Content uploaded by Matthias Hammer
Author content
All content in this area was uploaded by Matthias Hammer on Sep 08, 2020
Content may be subject to copyright.
Content uploaded by Taiana Haag
Author content
All content in this area was uploaded by Taiana Haag on Dec 07, 2017
Content may be subject to copyright.
Content uploaded by Marcelo Mazzolli
Author content
All content in this area was uploaded by Marcelo Mazzolli on May 27, 2018
Content may be subject to copyright.
http://journals.cambridge.org Downloaded: 13 May 2016 IP address: 88.111.106.26
Multiple methods increase detection of large and
medium-sized mammals: working with volunteers in
south-eastern Oman
MARCELO MAZZOLLI,TAIANA HAAG,BEATRIZ G. LIPPERT,EDUARDO EIZIRIK
MATTHIAS L.A. HAMMER and K HALID ALHIKMANI
Abstract We compared the effectiveness of various meth-
ods for surveying medium and large wild mammals in
southern Oman. Working with volunteers recruited by
Biosphere Expeditions, wildlife professionals and local ran-
gers, we used direct observation, camera traps, sign surveys
(tracks and/or dung) and molecular scatology to study
sampling units of ×km (grid cells) in an area of
× km during a -week period in February–March
. Sixteen mammal species were recorded, and the largest
numbers of species were recorded by sign surveys and cam-
era traps (both n = ); sign surveys, direct sightings and
DNA scatology recorded species across the largest number
of grid cells. For species with a sample size large enough for
comparison (i.e. detected in $grid cells), DNA scatology
proved most effective for detecting caracal Caracal caracal,
signs for hyaena Hyaena hyaena, ibex Capra nubiana, por-
cupine Hystrix indica and hyrax Procavia capensis, and signs
and direct sightings for mountain gazelle Gazella gazella.
Clustering, in which records from multiple methods are ei-
ther adjacent or overlapping, was highest ($%) for the
wolf Canis lupus, porcupine, ibex and gazelle. Our results in-
dicate thebest methods to detectand record the distributions
of individual species in the study area, and demonstrate the
advantage of using multiple methods to reduce the risk of
false absences or partial detections. Our findings also high-
light the potential of clustering as a means of cross-checking
results of observations that are skill-dependent, which is par-
ticularly useful when employing a large workforce.
Keywords Citizen science, Dhofar, mammals, methods,
Middle East, Oman, sampling, volunteer
Introduction
Knowing which methods are most efficient for record-
ing target species is fundamental to the success of
short-duration research expeditions and surveys. Without
such prior knowledge, efforts and resources may be wasted
by using methods that are not appropriate for recording the
species of interest. More broadly, failure to record species
that are present may result in misleading descriptions of
distribution and abundance. These potential biases have
not been adequately addressed in the scientific literature,
and most of the statistics used to infer density and presence
of species have been developed using a single field method
(e.g. Otis et al., ; Burnham et al., ; Boulinier et al.,
; Karanth & Nichols, ; MacKenzie et al., ;
MacKenzie & Nichols, ). More recently, models
have been developed that incorporate data from multiple
methods (e.g. Nichols et al., ), an acknowledgement
that single-method approaches may not be ideal in all re-
search situations, although not everyone agrees (Otto &
Roloff, ).
Earlier use of multiple survey methods (e.g. Zielinski &
Kucera, ) is now becoming more popular (Silveira et al.,
; Gompper et al., ; Nichols et al., ;Nomani
et al., ; Ausband et al., ). Previously, particular
methods were advocated for estimating the abundance
and occupancy of particular species or taxonomic groups
(e.g. Karanth et al., ; Balme et al., ;Mondol
et al., ). However, sampling rare species (or popula-
tions) using a single method, such as camera trapping,
necessitates increasing survey effort, often to a level that
may be logistically unrealistic (Shannon et al., ).
Furthermore, there is increasing evidence that different
methods yield different detection probabilities (e.g. Nichols
et al., ; Otto & Roloff, ) and may produce different
estimates of abundance or presence (e.g. Gompper et al.,
; Nomani et al., ; Otto & Roloff, ). It is therefore
possible that two methods may result in two different esti-
mates, even when detection probability statistics are used,
highlighting the relevance of analysing the efficiency of mul-
tiple methods.
Here we demonstrate how the efficiency of sampling
methods varies by species, and that single sampling meth-
ods cannot be prescribed in a generalized way for all study
situations. We also consider the potential for bias when
MARCELO MAZZOLLI (Corresponding author) Projeto Puma, Av. Castelo Branco
170, CP 525, 88509-900, Lages, Santa Catarina, Brazil
E-mail marcelo@projeto-puma.org
TAIANA HAAG,BEATRIZ G. LIPPERT and EDUARDO EIZIRIK, Laboratory of Genomic
and Molecular Biology of the Pontifícia Universidade Católica of Rio Grande do
Sul, Brazil
MATTHIAS L.A. HAMMER Biosphere Expeditions, UK
KHALID ALHIKMANI Office for Conservation of the Environment, Diwan of Royal
Court, Oman
Received May . Revision requested June .
Accepted July .
Oryx
, Page 1 of 8 ©2016 Fauna & Flora International doi:10.1017/S0030605315001003
http://journals.cambridge.org Downloaded: 13 May 2016 IP address: 88.111.106.26
there is a large team working in the field, a matter that may
be of particular concern when volunteers are involved.
When multiple sampling methods are used, particularly
those that are less dependent on observer skill, the results
may be cross-checked, and we examined how clustering of
grid cells from multiple methods may be used to do this for
individual species.
Study area
The × km study area in the south-west Dhofar
Mountains (part of the Nejd or Jabal Al Qara region) in
Oman, in the south-east Arabian Peninsula, was delimited
by Wadi Uyun in the north and the cliffs above the Salalah
plains in the south. The topography varies from wadis (sea-
sonally dry riverbeds) to mountain ridges and escarpments.
Vegetation coverage increases towards the southern
monsoon-fed regions but consists of scattered bushes and
does not hinder visibility.
Methods
Two groups, of and participants, carried out surveys dur-
ing – February and February–March ,respectively.
Each group comprised wildlife professionals, local rangers,
and volunteers recruited by Biosphere Expeditions, who re-
ceived training in data collection. An expedition leader (Paul
O’Dowd), the expedition scientist (MM) and the national sci-
entist (KH) were present throughout the expedition. Each
group was divided into –subgroups to maximize the area
surveyed.
Sampling and analysis of signs
We surveyed ×km sampling units (grid cells; Fig. )
for medium and large mammals during a -day period. The
size of the area was determined by the capacity of the survey
team to cover the area from the base camp, and the cell size
was determined by the need to cover areas large enough to
FIG. 1 The ×km cells
surveyed, by various methods
(Table ), for large and
medium-sized mammals in
south-eastern Oman, with the
locations of camera trap
stations.
2 M. Mazzolli et al.
Oryx
, Page 2 of 8 ©2016 Fauna & Flora International doi:10.1017/S0030605315001003
http://journals.cambridge.org Downloaded: 13 May 2016 IP address: 88.111.106.26
be relevant for describing the distributions of large and me-
dium mammals.
Identification methods included recording mammalian
signs (mainly tracks and herbivore dung), DNA analysis
of carnivore scats, visual recording, and camera traps.
Carnivore scats were not identified macroscopically be-
cause of the likelihood of significant error (Davison et al.,
; Harrison, ; Perez et al., ; Janecka et al.,
; Vanstreels et al., ; Kelly et al., ; Mazzolli &
Hammer, ), and therefore samples were collected for
DNA-based species identification.
The presence or absence and frequency of target species
was recorded using the general location given by a grid con-
sisting of ×km cells, the code of which was displayed in
TABLE 1 Large and medium-sized mammal species recorded in south-eastern Oman (Fig. ), with their global and regional Red List status,
the number of cells in which they were recorded (and the total number of records) by five methods (sightings, signs, camera traps, bones
and carcasses, and faecal DNA analysis) and, for the seven most commonly recorded species (i.e. recorded in .cells), the number of cells
in which two or more methods recorded presence in two or more adjoining cells (i.e. cells that were clustered, with the percentage of the
total number of cells in parentheses). Signs are mostly tracks and faecal samples (dung) identified by eye.
Species
Global
status
1
Regional
status
2
No. of cells in which species recorded (no. of records)
3
Cell clustering
(% of total
cells)Sighting Sign
Camera
trap
Bones &
carcasses
DNA
analysis Total
Carnivora
Leopard Panthera
pardus nimr
CR CR 1 (1)
Caracal Caracal
caracal
LC LC 1 (track, n = 1) 2 (2) 9 (10) 11 5 (45)
Gordon’s wildcat Felis
silvestris
LC NT 1 (1)
Striped hyaena
Hyaena hyaena
NT EN 26 (tracks, n = 40) 3 (20) 1 (1) 26 10 (38)
Grey wolf Canis lupus LC EN 7 (tracks, n = 9) 2 (2) 1 (1) 4 (10) 8 5 (63)
Red fox Vulpes vulpes LC LC 1 (1) 1 (1)
Blanford’s fox Vulpes
cana
LC VU 2 (2)
Honey badger
Mellivora capensis
LC NT 1 (3)
Small spotted genet
Genetta genetta
LC LC 1 (4)
White-tailed mon-
goose Ichneumia
albicauda
LC LC 1 (1)
Artiodactyla
Mountain gazelle
Gazella gazella
VU 18 (31) 41 (tracks, n = 47;
dung, n = 48)
1 (1) 46 41 (89)
Nubian ibex Capra
nubiana
VU 1 (1) 23 (tracks, n = 17;
dung, n = 34)
1 (1) 1 (1) 24 21 (88)
Hyracoidea
Rock hyrax Procavia
capensis
LC 1 (2) 28 (tracks, n = 10;
dung, n = 40)
1 (17) 2 (2) 28 11 (39)
Lagomorpha
Cape hare Lepus
capensis
LC 2 (track, n = 1;
dung, n = 2)
Rodentia
Indian crested porcu-
pine Hystrix indica
LC 31 (tracks, n = 26;
dung, n = 47;
quills, n = 9)
5 (35) 38 29 (76)
Hedgehog
Paraechinus aethiopi-
cus or P. hypomelas
LC 2 (2)
CR, Critically Endangered; EN, Endangered; VU, Vulnerable; NT, Near Threatened; LC, Least Concern; IUCN ().
Mallon & Budd ().
Blank cells indicate the species was not recorded by that method.
Mammals of the Arabian Peninsula 3
Oryx
, Page 3 of 8 ©2016 Fauna & Flora International doi:10.1017/S0030605315001003
http://journals.cambridge.org Downloaded: 13 May 2016 IP address: 88.111.106.26
the global positioning system (GPS) of each surveyor. Once
a species or signs of it were found in a given cell, it was
scored as containing the species. Species were recorded
only once for each cell during a given survey (i.e. there
was no double counting). Signs not identified directly in
the field were collected (in the case of scats) or photo-
graphed with a scale (in the case of tracks). Twenty passive
infrared camera traps (Cuddeback, Green Bay, USA) were
deployed pseudo-randomly in cells, at locations such as
waterholes and known animal trails, aiming for the widest
coverage possible. Camera traps were active day and night,
and set at cm above ground, with the beam directed
slightly downwards.
Training
Training of the survey group included an introduction to
conservation issues, followed by training on practical as-
pects of the survey, such as species identification from tracks
and dung, and the use of GPS and data recording sheets,
which lasted days. Before volunteers were allowed to
carry out surveys on their own, experienced personnel ac-
companied them for at least additional days during field
surveys, to provide further teaching and to check their
knowledge. To reduce identification error, group members
were instructed to bring herbivore dung to base camp if they
were unable to identify the species in the field. They were
also briefed on how to photograph tracks (using a scale)
for later identification.
DNA analysis of faecal samples
We used DNA analysis of scats to identify species of carni-
vores. Extractions were performed using the QIAamp DNA
Stool Mini Kit (QIAGEN, Hilden, Germany), following the
manufacturer’s instructions. The extractions were carried
out in a UV-sterilized laminar flow hood dedicated to the
analysis of DNA from non-invasive samples. Each batch
of extractions included one negative extraction control
to monitor the occurrence of contamination with extrinsic
DNA.
To identify species from each scat we used an assay that
targets a short segment of the mtDNA ATP synthase sub-
unit (ATP) gene, using the reverse primer ATP-DR
and the forward primer ATP-DF. We used polymerase
chain reactions (PCR) for the ATPgene, following the pro-
tocols described by Haag et al. (,).
The PCR products were visualized on a % agarose gel
stained with GelRed (Biotium, Hayward, USA), purified
with PEG , sequenced using the DYEnamic ET Dye
Terminator Sequencing Kit (GE Healthcare, Hatfield, UK)
and analysed in a MegaBACE automated sequencer
(GE Healthcare). Sequence chromatograms were edited
and analysed using FinchTV v. ..(Geospiza, Inc.,
Seattle, USA). The ATPgene fragment obtained from
each faecal sample was compared with reference sequence.
Geographical information system (GIS) and mapping
The mapping procedures and analysis were designed to be
easily integrated and replicated across multiple expedi-
tions by personnel with no formal training in GIS
(Mazzolli & Hammer, ). The main reference map
used was at :, scale (Uyūn, NE -F; National
Survey Authority, Sultanate of Oman), prepared using
aerial photographs from and field updates from
, with grid data in the Universal Transverse
Mercator projection (zones and ,WGS datum).
An image of the study area was imported and georefer-
enced in TrackMaker (Geo Studio Technology, Minas
Gerais, Brazil). A grid of ×km cells covering the area
was uploaded into GPS units, to aid navigation and data col-
lection. As the work progressed, additional features such as
access roads, base camp, trails and camera-trap locations
were added to the GPS units, and were later overlaid onto
a topographic map in TrackMaker, which was then edited
and redrawn in Adobe Photoshop (Adobe Systems Inc.,
San Jose, USA) to leave only the features of interest.
Cluster analysis
Maps were produced for each target species, with cells dis-
playing their recorded distribution and the methods by
which they were recorded. The number of overlapping or
adjacent cells in which two or more methods recorded the
presence of a given species was counted. If such clustered
cells occurred in .% of the total number of cells in
which a species was recorded, we considered that two or
more methods corroborated each other.
We analysed clustered features without using automated
GIS methods because the latter depend on data points or
other features that represent an ‘excess of events’in geo-
graphical space (Jacquez, ). Our data were not clustered
in this sense. This was done to avoid autocorrelation, to ap-
proximate the format of data collected to that of the pro-
cessed data (data were processed as clusters of cells, not as
data points), and to cover as much area as possible by avoid-
ing spending time on redundant recording of species in a
single cell. Furthermore, there are concerns regarding the
accuracy of automated GIS clustering (Hamfelt et al., ;
Murray et al., ).
Results
We recorded species of medium and large mammals
(Table ). The efficiency of the identification methods varied
for each species. Seven species were recorded exclusively by a
single method. Leopard Panthera pardus nimr and wildcat
4 M. Mazzolli et al.
Oryx
, Page 4 of 8 ©2016 Fauna & Flora International doi:10.1017/S0030605315001003
http://journals.cambridge.org Downloaded: 13 May 2016 IP address: 88.111.106.26
Felis silvestris gordoni were recorded in single cells exclusively
by DNA analysis. DNA analysis was also more efficient than
other methods in detecting caracal Caracal caracal, and
contributed substantially to detecting wolves Canis lupus.
Wolf and hyaena Hyaena hyaena were recorded predomin-
antly by tracks; gazelle Gazella gazella by tracks, dung and
sightings; ibex Capra nubiana by tracks and dung; and por-
cupine Hystrix indica and hyrax Procavia capensis by dung
(Table ).
Camera traps also recorded species that were not recorded
by other means, namely the honey badger Mellivora capensis,
little spotted genet Genetta genetta, mongoose Ichneumia al-
bicauda and Blanford’s fox Vupes cana. Camera traps yielded
high recording frequencies for the hyaena, hyrax and porcu-
pine, but they were localized in only a few cells compared with
results from other methods. The hedgehog Paraechinus sp.
was the only taxon exclusively recorded by direct observation.
Clustering of cells in the grid space was highest for wolf,
gazelle, ibex and porcupine; i.e. $% of the distribution of
these species were recorded by two or more methods in two
or more adjoining cells. For the other species with a suffi-
ciently large sample size (n .) for comparison (hyaena,
caracal and hyrax), clustering was ,%(Table ).
Discussion
Comparison of methods
Our results show that the efficiency of detection methods
varies by species, with one or two methods often outperform-
ing others. For several species different methods produced
different spatial distributions, suggesting a higher detection
efficiency of multiple methods used in combination.
Previous studies have demonstrated differences in detec-
tion rates across methods, even at longer sampling intervals
(e.g. Zielinski & Kucera, ; Gompper et al., ; Vine
et al., ). Although camera traps are one of the tools
most recommended for recording and monitoring wildlife
(e.g. Silveira et al., ; Balme et al., ), in our study
they did not detect six species, including the Critically
Endangered Arabian leopard, and recorded the hyaena in
only three of the cells where the species was recorded,
and the ibex in only one of cells where it was recorded.
Even if camera-trap sampling for hyaena and ibex were
equalized for the whole study area (simulating their deploy-
ment in all surveyed grid cells) by multiplying the num-
ber of grid cells recorded (camera traps were deployed in
cells) by .( ×.= cells), and presuming the same re-
cording rate, camera traps would have recorded ibex in six
times fewer cells and hyaena in half of the number of cells
in which they were recorded by other sampling methods.
Although gazelles were recorded in cells using other
methods, the species was not recorded by camera traps.
Thus there are cases in which meaningful parameter esti-
mates cannot be obtained, regardless of one’s statistical skills
(Guillera-Arroita et al., ). Camera traps similarly re-
turned a low sample size for ibex and leopards, and did
not detect gazelles, in neighbouring Yemen (Khorozyan
et al., ). Similarly, in Jabal Samhan recording rates for
some species were found to vary greatly depending on the
habitats sampled (Spalton et al., ); as other methods
were not used we cannot know whether these findings are
a true indication of the occurrence/absence of the species
or a sampling artefact.
Our findings indicate that no single method should be
relied on in all situations. The most appropriate method or
combination of methods will depend on the target species,
population and region, and on the parameters of interest.
In a study in Slovakia, bears Ursus arctos were detected by
tracks in grid cells, but in only one cell by camera traps,
and wolves and lynxes Lynx lynx were detected in up to
times more cells by tracks than by other methods (Hulik
et al., ). We do not know how the detection probabil-
ities of survey methods vary across the range of a single
species, a variation that is probably associated with density.
For leopards, for example, scrapes and tracks have been
shown to be an efficient method to detect their presence
in Jabal Samhan Nature Reserve in the east of Dhofar
(Spalton, ), even during short surveys, whereas cam-
era trapping is useful during longer surveys (Spalton et al.,
;Spalton&AlHikmani,). However, short sur-
veys of our study site in south-west Dhofar did not yield
a high frequency of records, suggesting that leopards are
rare in the area (Mazzolli, ; this study), and this has
since been confirmed (Al Hikmani et al., ). Similarly,
jaguars in the Atlantic Rainforest of Brazil now occur at
such low densities that they were not detected by camera
traps during a -year study, and were detected by tracks
in only four instances (Mazzolli et al., ); in contrast,
both camera traps and track surveys repeatedly recorded
jaguars during an -day survey in Madre de Dios, Peru
(Lee et al., ). We do not contest the value of camera
trapping as a survey method (it successfully detected spe-
cies that were not detected by other methods in our study)
but it may not always be reliable in detecting species
throughout their range.
Cross-checking results with cluster analysis
The role of volunteersin research has been widely recognized
and is increasing (Brightsmith et al., ). Volunteers are
particularly essential in large-scale monitoring programmes
(Howe et al., ; Newman et al., ; Sauer et al., ;
Kindberg et al., ; Schmeller et al., ). Participation
of volunteers requires protocols that are easy to follow, and
the data collected hasto be scrutinized carefully and discarded
if suspect or unreliable (Cohn, ).
Mammals of the Arabian Peninsula 5
Oryx
, Page 5 of 8 ©2016 Fauna & Flora International doi:10.1017/S0030605315001003
http://journals.cambridge.org Downloaded: 13 May 2016 IP address: 88.111.106.26
Training is fundamental for securing unbiased data. In a
deer monitoring study using pellet group counts volunteers
received hours of training using slides, yet they were only
able to identify correctly % of deer droppings during the
study (Buesching et al., ). However, in our study volun-
teers worked in groups, creating collaborative conditions
that probably resulted in improved accuracy. Furthermore,
they received days of intensive training and had the oppor-
tunity to practise with scientists in the field for at least
more days before working on their own.
In such studies data quality also needs to be assessed, for
which we used a post hoc evaluation with cluster analysis.
The idea is that any inconsistencies will become apparent
when using several methods for cross-validation. It would
be reasonable to assume that contradictory findings from
various methods could be an indication of observer error,
but this is not always the case. The chance of surveyed
cells clustering reduces when a single method notably out-
performs others. In this case even if cells with records from
different methods are overlapping or neighbouring, they
will be a low proportion of all cells and therefore the meas-
ure of clustering will be low. This cluster analysis requires
that at least two methods have records for a similar number
of cells. A useful future improvement would be to incorporate
an additional measure to corroborate the detection methods.
We found a substantial spatial similarity of results among
various methods for the wolf, gazelle, ibex and porcupine
(i.e. for those species, cluster analysis showed that the various
methods corroborated each other). For other species, how-
ever, the number of clusters of records from the various
methods was low, which is attributable to a single method
predominating in terms of recording success. This was the
case for the hyaena and hyrax, which were predominantly
recorded by skill-dependent methods (tracks and scats, re-
spectively). The caracal was also predominantly recorded
using a single, but non skill-dependent, method (faecal
DNA). The hyaena’s hind and front tracks differ in size
and shape, and hyrax dung is usually found clustered near
colonies and is markedly different from that of other
herbivores in the region.
Our results show that methods vary in their ability to de-
tect species of mammals. Some species would not have been
recorded if the single method that detected them had not
been used. The distribution of other species would have
been underestimated (i.e. the hyaena, detected mainly by
tracks; the hyrax, detected mainly from dung piles; and
the caracal, detected from faecal DNA).
Conclusions
Our results indicated the best methods for detecting and re-
cording distributions of individual mammal species in the
study area, and demonstrated the advantage of using mul-
tiple methods to reduce the risk of false absences or partial
detections. Our findings also highlight the potential of clus-
tering for cross-checking the results of observations that are
skill-dependent, which is particularly useful when employ-
ing a large workforce.
Our findings have broad applications for surveying ter-
restrial mammals. Single methods often fail to detect target
species, and thus a multiple-method approach is necessary
to identify the most appropriate methods for the target spe-
cies, region and habitat. We have shown that a combination
of methods can produce information at a faster rate and re-
sult in a more complete mammal survey than any single
method.
Acknowledgements
Local guiding, logistic and institutional support were pro-
vided by Hadi al Hikmani, Andrew Spalton, Mansoor
Hamed Al Jahdhami, Sheikh Mohammed Al Balushi and
Khalifa al Jahwari. We are also grateful for the participation
of rangers from the Ministry of Environment & Climate
Affairs. Corporate support came from Land Rover &
Swarovski Optik, and from The Ford Motor Company
Conservation and Environmental Grants. We thank the vo-
lunteers who participated in the expedition to Oman,
and Paul O’Dowd for his participation as expedition leader.
The manuscript was enhanced by the critiques of two an-
onymous reviewers and Andrew Spalton, and a language re-
view by Kimberly Ryan Mazzolli.
References
ALHICKMANI, H., ZAABANOOT,N.&ZAABANOOT, A. () Camera
trapping of Arabian leopard in the Nejd region of Dhofar
Mountains. Cat News,,.
AUSBAND, D.E., RICH, L.N., GLENN, E.M., MITCHELL, M.S., ZAGER,
P., MILLER, D.A.W. et al. () Monitoring gray wolf populations
using multiple survey methods. The Journal of Wildlife
Management,,–.
BALME, G.A., HUNTER, L.T.B. & SLOTOW, R. () Evaluating
methods for counting cryptic carnivores. The Journal of Wildlife
Management,,–.
BOULINIER, T., NICHOLS, J.D., SAUER, J.R., HINES, J.E. & POLLOCK,
K.H. () Estimating species richness: the importance of
heterogeneity in species detectability. Ecology,,–.
BRIGHTSMITH, D.J., STRONZA,A.&HOLLE, K. () Ecotourism,
conservation biology, and volunteer tourism: a mutually beneficial
triumvirate. Biological Conservation,,–.
BUESCHING, C.D., NEWMAN, C. & MAC DONALD, D.W. () How
dear are deer volunteers: the efficiency of monitoring deer using
teams of volunteers to conduct pellet group counts. Oryx,,
–.
BURNHAM, K.P., ANDERSON, D.R. & L AAKE, J. ()Estimation of
Density from Line Transect Sampling of Biological Populations.
Wildlife Monographs . Wildlife Society, Louisville, USA.
COHN, J.P. () Citizen science: can volunteers do real research?
BioScience,,–.
6 M. Mazzolli et al.
Oryx
, Page 6 of 8 ©2016 Fauna & Flora International doi:10.1017/S0030605315001003
http://journals.cambridge.org Downloaded: 13 May 2016 IP address: 88.111.106.26
DAVISO N, A., BIRKS, J.D.S., B ROOKES, R.C., BRAITHWAITE, T.C. &
MESSENGER, J.E. () On the origin of faeces: morphological
versus molecular methods for surveying rare carnivores from their
scats. Journal of Zoology,,–.
GOMPPER, M.E., KAYS, R.W., RAY, J.C., L APOINT, S.D., BOGAN, D.A.
&C
RYAN, J.R. () A comparison of noninvasive techniques to
survey carnivore communities in northeastern North America.
Wildlife Society Bulletin,,–.
GUILLERA-ARROITA, G., LAHOZ-MONFORT, J.J., M ACKENZIE, D.I.,
WINTLE, B.A. & MCCARTHY, M.A. () Ignoring imperfect
detection in biological surveys is dangerous: a response to
‘Fitting and interpreting occupancy models’.PLoS ONE,(),
e.
HAAG, T., SANTOS, A.S., D EANGELO, C., SRBEK-ARAUJ O, A., SANA,
D.A., MORATO, R.G. et al. () Development and testing of an
optimized method for DNA-based identification of jaguar
(Panthera onca) and puma (Puma concolor) faecal samples for use
in ecological and genetic studies. Genetica,,–.
HAAG, T., SANTOS, A.S., S ANA, D.A., MORATO, R.G., C ULLEN,JR, L.,
CRAWSHAW,JR, P.G. et al. () The effect of habitat
fragmentation on the genetic structure of a top predator: loss of
diversity and high differentiation among remnant populations of
Atlantic Forest jaguars (Panthera onca). Molecular Ecology,,
–.
HAMFELT, A., KARLSSON, M., THIERFELDER,T.&VALKOVSKY,V.
() Beyond K-means: clusters identification for GIS. In
Information Fusion and Geographic Information Systems (eds
V. Popovich, C. Claramunt, M. Schrenk & K. Korolenko), pp.
–. Springer Berlin Heidelberg, New York, USA.
HARRISON, R.L. () Evaluation of microscopic and macroscopic
methods to identify felid hair. Wildlife Society Bulletin,,–.
HOWE, R.W., WOLF, A.T. & RINALDI,T.() Monitoring birds in a
regional landscape: lessons from the Nicolet National Forest Bird
Survey. In Monitoring Bird Populations by Point Counts (eds
C.J. Ralph, J.R. Sauer & S. Droege), pp. –. General Technical
Report PSW-GTR-. U.S. Department of Agriculture, Forest
Service, Pacific Southwest Research Station, Albany, USA.
HULIK, T., MAZZOLLI,M.&HAMMER, M. ()True White
Wilderness: Tracking Lynx, Wolf and Bear in the Carpathian
Mountains of Slovakia. Biosphere Expeditions report. London, UK.
IUCN ()The IUCN Red List of Threatened Species v. ..Http://
www.iucnredlist.org [accessed July ].
JACQUEZ, G.M. () Spatial cluster analysis. In The Handbook of
Geographic Information Science (eds J.P. Wilson & S. Fotheringham),
pp. –. Blackwell Publishing, Malden, USA.
JANECKA, J.E., JACKSON, R., ZHANG, Y., L I, D., MUNKHTSOG, B.,
BUCKLEY-BEASON,V.&MURPHY, W.J. () Population
monitoring of snow leopards using noninvasive genetics. Cat News,
,–.
KARANTH, K.U. & NICHOLS, J.D. () Estimation of tiger densities
in India using photographic captures and recaptures. Ecology,,
–.
KARANTH, K.U., NICHOLS, J.D. & K UMAR, N.S. () Photographic
sampling of elusive mammals in tropical forests. In Sampling Rare
or Elusive Species (ed. W.L. Thompson), pp. –. Island Press,
Washington, DC, USA.
KELLY, M.J., BETSCH, J., WULTSCH, C., MESA,B.&MILLS, L.S. ()
Noninvasive sampling for carnivores. In Carnivore Ecology and
Conservation: A Handbook of Techniques (eds L. BOITANI & R.A.
POWELL), pp. –. Oxford University Press, Oxford, UK.
KHOROZYAN, I., STANTON, D., MOHAMMED, M., A L-RA’IL,W.&
PITTET, M. () Patterns of co-existence between humans and
mammals in Yemen: some species thrive while others are nearly
extinct. Biodiversity and Conservation,,–.
KINDBERG, J., ERICSSON, G. & S WENSON, J.E. () Monitoring rare
or elusive large mammals using effort-corrected voluntary
observers. Biological Conservation,,–.
LEE, A., MAZZOLLI, M., TATUM-HUME, E., K IRBY,C.&HAMMER, M.
()Icons of the Amazon: Jaguars, Pumas, Parrots and Peccaries in
Peru. Biosphere Expeditions report. London, UK.
MACKENZIE, D.I. & NICHOLS, J.D. () Occupancy as a surrogate
for abundance estimation. Animal Biodiversity and Conservation,
,–.
MACKENZIE, D.I., NICHOLS, J.D., LACHMAN , G.B., DROEGE, S.,
ROYLE, J.A. & LANGTIMM, C.A. () Estimating site occupancy
rates when detection probabilities are less than one. Ecology,,
–.
MALLON,D.&BUDD, K. ()Regional Red List Status of Carnivores
in the Arabian Peninsula. IUCN, Gland, Switzerland, and
Cambridge, UK. Https://portals.iucn.org/library/efiles/edocs/
RL--.pdf [accessed July ].
MAZZOLLI, M. () Arabian leopard, Panthera pardus nimr, status
and habitat assessment in northwest Dhofar, Oman (Mammalia:
Felidae). Zoology in the Middle East,,–.
MAZZOLLI,M.&HAMMER, L.A. ()Sampling and Analysis of Data
for Large Terrestrial Mammals During Short-Term Volunteer
Expeditions. Biosphere Expeditions, UK.
MAZZOLLI, M., OLIVEIRA,V.&HAMMER, M. ()Studying Jaguars,
Pumas and their Prey in Brazil’s Atlantic Rainforest: The Jaguar
Corridor. Expedition report. Biosphere Expeditions, UK.
MONDOL, S., KARANTH, K.U., K UMAR, N.S., GOPALASWAMY, A.M.,
ANDHERIA,A.&RAMAKRI SHNAN, U. () Evaluation of
non-invasive genetic sampling methods for estimating tiger
population size. Biological Conservation,,–.
MURRAY, A.T., GRUBESIC, T.H., REY, S.J. & A NSELIN, L. () Spatial
data uncertainty and cluster detection. Proceedings of the
GIScience Meeting. Columbus, USA.
NEWMAN, C., BUESCHING, C.D. & M ACDONAL D, D.W. ()
Validating mammal monitoring methods and assessing the
performance of volunteers in wildlife conservation—“Sed quis
custodiet ipsos custodies?”Biological Conservation,,–.
NICHOLS, J.D., BAILEY, L.L., O’CONNELL,JR, A.F., TALANCY, N.W.,
CAMPBELL GRANT, E.H., GILBERT, A.T. et al. () Multi-scale
occupancy estimation and modelling using multiple detection
methods. Journal of Applied Ecology,,–.
NOMANI, S.Z., CARTHY, R.R. & O LI, M.K. () Comparison of
methods for estimating abundance of gopher tortoises. Applied
Herpetology,,–.
OTIS, D.L., BURNHAM, K.P., WHITE, G.C. & ANDERSON, D.R. ()
Statistical Inference from Capture Data on Closed Animal
Populations. Wildlife Monographs . Wildlife Society, Louisville,
USA.
OTTO, C.R.V. & ROLOFF, G.J. () Using multiple methods to assess
detection probabilities of forest-floor wildlife. The Journal of
Wildlife Management,,–.
PEREZ, I., GEFFEN, E. & MOKADY, O. () Critically Endangered
Arabian leopards Panthera pardus nimr in Israel: estimating
population parameters using molecular scatology. Oryx,,–.
SAUER, J.R., FALLON, J.E. & JOHNSON, R. () Use of North
American Breeding Bird Survey data to estimate population change
for bird conservation regions. Journal of Wildlife Management,,
–.
SCHMELLER,D.S.,HENRY,P-Y.,JULLIARD,R.,GRUBER,B.,CLOBERT,J.,
DZIOCK,F.etal.() Advantages of volunteer-based biodiversity
monitoring in Europe. Conservation Biology,,–.
SHANNON, G., LEWIS, J.S. & GERBER, B.D. () Recommended
survey designs for occupancy modelling using motion-activated
cameras: insights from empirical wildlife data. PeerJ, ,e.
Mammals of the Arabian Peninsula 7
Oryx
, Page 7 of 8 ©2016 Fauna & Flora International doi:10.1017/S0030605315001003
http://journals.cambridge.org Downloaded: 13 May 2016 IP address: 88.111.106.26
SILVEIRA, L., JÁCOMO, A.T.A. & DINIZ-FILHO, J.A.F. () Camera
trap, line transect census and track surveys: a comparative
evaluation. Biological Conservation,,–.
SPALTON, J.A. () The Arabian leopard in Oman. Cat News,,–.
SPALTON, J.A. & ALHIKMANI, H. ()The Arabian Leopards of
Oman. Stacey International, London, UK.
SPALTON, J.A., AL HIKMANI, H.M., WILLIS,D.&SAID, A.S.B. ()
Critically Endangered Arabian leopards Panthera pardus nimr
persist in the Jabal Samhan Nature Reserve, Oman. Oryx,,
–.
VANSTREELS, R.E.T., RAMALHO, F.P. & ADANIA, C.H. ()
Microestrutura de pêlos-guarda de felídeos brasileiros: considerações
para a identificação de espécies. Biota Neotropical,,–.
VINE, S.J., CROWTHER, M.S., LAPIDGE, S.J., D ICKMAN, C.R., MOONEY,
N., PIGGOTT, M.P. & ENGLISH, A.W. () Comparison of
methods to detect rare and cryptic species: a case study using the red
fox (Vulpes vulpes). Wildlife Research,,–.
ZIELINSKI, W.J. & KUCERA, T.E. ()American Marten, Fisher,
Lynx, and Wolverine: Survey Methods for their Detection. General
Technical Report PSW-GTR-. U.S. Department of Agriculture,
Forest Service, Pacific Southwest Research Station, Albany, USA.
Biographical sketches
MARCELO MAZZOLLI has over decades of experience in wildlife re-
search, mainly on the ecology of felids in the context of broader envir-
onmental processes at landscape and regional scales. TAIANA HAAG’s
research focuses on the characterization and genetic diversity of large
felids in Brazil. BEATRIZ GARCIA LIPPERT has broad interests within
the fields of environment and education, focusing on the genetic char-
acterization of Brazilian mammals. EDUARDO EIZIRIK has studied
the genetic polymorphism of felids for years, while also exploring
their phylogenetic and evolutionary relationships. M ATTHIAS
HAMMER is the founder of Biosphere Expeditions, and has led
volunteer-based environmental research and conservation expeditions
to various regions. KHALID AL HIKMANI works as a researcher, and
guides wildlife research expeditions in Oman.
8 M. Mazzolli et al.
Oryx
, Page 8 of 8 ©2016 Fauna & Flora International doi:10.1017/S0030605315001003
