mongolia represents a relatively untouched
ecosystem despite having been inhabited by
nomadic herders for thousands of years at low
densities. The landscape has little development,
fencing or paved roads. This relatively untouched
status is changing rapidly due to unprecedented
levels of mining and commensurate infrastruc-
ture development in recent years. The Mongolian
ecosystems harbour most or all of its constituent
members that were historically present. Further-
more, many IUCN Red Listed species are still pre-
sent in higher relative numbers than in
surrounding countries where these species are
currently extant or have recently been extirpated
(e.g. ibex, argali and snow leopards). Finally, Mon-
golia may represent a continental bellwether for
climatic change due to its isolation from moderat-
ing maritime influences. For all of these reasons,
using the WPI as a monitoring tool to measure
trends in biodiversity, and also for important spe-
cies about which we know little in terms of preva-
lence and distribution, shows real promise for
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PArt 1 – cAmErA trAPPIng for AnImAL monItorIng: cAsE studIEs
temperate regions where biodiversity and ecosys-
tem health may rely on much larger geographic
Funding and support were provided by the Zoo-
logical Society of London, NEMO2,World Bank and
the United Nations Development Program (UNDP).
Additional invaluable in country support was pro-
vided by the Mongolia Program of the Wildlife
Conservation Society, the National University of
Mongolia, the Park Administration of Mongolia
and Border Defence Agency and, from the World
Bank, Tony Whitten.
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... Information on wildlife populations in Khonin Nuga, especially data on population size and density, is rare. A camera trapping study carried out in summer 2010 reports average rates of detection per 100 trap nights of 15.8 for wild ungulates, 3.2 for livestock and 0.1 for wolves (Townsend et al. 2010(Townsend et al. , 2014. The wolf density and home range size in Khonin Nuga is unknown. ...
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The increasing animosity towards wolves ( Canis lupus ) by livestock-keeping nomads in Mongolia and the accompanying conflicts highlight the urgent need for knowledge about the feeding behavior of wolves, since information on the feeding ecology of wolves in Mongolia is rare, especially in the mountain taiga and mountain forest steppe regions of Northern Mongolia. Those regions are characterized by a relatively high wildlife diversity and are sparsely populated by humans. To face this problem, 137 wolf scats were collected in the Khentii Mountain range in Northern Mongolia between 2008 and 2012. Almost all wolf faeces contained remnants of wild ungulates, which made up 89% of the consumed biomass. Siberian roe deer ( Capreolus pygargus ) was the most important and positively selected prey species. It was followed by red deer ( Cervus elaphus ) and wild boar ( Sus scrofa ), which was negatively selected by wolves. Wolves also fed on buffer prey species such as lagomorphs and small mammals. No evidence of domestic ungulates was found in the wolf diet. Thus, near-natural habitats with a diverse fauna of wild animals are important to limit livestock depredation.
... Furthermore, cameras can measure the success of conservation actions (Dajun et al., 2006), including protected area effectiveness (Burton et al., 2011), such that cameras are highlighted as tools to monitor local or regional biodiversity (Tobler et al., 2015). For example, a camera-specific diversity metric, the Wildlife Picture Index (WPI; O'Brien et al., 2010), has been used to measure trends in large-mammal communities of Mongolia (Townsend et al., 2014), Costa Rica (Ahumada et al., 2013) and most recently, on a entire network of forested tropical protected areas (). Remote camera projects usually target ground-dwelling vertebrates (mostly mammals), although there are examples focused on arboreal mammals (Gregory et al., 2014), and " phenocams " are an emerging technology for monitoring phenology, snow cover, and disturbance events (Brown et al., 2016). ...
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Large carnivores play important roles in structuring ecosystems, but carnivores are often the first species lost from an ecosystem. Carnivores are important conservation tools to protect, garner support for, and indicates changes in, biodiversity. One of the challenges in understanding both the ecological and conservation roles of carnivores, however, is that they tend to be rare and elusive. Furthermore, the need for conservation is increasing while budgets for conservation remain tight. Efficiency is needed to monitor multiple carnivore species. Remote cameras may offer an efficient means to meet these challenges collecting data simultaneously on many species. Data from cameras for species that are not uniquely identifiable can be used to estimate occupancy, i.e. the proportion of area occupied. Occupancy data is cost-effective and a recommended state variable to monitor population trends. As with any new monitoring method, prospective power analysis is essential. Using n = 183 remote cameras across 5 national parks, I test how camera-based occupancy models can monitor changes in grizzly bear occupancy. As hypothesized, statistical power increased with both the number of cameras and the number of days cameras were deployed, but power was not affected by the ecological mechanisms of decline. Furthermore, when monitoring multiple species, species-specific occupancy and detection probability estimates can affect statistical power. I also examined how sampling scales define occupancy and occupancy-abundance relationships (OA) in mobile animals. I found that the temporal scale of sampling greatly affected the definition of occupancy, which ranges from occupancy of single or multiple individuals, to partial use by one individual or many individuals. With point-sampling, however, spatial grain had little affect on occupancy estimates or the OA relationship, helping pave the way for robust multi-species monitoring. Ideally, multiple-species monitoring can also provide information on how species interact in food-webs. Using occupancy models for 16 mammal species in the Canadian Rockies (n = 698 cameras), I compare carnivores as candidate umbrella species and assesses their ecological role in food-web structure. Grizzly bear occupancy was highly correlated with other species’ occupancy, but wolves were more correlated with how food webs changed across the landscape. This corroborates the importance of wolves as a keystone species and advances the umbrella-species concept beyond conserving biodiversity. With the insights gained from my research, camera networks can be easily scaled-up to monitoring the planet's biodiversity. Remote cameras excite public support to ultimately help make successes in global conservation possible.
... In fact, the Tropical Ecology Assessment and Monitoring Network (TEAM; developed a conceptually simple camera-specific diversity metric called the Wildlife Picture Index (WPI; O' Brien et al. 2010), which summarizes the average proportional change in occupancy among species, and has been used to measure trends in the largemammal communities of Mongolia ( Townsend et al. 2014), Costa Rica ( Ahumada et al. 2013), and most recently an entire network of forested tropical protected areas ( Beaudrot et al. 2016). Remote-camera projects usually target ground-dwelling vertebrates (mostly mammals), although some projects focus on arboreal mammals ( Gregory et al. 2014), and "phenocams" are an emerging technology for monitoring phenology, snow cover, and disturbance events ( Brown et al. 2016). ...
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Countries committed to implementing the Convention on Biological Diversity's 2011–2020 strategic plan need effective tools to monitor global trends in biodiversity. Remote cameras are a rapidly growing technology that has great potential to transform global monitoring for terrestrial biodiversity and can be an important contributor to the call for measuring Essential Biodiversity Variables. Recent advances in camera technology and methods enable researchers to estimate changes in abundance and distribution for entire communities of animals and to identify global drivers of biodiversity trends. We suggest that interconnected networks of remote cameras will soon monitor biodiversity at a global scale, help answer pressing ecological questions, and guide conservation policy. This global network will require greater collaboration among remote-camera studies and citizen scientists, including standardized metadata, shared protocols, and security measures to protect records about sensitive species. With modest investment in infrastructure, and continued innovation, synthesis, and collaboration, we envision a global network of remote cameras that not only provides real-time biodiversity data but also serves to connect people with nature.
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Carnivore extinctions frequently have cascading impacts through an ecosystem, so effective management of ecological communities requires an understanding of carnivore vulnerability. This has been hindered by the elusive nature of many carnivores, as well as a disproportionate focus on large-bodied species and particular geographic regions. We use multiple survey methods and a hierarchical multi-species occupancy model accounting for imperfect detection to assess extinction risk across the entire carnivore community in Ghana’s Mole National Park, a poorly studied West African savanna ecosystem. Only 9 of 16 historically occurring carnivore species were detected in a camera-trap survey covering 253 stations deployed for 5469 trap days between October 2006 and January 2009, and our occupancy model indicated low overall likelihoods of false absence despite low per-survey probabilities of detection. Concurrent sign, call-in, and village surveys, as well as long-term law enforcement patrol records, provided more equivocal evidence of carnivore occurrence but supported the conclusion that many carnivores have declined and are likely functionally or fully extirpated from the park, including the top predator, lion (Panthera leo). Contrary to expectation, variation in carnivore persistence was not explained by ecological or life-history traits such as body size, home range size or fecundity, thus raising questions about the predictability of carnivore community disassembly. Our results imply an urgent need for new initiatives to better protect and restore West Africa’s embattled carnivore populations, and they highlight a broader need for more empirical study of the response of entire carnivore communities to anthropogenic impact.
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Few species are likely to be so evident that they will always be detected when present. Failing to allow for the possibility that a target species was present, but undetected, at a site will lead to biased estimates of site occupancy, colonization, and local extinction probabilities. These population vital rates are often of interest in long-term monitoring programs and metapopulation studies. We present a model that enables direct estimation of these parameters when the probability of detecting the species is less than 1. The model does not require any assumptions of process stationarity, as do some previous methods, but does require detection/nondetection data to be collected in a manner similar to Pollock's robust design as used in mark–recapture studies. Via simulation, we show that the model provides good estimates of parameters for most scenarios considered. We illustrate the method with data from monitoring programs of Northern Spotted Owls (Strix occiden-talis caurina) in northern California and tiger salamanders (Ambystoma tigrinum) in Min-nesota, USA.
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Although recent biodiversity loss has been compared with cataclysmic mass extinctions, we still possess few indicators that can assess the extent or location of biodiversity loss on a global scale. The Convention on Biological Diversity (CBD) has mandated development of indicators that can meet the needs of monitoring biodiversity by 2010. To date, many indicators rely on unwarranted assumptions, secondary data, expert opinion and retrospective time series. We present a new biodiversity indicator, the Wildlife Picture Index (WPI) that targets medium and large-sized terrestrial birds and mammals in forested and savannah ecosystems that. The WPI is a composite indicator based on the geometric mean of relative occupancy estimates derived from camera trap sampling at a landscape scale. It has been designed to meet the needs of a CBD indicator while avoiding many of the pitfalls that characterize some CBD indicators. We present an example using 8 years of camera trap data from Bukit Barisan Selatan National Park, Indonesia to show that the WPI is capable of detecting changes in the rate of loss of biodiversity, a key requirement of a CBD indicator. We conclude that the WPI should be effective at monitoring top trophic levels in forest and savannah ecosystems using primary data and can fill the gap in knowledge about trends in tropical biodiversity.
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The need to monitor trends in biodiversity raises many technical issues. What are the features of a good biodiversity index? How should trends in abundance of individual species be estimated? How should composite indices, possibly spanning very diverse taxa, be formed? At what spatial scale should composite indices be applied? How might change-points--points at which the underlying trend changes--be identified? We address some of the technical issues underlying composite indices, including survey design, weighting of the constituent indices, identification of change-points and estimation of spatially varying time trends. We suggest some criteria that biodiversity measures for use in monitoring surveys should satisfy, and we discuss the problems of implementing rigorous methods. We illustrate the properties of different composite indices using UK farmland bird data. We conclude that no single index can capture all aspects of biodiversity change, but that a modified Shannon index and the geometric mean of relative abundance have useful properties.
Rapid faunal assessments can use different methods depending on environmental conditions and costs. To compare the efficiency of three methods in detecting species richness and abundance, we tested them in the grasslands of Emas National Park, central Brazil. Track census was the most effective method for detecting richness, followed by camera-trapping and direct faunal counts. Track census reached an asymptote for number of species after only 12 days, but all methods converged on similar estimates of species richness after around 30 days. There was no significant spatial correlation for species richness or total abundance, between camera trap and tracks, across the 29 samples distributed in the park. However, for some species, abundance showed significant spatial correlation between methods. Also, these rates were significantly correlated across species and the spatial correlation between methods was significantly associated with log-transformed body mass across species. We conclude that, despite the high initial costs for camera-trapping, this method is the most appropriate for mammal inventory in all environmental conditions, allowing a rapid assessment of wildlife conservation status.
Conservation of a threatened species is reliant upon good quality monitoring information to provide population estimates and trends to inform management practices. Surveying to establish such data can be costly and difficult, particularly for cryptic species in forest habitats. We therefore used remotely triggered cameras to survey for the presence of the pygmy hippopotamus Choeropsis liberiensis in Sapo National Park in Liberia. In 1,247 trap days we obtained seven camera-trap photographs, the first photographic records of the species in Liberia. Habitat destruction, principally from illegal gold mining, is the greatest threat to the persistence of the pygmy hippopotamus within the Park. A range-wide survey of the pygmy hippopotamus is required to establish a robust baseline from which future conservation efforts can be developed. Understanding how this species is able to cope with the effects of habitat fragmentation across its range, and controlling commercial hunting, will dictate how it is able to survive the ongoing pressures of land conversion in West Africa.
Biodiversity monitoring is critical to assess the effectiveness of management activities and policy change, particularly in the light of accelerating impacts of environmental change, and for compiling national responses to international obligations and agreements. Monitoring methods able to identify species most likely to be affected by environmental change, and pinpoint those changes with the strongest impacts, will enable managers to target efforts towards vulnerable species and significant threats. Here we take a new approach to carnivore monitoring, combining camera-trap surveys with ecological niche factor analysis to assess distribution and patterns of habitat use of mammalian carnivore assemblages across northern Tanzania. We conducted 11 surveys over 430 camera-trap stations and 11 355 trap-days. We recorded 23 out of 35 carnivore species known to occur in Tanzania and report major extensions to the known distribution of the bushy-tailed mongoose Bdeogale crassicauda, previously thought to be rare. Carnivore biodiversity tended to be higher in national parks than in game reserves and forest reserves. We explored habitat use for seven species for which we had sufficient information. All species tended to be found near rivers and southern Acacia commiphora woodlands (except one mongoose species), and avoided deciduous shrubland, favouring deciduous woodland and/or open grassland. All species tended to avoid croplands suggesting that habitat conversion to agriculture could have serious implications for carnivore distribution. Our study provides a first example where camera-trap data are combined with niche analyses to reveal patterns in habitat use and spatial distribution of otherwise elusive and poorly known species and to inform reserve design and land-use planning. Our methodology represents a potentially powerful tool that can inform national and site-based wildlife managers and policy makers as well as international agreements on conservation.
For the vast majority of cases, it is highly unlikely that all the individuals of a population will be encountered during a study. Furthermore, it is unlikely that a constant fraction of the population is encountered over times, locations, or species to be compared. Hence, simple counts usually will not be good indices of population size. We recommend that detection probabilities (the probability of including an individual in a count) be esti- mated and incorporated into inference procedures. However, most techniques for estimating detection probability require moderate sample sizes, which may not be achievable when studying rare species. In order to improve the reliability of inferences from studies of rare species, we suggest two general approaches that researchers may wish to consider that incorporate the concept of imperfect detectability: (1) borrowing information about de- tectability or the other quantities of interest from other times, places, or species; and (2) using state variables other than abundance (e.g., species richness and occupancy). We illustrate these suggestions with examples and discuss the relative benefits and drawbacks of each approach.
Community structure and diversity of tropical forest mammals: data from a global camera trap network
  • J A Ahumada
  • Cef Silva
  • K Gajapersad
  • C Hallam
  • J Hurtado
  • E Martin
  • A Mcwilliam
  • B Mugerwa
  • T O'brien
  • F Rovero
  • D Sheil
  • W R Spironello
  • N Winarni
  • S J Andaelman
Ahumada JA, Silva CEF, Gajapersad K, Hallam C, Hurtado J, Martin E, McWilliam A, Mugerwa B, O'Brien T, Rovero F, Sheil D, Spironello WR, Winarni N, Andaelman SJ (2011) Community structure and diversity of tropical forest mammals: data from a global camera trap network. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 366, 2703-2711.
  • E L Clark
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  • Jem Baillie
  • N Batsaikhan
  • R Samiya
  • M Stubbe
Clark EL, Munkhbat J, Dulamtseren S, Baillie JEM, Batsaikhan N, Samiya R, Stubbe M (2006) Mongolian Red List of Mammals. Regional Red List Series Vol. 1. (Zoological Society of London, London).