Download Camera trap assessment of the mammalian assemblages within the

CAMERA TRAP ASSESSMENT OF THE MAMMALIAN ASSEMBLAGES WITHIN THE
TULI WILDERNESS AREA, BOTSWANA
By
John C. Lisek
A Thesis
presented in partial fulfillment
of the requirements for the degree of
Master of Science
in the Department of Biology and Earth Sciences
University of Central Missouri
March 2013
© 2013
John C. Lisek
ALL RIGHTS RESERVED
CAMERA TRAP ASSESSMENT OF THE MAMMALIAN ASSEMBLAGES WITHIN THE
TULI WILDERNESS AREA, BOTSWANA
by
John C. Lisek
May 2013
APPROVED:
Thesis Chair: Dr. Victoria L. Jackson
Thesis Committee Member: Dr. Stephen Wilson
Thesis Committee Member: Ms. Jennifer Mittelhauser
ACCEPTED:
Chair, Department of Biology and Earth Science: Dr. Fanson Kidwaro
UNIVERSITY OF CENTRAL MISSOURI
WARRENSBURG, MISSOURI
ACKNOWLEDGMENTS
In appreciation of all those who assisted and contributed to the success of my graduate
research, I would like to express my sincere gratitude for your time, effort, and advice. There
were many who supported me both in Africa and in the United States, and I would like to extend
a special thanks to the following groups and individuals. I would like to thank Dr. Joy Stevenson,
the UCMO International Office, Dean Alice Greife, Phd., and the Research Assistance Grant
(2011) from the College of Science and Technology for contributing financially toward my
efforts. The University of Central Missouri (UCMO) Department of Biology and Earth Science
and Dr. Victoria Jackson were also gracious in allowing me to utilize department cameras and
GPS equipment. I would like to thank Dr. Victoria Jackson and Mrs. Jennifer Mittelhauser, MS,
for assisting with the strategic planning of my trip and the logistics of transporting my equipment
to Africa.
A big thank you must also be extended to Stuart Quinn and the African Conservation
Experience (ACE) Program for welcoming my study to their site and for their assistance with
hunting bait, setting cameras, and providing me with food and shelter during my stay. Along
with Stuart and the ACE team, I would also like to thank Jennifer Mittelhauser, Barry Pabst,
Casey Zimmerman, David Penning, Yuki Tsunoda, Sarah Campion, Pippa Snell, Tina Bulley,
Rachel Kirbby, Bernie O’Neill, Alex Firth, and Jackie Nichol for assisting me with the
placement and monitoring of cameras.
Upon my return to the United States, there were many who assisted with the analysis of
my data. I would like to thank Dr. Keshav Bhattarai for assisting me with my education in
utilizing Geographic Information Systems (GIS) and for his assistance with any questions during
the creation of my maps. My advisor, Dr. Jackson, was very instrumental with the editing of my
thesis drafts, and I would like to thank her and the members of my committee for their
recommendations and support. Dr. Jackson, Dr. Stephen Wilson, and Jennifer Mittelhauser were
extremely helpful in providing constructive criticism and suggestions to help me best express my
findings and results.
Last, but not least, I would like to thank my wife, Andrea Lisek, for all the support and
assistance during late night reviewing and editing sessions. This has been an enlightening and
rewarding experience, and it is my hope that this research can further the protection of African
mammals and research techniques within the scientific community. Thank you for all of your
assistance with this opportunity.
Table of Contents
ACKNOWLEDGMENTS ............................................................................................................ IV
LIST OF TABLES ..................................................................................................................... VIII
LIST OF FIGURES ..................................................................................................................... XII
CHAPTER 1: INTRODUCTION AND LITERATURE REVIEW ......................................... 1
GENERAL BACKGROUND ..................................................................................................... 1
BOTSWANA’S NORTHERN TULI GAME RESERVE .......................................................... 2
IMPORTANCE OF MAINTAINING DIVERSITY .................................................................. 4
SURVEY METHODS FOR ELUSIVE SPECIES ..................................................................... 6
MAPPING AND MODELING TECHNIQUES ......................................................................... 8
CHAPTER OBJECTIVES ........................................................................................................ 11
LITERATURE CITED ............................................................................................................. 13
CHAPTER 2: MEASURING MAMMALIAN SPECIES RICHNESS WITHIN TULI
WILDERNESS AREA (TWA), BOTSWANA: UTILIZING CAMERA TRAP SURVEYS
AND GEOGRAPHICAL INFORMATION SYSTEM (GIS) ................................................. 23
ABSTRACT: ............................................................................................................................. 23
INTRODUCTION ..................................................................................................................... 24
METHODS................................................................................................................................ 27
RESULTS.................................................................................................................................. 33
DISCUSSION ........................................................................................................................... 35
LITERATURE CITED ............................................................................................................. 40
CHAPTER 3: PREDICTING OCCURRENCE/OCCUPANCY FROM A COMMUNITY
SURVEY OF MEDIUM TO LARGE-SIZED MAMMALS IN THE TULI GAME
RESERVE OF BOTSWANA. .................................................................................................... 63
ABSTRACT: ............................................................................................................................. 63
INTRODUCTION ..................................................................................................................... 65
METHODS................................................................................................................................ 69
RESULTS.................................................................................................................................. 74
DISCUSSION ........................................................................................................................... 78
LITERATURE CITED ............................................................................................................. 84
INCLUSIVE LITERATURE CITED ......................................................................................... 112
APPENDICES ............................................................................................................................ 130
VI
APPENDIX A ......................................................................................................................... 130
VII
LIST OF TABLES
CHAPTER 2:
TABLE 2.1—SUMMARY OF STRATIFIED RANDOM HABITAT SELECTION
PERCENTAGES FOR CAMERA TRAP PLACEMENT WITHIN TULI WILDERNESS
AREA, BOTSWANA. ........................................................................................................... 48
TABLE 2.2—RESULTS OF DESCRIPTIVE STATISTICS FOR MAMMALIAN SPECIES
RICHNESS WITHIN CAMERA TRAP SITES IN THE TULI WILDERNESS AREA,
BOTSWANA. MEAN VALUES, STANDARD DEVIATIONS (SD), AND 95%
CONFIDENCE INTERVALS (C.I.) ARE GIVEN WITH EXCEPTION OF 3 REMOVED
SITES (6, 12, AND 31). ........................................................................................................ 49
TABLE 2.3—MAMMALIAN SPECIES DETECTED FROM CAMERA TRAP SURVEYS
WITHIN THE TULI WILDERNESS AREA, BOTSWANA, 2011. SPECIES STATUS
INFORMATION PROVIDED FROM THE INTERNATIONAL UNION FOR
CONSERVATION OF NATURAL RESOURCES RED LIST OF THREATENED
SPECIES- LEAST CONCERN (LC), NEAR THREATENED (NT), VULNERABLE (VU),
ENDANGERED (EN), CRITICALLY ENDANGERED (CR), AND NOT LISTED BY
ICUN (UK). ........................................................................................................................... 57
TABLE 2. 4—MAMMALAIN SPECIES DETECTION BY INDIVIDUAL CAMERA TRAP
SITES..................................................................................................................................... 59
TABLE 2.5—LIST OF MAMMALIAN SPECIES OF POSSIBLE DETECTION BASED ON
SPECIES ECOLOGY AND DISTRIBUTION. SPECIES STATUS INFORMATION
PROVIDED FROM THE INTERNATIONAL UNION FOR CONSERVATION OF
NATURAL RESOURCES RED LIST OF THREATENED SPECIES- LEAST CONCERN
VIII
(LC), NEAR THREATENED (NT), VULNERABLE (VU), ENDANGERED (EN),
CRITICALLY ENDANGERED (CR), AND NOT LISTED BY ICUN (N/A).................... 60
CHAPTER 3:
TABLE 3.1—SUMMARY OF STRATIFIED RANDOM HABITAT SELECTION
PERCENTAGES FOR CAMERA TRAP PLACEMENTS WITHIN THE TULI
WILDERNESS AREA. ......................................................................................................... 93
TABLE 3.2—LIST OF A PRIORI MODELS WITH HYPOTHESES FOR EFFECT ON
OCCURRENCE (Ψ) FROM CAMERA TRAP SURVEY IN THE TULI WILDERNESS
AREA, BOTSWANA, 2011. ................................................................................................. 94
TABLE 3.3—LIST OF A PRIORI MODELS WITH HYPOTHESES FOR EFFECT ON
DETECTION PROBABILITY (P) FROM CAMERA TRAP SURVEY IN THE TULI
WILDERNESS AREA, BOTSWANA, 2011. ...................................................................... 95
TABLE 3.4—RESULTS FOR MAMMALIAN SPECIES DETECTED FROM CAMERA TRAP
SURVEYS WITHIN THE TULI WILDERNESS AREA, BOTSWANA, 2011. SPECIES
STATUS INFORMATION PROVIDED FROM THE INTERNATIONAL UNION FOR
CONSERVATION OF NATURAL RESOURCES RED LIST OF THREATENED
SPECIES- LEAST CONCERN (LC), NEAR THREATENED (NT), VULNERABLE (VU),
ENDANGERED (EN), CRITICALLY ENDANGERED (CR), AND NOT LISTED BY
ICUN (UK). ........................................................................................................................... 97
TABLE 3.5—RESULTS FOR MAMMALIAN SPECIES OCCUPANCY WITHIN 95%
CONFIDENCE INTERVAL, ESTIMATED PROBABILITY OF DETECTION, AND
NAÏVE OCCUPANCY ESTIMATES FOR UNDER-DISPERSED SPECIES.
FREQUENCIES OF INDIVIDUAL SPECIES DETECTED WITH THE NUMBER OF
IX
SITES DETECTED ARE DISPLAYED. DETECTED FROM CAMERA TRAP SURVEYS
WITHIN THE TULI WILDERNESS AREA, BOTSWANA, 2011. .................................. 100
TABLE 3.6—BEST FIT MODEL SELECTION OF SITE COVARIATE EFFECTS ON
OCCURRENCE MODELS (Ψ) FOR AFRICAN ELEPHANT (LOXODONTA AFRICANA).
............................................................................................................................................. 103
TABLE 3.7—BEST FIT MODEL SELECTION OF SITE COVARIATE EFFECTS ON
DETECTION PROBABILITY MODELS (P) FOR AFRICAN ELEPHANT (LOXODONTA
AFRICANA). ........................................................................................................................ 103
TABLE 3.8—BEST FIT MODEL SELECTION OF SITE COVARIATE EFFECTS ON
OCCURRENCE MODELS (Ψ) FOR AFRICAN LEOPARD (PANTHERA PARDUS). . 104
TABLE 3.9—BEST FIT MODEL SELECTION OF SITE COVARIATE EFFECTS ON
DETECTION PROBABILITY MODELS (P) FOR AFRICAN LEOPARD (PANTHERA
PARDUS). ............................................................................................................................ 104
TABLE 3.10—BEST FIT MODEL SELECTION OF SITE COVARIATE EFFECTS ON
OCCURRENCE MODELS (Ψ) FOR BROWN HYENA (PARAHYAENA BRUNNEA). . 105
TABLE 3.11—BEST FIT MODEL SELECTION OF SITE COVARIATE EFFECTS ON
DETECTION PROBABILITY MODELS (P) FOR BROWN HYENA (PARAHYAENA
BRUNNEA). ......................................................................................................................... 105
TABLE 3.12—BEST FIT MODEL SELECTION OF SITE COVARIATE EFFECTS ON
OCCURRENCE MODELS (Ψ) FOR SPOTTED HYENA (CROCUTA CROCUTA). ..... 106
TABLE 3.13—BEST FIT MODEL SELECTION OF SITE COVARIATE EFFECTS ON
DETECTION PROBABILITY MODELS (P) FOR SPOTTED HYENA (CROCUTA
CROCUTA). ......................................................................................................................... 106
X
TABLE 3.14—BEST FIT MODEL SELECTION OF SITE COVARIATE EFFECTS ON
OCCURRENCE MODELS (Ψ) FOR SCRUB HARE (LEPUS SAXATILIS). ................... 107
TABLE 3.15—BEST FIT MODEL SELECTION OF SITE COVARIATE EFFECTS ON
DETECTION PROBABILITY MODELS (P) FOR SCRUB HARE (LEPUS SAXATILIS).
............................................................................................................................................. 107
TABLE 3.16—RESULTS FOR MAMMALIAN SPECIES MEAN LATENCY TO
DETECTION (LTD) WITH ASSOCIATED STANDARD ERRORS IN PARENTHESES,
TOTAL NUMBER OF SITES DETECTED AND OVERALL FREQUENCY OF
DETECTION AT CAMERA SITES FROM CAMERA TRAP SURVEYS WITHIN THE
TULI WILDERNESS AREA, BOTSWANA, 2011. .......................................................... 110
XI
LIST OF FIGURES
CHAPTER 1:
FIGURE 1.1—MAP OF THE TULI WILDERNESS STUDY AREA WITHIN THE
NORTHERN TULI GAME RESERVE, BOTSWANA. ...................................................... 21
CHAPTER 2:
FIGURE 2.1—MAP DESCRIPTION OF THE TULI WILDERNESS AREA (TWA),
BOTSWANA......................................................................................................................... 50
FIGURE 2.2—MEAN SPECIES RICHNESS RATIO (SE) ANALYZED BY HABITAT
ASSOCIATIONS FROM CAMERA TRAP SURVEY IN THE TULI WILDERNESS
AREA, BOTSWANA, 2011. ................................................................................................. 51
FIGURE 2.3—MEAN SPECIES RICHNESS RATIOS ANALYZED BY PLACEMENT OF
CAMERA SITES WITHIN THREE SEPARETE FREATURES (ROADS, DRAINAGE
LINES, AND OTHER AREAS) FROM CAMERA TRAP SURVEY IN THE TULI
WILDERNESS AREA, BOTSWANA, 2011. NOTATIONS OF MEAN VALUES WITH
STANDARD ERRORS AND SIGNIFICANCE NOTED (ONE-WAY ANOVA,
F2,33=4.208, P=0.024) WITH * FROM COMPARISON TEST. ........................................... 52
FIGURE 2.4—MAMMALIAN SPECIES RICHNESS RATIOS FOR CARNIVORES AND
HERBIVORES FROM CAMERA TRAP SURVEY IN THE TULI WILDERNESS AREA,
BOTSWANA, 2011. MEAN VALUES REPRESENTED OF SPECIES RICHNESS
BASED ON DIET WITH STANDARD ERRORS (SE) AND NOTED SIGNIFICANCE OF
(MANN-WHITNEY RANK SUM TEST, U1,34=472.5, P=0.049) BY *. ............................. 53
FIGURE 2.5—MAP OF MAMMALIAN SPECIES RICHNESS WITHIN THE TULI
WILDERNESS STUDY AREA WITHIN THE NORTHERN TULI GAME RESERVE,
BOTSWANA, 2011............................................................................................................... 54
XII
FIGURE 2.6—MAP RESULTS FROM HOT SPOT ANALYSIS INDICATING
MAMMALIAN SPECIES RICHNESS HOT SPOTS WITHIN THE TULI WILDERNESS
STUDY AREA WITHIN THE NORTHERN TULI GAME RESERVE, BOTSWANA.
2011. ...................................................................................................................................... 55
FIGURE 2.7—MAP OF RESULTS OF CLUSTER/OUTLIER ANALYSIS OF MAMMALIAN
SPECIES RICHNESS WITHIN THE TULI WILDERNESS STUDY AREA WITHIN THE
NORTHERN TULI GAME RESERVE, BOTSWANA, 2011. ............................................ 56
CHAPTER 3:
FIGURE 3.1—MAP OF THE TULI WILDERNESS STUDY AREA WITHIN THE
NORTHERN TULI GAME RESERVE, BOTSWANA. ...................................................... 96
FIGURE 3.2—GRAPH OF RESULTS FOR ANALYSIS OF SPECIES RICHNESS RATIOS
DETECTED COMPARED TO LENGTH OF CAMERA TRAP DAYS IN OPERATION
FROM CAMERA TRAP SURVEY CONDUCTED WITHIN THE TULI WILDERNESS
AREA, BOTSWANA, 2011. MEAN SPECIES WITH STANDARD ERRORS (SE) AND
SIGNIFICANCE OF (KRUSKAL-WALLIS ONE-WAY MULTIPLE TEST, H2=13.073,
P=0.001) NOTED BY *. ..................................................................................................... 108
FIGURE 3.3—MAMMALIAN SPECIES FREQUENCY OF DETECTION RESULT FROM
CAMERA TRAP SURVEY WITHIN THE TULI WILDERNESS AREA, BOTSWANA,
2011. .................................................................................................................................... 109
XIII
CHAPTER 1: INTRODUCTION AND LITERATURE REVIEW
GENERAL BACKGROUND
Understanding a species’ ecology and life history is now more important than ever
before. As human population increases, destruction and conversion of habitat for human use
increases all over the world, and understanding how animals move through and occupy their
environment is extremely important. The increasing loss or modification of habitat can affect the
biodiversity within an area, especially those harboring habitat specific/dependent species
(Western and Maitumo 2004). Anthropomorphic influences on habitat, such as the introduction
of domestic animals and crops, historically led to the extirpation of some mammalian species
from their natural ranges (De Vos 1964). Agricultural practices such as clearing of forests and
draining of marshlands to plant crops produce significant changes to the environment (De Vos
1964).
Hackel (1999) summarized literature identifying potential factors that affect CommunityBased Conservation (CBC) within Africa such as, population growth and land-use pressures,
poverty, and democratization were researched. Hackel (1999) concluded that human population
growth and human encroachment near protected reserves posed threats to core wildlife areas and
diversity. Donald and Evans (2006) estimated that agriculture in sub-Saharan Africa and other
developing countries will increase 30% by the year 2050. Fragmentation of habitat affects
species by increasing competition, predation, and susceptibility to parasites (Rodriguez-Arroyo
and Dias 2009). Populations of large mammalian carnivores in North America may decline in
response to these habitat disturbances, while meso-carnivore (fox, coyote) numbers can increase
(Rodriguez-Arroyo and Dias 2009).
1
As habitat decreases, creating reserves to conserve and preserve mammalian species is
essential. The creation of such reserves has its own set of challenges. Africa has many large
reserves, but approximately 80% of the historic ranges of many large mammals reside outside
protected areas (Caro 2001, Hoare 1999). Researchers have examined the effects of
fragmentation and migration of species within reserve fragments (Caro 2001, Hoare 1999).
Walker et al. (1987) found fencing within reserves caused fragmentation and hindered
movements of species. Features such as roads, drainage lines, and dense tree rows can also be a
source of fragmentation within reserves (Verboom and Van Apeldoorn 1990). Landscape
characteristics within reserves can also pose challenges and create natural fragmentation (Walker
et al. 1987, Verboom and Van Apeldoorn 1990).These features act as barriers to small mammal
dispersal between habitat patches and can positively or negatively affect metapopulations
(Verboom and Van Apeldoorn 1990). Some landscape elements, such as roads or fence rows,
allow for range expansion of certain species (Verboom and Van Apeldoorn 1990). Landscape
features such as these can be termed habitat corridors; portions/strips of preferred habitat which
link habitat patches to facilitate movement (Bennett 1990). Corridors facilitate dispersal and
allow interaction between populations that may have otherwise been isolated (Bennett 1990).
They may also reduce vulnerability to stochastic events and, in the event of a local extinction,
provide a means for recolonization (Bennett 1990).
BOTSWANA’S NORTHERN TULI GAME RESERVE
Africa provides habitat for a large diversity of unique animal species (Lombard 1995).
Africa’s high rate of annual human population growth (mean of 5.2%) increases pressure to
manipulate natural lands for agriculture and pasture (Kwasi Fosu and Mwabu 2010, Hackel
1999). African nature reserves have historically served as recreation destinations for enthusiasts
2
or sportsmen (Fjeldsa et al. 2004). Northern Tuli Game Reserve (NTGR) was created by various
landowners with similar conservation interests (McKenzie 1990). This reserve is not recognized
by Botswana’s government and is maintained by private land owners (McKenzie 1990). The
reserve is located on the eastern boundary of Botswana between 21°55’S and 22°15’S and is
flanked by Zimbabwe between 28°55’E and 29°15’E (McKenzie 1990, Styles and Skinner 2000,
Walker et al. 1987). NTGR is bordered southerly by the Limpopo River which marks the border
between Botswana and The Republic of South Africa. The eastern side of the reserve is bordered
by the Shashe River, marking Botswana’s border with Zimbabwe.
NTGR is approximately 3000 km2 and consists mostly of unfenced land utilized for both
game reserve and agriculture (Kuhn 2012, McKenzie 1990). The reserve is contained within the
central district which covers 147,730 km2 (Kuhn 2012, McKenzie 1990). Vegetation in NTGR is
classified as Mopane Veld habitat (McKenzie 1990), and the mopane tree (Colophospermum
mopane) remains the dominate species (Styles and Skinner 2000). This vegetation helps support
many large mammalian herbivores (Kuhn 2012), such as elephants (Loxodonta africana), kudu
(Tragelaphus strepsiceros), blue wildebeest (Connochaetes taurinus), and impala (Aepyceros
melampus). Large carnivores include lions (Panthera leo), leopards (Panthera pardus pardus),
black-backed jackals (Canis mesomelas), and African wild dogs (Lycaon pictus), an endangered
species (Kuhn 2012, Woodroffe and Sillero-Zubiri 2012).
Private reserves such as NTGR have fewer financial and governmental constraints than
public reserves and are more flexible in allocating conservation efforts protecting endangered
species, serving as buffer zones between public areas, and providing local indigenous people
with employment (Langholz 1996). Data on human-animal incidents are collected for each
district within Botswana, and the central district is documented as having 55.3% of all
3
nationwide animal incidents (Kuhn 2012). Current human population in NTGR has been
estimated at 7,954 individuals (Kuhn 2012). People live just outside the edges of the reserve and
use livestock farming as a means of support. With this comes risk, as animal-human conflicts
increase, including territorial defense by elephants and depredation by large carnivores (Kuhn
2012). In a recent study, Kuhn (2012) examined bone accumulations of prey at spotted hyena
dens in order to monitor and quantify predation on livestock. Bones were collected over a three
month period and identified to species or class using reference collections at the University of
Witwatersrand. A total of 976 specimens were collected, with 69.4% identified by skeletal
elements and the remaining 55.2% by species/class. Of the collected and identified specimens,
fewer than 3% of remains were domestic livestock. These results indicated that spotted hyena
residing within the NTGR prey mainly on wild species of impala, ostrich (Struthio camelus), and
kudu (Kuhn 2012).
IMPORTANCE OF MAINTAINING DIVERSITY
Biological surveys are necessary in order to determine the effects of fragmentation due to
anthropogenic factors. Major anthropogenic factors leading to the fragmentation or degradation
of habitats include the increased demand for agriculture and mining for mineral resources
(Kruess and Tscharntke 1994). Predators help regulate prey species, but as habitat connectivity is
reduced or removed altogether, their ability to control prey populations is also reduced (Kruess
and Tscharntke 1994). Kruess and Tscharntke (1994) studied the effects of habitat fragmentation
leading to the isolation of parasitoid insects. They sampled isolated fragmented clover fields and
found 8 to 12 species within larger meadows, while smaller, isolated 500 m patches contained
only 1 or 2 species. They concluded that habitat isolation had negative effects on species
diversity and mammalian herbivores had decreasing effects on isolated parasitoids (Kruess and
4
Tscharntke 1994). In sampling areas of fragmentation and isolation, researchers must take into
account the species-area relationship: as area increases the number of new species within the area
increases (Hillebrand and Blenckner 2002). Obtaining ecological information not only provides
data on species’ requirements for hunting/foraging, resting, and breeding, but data are also
provided on the types and quantity of habitat required to maintain species richness and protect
species from endangerment (Davis and Wagner 2003).
A major factor that affects mammalian species within the vast open areas of Africa is
pressure from poachers. These areas are often not patrolled or are under the protection of local
tribes which lack the resources to protect wildlife (Caro 1999). Game reserves have greater
species richness and higher densities of mammals than non-regulated areas of Africa (Caro
1999). Despite higher diversity levels, some species within game reserve areas such as leopards,
lions, bushbucks (Tragelaphus sylvaticus), and impala have declined due to hunting by tourists
(Caro 1999). Caro (1999) examined mammalian densities around and within three protected
reserves in Tanzania to determine the factors affecting densities. Observations of animals were
conducted by traveling along pre-determined transects. Species variability and densities were
significantly lower outside of reserves where legal and illegal hunting occurs (Caro 1999).
Increases in fleeing behaviors have been seen by giraffes (Giraffa camelopardalis), Cape
buffaloes (Syncerus caffer), zebras (Equus quagga), and reedbucks (Redunca spp.) in areas
outside reserve boundaries (Caro 1999).
Species richness, or α-richness, is referred to as the total number of species that occur
within a given area or habitat (Orians and Groom 2006). Examining spatial and temporal trends,
along with the effects of environmental stochastisity on occurrence of species helps answer
questions regarding diversity change (Boulinier et al. 1998). Boulininer et al. (1998) found that
5
detectability of species and richness increased when the environment was most heterogeneous.
Additionally, when heterogeneity of the study area is added into models, it increases the
detectability of species inhabiting the area (Boulinier et al. 1998).
There are some considerations in surveying species richness that should be taken into
account. Researchers should ask three questions before surveying an area: (1) why survey or
monitor, (2) what variables need to be sampled, and (3) how is the sampling or monitoring going
to be conducted (Weber et al. 2004). Another consideration is the size of the area to be sampled
because the number of species detected will be proportional to the size of the area (Baltanas
1992). Therefore, a larger land/habitat area will result in larger species richness (Baltanas 1992).
Several factors affect species diversity in reserves or other protected areas. Large predators
enhance diversity when they are not impacting prey extinction rates, allowing prey competition
to exist (Shurin and Allen 2001). By reducing target prey populations, increases in predator
colonization rates produce open habitat patches for new prey species to immigrate (Shurin and
Allen 2001). Shurin and Allen (2001) indicated that multiple predators can promote coexistence
of prey species that would normally be in competition.
SURVEY METHODS FOR ELUSIVE SPECIES
Mammals can be difficult to study because many are nocturnal, elusive, and have diverse
life history traits. Carnivores are particularly hard to monitor because of their large home ranges
and small populations (Long et al. 2011). Camera trapping is effective for investigating many
mammalian species (Foresman and Pearson 1998; Khoroyzyan et al. 2008, Trolle and Kery
2003). Large carnivores can become camera shy when exposed to flash cameras (Wegge et al.
2004). Infrared camera traps in conjunction with other techniques (track plates, track counts, and
6
visual sightings) can provide more thorough species inventories (Foresman and Pearson 1998,
Srbek-Araujo and Chiarello 2005, Wegge et al. 2004). Other benefits of infrared camera traps
include individual photographs of animals, decreased man-power, and this method requires little
training (Srbek-Araujo and Chiarello 2005). Foresman and Pearson (1998) compared the use of
camera traps and track plates baited with white-tailed deer (Odocoileus virginianus), commercial
lures, and chicken parts for detecting species in forested areas of Montana. Their research
supported the use of camera trapping for mammalian carnivores due to greater detection abilities,
more accurate species identification, and decreased required effort (Foresman and Pearson
1998). Camera trapping is an effective method in determining occupancy and abundance of
carnivores (Thorn et al. 2009), and Balme et al. (2007) revealed camera trapping to be more cost
effective, when compared to other methods. Researchers are not only using cameras for density
estimations and occupancy, but also in observing scent marking behaviors (Marnewick et al.
2006).
Camera trapping can be effective in both small and large areas. Balme et al. (2007)
sampled 210 km2 of the Phinda Game Reserve in South Africa, and in order to deal with this
large area, researchers divided the land into two separate zones for sampling. In areas of
changing elevations, separate sampling occasions should be completed to measure species’
responses to environmental changes (Khoroyzyan et al. 2008). Suitable camera placement
locations include areas on game paths, trails, and dirt roads rarely used by humans (Balme et al.
2007, Kauffman et al. 2007). Height placement of cameras to detect various species should be
between 20-70 cm above the ground (Balme et al. 2007, Kelly and Holub 2008, Negroes et al.
2010). Trolle and Kery (2003) deployed active and passive cameras along game trails and
corridors at a height of 20 cm. Camera sensitivity was set to take photographs in a series every
7
30 seconds once the infrared beam was triggered (Trolle and Kery 2003). Sites were baited with
meat to encourage animals to remain in front of cameras so that flanking pictures could be
captured for individual identification (Trolle and Kery 2003). Detecting leopards involves
mounting cameras on trees or wooden posts 2-3 m from trails or paths. The use of two cameras
at a site can provide a full picture for identification (Balme et al. 2007). Cameras should be set to
record photos 24 hours/day with a delay interval of 20-30 seconds to increase chances of
capturing photos of both flanks of the animal (Marnewick et al. 2006, Negroes et al. 2010, Trolle
and Kery 2003). Cameras should be set to include recorded date and time stamps (Khoroyzyan et
al. 2008), and the distance between camera trap sets of less than 2 km is effective for capturing
images used to determine occupancy of large carnivores (Balme et al. 2009). Baiting camera
traps with flesh of prey animals, such as deer or liquid scents of carnivores, is also effective
(Foresman and Pearson 1998; Kauffman et al. 2007, Trolle and Kery 2003).
MAPPING AND MODELING TECHNIQUES
Theoretical modeling of occupancy, predictability, and habitat preferences has become
popular among ecologists. Occupancy modeling uses data based on the detection or nondetection of species and can account for detection probability less than one (MacKenzie et al.
2002). Modeling that utilizes this approach allows for estimation of species occurrence (Ψ) and
species detectability ( ̂ ). Additional covariates can include habitat characteristics, such as patch
size, elevation, and climatic conditions (MacKenzie et al. 2002, 2006). Models are validated
using Akaike’s Information Criterion (AIC) which sums to 1, with the best model selection
having the highest value (MacKenzie et al. 2006). Finley et al. (2005) examined swift fox
(Vulpes velox) occupancy rates within a Colorado prairie using methods by MacKenzie et al.
8
(2002). Detection/non-detection data were gathered by live-trapping foxes in pre-selected grids.
Top models for detectability included short grass prairie alone, and other top models included
density of animals and percentage of short-grass prairie within trapping grids (Finley et al. 2005)
Geographic Information Systems (GIS) has developed into an essential tool for ecologists
(Johnson 1990). GIS utilizes points, lines, and polygons to represent key features in the
landscape and combines them to specific attribute data (e.g., species type, sex, or age) to answer
hypotheses (Johnson 1990). Researchers are able to plot and store field data from Global
Positioning Systems (GPS) that can be used to study the ecology of animals. Ecologists use GIS
to measure one dimension of two dimensional areas (i.e. habitat patches), to analyze spatial
heterogeneity and temporal change, to analyze proximity of key species, and to interpret data for
use in predictability models (Johnson 1990, Pickett and White 1985). Descriptions of areas are
shown geographically within a known coordinate system, which accurately places the data on the
earth’s surface (Johnson 1990). Abiotic and biotic characteristics (e.g., soil types, rock
formations, and species) can be expressed as a function of the features on the map (Johnson
1990). Measurements of a feature class (physical characteristic associated with data) or classes
within areas are determined by using aerial photographs (Johnson 1990). The method of
buffering or extracting these areas can be utilized to determine effects of land use on the
condition of a habitat (Johnson 1990).
Several methods have been used to map species distributions, such as percolation theory
which analyzes the amount of energy an organism uses when moving across landscapes (O’Neill
et al. 1988). An effective procedure for analyzing spatial data of mammalian species involves
overlaying spatial data base layers of land use/land cover with separate shapefiles that contain
information gathered on species movements across habitats (Johnson 1990). This method aids in
9
the evaluation of spatial and temporal changes within protected reserve areas. Osborne et al.
(2001) modeled habitat use of great bustards (Otis tarda) with GIS. Point location data from
observations were imported into GIS, and satellite images were edited to obtain normalized
difference vegetation indices. Spatial correlations and regression analyses were included in
models shown on maps (Osborne et al. 2001). Negative human impacts were found by
overlaying models that indicated lower densities of bustards around roads, railways, and
buildings (Osborne et al. 2001). Researchers can determine the relative importance of habitats
based on overlaying species distributions upon thematic maps that depict habitat parameters,
such as land cover, distance to water bodies and soils. Overlays of habitat parameters can
provide insight into species habitat requirements, and weighted values, based on the importance
of the habitat type, can be assigned (Johnson 1990). Additionally, by acquiring descriptions and
weights of the habitat characteristics (e.g., patch size, arrangement, and distance between
patches) quantity and quality of the habitat can be approximated. Ordinal modeling provides a
means for estimating desired features by assigning weighted values to features within habitats in
order to provide spatial descriptions of the variables (Johnson 1990). Factors such as climate,
terrain, vegetative cover, and tree density should be considered when modeling vertebrate
distributions on spatial and temporal scales (Mackey and Lindenmayer 2001). Jaberg and Guisan
(2001) mapped bat communities not only by evaluating the above characteristics, but also by
including species density, buildings, and agricultural lands. Models used elevation, presence of
isolated trees, and distance to nearest water source as predictors for detecting occurrence of bat
species (Jaberg and Guisan 2001).
Applying a multiple disciplinary approach (camera trapping, GIS, occupancy modeling,
etc.) for surveying mammalian assemblages in reserves can enhance understanding of species.
10
Utilizing camera traps for surveying large reserves provides a means to collect vast amounts of
data on species presence/absence, richness/diversity, landscape use, and anthropogenic factors
affecting mortality. With a growing human population and the encroachment of natural
landscapes, occupancy modeling is important for habitat protection and monitoring. As concerns
for habitat availability for endangered species increase, so does the need for accurate data.
Gaining information for as many species as possible during a survey gives researchers a more
complete look within a reserve area. This allows for better management of mammalian species
and allocation of reserve resources.
STUDY OBJECTIVES
The first objective of this study was to examine species richness of medium to large-sized
mammalian assemblages within the Tuli Wilderness Area (TWA) using camera trap surveys and
to examine data using occupancy analysis. Chapter 2 identifies mammalian carnivore and
herbivore species richness within the TWA. I also analyzed trophic levels of taxa to reveal
differences between carnivores and herbivores. I used GIS in order to identify hot and cold spots
of richness. The last objective for chapter 2 was to examine camera placement in habitat types
and among natural/anthropogenic features for optimum placement. My goal was to adequately
describe the mammalian assemblages in order to provide updated information of mammalian
species for wildlife managers.
Chapter 3 focuses on the use of camera trapping data to define occupancy for the
mammalian assemblages within the TWA. Detection/non-detection histories were used to
estimate occupancy and detection probabilities for species detected in the TWA. I used covariate
models of occupancy and probability to determine factors that affect monitoring for the
11
following species: brown hyenas (Parahyaena brunnea), spotted hyenas (Crocuta crocuta),
African leopards, scrub hares (Lepus saxatilis), and African elephants. In examining species
richness at sites compared with the time interval use for each camera at each site, I was able to
provide support for shorter sampling periods for species monitoring at sites within reserves with
similar characteristics.
12
LITERATURE CITED
BALME, G., L. HUNTER,
AND
R. SLOTOW. 2007. Feeding habitat selection by hunting leopards
Panthera pardus in a woodland savanna: prey catchability versus abundance. Animal
Behaviour 74:589-598.
BALME, G. A., L. T. B. HUNTER,
AND
R. SLOTOW. 2009. Evaluating methods for counting
cryptic carnivores. Journal of Wildlife Management 73(3):433-441.
BALTANAS, A. 1992. On the use of some methods for the estimation of species richness. Oikos
5(3):484-492.
BENNETT, A. F. 1990. Habitat corridors and the conservation of small mammals in fragmented
forest environment. Landscape Ecology 4:109-122.
BOULINIER, T., J. D. NICHOLS, J. R. SAUER, J. E. HINES,
AND K.
H. POLLOCK. 1998.
Estimating species richness: the importance of heterogeneity in species detectability. Ecology
79(3):1018-1028.
CARO, T. M. 1999. Densities of mammals in partially protected areas: the Katavi ecosystem of
western Tanzania. Journal of Applied Ecology 36:205-217.
13
CARO, T. M. 2001. Species richness and abundance of small mammals inside and outside an
African national park. Biological Conservation 98:251-257.
DAVIS, A.
AND J.
R. WAGNER. 2003. Who knows? On the importance of identifying “experts”
when researching local ecological knowledge. Human Ecology 31(3):463-489.
DE VOS, A. 1964. Range changes of mammals in the Great Lakes Region. American Naturalist
71(1):210-231.
DONALD, P. F.
AND
A. D. EVANS. 2006. Habitat connectivity and matrix restoration: the wider
implications of agri-environment schemes. Journal of Applied Ecology 43:209-218.
FINLEY, D. J., G. C. WHITE,
AND J.
P. FITZGERALD. 2005. Estimation of swift fox population
size and occupancy rates in eastern Colorado. Journal of Wildlife Management 69(3):861873.
FJELDSA, J., D. D. BURGESS, S. BLYTH, AND H. M. DEKLERK. 2004. Where are the major gaps
in the reserve network for Africa’s mammals? Oryx 38(1):17-25.
FORESMAN, K. R. AND D. E. PEARSON. 1998. Comparison of proposed survey procedures for
detection of forest carnivores. The Journal of Wildlife Management 62(4):1217-1226.
14
HACKEL, J. D. 1999. Community conservation and the future of Africa’s wildlife. Conservation
Biology 13(4):726-734.
HILLEBRAND, H. AND T. BLENCKNER. 2002. Regional and local impact on species diversity-from
pattern to process. Oecologia 132:479-491.
HOARE, R. E. 1999. Determinants of human-elephant conflict in a land-use mosaic. Journal of
Applied Ecology 36:689-700.
JABERG, C.
AND
A. GUISAN. 2001. Modeling the distribution of bats in relation to landscape
structure in a temperate mountain environment. Journal of Applied Ecology 38(6):11691181.
JOHNSON, L. B. 1990. Analyzing spatial and temporal phenomena using geographical
information systems. Landscape Ecology 4(1):31-43.
KAUFFMAN, M. J., N. VARLEY, D. W. SMITH, D. R. STAHLER, D. R. MACNULTY,
AND M.
S. BOYCE. 2007. Landscape heterogeneity shapes predation in a newly restored predator-prey
system. Ecology Letters 10:690–700.
15
KELLY, K. J.
AND
E. L. HOLUB. 2008. Camera trapping of carnivores: trap success among
camera types and across species, and habitat selection by species, on Salt Pond Mountain,
Giles County, Virginia. Northeastern Naturalist 15(2):249-262.
KHOROZYAN, I. G., A. G. MALKHASYAN, AND A. V. ABRAMOV. 2008. Presence-absence
surveys of prey and their use in predicting leopard (Panthera pardus) densities: a case study
from Armenia. Integrative Zoology 3:322-332.
KRUESS, A.
AND
T. TSCHARNTKE. 1994. Habitat fragmentation, species loss, and biological
control. Science 264:1581-1584.
KUHN, B. F. 2012. Bone accumulations of spotted hyaenas (Crocuta crocuta, Erxleben, 1777) as
indicators of diet and human conflict; Mashatu, Botswana. International Journal of Ecology
2012:1-6
KWASI FOSU, A.
AND G.
MWABU. 2010. Human Development in Africa. World Institute for
Development Economic Research, United Nations University, Helsinki, Finland.
LANGHOLZ, J. 1996. Economics, objectives, and success of private nature reserves in subSaharan Africa and Latin America. Conservation Biology 10(1):271-280.
16
LOMBARD, A. T. 1995. Introduction to an evaluation of the protection status of South Africans
vertebrates. South African Journal of Zoology 30(3):63-70.
LONG, R. A., T. M. DONOVAN, P. MACKAY, W. J. ZIELINSKI,
AND
J. S. BUZAS. [online]
2011. Predicting carnivore occurrence with noninvasive surveys and occupancy modeling.
Landscape Ecology 26:327-340.
MACKENZIE, D. I., J. D. NICHOLS, G. B. LACHMAN, S. DROEGE, J. A. ROYLE,
AND
C. A.
LANGTIMM. 2002. Estimating site occupancy rates when detection probabilities are less than
one. Ecology 83(8):2248-2255.
MACKENZIE, D. I., J. D. NICHOLS, J. A. ROYLE, K. H. POLLOCK, L. L. BAILY,
AND J. E.
HINES. 2006. Occupancy estimation and modeling. Academic Press, Burlington,
Massachusetts
MACKEY, B. G.
AND D.
B. LINDENMAYER. 2001. Towards a hierarchical framework for
modeling the spatial distribution of animals. Journal of Biogeography 28(9):1147-1166.
MARNEWICK, K. A., J. DU P. BOTHMA,
AND
G. H. VERDOORN. 2006. Using camera-trapping
to investigate the use of a tree as a scent-marking post by cheetahs in the Thabazimbi district.
South African Journal of Wildlife Research 36(2):139-145.
17
MCKENZIE, A. A. 1990. Co-operative hunting in the black-backed jackal (Canis mesomelas, Schreber).
PhD Thesis, University of Pretoria, Pretoria, South Africa.
NEGROES, N., P. SARMENTO, J. CRUZ, C. EIRA, E. REVILLA, C. FONSECA, R. SOLLMANN, N.
M. TORRES, M. M. FURTADO, A. T. A. JACOMO,
AND
L. SILVEIRA. 2010. Use of camera-
trapping to estimate puma density and influencing factors in central Brazil. Journal of
Wildlife Management 74(6):1195-1203.
O'NEILL, R. V., J. R. KRUMMEL, R. H. GARDNER, G. SUGIHARA, B. JACKSON, D. L.
DEANGELIS, B. T. MILNE, M. G. TURNER, B. ZYGMUNT, S. CHRISTENSEN, V. H. DALE,
AND R.
ORIANS, G.
L. GRAHAM. 1988. Indices of landscape pattern. Landscape Ecology 1(3):153-162.
AND M. GROOM.
2006. Global biodiversity patterns and processes. Pp 27-62 in
Principles of Conservation Biology (M. J. Groom, G. K. Meffe, C. R. Carroll, and
Contributors. Sinauer Associates, Inc. Third Edition.
OSBORNE, P. E., J. C. ALONSO,
AND R.
G. BRYANT. 2001. Modeling landscape-scale habitat
using GIS and remote sensing: a case study with great bustards. Journal of Applied Ecology
38:458-471.
PICKETT, S. T. A.
AND P.
S. WHITE. 1985. The ecology of natural disturbance and patch
dynamics. Academic Press, New York.
18
RODRIGUEZ-ARROYO, V.
AND
PA. D. DIAS. 2009. Effects of habitat fragmentation and
disturbance on howler monkeys: a review. American Journal of Primatology 71(1):1-16.
SHURIN, J. B.
AND
E. G. ALLEN. 2001. Effects of competition, predation, and dispersal, on
species richness at local and regional scales. The American Naturalist 158(6):624-637.
SRBEK-ARAUJO, A. C.
AND A.
G. CHIARELLO. 2005. Is camera-trapping an efficient method for
surveying mammals in Neotropical forests? A case study in south-eastern Brazil. Journal of
Tropical Ecology 21:121-125.
STYLES, C. V.
AND J.
D. SKINNER. 2000. The influence of large mammalian herbivores on
growth form and utilization of mopane trees, Colophosperum mopane, in Botswana’s
Northern Tuli Game Reserve. African Journal of Ecology 38:95-101.
THORN, M., D. M. SCOTT, M. GREEN, P. W. BATEMAN,
AND
E. Z. CAMERON. 2009.
Estimating brown hyena occupancy using baited camera traps. South Africa Journal of
Wildlife Research 39(1):1-10.
TROLLE M.
AND
M. KERY. 2003. Estimation of ocelot density in the pantanal using capture-
recapture analysis of camera-trapping data. Journal of Mammalogy 84(2):607-614.
19
VERBOOM, B.
AND
R. VAN APELDOORN. 1990. Effects of habitat fragmentation on the red
squirrel, Sciurus vulgaris L. Landscape Ecology 4:171-176.
WALKER, B. H., R. H. EMSLIE, R. N. OWEN-SMITH,
AND R.
J. SCHOLES. 1987. To cull or not
to cull: lessons from a Southern African drought. Journal of Applied Ecology 24(2):381-401.
WEBER, D., U. HINTERMANN,
AND A.
ZANGGER. 2004. Scale and trends in species richness:
condsiderations for monitoring biological diversity for political purposes. Global Ecology
and Biogeography 13:97-104.
WEGGE P, C. P. D. POKHERAL,
AND S.
R. JNAWALI. 2004. Effects of trapping effort and trap
shyness on estimates of tiger abundance from camera trap studies. Animal Conservation
7:251–256.
WESTERN, D.,
AND
D. MAITUMO. 2004. Woodland loss and restoration in a savanna park: a 20-
year experiment. African Journal of Ecology 42:111-121.
WOODROFFE, R.
AND
C. SILLERO-ZUBIRI, 2012. Lycaon pictus. In: IUCN 2012. IUCN Red List
of Threatened Species. Version 2012.2. <www.iucnredlist.org>. Downloaded on 06
November 2012.
20
Figure 1.1—Map of The Tuli Wilderness Study Area within the Northern Tuli Game
Reserve, Botswana.
21
Chapters 2 and 3 are written in the format set forth and accepted by the Journal of Mammalogy
22
CHAPTER 2: MEASURING MAMMALIAN SPECIES RICHNESS
WITHIN TULI WILDERNESS AREA (TWA), BOTSWANA: UTILIZING
CAMERA TRAP SURVEYS AND GEOGRAPHICAL INFORMATION
SYSTEM (GIS)
ABSTRACT:
Surveying mammalian communities for species richness is enhanced by utilizing camera
traps as a sampling tool. I conducted a survey of mammals within The Tuli Wilderness Area
(TWA) located within The Northern Tuli Game Reserve (NTGR) of Botswana. In the 34 day
sampling period, I had a total of 248 trap nights at 36 individual sites within the reserve. I
surveyed 38 of the expected 53 mammal species resulting in a 71% success rate. A total of 18
mammalian families and 9 sub-families were detected. Several questions were addressed
regarding species richness such as, how it is affected by habitat type, and how carnivore and
herbivore richness differs within the TWA. Species richness varied greatly between individual
camera sites. Using ArcGIS, I determined that 2 of the 36 sites were found to be hot spots and 2
of the 36 sites were cold spots of mammalian species richness. There were no differences in
species richness among the three main habitat types (mopane, forest/scrub, or riverine). When
compared to other camera trap locations, such as drainage lines, I determined that placement of
camera traps along roads or trails increased detection of mammal species. Carnivore and
herbivore species richness differed significantly within the TWA, with greater carnivore species
richness. Species of ecological concern, such as the African wild dog (Lycaon pictus), African
leopard (Panthera pardus), and African lion (Panthera leo) were found at camera sites,
indicating that baited camera trap research can be a valuable tool in monitoring species of
concern within African reserves.
Key Words: Species richness, camera trapping, medium and large sized mammals, carnivore,
herbivore.
23
INTRODUCTION
Studies conducted to assess community assemblages and the health of ecological areas
typically utilize species richness and diversity data (Boulinier et al. 1998). Concerns have arisen
in the last decade regarding a decline in mammalian species richness (Ceballos et al. 1998,
Woodroffe 2000). Species richness within an area depicts the total number of species which are
present at a given time (Ceballos et al. 1998, Woodroffe 2000). Anthropogenic impacts result in
decreased mammalian species richness, especially of large carnivores (Woodroffe 2000). Human
population increases adversely affect mammalian populations due to human/animal conflicts,
poaching, and encroachment into wilderness areas (Hayward et al. 2005). Surveys within Africa
have focused on determining mammalian species richness and diversity in areas where data are
inaccurate or nonexistent (Hayward et al. 2005).
Stochastic events and habitat fragmentation have been implicated in the local extirpation
of carnivores outside of protected areas. The effects of fragmentation can be examined by
reviewing species presence or absence data, protected area size based on geographic ranges, and
carnivore extinction rates. Wide-ranging carnivores such as the African wild dog (Lycaon
pictus), African lion (Panthera leo), African leopard (Panthera pardus pardus), and spotted
hyena (Crocuta crocuta) are at greater risk of extinction than species with smaller home ranges.
Examining ranging behavior has indicated increases in mortality of carnivores as human
populations expand into carnivore home ranges (Woodroffe and Ginsberg 1998).
Mammalian diversity can be affected by herbivorous populations, where mammalian
herbivores can negatively influence other organisms within the community. Herbivores can
affect plant diversity by over grazing if wild populations are not monitored (Bakker et al. 2006).
24
Conversely, large herbivores can increase plant diversity by spreading native seed. Large native
herbivores are affected by poaching, predation by carnivores, and habitat loss. Surveying
herbivore diversity is necessary in order to create more appropriate management action plans
(Bakker et al. 2006). Patterns of status change for mammalian species, including biodiversity
changes, extinction, extirpation, and endangered status in certain areas, have been attributed to
lack of consistent and accurate data (Ceballos and Brown 1995, Mills et al. 1993). Surveying
mammalian species diversity provides a means to observe effects caused by removal of large
keystone predators such as the African leopard, which has a varied catholic diet, and is
considered a carnivore generalist (Hayward et al. 2006). Maintaining a healthy and diverse
population of large herbivores is important for maintaining community structure (Mills et
al.1993). Keystone modifiers are species that affect habitat features without directly affecting
trophic levels of other species inhabiting a certain area. In veld (African grasslands) reserves and
protected areas within Africa, keystone herbivores are essential for maintaining vegetative
communities. Keystone herbivores can modify habitat (Mills et al. 1993) and cause a community
shift from dense woodland thickets to open veld (Augustine and McNaughton 1998, Van de
Kopple and Prins 1998). African elephants have been blamed for large amounts of habitat
destruction within Africa, and have been known to cause plant community shifts and effects on
carnivores (Van de Koppel and Prins 1998).
Surveying carnivore diversity can be difficult due to their rare and elusive nature (Balme
et al. 2007, Bowkett et al. 2006, Kauffman et al. 2007). Camera trapping is a valuable tool for
surveying large carnivores (Jackson et al. 2005). This method of examining mammalian
assemblages gives insight into species richness, territorial behaviors, population density, habitat
selection, and estimation of occupancy rates (Kelly and Holub 2008, Marnewick et al. 2006,
25
Negroes et al. 2010, Rowcliffe and Carbone 2008, Thorn et al. 2009). Utilizing remotely placed
infrared camera traps enhances temporal and spatial sampling of species, and provides a
permanent record of species within specific areas (Kays and Slauson 2008). Moruzzi et al.
(2002) examined species-specific habitat relationships within a mammalian community in order
to determine forest habitat preferences. By utilizing camera trap data researchers were able to
estimate species distributions for species, such as bobcats (Lynx rufus) and red foxes (Vulpes
vulpes). Additionally, their results indicate a reduction of the length of time for camera sets from
21 to 15 days would still yield a 90% capture rate (Moruzzi et al. 2002). Infrared camera
trapping provides more precise identification of large mammals (Lyra-Jorge and Ciocheti 2008).
Camera traps have been enhanced to detect extremely rare mammalian carnivores within sites by
utilizing baits or lures placed along roads and game trails and knowledge from skilled trackers
(Karanth et al. 2010, Rowcliffe and Carbone 2008). Placing cameras in greater frequency within
areas in shorter sampling periods can be effective in increasing sample sizes for analysis
(Moruzzi et al. 2002). Identification of travel corridors allows researchers to determine preferred
areas in which to place cameras (Karanth et al. 2010).
The first objective of my study was to determine medium and large-sized mammalian
carnivore and herbivore species richness within the Tuli Wilderness Area (TWA). Another factor
I examined was the mammalian species of ecological concern within my study area. My second
objective was to evaluate the carnivore/herbivore species richness. The third objective was to
examine camera placement and duration of survey to establish optimum placement and timing
for greater species detection. Lastly, I sought to determine which areas were hot/cold spots of
detection. My goal was to be able to adequately describe mammalian assemblages in order to
provide updated information to managers.
26
METHODS
Study Area
I conducted a survey of mammals within The Tuli Wilderness Area (TWA) located
within The Northern Tuli Game Reserve (NTGR) of Botswana (Fig. 2.1). The Northern Tuli
Game Reserve was created by various landowners with similar conservation interests; this
reserve is not recognized by Botswana’s government, but is maintained by private land owners
(McKenzie 1990). The reserve is located on the eastern boundary of Botswana between 21°55’S
and 22°15’S and flanked by Zimbabwe between 28°55’E and 29°15’E (McKenzie 1990, Styles
and Skinner 2000, Walker et al. 1987). NTGR is bordered southerly by The Republic of South
Africa and the Limpopo River. The eastern boundary is with Zimbabwe and the Shashe River.
The Tuli block is approximately 3000 km2 of mostly unfenced land with a mixture of agricultural
land, and is it situated in the central district which covers 147,730 km2 (Kuhn 2012, McKenzie
1990). Vegetation in NTGR is classified as Mopane veld (McKenzie 1990), and mopane
(Colophospermum mopane) the dominant vegetative species (Styles and Skinner 2000). NTGR
has an extensive system of drainage lines running throughout the reserve (McKenzie 1990).
Camera sites were located within The Tuli Wilderness Area (TWA) which consists of
approximately 4500-5000 hectares (Fig. 2.1) of Mopane veld with a mixture of forested and
rocky koppie habitats (McKenzie 1990). TWA lacks fences and thus provides animals freedom
of movement between other farms, with the exception of a northern fence line that separates
villages from the reserve (Selier 2007). Similar to NTGR, TWA maintains meandering rivers and
drainage lines throughout the area. Drainage lines and river areas provide riverine habitat during
wet seasons, and during dry seasons animals utilize this habitat as corridors.
27
Camera Trapping
I conducted camera trap surveys from 12 May to 13 June 2011. Camera traps were used
to survey medium and large sized mammals. Medium sized species were those with a weight of
2.0-20 kg and large species were ones < 20 kg. A total of 15 camera traps was employed
throughout TWA based on a stratified randomized design (Table 2.1), and 39 individual sites
were randomly chosen within a 500 x 500 m grid square based on information from skilled
trackers, accessibility, and low risk of harm to researchers by wildlife (Karanth et al. 2010). All
camera stations consisted of one remotely triggered infrared camera trap (Reconyx HyperFire
HC500 or Primos TRUTH CAM60). Camera sites were set along roads, drainage lines, or other
areas (open areas or water holes) at distances of 300 m to 500 m apart (Kays and Slauson 2008,
Negroes et al. 2010). Each was set at a height of 0 to 0.5 m, attached to trees with nylon straps,
and positioned at optimum angles for sensor detection of multiple species. Angles were
determined by utilizing test photos which captured the whole study site clearly and without
obstruction. Camera sensor sensitivity was set to medium to reduce triggering by wind and
moving vegetation. The number of photographs taken per detection was set to five with a five
second delay between photo series. Once placed, reinforced cable locks were used to prevent
damage or theft of cameras (Kays and Slauson 2008). To enhance detection of multiple species,
sites were baited (Kauffman et al. 2007, Thorn et al. 2009) using impala (Aepyceros melampus)
harvested by the land manager of the TWA in accordance with pre-allocated allowances for
research and local subsistence. Bait was placed in front of cameras, no more than 16 m away,
and consisted of various muscles from hind and forelimbs along with entrails. The length of
surveys ranged from 6 to 34 days; cameras were moved to increase the number of sites surveyed
(Kays and Slauson 2008). Cameras were checked daily or weekly.
28
At the end of the 34 day survey period, photographs were arranged by individual site
locations. Sites 6, 12, and 31 were removed due to camera trap malfunction or disturbance by
wildlife. As a result of removing these three sites, all analyses were calculated using the
remaining 36 sites. Photographs were examined to identify individual species detected using
“Walker’s Mammals of the World” text (Nowak 1999) and arranged by taxon (Table 2.3). Data
at each site were examined and species lists were compiled for each camera location (Table 2.4).
The total number of species was calculated for each camera site, and the status of concern and
population for each species was determined using The International Union for Conservation of
Natural Resources (IUCN) Red List for threatened or endangered species (IUCN 2012).
I followed guidelines set forth by the American Society of Mammalogists for the care and
ethical use of mammals (Sikes et al. 2011). All methods and protocols were approved by the
University of Central Missouri Institutional Animal Care and Use Committee on 13 May 2011
(IACUC-Approved Permit No. 11-3217).
Statistical Analysis
All species richness analyses were performed using statistical software SigmaPlot version
12.0 (Systat Software, San Jose, CA). I sought to determine mammalian species richness within
the TWA and among different camera trap sites. Descriptive statistics (mean, standard deviation,
and 95% confidence intervals) were used to describe species richness at camera sites within
TWA. Data for all statistical tests were converted from discrete to continuous by converting data
into ratios by dividing observed richness by the total number of expected inhabitants based on
geographic distribution. All assumptions of normality, homosedasticity, and independence were
assessed. Assumptions of normality were tested using the Shapiro-Wilk normality test.
29
I also tested whether there was a difference in carnivore and herbivore species richness
within TWA. Data were configured by carnivore/herbivore richness ratios for sites and compiled
for all sites into the two categories. Mann-Whitney Rank Sum tests were used to evaluate
differences between carnivore and herbivore species richness. This non-parametric test was used
because data did not meet the assumption of normality. The null hypothesis for this test was Ho:
There are no differences between carnivore and herbivore species richness within TWA.
My third question for analysis was whether species richness values were greater along
roads, drainage lines, or other areas. In order to determine differences among species richness
within placement categories an ANOVA was used, ratios of richness for each camera site was
placed into one of three categories: roads, drainage line, and other areas. I used a one-way
analysis of variance (ANOVA) with a Tukey Multiple Comparison Test to determine if
differences existed in species ratios among the three categories. All assumptions of normality,
independence, and equal variances were met. The null hypothesis for this was: Ho: There are no
differences in the species richness at camera trap sites among placement categories of roads,
drainage lines, or other areas.
For all other remaining statistical analyses, nonparametric tests were used because data
did not meet required assumptions of normality. I also examined whether species richness within
different habitat types varied among mopane vegetation, forest/scrub, and riverine habitats.
Species richness ratios for camera sites were organized by habitat category for analysis. A
Kruskal-Wallis one-way analysis of variance on ranks, a nonparametric alternative to the
standard ANOVA, was used to compare species richness between habitat types. My null
hypothesis was Ho: There is no difference in the amount of species richness between the three
habitat types of mopane vegetation, forest/scrub, and riverine habitat.
30
Spatial Analysis
I used ArcGIS Desktop v10.0 (ESRI – Redlands, California) to determine if and where
hot/cold spots of mammalian species richness occurred within TWA. Maps were created for
species richness based on camera site locations. I performed a hotspot analysis to identify
clustering of mammalian species richness, which calculates a Getis Ord Gi* (* represents
pronunciation for star, G-i-star) statistic for each individual feature (site) within a user-specified
distance to indicate clustering of high/low values (Chang 2012). A feature needs to be
statistically significant and must have a high value surrounded by other high values to be
considered a hot spot. Where in the equation xj is the site value of j site and wij is the spatial
weight assigned between i and j (Fig. 2.1a), and represents the total number features or sites.
Getis-Ord Gi* Statistic Equation.
̅ ∑
∑
√[ ∑
(∑
)]
Individual features, while taking into account neighboring features, are assigned a z-score and pvalue as a result of the statistic. I set the user specified distance to within 500 m of set grids, as
site selection was based on 500 m grid squares. I based final determination of significance on zscore and p-values. Assessment of significance of the p-value was adjusted and set to α=0.1 for
both spatial analysis functions as most were slightly above 0.05 (Stapp 2007). Alpha was
adjusted because I believe there is ecological significance between 0.05 – 0.1 within TWA.
Significant z-values that were positive indicated a hot spot. If the z-score was larger, this
31
indicated a more intense clustering (more intense hot spot). Negative z-values that are deemed
significant on the other hand indicate a cold spot (Chang 2012).
Cluster and outlier analyses determined areas with a cluster of high (HH), low (LL), high
surrounded by low amounts (HL), or outlier low richness values surrounded by higher richness
values (LH). This method identified features which were spatial outliers exercising the Anselin
Local Moran’s I statistic (Change 2012). Where xi represents an attribute for a feature (site) i and
̅ is the mean value of attributes. Spatial weight wij describes the spatial weight between features
i and j.
Anselin Local Moran’s I statistic equation.
̅
∑
(
̅)
Outputs for representation of individual features consist of a z-score, p-value, and I-value.
Features having a positive I value indicate that a feature has neighbors with similar high or low
values. Sites with negative values and neighbors having dissimilar values indicate outlier sites. I
also assessed results for this test using the output field cluster/outlier types high surrounded by
high values (HH – hot spot), low surrounded by low values (LL – cold spot), high surrounded by
low amounts (HL – hot spot), or outlier low richness values surrounded by higher richness values
(LH – cold spot).
Research design and implementation was conducted using guidelines set forth by the
American Society of Mammalogists (Sikes et al. 2011). All methods and protocols were
approved by the University of Central Missouri Institutional Animal Care and Use Committee on
13 May 2011 (IACUC-Approved Permit No. 11-3217).
32
RESULTS
Camera Trapping Survey of Species Richness
In the 34 day sampling period, I had a total of 248 trap nights at 36 separate sites. Camera
traps yielded over 50,000 photographs of mammalian carnivores, herbivores, omnivores,
folivores, and insectivores (Table 2.3). I found 38 of the 53 total species (71%) expected to exist
within the study area based on previous studies (Table 2.5). I detected 9 different mammalian
orders: Carnivora, Artiodactyla, Rodentia, Probocidea, Perissodactyla, Hyracoidea, Primates,
Lagomorpha, and Tubulidentata (Table 2.3). Within the orders, a total of 18 mammalian families
and 9 separate sub-families were observed. Not all species were detected at all sites and richness
ranged from 1-19, with sites 11 and 19 containing the highest species richness (Table 2.2, Fig.
2.5). Species richness varied among individual sites (Fig. 2.5). Camera sites 1, 11, 19, and 21 had
species richness greater than 15 species (Table 2.2).
Effects of Diet on Species Detection and Camera Placement (mopane, forest/scrub, or riverine
habitat)
A total of 36 individual survey sites were analyzed for photographic detection of species
(Table 2.2). Differences in herbivore and carnivore species richness were determined. In this
analysis, folivores were treated as herbivores, and insectivores and omnivores were placed with
carnivores. A record of 21 carnivores and 17 herbivores was observed, and I determined that
carnivore and herbivore richness differed significantly (Mann-Whitney Rank sum test,
U1,34=472.5, P=0.049). Carnivore species richness was greater within the TWA during camera
trap surveys (Fig. 2.4).
33
Results indicated that species richness did not differ significantly between mopane,
forest/scrub, or riverine habitat types (Kruskal-Wallis one-way multiple test, H2=2.771, P=0.250,
Fig. 2.2). Camera success was not dependent upon placement within the defined habitats.
Richness was significantly different among camera sites placed on roads, drainage lines, and
along other areas (one-way ANOVA, F2,33=4.208, P=0.024, Fig. 2.3). Species richness between
roads and other areas was not significantly different (Tukey test, q=2.659, P=0.161), nor were
other areas compared to drainage lines (Tukey test, q=1.549, P=0.524). Significant differences
were found between placement on roads and drainage lines (Fig. 2.3, Tukey test, q=3.969,
P=0.022). Roads were found to have higher richness than both drainage lines and other areas
(Fig. 2.3). This indicates that placement of camera traps along roads or features similar to roads
increased detection of mammalian species richness.
GIS Hotspot Analysis
I included 36 camera trap sites to spatially analyze species richness hot or cold spots
within the TWA. Sites 11 and 19 were determined to be hot spots (Fig. 2.6). The highest species
richness was detected at site 19 (Table 2.2, Fig.2.5), and this site also had a slightly higher zscore value than site 11 (Getis-Ord test, Z=2.23, P=0.02, Fig. 2.6). Site 11 had the second highest
species richness detected (Table 2.2, Fig. 2.5) and was also determined to be a mammalian
richness hot spot (Getis-Ord test, Z=2.00, P=0.04, Fig. 2.6).
Cluster and outlier analysis showed some similarities to the hot spot analysis. This also
identified site 11 as a hot spot for species richness and identified sites surrounding it as having
lower richness (Anselin Local Moran’s I, Z=2.97, P=0.002). Two sites within the TWA were
identified as cold spots for species richness (Fig. 2.7). Site 25 was found to be the coldest area
34
(Anselin Local Moran’s I, Z=-2.12, P=0.03) with the second lowest average species richness per
day, but site 25 was surrounded by sites with higher species richness detection ( ̅ =0.79
SD=0.80, Table 2.2, Fig. 2.7). Site 7 was also identified as a cold spot which had a higher
number of species richness per day than 13 other sites ( ̅ =1.80, SD=0.84, Table 2.2)
DISCUSSION
In my survey of mammalian assemblages within the Tuli Wilderness Area (reserve),
baited camera traps were successful in capturing photographic data over 248 camera trap
nights, and detected 38 of the 53 mammalian species believed to be distributed in
southeastern Botswana (Nowak 1999). This shows that 71% of the medium to large sized
mammals described to be distributed in southeastern Botswana can be found in the TWA,
showing the area to be rich in mammalian diversity. Determining species richness in the
TWA provides insight for conservation planning and helps determine possible influencing
factors affecting presence or absence of certain species. A variety of carnivores and
herbivores were photographed throughout the survey. Carnivore species richness was greater
than that of herbivore richness during camera trapping. Abundance was not examined in my
study, as it was not a component of the overall objective. Baiting camera site may account for
a slight increase in carnivores, but herbivores did not indicate a negative response to carrion
odors at sites. Results from my research of mammalian species richness provide a
representation of large species (African lion, leopard, elephant, and hyenas) to medium sized
species (scrub hare (Lepus saxatilis), Smith’s bush squirrel (Paraxerus cepapi), and slender
mongoose (Galerella sanguinea).
Mesopredators serve key roles in many communities (Roemer et al. 2009). The
35
medium-sized carnivores within TWA included black-backed jackals (Canis mesomelas),
African wildcats (Felis silvestris lybica), bat-eared foxes (Otcyon megalotis), and whitetailed mongoose (Ichneumia albicauda) detected at sites (Table 2.4). At least one
mesopredator was observed at all camera trap sites (Fig. 2.4). Mesopredator monitoring is
important for monitoring the African wild dog populations. I was able to observe a pack of
wild dogs within the TWA at site 11 where species richness was 18. Observing wild dogs at a
site that indicates many other carnivores may warrant further investigation, as
mesocarnivores are known to be reservoirs for diseases like parvo, rabies, and canine
distemper which threaten recovery efforts (Roemer et al. 2009). Van de Bildt et al. (2002)
examined the effects of distemper on the conservation of wild dogs in a captive breeding
program within the Mkomazi Game Reserve (MGR). During breeding, wild dogs became
infected with the disease and the virus was traced to strains from domestic dogs (Canis
familiaris), bat-eared foxes, and lions.
Detection of species other than carnivores was successful during the 248 trap nights
such as browsing herbivore species in particular, the impala (Aepyceros melampus) was
detected at 75% of camera sites (Table 2.3). This species is commonly observed throughout
Africa, but if populations are not managed carefully they can affect acacia seedling
reestablishment (Prins and Van Der Jeugd 1993). The presence of this species should not
have a negative effect on the vegetation in the TWA because of the dominance of Mopane
veld. Styles and Skinner (2000) found that within the NTGR, large browsing species were
responsible for increases in mopane scrub vegetation, and their browsing stimulated buds to
produce early leaves. Large browsing species aid in vegetative processes within the African
reserves.
36
Maintaining reserves with minimal fragmentation is essential to maintaining species
diversity and reducing risk of extinction (Burkey 1989). The TWA reduces these effects by
eliminating anthropogenic barriers and allowing animals to move naturally (McKenzie 1990).
With movement of animals comes risk of predation by illegal hunters for bushmeat, ivory,
and pelage. This risk is a reality in TWA as snare traps were found and removed during our
survey. Site 3 camera traps identified a poacher in areas where African elephant, kudu, and
brown/spotted hyena were observed. Caro (2008) observed declines of large mammal
populations in western Tanzania and indicated that a major cause was illegal hunting. Species
richness surveys can help detect and prosecute poachers (Caro 2008).
Gelderblom et al. (1995) determined that the southern half of Africa’s grasslands had
the highest richness of carnivores. I showed that carnivores had the highest amount of species
richness (16), including the African leopard, African lion, brown hyena, and spotted hyena.
Other species in the area included the Caracal (Caracal caracal) and white-tailed mongoose
(Ichneumia albicauda).
Kelly and Holub (2008) found camera traps to be successful in detecting mammalian
species within several habitat types in Giles County, Virginia. Results of our study found
camera trap effort along roads have the greatest detection (Fig. 2.3), and Kelly and Holub
(2008) found that felid and canid trap success increased when traps were located closer to
roads. Trolle and Kery (2003) found 96% of captures of carnivores were along roadways.
Placement of cameras, together with baiting of sites, increased detection of species within the
TWA and coincides with their findings. Another contributing factor to increasing the overall
detection of mammalian species in the TWA involved bating trap sites. Numerous studies
indicated that baiting of camera sites increased trap success of carnivores (Foresman and
37
Pearson 1998, Kelly and Holub 2008, Trolle and Kery 2003). Thus, baited camera traps
placed near roads can increase detection of mammalian species for surveying/monitoring.
Hot spots of mammalian species richness were found using GIS hot spot analysis at
sites 11 and 19 (Fig. 2.6). Within site 11, I observed 18 species such as Cape porcupine
(Hystrix africaeaustralis), African leopard, and common eland (Table 2.4). Observing
endangered species such as the African wild dog at this hot spot of species richness indicates
these areas as having increased ability to monitor species of concern. Sites 14, 15, 19, and 28
had high species richness and appear to be corridors between habitat patches that naturally
funnel species along ridges, koppies, or dirt roads intersecting multiple game trails. My
results are similar to those of Haddad et al. (2003), where photographic data and hot spot
analysis indicated multiple species using corridors for movement between habitat patches.
These sites are of importance for species monitoring because they are on roads which have
high rates of detection and are hot spots for species richness. Tewksbury et al. (2002)
suggested that corridors can affect patch use within areas by diverting species into connected
habitat patches. My results were not different from this hypothesis, as animals within TWA
used sites identified as hot spots as selected natural corridors.
Analysis of species cold spots indicated that camera trap sites 7 and 25 were areas
where few mammalian species were detected. Lack of species detection in these areas could
possibly be explained by a lack of sufficient cover or susceptibility to predatory ambush.
Utilizing GIS, I was able to determine hot spot areas within the TWA which identified areas
where species of critically endangered (African wild dog), near threatened (African leopard),
or vulnerable (African lion) levels were detected. The approach of using hot spot, cluster, and
outlier analysis together provides a means within the TWA to accurately determine areas to
38
maximize surveys. Areas identified as hot spots were consistent with feeding habits of
African leopards (Hayward et al. 2005). These hot spots are characteristically high quality
habitat for prey species and give leopards advantageous hunting areas. In these areas, denser
patches of habitat provided leopards camouflage in order to ambush prey. Ungulate species
such as the impala, common duiker, and bushbuck were species surveyed at sites that were
indicated by Hayward et al. (2005) as preferred prey of large felids. Prey preferences of wild
dogs are similar to those of the leopards, and both sites 11 and 19 illustrate areas preferred by
wild dogs and leopards where increased competition for prey between carnivore species will
occur (Hayward et al. 2006).
Using such tools as baited camera traps along road-type paths, species surveys can
continue within TWA and be expanded into other areas of the NTGR to help monitor species
richness. Surveys could focus on habitat use throughout different times of the year. Creation
of an electronic database would help organize and track photographic data of mammalian
species. This would provide researchers the ability to monitor richness and diversity over not
only a spatial, but also a temporal scale.
39
LITERATURE CITED
AUGUSTINE, D. J.
AND
S. J. MCNAUGHTON. 1998. Ungulate effects on the functional species
composition of plant communities: herbivore selectivity and plant tolerance. Journal of
Wildlife Management 62(4):1165-1183.
BAKKER, E. S., M. E. RITCHIE, H. OLFF, D. G. MILCHUNAS,
AND
J. M. H. KNOPS. 2006.
Herbivore impact on grassland plant diversity depends on habitat productivity and herbivore
size. Ecology Letters 9: 780-788.
BALME, G., L. HUNTER,
AND
R. SLOTOW. 2007. Feeding habitat selection by hunting leopards
Panthera pardus in a woodland savanna: prey catchability versus abundance. Animal
Behaviour 74:589-598.
BOULINIER, T., J. D. NICHOLS, J. R. SAUER, J. E. HINES,
AND
K. H. POLLOCK. 1998.
Estimating species richness: the importance of heterogeneity in species detectability. Ecology
79(3):1018-1028.
BOWEKETT, A. E., N. R. LUNT,
AND
A. B. PLOWMAN. 2006. How do you monitor rare and
elusive mammals? Counting duikers in Kenya, Tanzania and Zimbabwe Pp. 21-28 in
Animals, zoos, and conservation (Ewa Zgrabczynska, Piotr Cwiertnia, and Joanna Ziomek)
The Zoological Garden, Poznan, Poland.
40
BURKEY, T. A. 1989. Extinction in nature reserves: the effect of fragmentation and the
importance of migration between reserve fragments. Oikos 55(1):75-81.
CARO, T. 2008. Decline of large mammals in the Katavi-Rukwa ecosystem of western Tanzania.
African Zoology 43(1):99-116.
CEBALLOS, G.,
AND
J. H. BROWN. 1995. Global patterns of mammalian diversity, endemism,
and endangerment. Conservation Biology 9:559-568.
CEBALLOS, G., P. RODRIGUEZ,
AND
R. A. MEDELLIN. 1998. Assessing conservation priorities
in magadiverse Mexico: mammalian diversity, endemicity, and endangerment. Ecological
Applications 8(1):8-17.
CHANG, K. T. 2012. Introduction to geographic information systems. 6th ed. McGraw-Hill, New
York, NY.
ESRI 2011. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research
Institute.
FORESMAN, K. R.,
AND
D. E. PEARSON. 1998. Comparison of proposed survey procedures for
detection of forest carnivores. Journal of Wildlife Management 62(4):1217-1226.
41
GELDERBLOM, C. M., G. N. BRONNER, A. T. LOMBARD,
AND
P. J. TAYLOR. 1995. Patterns of
distribution and current protection status of the Carnivora, Chiroptera and Insectivora in
South Africa. South African Journal of Zoology 30(3):103-114.
HADDAD, N. M., D. R. BOWNE, A. CUNNINGHAM, B. J. DANIELSON, D. J. LEVEY, S.
SARGENT,
AND T.
SPIRA. 2003. Corridor use by diverse taxa. Ecology 84(3):609-615.
HAYWARD, M. W., P. HENSCHEL, J. O’BRIEN, M. HOFMEYR, G. BALME,
AND
G. I. H.
KERLEY. 2006. Prey preferences of the leopard (Panthera pardus). Journal of Zoology
270:298-313.
HAYWARD, M. W., R. M. WHITE, K. M. MABANDLA,
AND
P. PAKAMA. 2005. Mammalian
fauna of indigenous forest in the Transkei region of South Africa: an overdue survey. South
African Journal of Wildlife Research 35(2):117-124.
IUCN 2012. IUCN Red List of Threatened Species. Version 2012.2. <www.iucnredlist.org>.
Downloaded on 05 February 2013.
JACKSON, R. M., J. D. ROE, R. WANGCHUK,
AND
D. O. HUNTER. 2005. Surveying snow
leopard populations with emphasis on camera trapping: a handbook. The Snow Leopard
Conservancy, Sonoma, California (73 pages)
42
KARANTH, K. K., J. D. NICHOLS, K. U. KARANTH, J. E. HINES,
AND
N. L. CHRISTENSEN JR.
2010. The shrinking ark: patterns of large mammal extinctions in India. Proceedings of The
Royle Society 277:1971-1979.
KAUFFMAN, M. J., M. SANJAYAN, J. LOWENSTEIN, A. NELSON, R. M. JEO,
AND K.
R.
CROOKS. 2007. Remote camera-trap methods and analyses reveal impacts of rangeland
management on Namibian carnivore communities. Oryx 41(1):70-78.
KAYS, R.
AND K.
SLAUSON. 2008. Remote Cameras Pp. 110-140 in Noninvasive survey
methods for carnivores (R. Long, P. MacKay, W. Zielinski, and J. Ray eds.). Island Press,
Washington DC.
KELLY, K. J.
AND
E. L. HOLUB. 2008. Camera trapping of carnivores: trap success among
camera types and across species, and habitat selection by species, on Salt Pond Mountain,
Giles County, Virginia. Northeastern Naturalist 15(2):249-262.
KUHN, B. F. 2012. Bone accumulations of spotted hyaenas (Crocuta crocuta, Erxleben, 1777)
as indicators of diet and human conflict; Mashatu, Botswana. International Journal of
Ecology 2012: Article ID 178348, 6 pages, 2012. doi:10.1155/2012/178348.
43
LYRA-JORGE, M. C.
AND
G. CIOCHETI. 2008. Comparing methods for sampling large-and
medium sized mammals: camera traps and track plots. European Journal of Wildlife
Research 54:739-744
MARNEWICK, K. A., J. DU P. BOTHMA,
AND
G. H. VERDOORN. 2006. Using camera-trapping
to investigate the use of a tree as a scent-marking post by cheetahs in the Thabazimbi district.
South African Journal of Wildlife Research 36(2):139-145.
MCKENZIE, A .A. 1990. Co-operative hunting in the black-backed jackal (Canis mesomelas
Schreber). PhD Thesis, University of Pretoria, Pretoria, South Africa.
MILLS, L. S., M. E. SOULE,
AND
D. F. DOAK. 1993. The keystone-species concept in ecology
and conservation. BioScience 43(4):219-224.
MORUZZI, T. L., T. K. FULLER, R. M. DEGRAFF, R. T. BROOKS,
AND
W. LI. 2002. Assessing
remotely triggered cameras for surveying carnivore distribution. Wildlife Society Bulletin
30(2):380-386.
NEGROES, N., P. SARMENTO, J. CRUZ, C. EIRA, E. REVILLA, C. FONSECA, R. SOLLMANN, N.
M. TORRES, M. M. FURTADO, A. T. A. JACOMO,
AND
L. SILVEIRA. 2010. Use of camera-
trapping to estimate puma density and influencing factors in central Brazil. Journal of
Wildlife Management 74(6):1195-1203.
44
NOWAK, R. M. 1999. Walker’s mammals of the world. Vol. 1 & 2. The Johns Hopkins
University Press, Baltimore, Maryland.
PRINS, H. H. T.
AND
H. P. VAN DER JEUGD. 1993. Herbivore population crashes and woodland
structure in East Africa. Journal of Ecology 81:305-314.
ROEMER, G. W., M. E. GOMPPER,
AND
B. VAN VALKENBURGH. 2009. The ecological role of
the mammalian mesocarnivore. BioScience 9(2):165-173.
ROWCLIFFE, J. M.,
AND C.
CARBONE. 2008. Surveys using camera traps: are we looking to a
brighter future? Animal Conservation 11:185-186.
SELIER, S. A. J. 2007. The social structure, distribution and demographic status of the African
elephant population in the Central Limpopo River Valley of Botswana, Zimbabwe and South
Africa. Centre of Wildlife Management, Faculty of Natural and Agricultural Sciences,
University of Pretoria, Dissertation.
SIKES, R. S., W. L. GANNON,
AND THE ANIMAL CARE AND USE COMMITTEE OF THE AMERICAN
SOCIETY OF MAMMALOGISTS. 2011. Guidelines of the American Society of Mammalogists for
the use of wild animals in research. Journal of Mammalogy 92(1):235-253.
45
STAPP, P. 2007. Rodent communities in active and inactive colonies of black-tailed prairie dogs
in shortgrass steppe. Journal of Mammalogy 88(1):241-249.
STYLES, C. V.
AND J.
D. SKINNER. 2000. The influence of large mammalian herbivores on
growth form and utilization of mopane trees, Colophosperum mopane, in Botswana’s
Northern Tuli Game Reserve. African Journal of Ecology 38(2):95-101.
TEWKSBURY, J. J., D. J. LEVEY, N. M. HADDAD, S. SARGENT, J. L. ORROCK, A. WELDON,
B. J. DANIELSON, J. BRINKERHOFF, E. I. DAMSCHEN,
AND P.
TOWNSEND. 2002. Corridors
affect plants, animals, and their interactions in fragmented landscapes. Proceedings of the
National Academy of Sciences of the United States of America 99(20):12923-12926.
THORN, M., D. M. SCOTT, M. GREEN, P. W. BATEMAN
AND
E. Z. CAMERON. 2009.
Estimating brown hyena occupancy using baited camera traps. South Africa Journal of
Wildlife Research 39(1):1-10.
TROLLE M.
AND
M. KERY. 2003. Estimation of ocelot density in the pantanal using capture-
recapture analysis of camera-trapping data. Journal of Mammalogy 84(2): 607-614.
WALKER, B. H., R. H. EMSLIE, R. N. OWEN-SMITH,
AND R.
J. SCHOLES. 1987. To cull or not
to cull: lessons from a Southern African drought. Journal of Applied Ecology 24(2):381-401.
46
WOODROFFE, R. 2000. Predators and people: Using human densities to interpret declines of large
carnivores. Animal Conservation 3:165-173.
WOODROFFE, R.
AND J.
R. GINSBERG. 1998. Edge effects and the extinction of populations
inside protected areas. Science 280:2126-2128.
VAN DE BILDT, M. W. G., T. KUIKEN, A. M. VISEE, S. LEMA, T. R. FITZJOHN,
AND A.
D.
M. E. OSTERHUS. 2002. Distemper outbreak and its effect on African wild dog conservation.
Emerging Infectious Diseases 8(2):211-213.
VAN DE KOPPEL, J.
AND
H. H. T. PRINS. 1998. The importance of herbivore interactions for the
dynamics of African savanna woodlands: an hypothesis. Journal of Tropical Ecology 14:565576.
47
Table 2.1—Summary of stratified random habitat selection percentages for camera trap
placement within Tuli Wilderness Area, Botswana.
Habitat Type
Percent Cover (%)
Number of Camera placements
Riverine
15
3
Acacia woodland
65
10
Shrub-lands
20
3
Total
100
16
48
Table 2.2—Results of descriptive statistics for mammalian species richness within camera
trap sites in The Tuli Wilderness Area, Botswana. Mean values, standard deviations (SD),
and 95% confidence intervals (C.I.) are given with exception of 3 removed sites (6, 12, and
31).
Site Site Name
Total Species
richness within site
Mean richness
per day
SD
95% CI
value
15
4
9
9
14
6
1
25
9
1
68
1
17
15
9
15
1
9
10
15
10
11
13
5
8
8
1
70
4
1
18
2
4
1
14
84
7
5.00
1.80
3.75
3.00
5.09
1.80
1.86
1.29
1.83
4.25
1.00
1.80
4.58
2.00
2.00
3.18
4.13
1.73
3.18
1.50
2.16
4.60
0.79
1.50
1.55
1.64
1.00
1.63
2.00
2.70
0.70
0.64
2.91
3.09
2.50
1.11
2.39
1.30
2.22
0.93
1.88
0.84
1.20
1.11
0.75
1.55
1.00
1.32
1.68
2.45
1.23
1.33
1.50
1.16
1.70
0.91
1.39
1.95
0.80
1.41
1.57
0.81
0.71
1.19
1.41
1.34
0.48
0.67
1.38
1.58
1.38
0.78
2.00
1.62
3.53
0.77
0.81
1.04
0.54
1.03
0.79
0.56
1.71
0.62
0.81
0.97
0.94
0.69
0.80
0.64
0.88
0.58
0.67
2.42
0.46
1.18
1.06
0.54
0.54
0.99
0.95
0.96
0.35
0.45
0.92
1.06
1.45
0.60
1
Elephant Pans
2
Malema Cultline
3
Leopard Loppies
4
Bed Rock
5
Eagle Entrance
7
Eagle Rock Funnel near bushman
8
Northern Eagle
9
Red Plains
10 Lonely Road
11 Elephant Neck
13 Pels Pools
14 Leopard Road Drainage Line
15 Serolo Pool Water Hole
16 River Road East
17 Cross Plain Cross Road
18 Leopard Rock West
19 Fairfield Cultine South
20 Meadow Meander
21 Molema Mall
22 Lonely Hill
23 Ridgeback
24 Sesame Street
25 Mohave River East
26 Wildebeest Wonder
27 Cross Plains Plains
28 Cross Plains Molema Cutline
29 10P Drainage Line
30 Three Trees
32 Boebean Crossing
33 Great Wall of Tuli NE
34 Grain Pits Leopard Road
35 Lonely Road Drainage Line
36 Boom Gate
37 Sycamore Fig Crossroad
38 Cross Plains Copse
39 Mohave Highway
49
Figure 2.1—Map description of the Tuli Wilderness Area (TWA), Botswana.
50
Figure 2.2—Mean species richness ratio (± SE) analyzed by habitat associations from
camera trap survey in The Tuli Wilderness Area, Botswana, 2011.
51
Figure 2.3—Mean species richness ratios analyzed by placement of camera sites within
three separete freatures (roads, drainage lines, and other areas) from camera trap survey
in The Tuli Wilderness Area, Botswana, 2011. Notations of mean values with standard
errors (± SE) and significance noted (one-way ANOVA, F2,33=4.208, P=0.024) with * from
comparison test.
52
Figure 2.4—Mammalian species richness ratios for carnivores and herbivores from camera
trap survey in The Tuli Wilderness Area, Botswana, 2011. Mean values represented of
species richness based on diet with standard errors (± SE) and noted significance of (MannWhitney Rank sum test, U1,34=472.5, P=0.049) by *.
53
Figure 2.5—Map of mammalian species richness within The Tuli Wilderness Study Area
within the Northern Tuli Game Reserve, Botswana, 2011.
54
Figure 2.6—Map results from hot spot analysis indicating mammalian species richness hot
spots within The Tuli Wilderness Study Area within the Northern Tuli Game Reserve,
Botswana. 2011.
55
Figure 2.7—Map of results of cluster/outlier analysis of mammalian species richness within
The Tuli Wilderness Study Area within the Northern Tuli Game Reserve, Botswana, 2011.
56
Table 2.3—Mammalian species detected from camera trap surveys within The Tuli Wilderness
Area, Botswana, 2011. Species status information provided from The International Union for
Conservation of Natural Resources Red List of Threatened Species- least concern (LC), near
threatened (NT), vulnerable (VU), endangered (EN), critically endangered (CR), and not listed
by ICUN (UK).
Order
Carnivora
Number of
sites detected Status Population Status (ICUN)
Common Name
Species Name
Diet
Aardwolf
African Civet
African Leopard
African Lion
African Wild dog
African Wildcat
Banded Mongoose
Bat-eared Fox
Black-Backed Jackal
Brown Hyena
Caracal
Honey Badger
Large-Spotted Genet
Slender Mongoose
Spotted Hyena
White-tailed Mongoose
Proteles cristatus
Civetticis civetta
Panthera pardus pardus
Panthera leo
Lycaon pictus
Felis silvestris lybica
Mungos mungo
Otcyon megalotis
Canis mesomelas
Parahyaena brunnea
Caracal caracal
Mellivora capensis
Genetta maculataenet
Galerella flavescenson
Crocuta crocuta
Ichneumia albicauda
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
1
14
13
3
1
9
5
4
22
32
1
4
9
10
13
1
LC
LC
NT
VU
EN
UK
LC
LC
LC
UK
LC
LC
LC
LC
UK
LC
Stable
Unknown
Decreasing
Decreasing
Decreasing
Unknown
Stable
Unknown
Stable
Unknown
Unknown
Decreasing
Unknown
Stable
Unknown
Stable
Blue Wildebeest
Bushbuck
Bushpig
Common Duiker
Common Eland
Common Warthog
Giraffe
Greater Kudu
Impala
Connochaetes taurinus
Tragelas scriptus
Potamochoerus larvatus
Sylvicapra grimmia
Tragelaphus oryx
Phacochoerus africanus
Giraffa camelepardalis
Tragelaphus strepsiceros
Aepyceros melampus
Herbivore
Herbivore
Omnivore
Herbivore
Herbivore
Omnivore
Herbivore
Herbivore
Herbivore
11
4
6
6
12
24
2
21
27
LC
LC
LC
LC
LC
LC
LC
LC
LC
Stable
Stable
Stable
Stable
Stable
Stable
Decreasing
Stable
Stable
Artiodactyla
57
Table 2.3—Continued
Order
Artiodactyla
Number of
sites detected Status Population Status (ICUN)
Common Name
Species Name
Diet
Klipspringer
Steenbok
Waterbuck
Orertragus oreotragus
Raphicerus campestris
Kobus ellipsiprymnus
Herbivore
Herbivore
Herbivore
2
8
4
LC
LC
LC
Stable
Stable
Decreasing
Cape Porcupine
Smiths Brush Squirrel
Spring Hare
Hystrix africaeaustralis
Paraxerus cepapi
Pedetes capensis
Herbivore
Herbivore
Herbivore
8
2
5
LC
UK
LC
Stable
Unknown
Unknown
African Elephant
Loxodonta africana
Herbivore
21
VU
Increasing
Plains Zebra
Equus quagga
Herbivore
14
LC
Stable
Rock Hyrax
Procavia capensis
Herbivore
2
LC
Unknown
Chacma Baboon
Vervet monkey
Papio hamadryas ursinus
Chlorocebus pygerythrus
Omnivore
Omnivore
9
2
LC
LC
Stable
Stable
Scrub Hare
Lepus saxatilis
Folivore
12
LC
Decreasing
Aardvark
Orycteropus afer
Insectivore
4
UK
Unknown
Rodentia
Proboscidea
Perissodactyla
Hyracoidea
Primates
Lagomorpha
Tubulidentata
58
Table 2. 4—Mammalian Species Detection by individual Camera Trap Sites.
Camera Sites
Species
Common Name
1
Aepyceros melampus
Impala
x
Canis mesomelas
Black-Backed Jackal
x
Caracal caracal
Caracal
Chlorocebus pygerythrus
Vervet monkey
Civetticis civetta
African civet
x
Connochaetes taurinus
Blue Wildebeest
x
Crocuta crocuta
Spotted Hyena
x
Equus quagga
Plains Zebra
x
Felis silvestris lybica
African Wildcat
Galerella flavescenson
Slender Mongoose
Genetta maculataenet
Large-Spotted Genet
Giraffa camelepardalis
Giraffe
Hystrix africaeaustralis
Cape Porcupine
-
Ichneumia albicauda
White-tailed Mongoose
-
Kobus ellipsiprymnus
Waterbuck
-
Lepus saxatilis
Scrub Hare
x
Loxodonta africana
African Elephant
x
Mellivora capensis
Honey Badger
Mungos mungo
Banded Mongoose
Otcyon megalotis
Bat-eared Fox
-
Orertragus oreotragus
Klipspringer
-
Orycteropus afer
Aardvark
-
Panthera leo
African lion
Panthera pardus pardus
African leopard
Papio hamadryas ursinus
Chacma Baboon
Parahyaena brunnea
Brown Hyena
Paraxerus cepapi
Smiths Brush Squirrel
Pedetes capensis
Spring Hare
Phacochoerus africanus
Common Warthog
Potamochoerus larvatus
Bushpig
Procavia capensis
Rock Hyrax
Raphicerus campestris
Steenbok
Sylvicapra grimmia
Common Duiker
x
x
Tragelaphus oryx
Common Eland
x
x
Tragelaphus strepsiceros
Greater Kudu
x
x
Lycaon pictus
African Wild dog
Tragelas Scriptus
Bushbuck
Proteles cristatus
Aardwolf
Notes: x indicates the detection of a species within the predetermined camera site.
59
2
x
3
4
5 6 7
8
9
x
x
x
-
x
x
x
x
x
-
x
x
10
11
12
13
x
x
14
15
16
x
x
x
x
17
x
18
19
20
21
22
23
24
25
26
x
x
x
x
X
x
x
x
x
x
x
x
x
X
x
x
x
x
27
x
x
x
x
-
x
-
x
-
x
x
x
x
x
x
-
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
-
x
x
-
x
-
x
-
x
-
x
-
x
-
x
x
x
x
x
x
x
x
-
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
X
x
X
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
-
x
-
x
x
-
x
-
x
-
x
x
x
x
x
x
x
x
x
x
x
-
X
x
X
x
-
x
x
x
x
x
x
x
x
x
x
x
X
x
x
x
x
-
x
-
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
-
x
-
x
x
X
x
x
x
-
x
x
x
-
x
x
x
39
x
x
x
x
x
x
x
x
-
x
-
x
-
x
-
x
-
x
-
x
x
x
x
x
38
x
x
x
x
x
x
x
x
37
x
x
x
x
x
36
x
x
x
x
35
-
-
x
x
x
x
x
x
x
x
x
x
34
x
-
x
-
33
x
-
x
-
-
x
X
x
-
x
x
x
x
x
x
x
x
x
-
x
x
x
x
x
-
-
x
x
32
-
-
x
x
x
31
x
-
x
x
x
x
-
x
x
x
-
30
x
-
x
29
x
-
x
28
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
X
x
x
x
x
x
x
-
x
-
x
x
x
x
x
x
x
-
x
x
Table 2.5—List of mammalian species of possible detection based on species ecology and distribution. Species status information
provided from The International Union for Conservation of Natural Resources Red List of Threatened Species- least concern
(LC), near threatened (NT), vulnerable (VU), endangered (EN), critically endangered (CR), and not listed by ICUN (N/A).
Order
Carnivora
Common Name
Species Name
Diet
Aardwolf
African Civet
African Clawless Otter*
African (Common) Genet*
African Leopard
African Lion
African Striped Weasel*
African Wild Dog
African Wildcat
Banded Mongoose
Bat-eared Fox
Black-Backed Jackal
Black-Footed Cat*
Brown Hyena
Cape Fox*
Caracal
Cheetah*
Common Dwarf Mongoose*
Domestic Cat*
Honey Badger
Large-Spotted Genet
Marsh Mongoose*
Selous' Mongoose*
Serval*
Slender Mongoose
Proteles cristatus
Civettictis civetta
Aonyx capensis
Genetta genetta
Panthera pardus pardus
Panthera leo
Poecilogale albinucha
Lycaon pictus
Felis silvestris lybica
Mungos mungo
Otcyon megalotis
Canis mesomelas
Felis nigripes
Parahyaena brunnea
Vulpes chama
Caracal caracal
Acinonyx jubatus
Helogale parvula
Felis catus
Mellivora capensis
Genetta maculata
Atilax paludinosus
Paracynictis selousi
Leptailurus serval
Galerella sanguinea
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
60
Status
Population Status (ICUN)
LC
LC
LC
LC
NT
VU
LC
EN
LC
LC
LC
LC
VU
NT
LC
LC
VU
LC
N/A
LC
LC
LC
LC
LC
LC
Stable
Unknown
Stable
Stable
Decreasing
Decreasing
Unknown
Decreasing
Decreasing
Stable
Unknown
Stable
Decreasing
Decreasing
Stable
Unknown
Decreasing
Stable
N/A
Decreasing
Unknown
Decreasing
Unknown
Stable
Stable
Table 2.5—Continued
Spotted Hyena
White-tailed Mongoose
Crocuta crocuta
Ichneumia albicauda
Carnivore
Carnivore
LC
LC
Decreasing
Stable
Blue Wildebeest
Bushbuck
Bushpig
Common Duiker
Common Eland
Common Warthog
Giraffe
Greater Kudu
Impala
Connochaetes taurinus
Tragelas scriptus
Potamochoerus larvatus
Sylvicapra grimmia
Tragelaphus oryx
Phacochoerus africanus
Giraffa camelopardalis
Tragelaphus strepsiceros
Aepyceros melampus
Herbivore
Herbivore
Omnivore
Herbivore
Herbivore
Omnivore
Herbivore
Herbivore
Herbivore
LC
LC
LC
LC
LC
LC
LC
LC
LC
Stable
Stable
Stable
Stable
Stable
Stable
Decreasing
Stable
Stable
Klipspringer
Steenbok
Waterbuck
Oreotragus oreotragus
Raphicerus campestris
Kobus ellipsiprymnus
Herbivore
Herbivore
Herbivore
LC
LC
LC
Stable
Stable
Decreasing
Bushpig
Potamochoerus larvatus
Omnivore
LC
Stable
Cape Porcupine
Smiths Brush Squirrel
Spring Hare
Hystrix africaeaustralis
Paraxerus cepapi
Pedetes capensis
Herbivore
Herbivore
Herbivore
LC
LC
LC
Stable
Stable
Unknown
African Elephant
Loxodonta africana
Herbivore
VU
Increasing
Plains Zebra
Equus quagga
Herbivore
LC
Stable
Bush Hyrax*
Rock Hyrax
Heterohyrax brucei
Procavia capensis
Herbivore
Herbivore
LC
LC
Unknown
Unknown
Artiodactyla
Artiodactyla
Cetartiodactyla
Rodentia
Proboscidea
Perissodactyla
Hyracoidea
61
Table 2.5—Continued
Primates
Chacma Baboon
Vervet Monkey
Greater Bushbaby*
Papio ursinus
Chlorocebus pygerythrus
Otolemur crassicaudatus
Omnivore
LC
Omnivore
LC
Gumivore/F LC
Stable
Stable
Stable
Lagomorpha
Jameson's Red Rockhare (red rabbit)* Pronolagus randensis
Scrub Hare
Lepus saxatilis
Folivore
Folivore
LC
LC
Unknown
Decreasing
Insectivore
LC
Unknown
Tubulidentata
Aardvark
*indicates species not photographed.
Orycteropus afer
62
CHAPTER 3: PREDICTING OCCURRENCE/OCCUPANCY FROM A
COMMUNITY SURVEY OF MEDIUM TO LARGE-SIZED MAMMALS IN
THE TULI GAME RESERVE OF BOTSWANA
ABSTRACT:
Using baited and non-baited camera traps I conducted a survey of mammals in The Tuli
Wilderness Area (TWA) located within The Northern Tuli Game Reserve (NTGR) of Botswana.
In the 34 day sampling period I had a total of 248 trap nights at 36 individual sites within the
reserve. I used camera traps to determine occupancy and detection probabilities for 38 species
detected from 12 May to 13 June 2011. Species detected included large and medium carnivores,
herbivores, omnivores, insectivores, and folivores. Detection probabilities and occurrence
estimates varied among taxonomic groups, and the species with the highest probability of
detection (47%) were the scrub hare (Lepus saxatilis) and impala (Aepyceros melampus). Six
species had probability of detection estimates < 5% indicating that these species as extremely
hard to detect. Occupancy results indicated that species occupancy varies within the TWA.
Carnivore occupancy was the highest within the TWA, followed by herbivores. Insectivores had
the lowest occupancy rates. I created occupancy covariate models for 5 species in order to
determine the effect of covariates (mopane vegetation, mixed forest, roads, drainage lines,
distance to water, baited/non-baited) on detection probabilities and occurrence. These effects
varied based on species. Camera trap operation was most efficient with fewer than 6 days of
operation, which coincided with the average Latency to Detection (LTD) for species with a mean
of ̅ = 6.6 days. Results from occupancy modeling can help establish factors affecting species of
concern or endangered status found within the TWA. With calculated rates of species occupancy,
detection probabilities, and habitat covariate modeling, managers of the TWA can focus research
efforts with increased species data.
63
Key words: Mammals, occupancy modeling, presences/absence, reserves, Northern Tuli Game
Reserve (NTGR), carnivore, occurrence, latency to detection (LTD).
64
INTRODUCTION
The African continent is home to many mammalian megafauna which exist within
wildlife reserves either for conservation protection or for sport to attract outdoorsmen. Reserves
provide habitat for the approximately 194 large and 140 medium to small mammal species
(Fjeldsa et al. 2004). Efforts to conserve and maintain biodiversity have focused on particular
taxa with emphasis on large carnivores due to their life history characteristics, which include
habitat requirements, low population densities, and role as top predators (Ray et al. 2005). Large
carnivores have served as targets for conservation, but most importantly as tools for helping to
create conservation action plans for improving and maintaining mammalian biodiversity. There
are several proposed ways that these mammals can be utilized as tools for conservation, such as
for ecosystem conservation (umbrella, keystone species), ecosystem restoration (keystone
species), as conservation symbols (flagship species), to prioritize areas for conservation (focal or
umbrella species), for site-based conservation planning (focal or umbrella species), and for
maintaining biodiversity (indicator species). The World Wildlife Fund (WWF) Global Species
Program (GSP) utilizes large carnivores as flagship species, ambassadors, poster animals to
support regional or border conservation goals, and for maintaining/preserving habitat. In
protecting large mammalian carnivores, such as the mountain lion (Puma concolor), habitat
corridors and reserve networks have been conserved, benefiting many other species (Ray et al.
2005).
Due to the establishment of new reserves and maintenance of existing reserves there has
been a decrease in mortality of large mammals. In Kruger National Park in South Africa, 93% of
leopard (Panthera pardus) mortality is caused by natural occurrences (interspecific competition,
disease, or starvation) and the remaining 7% is due to anthropogenic effects (Loverridge et al.
65
2010). Reserves are areas with specific boundaries that focus on site-based conservation, which
has been used to configure area size, habitat patch connectivity by corridors, or even connectivity
with other reserves (Ray et al. 2005). The selection of any mammalian species for use as a focal
or indicator species begins with surveying and monitoring species. Fjeldsa et al. (2004)
examined reserve network patches throughout Africa to determine deficiencies and restrictions
between habitats that affect mammalian species. Results indicated a greater need for additional
reserves to fill gaps within areas of southern Africa and to create a reserve network detailing all
biodiversity within them (Fjeldsa et al. 2004). The proposed Limpopo/Shashe Trans Frontier
Conservation Area (TFCA) will connect numerous reserves in northeastern South Africa,
Botswana, and Zimbabwe and will be composed of private reserves, national parks, and state
owned lands (Hughes 2005). Connecting once restricted gaps between protected habitats requires
detailed management action plans. The proposed habitat area is ideal for increased reintroduction
of endangered species such as black rhinoceros (Diceros bicornis), African wild dog (Lycaon
pictus), and other species which may be near threatened status (IUCN 2012).
Surveying mammalian carnivore diversity can be difficult due to their rare and elusive
nature (Balme et al. 2007, Bowekett et al. 2006, Kauffman et al. 2007). Camera trapping is a
valuable tool for surveying large carnivores (Jackson et al. 2005). This method of examining
mammalian assemblages aids in conservation assessments (richness), insights into territorial
behaviors, population density, habitat selection, and estimating occupancy rates (Kelly and
Holub 2008, Marnewick et al. 2006, Negroes et al. 2010, Rowcliffe and Carbone 2008, Thorn et
al. 2009). Utilizing remotely placed infrared camera traps enhances sampling of species
temporally and spatially, and results in a permanent record of species inhabiting specific habitats
(Kays and Slauson 2008). Moruzzi et al. (2002) examined species-specific habitat relationships
66
within a mammalian community in order to determine habitat preferences. By utilizing camera
trap data they were able to estimate distributions of most species such as bobcats (Lynx rufus)
and red foxes (Vulpes vulpes). Infrared camera trapping provides more precise identification of
large mammals (Lyra-Jorge and Ciocheti 2008). Camera traps have been used to detect
extremely rare mammalian carnivores by using baits or lures placed along roads and game trails
and with the assistance of skilled trackers (Karanth and Nichols 2010, Rowcliffe and Carbone
2008). Identification of travel corridors allows researchers to determine preferred areas in which
to place cameras (Karanth and Nichols 2010). Setting cameras out more often and for shorter
sampling periods has been effective in increasing sample size (Moruzzi et al. 2002). Evaluation
of the effectiveness of camera traps has been compared to conventional techniques used in the
conservation biology field since the increased use of camera traps in the 1980s (Gompper et al.
2006, Foresman and Pearson 1998, Karanth et al. 2010, Rowcliffe and Carbone 2008). The
effectiveness of camera traps or any other technique must be examined and tailored to fit the
study area being surveyed (Kays and Slauson 2008). Evaluating effectiveness of camera trapping
in areas like private or state-owned reserves can involve latency to detect (LTD) for specific
species (Karanth et al. 2010). This measure yields information about the camera’s ability to
accurately detect species, and also the amount of effort required to capture a species of interest
(Foresman and Pearson 1998, Gommper et al. 2006).
Camera traps can be used to create detection histories for species (MacKenzie et al. 2002,
2006). This approach utilizes maximum likelihood estimation to predict the occurrence and
probability of detecting an animal (Karanth and Nichols 2010). Researchers can use detection
histories to produce estimates of site occupancy even when detection histories at sites are <1
(MacKenzie et al. 2002). Occupancy modeling provides researchers with a robust method that
67
has been adapted from standard capture-recapture analysis and allows for potential detection of
species that might go otherwise undetected (MacKenzie et al. 2002, 2006). Occupancy models
allow researchers to evaluate covariates associated with occupancy (e.g. habitat) and detection
(e.g. survey method). This method also allows use of smaller sample sizes and thus is more cost
effective than other techniques (MacKenzie et al. 2006). These parameters are known as
covariates and can include variables such as habitat type or distance to certain geological features
(MacKenzie et al. 2006, MacKenzie and Bailey 2004). Modeling for species occurrence provides
information on occurrence within specific covariate parameters. Identifying these habitat
covariates can increase detection of a species (Harris and Ogan 1997). Thorn et al. (2009)
examined occupancy of brown hyenas (Parahyaena brunnea) to evaluate the effectiveness of
camera traps in determining occupancy rate and covariate modeling using environmental and
lure covariates. They found the probability of detection to be 10%, with the model indicating
habitat and lure covariates as being the best. Model results indicated that hyenas were most
likely to be found in scrub or woodland habitat, and fish lures had the greatest detection
probability (Thorn et al. 2009). Occupancy models provide additional information for rare or
elusive species regarding the proportion of area (sites) they occupy. Managers then have the
ability to map, predict, and assess distribution of species to establish plans for increased
monitoring and detection of animals.
For the purposes of this study, I used camera traps to determine occupancy for the
mammalian assemblages within the Tuli Wilderness Area (TWA). The goal of my survey was to
create estimates for occupancy and detection probabilities. Also, I aimed to examine the
frequency of detection by species, along with the amount of survey effort needed to detect
individuals and the most appropriate length of time required to have camera traps in operation
68
within this type of African reserve. I determined covariate models for five species: brown hyena,
spotted hyena (Crocuta crocuta), African leopard, scrub hare (Lepus saxatilis), and the African
elephant (Loxodonta africana).
METHODS
Study Area
I conducted a survey of medium to large mammals within The Tuli Wilderness Area
(TWA) located in The Northern Tuli Game Reserve (NTGR) of Botswana (Fig. 3.1). The
Northern Tuli Game Reserve was created by various landowners with similar conservation
interests (McKenzie 1990); this reserve is not recognized by Botswana’s government but is
maintained by private land owners. The reserve is on the eastern boundary of Botswana between
21°55’S and 22°15’S and flanked by Zimbabwe between 28°55’E and 29°15’E (McKenzie 1990,
Styles and Skinner 2000, Walker et al. 1987). The NTGR is bordered southerly by The Republic
of South Africa and the Limpopo River. The eastern boundary is with Zimbabwe and the Shashe
River. The Tuli block is approximately 3000 km2 of mostly unfenced land with a mixture of
agriculture land and is situated in the central district which covers 147,730 km2 (Kuhn 2012,
McKenzie 1990, Fig. 3.1) Vegetation in the NTGR is classified as mopane veld (McKenzie
1990, Styles and Skinner 2000). NTGR has an extensive system of drainage lines running
throughout the reserve, and water also moves via the Majale River that joins the Limpopo River
and continues to the south (McKenzie 1990).
Study sites within the TWA consisted of approximately 4500-5000 hectares of mopane
veld with a mixture of forested and rocky koppie habitats (McKenzie 1990). By lacking fenced
borders the TWA provides animals freedom of movement between other farms, with the
69
exception of a northern fence line that separates villages from the reserve (Selier 2007). Similar
to the NTGR, the TWA maintains meandering rivers and drainage lines throughout the area that
move water and provide habitat for animals during dry seasons.
Camera Trapping
I conducted camera trap surveys from 12 May to 13 June 2011. Camera traps were used
to survey medium and large sized mammals. Medium sized species were those with a weight of
2.0-20 kg and large species were ones < 20 kg. A total of 15 camera traps was deployed
throughout TWA based on a stratified randomized design (Table 3.1) Thirty-nine individual sites
were randomly chosen within a 500x500 m grid square based on information from skilled
trackers, accessibility, and risk of harm to researchers by wildlife (Karanth and Nichols 2010).
All camera stations consisted of one remotely triggered infrared camera trap (Reconyx HyperFire
HC500 or Primos TRUTH CAM60). Camera sites were set along roads, drainage lines, or other
areas (open areas or water holes) at distances of 300 m to 500 m apart (Kays and Slauson 2008,
Negroes et al. 2010). Each was set at a height of 0 to 0.5 m, attached to trees with a nylon strap,
and positioned at optimum angles for sensor detection of multiple species. Angles were
determined by test photos which captured the whole study site clearly and without obstruction.
Camera sensor sensitivity was set to medium to reduce triggering by wind and moving
vegetation. Number of photographs taken per detection was set to 5 with a 5 second delay
between photo series. Once placed, reinforced cable locks were used to prevent damage or theft
of cameras (Kays and Slauson 2008). To enhance detection of multiple species, certain sites were
baited (Kauffman et al. 2007, Thorn et al. 2009) using impala (Aepyceros melampus) harvested
by the land manager of the TWA in accordance with pre-allocated allowances for research and
local subsistence. Bait was placed in front of cameras no more than 16 m away, and consisted of
70
various muscles from hind and forelimbs along with entrails. Length of surveys ranged from 6 to
34 days (Gompper et al. 2006); cameras were moved to increase the number of site surveys
(Kays and Slauson 2008). Cameras were checked daily or weekly.
At the end of the 34 day survey period photographs were arranged by individual site
location. Photographs were examined to identify individual species detected and arranged by
taxonomic order. Each of the sites’ data were examined and species lists were arranged by taxon
to indicate species detected at separate camera locations. Species richness was calculated for
each camera site. I used the IUCN Redlist to categorize each species’ status of concern as least
concern (LC), near threatened (NT), vulnerable (VU), endangered (EN), critically endangered
(CR), and not listed by IUCN as unknown (UK) and population for each species was determined
using The International Union for Conservation of Natural Resources (IUCN) Red List for
threatened or endangered species (IUCN 2012).
Modeling Occupancy
In order to conduct occupancy analysis, I organized photographic data from camera
locations into a detection history. This history was determined by the detection of an individual
species and assigned a binary value of “1” for detection and “0” as non-detection (MacKenzie et
al. 2006). For each species that was detected I maintained an individual history, and site data
were arranged by sampling occasions for a total of 11, with no fewer than 3 per survey site.
Individual sampling occasions were organized into 3 trap nights per occasion in order to reduce
the amount of missing data. Estimates of species occupancy and probability of detection rates
were analyzed using single-species single-season occupancy modeling methods (MacKenzie et
al. 2002, 2006). All occupancy analyses were conducted using PRESENCE 2 software version
71
4.2 (Hines 2006). This software allows the generation of maximum likelihood estimates to be
determined for occupancy (Ψ) and probability of detection (p) of species. The results for each of
these outputs are identified by ̂ for individual detection probabilities and Ψ indicating the
proportion of sites within the study area that were occupied by an individual species and ̂ being
the estimated parameter of the area that a species occupies (MacKenzie et al. 2002, 2006).
I also used single-species single-season occupancy modeling methods proposed by
MacKenzie et al. (2002, 2006) and PRESENCE 2 software version 4.2 for additional analysis of
covariate modeling (Hines 2006). In this analysis, 5 species were selected to have models
constructed based on their current status by the IUCN and population trends. The selected
species were brown hyena, spotted hyena, leopard, scrub hare, and African elephant. A list of 10
a priori covariate models were used to evaluate occupancy (Table 3.2). To model detection
probability, I used 11 a priori models created using 6 separate site covariates: roads, drainage
lines, mopane vegetation, mixed forest vegetation, baited sites, and site distance to nearest water
source (Table 3.3). Geographical Information Systems (GIS) were used to create layers of water
features and site locations. Using Esri ArcGIS v10.0 (ArcMap) software, I calculated distances to
nearest water sources for individual sites. Models were created to predict factors that would
affect probability of detecting species in reserve areas and ultimately in monitoring species
occurrence (Thorn et al. 2009). Evaluation of models was done using the Akaike weight (wi) and
Akaike Information Criterion (AIC) which was corrected for smaller sample sizes (AICc) and is
recommended unless sample sizes are large compared to the number of parameters (MacKenzie
et al. 2006). Global models were checked for over-dispersion using a chi-square test with 10,000
bootstraps and evaluation of ̂ , as at least 1,000 are required (Thorn et al. 2009, MacKenzie et al.
2006). This check of over-dispersion was only done for global models and not subsequent
72
models; if found to be over-dispersed ( ̂ > 1), an inflation correction factor of
̂ was added to
standard errors (Burnham and Anderson 2002). In situations when one must account for over
dispersion, analysis model selection criteria of quasi-AICc (QAICc) was used to select the most
appropriate model (Burnham and Anderson 2002).
Species Frequencies and Camera Effort
For determining frequencies of detection for the 38 species observed, data were pooled
for all sites, and a detection history was created based on only the detection of a species at each
site. The frequency was calculated not for individual sites, but for the frequency of detection
during the entire 34 day sampling period within the TWA. In order to examine if there were
potential differences in the frequencies of species detected, data were organized by low, medium,
and high frequencies of detection. A non-parametric Kruskal-Wallis One Way Analysis of
Variance on Ranks test with a Dunn’s multiple comparison were performed on frequencies
because they failed to meet assumptions of normality for a parametric test. To determine the
most appropriate number of days to leave camera traps in operation within African reserves such
as the TWA, camera effort was separated into three categories for analysis: days in operation 06, 7-12, and >13. I conducted a Kruskal-Wallis One-way Analysis of Variance on Ranks with a
Dunn’s multiple comparison procedure to investigate where the differences occurred. A nonparametric statistical test was used as a result of not meeting assumptions of normality. Latency
to initial detection (LTD) allows examination of the time required to detect individual mammal
species, but also helps evaluate efficiency of cameras traps (Gompper et al. 2006). This metric
was determined by the number of trap nights required to detect an individual target species
(Foresman and Pearson 1998, Gompper et al. 2006).
73
Research design and implementation was conducted using guidelines set forth by the
American Society of Mammalogists (Sikes et al. 2011). All methods and protocols were
approved by the University of Central Missouri Institutional Animal Care and Use Committee on
13 May 2011 (IACUC-Approved Permit No. 11-3217).
RESULTS
Throughout the 34 day sampling survey I was able to capture over 50,000 photographs,
and I detected 38 separate mammalian species within 248 trap nights. These detections included
large and medium carnivores, herbivores, omnivores, and insectivores (Table 3.4). Overall,
occupancy rates for individual species varied within each Order (Table 3.5). Occupancy rates for
species ranged from high Ψ=0.87±0.08, including the brown hyena, impala, and common
warthog (Phacochoerus africanus ); to extremely low rates of naïve occupancy of Ψ=0.02±0.00
for the caracal (Caracal caracal) and white-tailed mongoose (Ichneumia albicauda); (Table 3.5).
Carnivore occupancy was determined to be the highest at Ψ=0.69±0.08, and this was followed by
herbivores with Ψ=0.44±0.08, folivores with Ψ=0.42±0.09, omnivores with Ψ=0.31±0.14, and
the lowest occupancy was insectivores with Ψ=0.17±0.13. Detection probabilities for individual
species also varied among taxonomic groups (Table 3.5). The 2 species which had the highest
estimated detection probability were the impala and scrub hare (47% , Table 3.5). Six species,
the caracal, white-tailed mongoose, honey badger (Mellivora capensis), African lion (Panthera
leo), African leopard, Smith’s bush squirrel (Paraxerus cepapi), and klipspringer (Oreotragus
oreotragus) had detection probability estimates ≤ 5% indicating harder detection ability (Table
3.5). All other mammalian species had detection probability estimates > 6% (Table 3.5).
74
Models selected as top ranked models are those which yield the majority of the model
weight (wt), with ∆AICk or ∆QAICk values > 2, and contain the fewest number of parameters.
Top models will carry the greatest impact on either Ψ or p, but other models having ∆AICk
values between 4 and 7 will still have influence, but with less support (MacKenzie et al. 2006).
Models with values < 10 have no support or effect on detection or occurrence (MacKenzie et al.
2006). Occupancy covariate modeling for brown hyena, spotted hyena, leopard, scrub hare, and
African elephant resulted in differing model outcomes between species (Table 3.6-3.15). Global
models for scrub hare and brown hyena had ̂ values > 1, indicating over-dispersal. This was
corrected using the procedure by MacKenzie et al. (2006). The top ranked covariate model for
occupancy of brown hyena was determined using ∆QAICk with a value of < 2 indicating
significant support was the constant (Ψ) model, with 26% of the model weight and indicating no
effect of covariates on Ψ (Table 3.10). Evidence of ∆QAIC values of < 2 indicated roads (14%)
and drainage lines (10%) having some influence on Ψ (Table 3.10). Covariate probability models
for brown hyena indicated mixed forest (25%), drainage lines (15%), constant detection “p”
(13%), bait (12%), and distance to water (11%) affecting detection probability with mixed forest
as the greatest effect (Table 3.11). Top ranked covariate models for brown hyenas showed higher
occupancy within roads, drainage lines, and areas which have been baited.
Results from spotted hyena occupancy models indicate that the global model was the top
model having lowest ∆QAICk with most of the model weight at 32% (Table 3.12). Other models
with ∆QAICk values < 2, for spotted hyena occupancy were distance to water (20%), roads +
drainage lines + mixed forest (14%), and roads (12%). Of all of the top covariate models,
distance to water and roads were revealed as the best model choices based on parsimony (Table
3.12). Final models for effects of covariates on detection probability for spotted hyena
75
designated a constant “p” model as the top ranked model with 81% of the weight (Table 3.13).
Thus, there is no support for any effects of covariates on the detection probability of spotted
hyena. Hyena occupancy increased along roads and when distance to water increased.
African leopard covariate models for occurrence provide 25% of the model weight for
constant occurrence (Ψ) having a ∆AICk value < 0.001, indicating this as the top model (Table
3.8). Other covariate models having less support with ∆AICk values between 2-4 were distance
to water (23%), mopane vegetation (14%), and drainage lines (12%). Although these models
indicated less support, they still affected occurrence based on ∆AICk values (Table 3.8).
Covariate modeling for detection probability showed 100% of the model weight for constant “p”,
indicating no effect of covariates on detection probability (Table 3.9). No other model provided
moderate support for covariate effects as all ∆AICk values were greater than 10 (Table 3.9).
Leopard occurrence increased within the TWA within mopane vegetation, drainage lines, and
when distance to water increased, were detection probability was not affected by covariates.
The highest ranked models for scrub hare occurrence were shown to be constant Ψ
(24%), mopane (20%), distance to water (18%), and mixed forest (16%; Table 3.14). Based on
the number of parameters and the rule of parsimony, the mopane and constant Ψ models would
be good covariate models because they have the lowest number of parameters. Detection
probability covariate models found that roads (14%), mopane (13%), constant “p” (13%),
distance to water (13%), drainage lines (13%), mixed forest (13%), and bait (13%) all affected
scrub hare detection probability (Table 3.15). Scrub hares were expected to have increased
occurrence within areas of mopane vegetation or mixed forest with greater distances to water.
Detection probability of this species was shown to be affected by 7 of the covariates (Table
3.15).
76
Top covariates for the African elephant were constant Ψ (23%), mixed forest (20%),
distance to water (13%), mopane vegetation (12%), and roads (10%). The overall top model was
the constant Ψ model, based on ∆AICk and having the fewest of parameters. Other models were
still top covariates because they had ∆AICk values < 2, but the number of parameters was higher
than the constant Ψ model. These models carried at least 10% of the weight individually, and
combined they represented 55% of the total weight (Table 3.6). Covariate models listed in Table
3.7 affected detection probability of the African elephant and top models were shown to be bait
(29%), distance to water (18%), mixed forest (15%), and drainage lines (12%). Overall
occurrence of the African elephant was found to be positive in areas such as mopane vegetation,
mixed forest habitats along roads, and areas having adequate distances to water sources.
Detection probability could be affected by bait, distance to water, mixed forest, and drainage line
covariates.
The frequency of detection varied for the species observed during the sampling season
(Fig. 3.3, Table 3.16). This detection frequency was highly variable, similar to the occupancy
rates, ranging from highest (0.82) for brown hyena to lowest (0.03) for aardwolf (Proteles
cristata), African wild dog, and caracal (Table 3.5). High, medium, and low categories of
frequency species were significantly different (Kruskal-Wallis one-way multiple test, H2 =
27.161 P = <0.001). Species with high frequencies of detection were significantly different when
compared to low category species (Dunn’s test, q=3.999, P<0.05). Results also indicate
significant differences between medium and low frequency species, and medium frequency
species having increased species detection (Dunn’s test, q= 4.064, P<0.05). Species richness
ratios were significantly different among categories of length in operation (Kruskal-Wallis oneway multiple test, H2=13.073, P=0.001, Fig. 3.2). There were significant differences between
77
categories 0-6 and >13 (Dunn’s test, q=3.607, P<0.05), with species detection most efficient with
length of camera trap operation ≤ 6 days. This coincides with the Latency for Detection (LTD)
values estimated for species as 28 of the 38 species had LTD values ranging from 2-6.6 (Table
3.16). Individual species LTD values varied among the 38 species detected within the TWA
(Table 3.16).
DISCUSSION
Modeling species occupancy has been utilized for examining mammalian species
occurrence within reserve areas (Karanth et al. 2009). I was able to use occupancy models as an
alternative to abundance for estimating habitat selection (MacKenzie et al. 2005). Occupancy
results in my study were similar to carnivore results published by Karanth et al. (2009) regarding
occupancy within protected areas of India. They were able to determine overall occupancy rates
for 20 mammalian species, including carnivores and herbivores. Rate was determined by sighting
surveys from professionals throughout India and results revealed an average occupancy of 0.55
(Karanth et al. 2009). These results correspond to my data within the TWA as the overall average
was 0.50. Detection probabilities provide data for the likelihood of detecting a species of interest.
The estimated detection probabilities varied among species, from 0.01 to 0.47. Results for
herbivore detection probabilities of 0.08-0.47 were found to be similar to those of Collier et al.
(2011). Their study assessed low veld savannah habitat in eastern Swaziland using multiple
surveyors, and they observed an overall average herbivore detection probability of 0.22-0.57.
Collier et al. (2011) found that 50% of species can be missed in surveying dense habitat, but their
findings also support our survey design for sampling several features (roads, open patches, and
dense forest) for species detection. This suggests that managers incorporate multiple survey
techniques, such as visual surveys and track and sign surveys, along with camera trapping, to
78
help monitor mammals (Long et al. 2010, Collier et al. 2011). Interpretations of the health of the
mammalian community can be drawn from the 50% rate of occurrence for herbivores, which
provide sustenance for the larger carnivore species within the reserve. Ramakrishnan et al.
(1999) examined the effects of low prey occurrence on large carnivore density within the
Mudumalai Wildlife Sanctuary of India. They surveyed the sanctuary for sightings of large
carnivores and prey, and found a decrease in large carnivore occurrence or detection where
larger herbivores are absent. The inverse effect can be seen within the TWA from our survey
results indicating that the mean carnivore occupancy was 64% with a mean probability of
detection of 15%. Evidence suggests that having a high enough and stable occurrence of medium
and large prey will support the diverse carnivore community within the TWA.
By utilizing occupancy modeling methods from MacKenzie et al. (2002, 2006), I was
able to construct separate covariate models for brown hyena, spotted hyena, African leopard,
scrub hare, and African elephant. Effects of covariates for occurrence and detection probability
varied between species as expected. For the brown hyena, my results correspond to those of
Thorn et al. (2009) where brown hyena detection increased with use of a baited lure and camera
placement in scrub (mopane) vegetation. My model provides evidence that increased occurrence
can be observed with camera placement on road-type features and dry drainage lines. The overall
rate of brown hyena occupancy of 0.87 was higher in the TWA than those obtained by Thorn et
al. (2011) in areas where hyena occupancy was lower at a rate of 0.74. Modifications to survey
design for brown hyena would include increasing sites within scrub (mopane) vegetation and
having an additional camera at an opposing angle for individual identification in population
estimates. The spotted hyena models that were assessed indicated that the covariates’ affect on
occurrence resulted in the global model, which included all 6 covariates. This model indicates
79
that all individual covariates have the ability to impact occurrence of spotted hyena. My results
are comparable to Holekamp and Dloniak (2010) where spotted hyenas were detected within a
variety of habitats, especially in forest-savanna mosaics. Furthermore, they found detection of
this species is invariant (unchanging) among study areas (Holekamp and Dloniak 2010). My best
fit model for detection indicated that covariates had no effect on the ability to detect individuals.
These models reveal that spotted hyena occurrence can be affected by habitat factors such as
mopane vegetation, mixed forest, distances to water, presence of drainage lines, roads, and a
combination of these factors. The ability of detecting an individual for this species on the other
hand has no covariate factors that would hinder the detection. For continued monitoring of
spotted hyena in the TWA using camera traps, a recommendation would be to increase sampling
within all habitat areas by placing an additional camera within individual sites for population
estimation.
Measuring occurrence of African leopard within the TWA is affected by distance to
water, mopane vegetation, and drainage lines. African leopard occurrence increased when
camera traps were baited, and these results are similar to those of Kauffman et al. (2007) who
examined rangeland impacts on carnivores within Namibia using baited camera traps. They
found that baited traps increased detection and researchers were able to determine leopard
declines as a result of increased persecution of poachers, human population density, and a
reduction in prey availability (Kauffman et al. 2007). There was no effect of covariates on
detection probability, and this can be explained by Hayward et al. (2006) who studied the life
history of the African leopard. They conducted a comprehensive literature survey of leopard
ecology in 29 areas. Their findings, along with those of Gavashelishvili and Lukarevskiy (2008),
suggest that leopards have adapted to become carnivore generalists. My recommendation for
80
further monitoring of the African leopard within the TWA would be to increase camera trap
surveys with additional cameras at site locations. Also, I would recommend incorporating the use
of photographic data with track signs to monitor individuals and determine population status
Subsequent habitat models with a combined weight of 41% indicated savannah veld positively
affected occurrence, and this has been indicated as preferred hare habitat (Lombardi et al. 2003).
My results demonstrate that African elephants within the TWA had a 64% rate of
occupancy with a 41% probability of detection. Covariate modeling suggests that there were no
effects on occurrence from habitat covariates. There were also fewer substantial models shown to
have possible effects which carried a combined 45% of the overall weight. In using additional
models, I agree with results from Harris et al. (2008), who conducted a study on habitat use by
African elephants. They found that elephant occurrences were higher in areas of mopane
vegetation, forested areas, and areas with an increased proximity to a water source (Harris et al.
2008). Continued monitoring of African elephants within the TWA should be conducted with
additional considerations for monitoring movements between local, unrestricted private farms
and reserves. A complete vegetation analysis is recommended to aid in furthering the comparison
of all mammalian species interactions and suitable habit.
Camera trapping involves various considerations when monitoring and surveying
mammalian species, from distribution of cameras to length of their deployment (Kays and
Slauson 2008). Results from our study differ from those of Moruzzi et al. (2002), where they
found that length of camera operation should be set at periods of 15-21 days. My results
concluded that for reserve areas similar to the TWA, camera trap operation is most efficient in
lengths of up to 6 days. These results are further shown to be effective because rates of species
LTD averaged 6.6 days. There were exceptions on LTD for rare or endangered species, such as
81
the African wild dog, which had a LTD of 21 days. Overall in observing mammalian species
diversity within the TWA, the reduction in the length of days in operation resulted in a greater
number of individual species detected while still allowing for the survey of a greater proportion
of the reserve. When monitoring rare species with camera traps, increasing the length of days in
operation would likely capture rare animals, as their LTD is much greater.
Although camera trapping was very successful within the TWA there are a few
disadvantages to using camera traps. Camera traps are stationary and thus cannot capture species
when moving to forage, escape predators, or disperse to new areas. Camera traps are not capable
of collecting biological data such as hair, blood, and scat samples. Also, cameras may not detect
individuals which move along the rear of the camera outside of the detection beam. Mechanical
malfunctions can also occur when using camera traps such as blank black photos, inaccurate
day/time information (if set incorrectly), or spontaneous complete shutdown. The initial cost of
camera trap equipment can be costly ranging from $70 to over $500 and depend on the data
storage and battery capabilities (Ryan 2011). Even with disadvantages camera traps are very
effective in detecting rare species and collecting data in areas which are large or areas with
logistical implications.
I was able to demonstrate that occupancy modeling is an effective tool for surveying and
monitoring species within an African reserve. With calculated rates of species occupancy and
detection probabilities, managers of the TWA can focus further research efforts based on
increased species data. Results of covariate models will help pinpoint factors affecting selected
species, especially those with declining or unknown population status and those considered
endangered or near threatened. I have demonstrated that TWA supports several large mammals
which could be used as flagship or umbrella species to increase conservation efforts within the
82
NTGR as a whole. Expanding camera trap surveys of the mammalian assemblages within the
TWA and building a complete database of individual species and individual capture data will
provide the tools to analyze differences in species occurrence between seasons. This will also
help determine population estimates for individual species. Having such detailed information
will allow managers to examine the effects of expanding protected areas for the movement of
species, as in the proposed Limpopo/Shashe Trans Frontier Conservation Area (TFCA).
83
LITERATURE CITED
BALME, G., L. HUNTER
AND
R. SLOTOW. 2007. Feeding habitat selection by hunting leopards
Panthera pardus in a woodland savanna: prey catchability versus abundance. Animal
Behaviour 74:589-598.
BOWKETT, A. E., N. R. LUNT,
AND
A. B. PLOWMAN. 2006. How do you monitor rare and
elusive mammals? Counting duikers in Kenya, Tanzania and Zimbabwe Pp. 21-28 in
Animals, zoos, and conservation (Ewa Zgrabczynska, Piotr Cwiertnia, and Joanna Ziomek)
The Zoological Garden, Poznan, Poland.
BURNHAM, K . P.,
AND
D. R. ANDERSON. 2002. Model selection and multimodel inference: a
practical information-theoretic approach. Second edition. Springer-Verlag, New York, New
York. 488 pp.
COLLIER, B. A., R. A. MCCEERY, K. W. CALHOUN, K. G. ROQUES,
AND A.
MONADJEM.
2011. Detecion probabilities of ungulates in the eastern Swaziland lowveld. Journal of South
African Wildlife Research 41(1):61-67.
ESTES, R. D. 1991. The Behavior Guide to African Mammals. University of California Press,
Ltd. Oxford, England: 366-369.
84
FORESMAN, K. R.
AND PEARSON
D. E. 1998. Comparison of proposed survey procedures for
detection of forest carnivores. The Journal of Wildlife Management 62(4):1217-1226.
FJELDSA, J., N. D. BURGESS, S. BLYTH,
AND H.
M.
DEKLERK.
2004. Where are the major gaps
in the reserve network for Africa’s mammals? Oryx 38(1):17-25.
GAVASHELISHVILI, A.,
AND
V. LUKAREVSKIY. 2008. Modeling the habitat requirements of
leopard Panthera pardus in west and central Asia." Journal of Applied Ecology 45.2
(2008):579-588.
GOMPPER, M. E., R. W. KAYS, J. C. RAY, S. D. LAPOINT, D. A.. BOGAN,
AND
J. R.
CRYAN. 2006. A comparison of noninvasive techniques to survey carnivore communities in
northeastern North America. Wildlife Society Bulletin 34(4):1142-1151.
HARRIS, G. M., G. J. RUSSELL, R. I.
VANAARDE, AND S.
L. PIMM. 2008. Rules of habitat use
by elephants Loxodonta africana in southern Africa: insights for regional management. Oryx
42(1):66-75.
85
HARRIS, J. E.,
AND
C. V. OGAN., EDS. 1997. Mesocarnivores of Northern California: Biology,
Management, and Survey Techniques, Workshop Manual. August 12-15, 1997, Humboldt
State Univ., Arcata, CA. The Wildlife Society, California North Coast Chapter, Arcata, CA.
127 p.
HAYWARD, M. W., P. HENSCHEL, J. O’BRIAN, M. HOFMEYR, G. BALME, G. I. KERLEY.
2006. Prey preferences of the leopard (Panthera pardus). Journal of Zoology 270:298-313.
HINES, J. E. 2006. PRESENCE2-Software to estimate patch occupancy and relates parameters.
USGS-PWRC. http://www.mbr-pwrc.usgs.gov/software/presence.html.
HOLEKAMP, K. E.,
AND
S. M DLONIAK. 2010. Intraspecific variation in the behavioral ecology
of a tropical carnivore, the spotted hyena. Advances in the Study of Behavior 42:189-229.
HUGHES, D. M. 2005. Third nature: Making space and time in the great Limpopo Conservation
Area. Cultural Anthropology 20(2):157-184.
IUCN 2012. IUCN Red List of Threatened Species. Version 2012.2. <www.iucnredlist.org>.
Downloaded on 11 February 2013.
86
JACKSON, R. M., J. D. ROE, R. WANGCHUK,
AND
D. O. HUNTER. 2005. Surveying Snow
Leopard Populations with Emphasis on Camera Trapping: A Handbook. The Snow Leopard
Conservancy, Sonoma, California (73 pages)
KAUFFMAN, M. J., N. VARLEY, D. W. SMITH, D. R. STAHLER, D. R. MACNULTY,
AND M. S.
BOYCE. 2007. Landscape heterogeneity shapes predation in a newly restored predator-prey
system. Ecology Letters 10:690–700.
KARANTH, K. U.
AND
J. D. NICHOLS. 2010. Non-invasive survey methods for assessing tiger
populations. Pp. 241-261 in Tigers of the world: the science , politics, and conservation of
Panthera tigris (R. Tilson and P. J. Nyhus). Academic Press, London, United Kingdom.
KARANTH, K . K., J. D. NICHOLS, K. U. KARANTH, J . E. HINES,
AND
N. L. CHRISTENSEN JR.
2010. The shrinking ark: patterns of large mammal extinctions in India. Proceedings of The
Royle Society 277:1971-1979.
KARANTH, K. K., , J. D. NICHOLS, J. E. HINES, K. U. KARANTH,
AND
N. L. CHRISTENSEN.
2009. Patterns and determinants of mammal species occurrence in India. Journal of Applied
Ecology 46(6):1189-1200.
87
KAYS, R.
AND K.
SLAUSON. 2008. Remote Cameras Pp. 110-140 in Noninvasive survey
methods for carnivores (Robert Long, Paula MacKay, Willian Zielinski, and Justina Ray).
Island Press, Washington DC.
KELLY, K. J.
AND
E. L. HOLUB. 2008. Camera trapping of carnivores: trap success among
camera types and across species, and habitat selection by species, on Salt Pond Mountain,
Giles County, Virginia. Northeastern Naturalist 15(2):249-262.
KUHN, B. F. 2012. Bone accumulations of spotted hyaenas (Crocuta crocuta, Erxleben, 1777) as
indicators of diet and human conflict; Mashatu, Btswana. International Journal of Ecology
2012: Article ID 178348, 6 pages, 2012. doi:10.1155/2012/178348
LONG, R. A., T. M. DONOVAN, P. MACKAY, W. J. ZIELINSKI,
AND
J. S. BUZAS. [online]
2011. Predicting carnivore occurrence with noninvasive surveys and occupancy modeling.
Landscape Ecology 26:327-340
LOMBARDI, L., N. FERNANDEZ, S. MORENO,
AND R.
VILLAFUERTE. 2003. Habitat-related
differences in rabbit (Oryctolagus cuniculus) abundance, distribution, and activity. Journal of
Mammalogy 84(1):26-36.
88
LOVERRIDGE, A. J., S. W. WANG, L. G. FRANK,
AND
J SEIDENSTICKER. 2010. Biology and
conservation of wild felids. Pp 161-195 People and wild felids: conservation of cats and
management of conflicts (MacDonald, D. W. and A. J. Loverridge). Oxford University
Press, United States, NY.
LYRA-JORGE, M. C.
AND
G. CIOCHETI. 2008. Comparing methods for sampling large-and
medium sized mammals: camera traps and track plots. European Journal of Wildlife
Research 54:739-744.
MACKENZIE, D. I., J. D. NICHOLS, J. A. ROYLE, K. H. POLLOCK, L. L. BAILY,
AND J. E.
HINES. 2006. Occupancy estimation and modeling. Academic Press, Burlington,
Massachusetts.
MACKENZIE, D. I.
AND
L. L. BAILEY. 2004. Assessing the fit of site-occupancy models.
Journal of Agricultural, Biological and Environmental Statistics 9(3):300-318.
MACKENZIE, D. I., J. D. NICHOLS, G. B. LACHMAN, S. DROEGE, J. A. ROYLE,
AND
C. A.
LANGTIMM. 2002. Estimating site occupancy rates when detection probabilities are less than
one. Ecology 83(8):2248-2255.
89
MACKENZIE, D. I., J. D. NICHOLS, N. SUTTON, K. KAWANISHI,
AND
L. L. BAILEY. 2005
Improving inferences in population studies of rare species that are detected imperfectly.
Ecology 86(5):1101-1113.
MARNEWICK, K. A., J. DU P. BOTHMA,
AND
G. H. VERDOORN. 2006. Using camera-trapping
to investigate the use of a tree as a scent-marking post by cheetahs in the Thabazimbi district.
South African Journal of Wildlife Research 36(2):139-145.
MCKENZIE, A.A. 1990. Co-operative hunting in the black-backed jackal (Canis mesomelas
Schreber). PhD Thesis, University of Pretoria, Pretoria, South Africa.
MORUZZI, T. L., T. K. FULLER, R. M. DEGRAFF, R. T. BROOKS,
AND
W. LI. 2002. Assessing
remotely triggered cameras for surveying carnivore distribution. Wildlife Society Bulletin
30(2):380-386.
NEGROES, N., P. SARMENTO, J. CRUZ, C. EIRA, E. REVILLA, C. FONSECA, R. SOLLMANN, N.
M. TORRES, M. M. FURTADO, A. T. A. JACOMO,
AND
L. SILVEIRA. 2010. Use of camera-
trapping to estimate puma density and influencing factors in central Brazil. Journal of
Wildlife Management 74(6):1195-1203.
RAMAKRISHNAN, U., R. G. COSS,
AND N.
W. PELKEY. 1999. Tiger decline caused by the
reduction of large ungulate prey: evidence from a study of leopard diets in southern India.
Biological Conservation 89:113-120.
90
RAY, J. C., K. H. REDFORD, R. S. STENECK,
AND
J. BERGER. 2005. Large carnivores and the
conservation of biodiversity. Pp. 34-59 Large carnivorous animals as tools for conserving
biodiversity: Assumptions and uncertainties (Justina C. Ray). Island Press, Washington, DC.
ROWCLIFFE, J. M.,
AND C.
CARBONE. 2008. Surveys using camera traps: are we looking to a
brighter future? Animal Conservation 11:185-186.
RYAN, J. M. 2011. Mammalogy techniques manual. 2nd ed. Lulu, Raleigh, NC.
SELIER, S. A. J. 2007. The social, distribution and demographic status of the African elephant
population in the Central Limpopo River Valley of Botswana, Zimbabwe and South Africa.
M.S. Thesis, University of Pretoria, Pretoria, South Africa.
SIKES, R. S., W. L. GANNON,
AND THE ANIMAL CARE AND USE COMMITTEE OF THE AMERICAN
SOCIETY OF MAMMALOGISTS. 2011. Guidelines of the American Society of Mammalogists
for the use of wild animals in research. Journal of Mammalogy 92(1):235-253.
STYLES, C. V.
AND J.
D. SKINNER. 2000. The influence of large mammalian herbivores on
growth form and utilization of mopane trees, Colophosperum mopane, in Botswana’s
Northern Tuli Game Reserve. African Journal of Ecology 38(2):95-101.
91
THORN, M., D. M. SCOTT, M. GREEN, P. W. BATEMAN
AND
E. Z. CAMERON. 2009.
Estimating brown hyena occupancy using baited camera traps. South Africa Journal of
Wildlife Research 39(1):1-10.
THORN, M., M. GREEN, P. W. BATEMAN, S. WAITE,
AND
D. M. SCOTT. 2011. Brown
hyaenas on roads: Estimating carnivore occupancy and abundance using spatially autocorrelated sign survey replicates. Biological Conservation (Inpress).
WALKER, B. H., R. H. EMSLIE, R. N. OWEN-SMITH,
AND R.
J. SCHOLES. 1987. To cull or not
to cull: lessons from a Southern African drought. 24(2):381-401.
92
Table 3.1—Summary of stratified random habitat selection percentages for camera trap
placements within the Tuli Wilderness Area.
Habitat Type
Percent Cover (%)
Amount of Camera placements
Riverine
15
3
Acacia woodland
65
10
Shrub-lands
20
3
Total
100
16
93
Table 3.2—List of a priori models with hypotheses for effect on occurrence (Ψ) from
camera trap survey in The Tuli Wilderness Area, Botswana, 2011.
Hypothesis
Model
No habitat effects on model
Ψ(.)
Effect of mopane habitat on occurrence
Ψ(mopane)
Effect of mixed forest on occurrence
Ψ(mixed forest)
Effect of roads on occurrence
Ψ(roads)
Effect of drainage lines on occurrence
Ψ(drainage lines)
Effect of distance to water on occurrence
Ψ(distance to water)
Mopane, mixed forest, roads, drainage lines, distance to
water, and bait all affect occurrence
Effect of roads, drainage line, and mixed forest on
occurrence
Effect of distance to water, roads, and mopane on
occurrence
Effect of roads and drainage lines on occurrence
94
Ψ(global)
Ψ(roads, drainage lines, mixed
forest)
Ψ(distance to water, roads, mopane)
Ψ(roads, drainage lines)
Table 3.3—List of a priori models with hypotheses for effect on detection probability (p)
from camera trap survey in The Tuli Wilderness Area, Botswana, 2011.
Hypothesis
Model
No habitat effects on detection model
p(.)
Effect of mopane habitat on detection
p(mopane)
Effect of mixed forest on detection
p(mixed forest)
Effect of roads on detection
p(roads)
Effect of drainage lines on detection
p(drainage lines)
Effect of distance to water on detection
p(distance to water)
Effect of bait on detection
p(bait)
Mopane, mixed forest, roads, drainage lines, distance to
water, and bait on detection
Effect of roads, drainage line, and mixed forest on
detection
Effect of distance to water, roads, and mopane on
detection
Effect of roads and drainage lines on detection
95
p(global)
p(roads, drainage lines, mixed
forest)
p(distance to water, roads, mopane)
p(roads, drainage lines)
Figure 3.1—Map of The Tuli Wilderness Study Area within the Northern Tuli Game
Reserve, Botswana.
96
Table 3.4—Results for mammalian species detected from camera trap surveys within The Tuli Wilderness Area, Botswana,
2011. Species status information provided from The International Union for Conservation of Natural Resources Red List of
Threatened Species- least concern (LC), near threatened (NT), vulnerable (VU), endangered (EN), critically endangered (CR),
and not listed by ICUN (UK).
Order
Carnivora
Number of
sites detected
Common Name
Species Name
Diet
Status Population Status (ICUN)
Aardwolf
African civet
African leopard
African lion
African Wild dog
African Wildcat
Banded Mongoose
Bat-eared Fox
Black-Backed Jackal
Brown Hyena
Caracal
Honey Badger
Large-Spotted Genet
Slender Mongoose
Spotted Hyena
White-tailed Mongoose
Proteles cristatus
Civetticis civetta
Panthera pardus pardus
Panthera leo
Lycaon pictus
Felis silvestris lybica
Mungos mungo
Otcyon megalotis
Canis mesomelas
Parahyaena brunnea
Caracal caracal
Mellivora capensis
Genetta maculataenet
Galerella flavescenson
Crocuta crocuta
Ichneumia albicauda
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
Carnivore
1
14
13
3
1
9
5
4
22
32
1
4
9
10
13
1
LC
LC
NT
VU
EN
UK
LC
LC
LC
UK
LC
LC
LC
LC
UK
LC
Stable
Unknown
Decreasing
Decreasing
Decreasing
Unknown
Stable
Unknown
Stable
Unknown
Unknown
Decreasing
Unknown
Stable
Unknown
Stable
Blue Wildebeest
Bushbuck
Bushpig
Common Duiker
Common Eland
Common Warthog
Connochaetes taurinus
Tragelas scriptus
Potamochoerus larvatus
Sylvicapra grimmia
Tragelaphus oryx
Phacochoerus africanus
Herbivore
Herbivore
Omnivore
Herbivore
Herbivore
Omnivore
11
4
6
6
12
24
LC
LC
LC
LC
LC
LC
Stable
Stable
Stable
Stable
Stable
Stable
Artiodactyla
97
Table 3.4—Continued
Order
Common Name
Giraffe
Greater Kudu
Impala
Species Name
Giraffa camelepardalis
Tragelaphus strepsiceros
Aepyceros melampus
Diet
Herbivore
Herbivore
Herbivore
Klipspringer
Steenbok
Waterbuck
Orertragus oreotragus
Raphicerus campestris
Kobus ellipsiprymnus
Herbivore
Herbivore
Herbivore
Cape Porcupine
Hystrix africaeaustralis
Smith’s Brush Squirrel Paraxerus cepapi
Spring Hare
Pedetes capensis
African Elephant
Number of
sites detected
2
21
27
Status
LC
LC
LC
Population Status (ICUN)
Decreasing
Stable
Stable
2
8
4
LC
LC
LC
Stable
Stable
Decreasing
Herbivore
Herbivore
Herbivore
8
2
5
LC
UK
LC
Stable
Unknown
Unknown
Loxodonta africana
Herbivore
21
VU
Increasing
Plains Zebra
Equus quagga
Herbivore
14
LC
Stable
Rock Hyrax
Procavia capensis
Herbivore
2
LC
Unknown
Chacma Baboon
Papio hamadryas ursinus
Omnivore
9
LC
Stable
Artiodactyla
Rodentia
Proboscidea
Perissodactyla
Hyracoidea
Primates
98
Table 3.4—Continued
Order
Number of
sites detected Status Population Status (ICUN)
2
LC
Stable
Common Name
Vervet monkey
Species Name
Chlorocebus pygerythrus
Diet
Omnivore
Scrub Hare
Lepus saxatilis
Folivore
12
LC
Decreasing
Aardvark
Orycteropus afer
Insectivore
4
UK
Unknown
Lagomorpha
Tubulidentata
99
Table 3.5—Results for mammalian species occupancy within 95% confidence interval, estimated probability of detection, and naïve
occupancy estimates for under-dispersed species. Frequencies of individual species detected with the number of sites detected are
displayed. Detected from camera trap surveys within The Tuli Wilderness Area, Botswana, 2011.
Mammalian Species
Carnivore
Black-Backed Jackal
Caracal
African Civet
Spotted Hyena
African Wildcat
Slender Mongoose
Large-Spotted Genet
White-tailed Mongoose
Honey Badger
Banded Mongoose
Bat-eared Fox
African Lion
African Leopard
Brown Hyena
African Wild dog
Aardwolf
0.45
0.01
0.35
0.19
0.14
0.13
0.18
0.00
0.02
0.18
0.15
0.02
0.05
0.46
0.12
0.08
Ψ
Standard
Error
Confidence
Interval
(95%)
Frequency
of Detection
at sites
Number of
sites detected
0.46
1.00
0.47
0.63
0.43
0.57
0.44
1.00
1.00
0.23
0.22
1.00
1.00
0.87
0.06
1.00
0.09
0.00
0.10
0.17
0.19
0.25
0.16
0.00
0.00
0.13
0.15
0.00
0.00
0.08
0.08
0.00
0.29-0.64
0.00-1.00
0.28-0.66
0.27-0.88
0.130.77
0.15-0.90
0.18-0.73
0.00-1.00
0.00-1.00
0.07-0.54
0.05-0.62
0.00-1.00
0.00-1.00
0.62-0.96
0.00-0.46
0.00-1.00
0.56
0.03
0.36
0.33
0.23
0.26
0.23
0.03
0.10
0.13
0.10
0.08
0.33
0.82
0.03
0.03
22
1
14
13
9
10
9
1
4
5
4
3
13
32
1
1
100
Naïve
Occupancy
Estimate
0.028
0.028
0.111
0.083
0.250
0.028
Table 3.5—Continued
Ψ
Standard
Error
Confidence
Interval
(95%)
Frequency
of Detection
at sites
Number of
sites detected
0.47
0.43
0.36
0.08
0.08
0.41
0.32
0.40
0.21
0.34
0.34
0.01
0.42
0.19
0.01
0.33
0.81
0.28
0.47
0.08
0.19
0.64
0.28
0.19
0.69
0.64
0.14
1.00
0.06
0.23
1.00
0.28
0.07
0.08
0.10
0.39
0.20
0.10
0.09
0.07
0.16
0.12
0.07
0.00
0.04
0.11
0.00
0.09
0.64-0.91
0.14-0.46
0.28-0.66
0.00-0.99
0.1-0.75
0.43-0.80
0.13-0.48
0.08-0.37
0.33-0.90
0.39-0.82
0.04-0.32
0.00-1.00
0.01-0.22
0.08-0.51
0.00-1.00
0.13-0.48
0.69
0.28
0.36
0.05
0.10
0.54
0.21
0.15
0.31
0.54
0.10
0.05
0.05
0.13
0.05
0.21
27
11
14
2
4
21
8
6
12
21
4
2
2
5
2
8
0.43
0.29
0.41
0.36
0.70
0.18
0.06
0.30
0.08
0.08
0.04
0.09
0.53-.082
0.07-0.39
0.01-0.22
0.15-0.50
0.62
0.15
0.05
0.23
24
6
2
9
0.47
0.42
0.09
0.26-0.59
0.31
12
0.15
0.17
0.13
0.02-0.57
0.10
4
Mammalian Species
Herbivore
Impala
Blue Wildebeest
Plains Zebra
Giraffe
Waterbuck
African Elephant
Steenbok
Common Duiker
Common Eland
Greater Kudu
Bushbuck
Klipspringer
Rock Hyrax
Spring Hare
Smith’s Brush Squirrel
Cape Porcupine
Omnivore
Common Warthog
Bushpig
Vervet monkey
Chacma Baboon
Folivore
Scrub Hare
Insectivore
Aardvark
101
Naïve
Occupancy
Estimate
0.028
0.056
102
Table 3.6—Best fit model selection of site covariate effects on occurrence models (Ψ) for
African elephant (Loxodonta africana).
Model
Ψ(.)
Ψ(mixed forest
Ψ(distance to water)
Ψ(mopane)
Ψ(roads)
Ψ(drainage lines)
Ψ(bait)
Ψ(roads+drainage lines)
Ψ(dist. to water, roads, mopane)
Ψ(roads+drainage lines, mixed forest)
Ψ(global)
AICc
186.19
186.54
187.38
187.45
187.82
188.58
188.58
190.19
190.93
191.35
194.45
∆AICk
0.00
0.35
1.19
1.26
1.63
2.39
2.39
4.00
4.74
5.16
8.26
wt
0.23
0.20
0.13
0.12
0.10
0.07
0.07
0.03
0.02
0.02
0.00
-2l
181.83
179.79
180.63
180.70
181.07
181.83
181.83
180.90
178.93
179.35
176.45
K
2
3
3
3
3
3
3
4
5
5
8
Corrected for effective sample size (AICc), (wt) model weight, (-2l) twice negative likelihood,
(k) number of parameters.
Table 3.7—Best fit model selection of site covariate effects on detection probability models
(p) for African elephant (Loxodonta africana).
Model
p(bait)
p(distance to water)
p(mixed forest
p(drainage lines)
p(.)
p(dist. to water, roads, mopane)
p(roads+drainage lines, mixed forest)
p(roads+drainage lines)
p(mopane)
p(roads)
p(global)
AICc
183.72
184.66
185.05
185.50
186.19
187.09
187.69
187.79
188.94
188.95
192.12
∆AICk
0.00
0.94
1.33
1.78
2.47
3.37
3.97
4.07
5.22
5.23
8.40
wt
0.29
0.18
0.15
0.12
0.08
0.05
0.04
0.04
0.02
0.02
0.00
-2l
179.36
180.30
180.69
181.14
181.83
177.80
178.40
181.04
184.58
184.50
174.12
K
2
2
2
2
2
4
4
3
2
2
7
Corrected for effective sample size (AICc), (wt) model weight, (-2l) twice negative likelihood,
103
Table 3.8—Best fit model selection of site covariate effects on occurrence models (Ψ) for
African leopard (Panthera pardus).
Model
Ψ(.)
Ψ(distance to water)
Ψ(mopane)
Ψ(drainage lines)
Ψ(mixed forest
Ψ(roads)
Ψ(roads+drainage lines)
Ψ(dist. to water, roads, mopane)
Ψ(roads+drainage lines, mixed forest)
Ψ(global)
AICc
80.55
80.77
81.76
82.08
82.60
82.93
84.36
85.51
86.85
88.35
∆AICk
0.00
0.22
1.21
1.53
2.05
2.38
3.81
4.96
6.30
7.80
wt
0.25
0.23
0.14
0.12
0.09
0.07
0.03
0.02
0.01
0.00
-2l
76.19
74.02
75.01
75.33
75.85
76.18
75.07
73.51
74.85
67.02
K
2
3
3
3
3
3
4
5
5
8
Corrected for effective sample size (AICc), (wt) model weight, (-2l) twice negative
likelihood, (k) number of parameters.
Table 3.9—Best fit model selection of site covariate effects on detection probability models
(p) for African leopard (Panthera pardus).
Model
p(.)
p(bait)
p(global)
p(distance to water)
p(mopane)
p(dist. to water, roads, mopane)
p(roads+drainage lines, mixed forest)
p(mixed forest)
p(roads+drainage lines)
p(drainage lines)
p(roads)
AICc
80.18
93.83
97.61
100.19
100.30
101.09
102.77
107.93
108.34
113.77
113.78
∆AICk
0.00
13.65
17.43
20.01
20.12
20.91
22.59
27.75
28.16
33.59
33.60
wt
1.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
-2l
75.82
89.47
79.61
95.83
95.94
91.80
93.48
103.57
101.59
109.41
109.42
Corrected for effective sample size (AICc), (wt) model weight, (-2l) twice negative
likelihood, (k) number of parameters.
104
K
2
2
7
2
2
4
4
2
3
2
3
Table 3.10—Best fit model selection of site covariate effects on occurrence models (Ψ) for
brown hyena (Parahyaena brunnea).
Model
Ψ(.)
Ψ(roads)
Ψ(drainage lines)
Ψ(mopane)
Ψ(mixed forest)
Ψ(roads+drainage lines)
Ψ(roads+drainage lines, mixed forest)
Ψ(distance to water)
Ψ(dist. to water, roads, mopane)
Ψ(global)
QAICc
155.01
156.31
156.95
157.04
157.30
157.57
159.89
160.00
165.25
171.25
∆QAICk
0.00
1.30
1.94
2.03
2.29
2.56
4.88
4.99
10.24
16.24
wt
0.26
0.14
0.10
0.09
0.08
0.07
0.02
0.02
0.00
0.00
-2l
227.48
225.84
226.80
226.94
227.33
223.90
223.32
231.41
231.41
231.41
K
2
3
3
3
3
4
5
3
5
8
Corrected for over-dispersal and effective sample size (QAICc), (wt) model weight, (-2l)
twice negative likelihood, (k) number of parameters.
Table 3.11—Best fit model selection of site covariate effects on detection probability models
(p) for brown hyena (Parahyaena brunnea).
Model
p(mixed forest)
p(drainage lines)
p(.)
p(bait)
p(distance to water)
p(roads)
p(mopane)
p(roads+drainage lines)
p(roads+drainage lines, mixed forest)
p(dist. to water, roads, mopane)
p(global)
AICc
230.5
231.57
231.84
232.02
232.22
232.62
232.63
233.96
234.79
236.64
243.26
∆AICk
0
1.07
1.34
1.52
1.72
2.12
2.13
3.46
4.29
6.14
12.76
wt
0.25
0.15
0.13
0.12
0.11
0.09
0.09
0.04
0.03
0.01
0.00
-2l
226.14
227.21
227.48
227.66
227.86
228.26
228.27
227.21
225.5
227.34
225.26
Corrected for effective sample size (AICc), (wt) model weight, (-2l) twice negative
likelihood, (k) number of parameters.
105
K
2
2
2
2
2
2
2
3
4
4
7
Table 3.12—Best fit model selection of site covariate effects on occurrence models (Ψ) for
spotted hyena (Crocuta crocuta).
Model
Ψ(global)
Ψ(distance to water)
Ψ(roads+drainage lines, mixed forest)
Ψ(roads)
Ψ(drainage lines)
Ψ(.)
Ψ(dist. to water, roads, mopane)
Ψ(mopane)
Ψ(mixed forest)
Ψ(roads+drainage lines)
AICc
117.29
118.23
118.90
119.24
119.46
121.36
121.95
122.96
123.73
132.78
∆AICk
0.00
0.94
1.61
1.95
2.17
4.07
4.66
5.67
6.44
15.49
wt
0.32
0.20
0.14
0.12
0.11
0.04
0.03
0.02
0.01
0.00
-2l
95.96
111.48
106.90
112.49
112.71
117.00
109.95
116.21
116.86
126.03
K
8
3
5
3
3
2
5
3
3
3
Corrected for effective sample size (AICc), (wt) model weight, (-2l) twice negative likelihood,
(k) number of parameters.
Table 3.13—Best fit model selection of site covariate effects on detection probability models
(p) for Spotted hyena (Crocuta crocuta).
Model
p(.)
p(distance to water)
p(drainage lines)
p(mixed forest)
p(mopane)
p(roads)
p(bait)
p(roads+drainage lines)
p(dist. to water, roads, mopane)
p(roads+drainage lines, mixed forest)
p(global)
AICc
121.36
127.84
135.22
129.53
133.83
136.16
131.42
132.78
127.65
126.16
128.68
∆AICk
0.00
6.48
13.86
8.17
12.47
14.80
10.06
11.42
6.29
4.80
7.32
wt
0.81
0.03
0.01
0.02
0.00
0.00
0.01
0.00
0.04
0.07
0.02
-2l
117.00
123.48
130.86
125.17
129.47
131.80
127.06
126.03
118.36
116.87
110.68
K
2
2
2
2
2
2
2
3
4
4
7
Corrected for effective sample size (AICc), (wt) model weight, (-2l) twice negative likelihood,
(k) number of parameters.
106
Table 3.14—Best fit model selection of site covariate effects on occurrence models (Ψ) for
scrub hare (Lepus saxatilis).
Model
Ψ(.)
Ψ(mopane)
Ψ(distance to water)
Ψ(mixed forest)
Ψ(roads)
Ψ(drainage lines)
Ψ(roads+drainage lines)
Ψ(dist. to water, roads, mopane)
Ψ(roads+drainage lines, mixed forest)
Ψ(global)
QAICc ∆QAICk
90.29
0.00
90.69
0.40
90.88
0.59
91.11
0.81
92.56
2.27
92.58
2.29
94.81
4.52
95.09
4.80
96.11
5.82
103.05
12.76
wt
0.24
0.20
0.18
0.16
0.07
0.07
0.02
0.02
0.01
0.00
-2l
137.49
138.12
134.61
134.97
137.30
137.33
136.83
132.94
134.58
130.74
K
2
2
3
3
3
3
4
5
5
8
Corrected for over-dispersal and effective sample size (QAICc), (wt) model weight, (-2l)
twice negative likelihood, (k) number of parameters.
Table 3.15—Best fit model selection of site covariate effects on detection probability models
(p) for scrub hare (Lepus saxatilis).
Model
p(roads)
p(mopane)
p(.)
p(distance to water)
p(drainage lines)
p(mixed forest)
p(bait)
p(roads+drainage lines)
p(dist. to water, roads, mopane)
p(roads+drainage lines, mixed forest)
p(global)
QAICc
44.31
44.40
44.45
44.45
44.45
44.45
44.51
46.63
48.86
49.09
57.00
∆QAICk
0.00
0.09
0.14
0.14
0.14
0.14
0.20
2.32
4.55
4.78
12.69
wt
0.14
0.13
0.13
0.13
0.13
0.13
0.13
0.04
0.01
0.01
0.00
-2l
137.00
137.31
137.49
137.49
137.50
137.51
137.69
136.80
135.73
136.53
133.78
Corrected for over-dispersal and effective sample size (QAICc), (wt) model weight, (-2l)
twice negative likelihood, (k) number of parameters.
107
K
2
2
2
2
2
2
2
3
4
4
7
Figure 3.2—Graph of results for analysis of species richness ratios detected compared to
length of camera trap days in operation from camera trap survey conducted within The Tuli
Wilderness Area, Botswana, 2011. Mean species with standard errors (SE) and significance
of (Kruskal-Wallis one-way multiple test, H2=13.073, P=0.001) noted by *.
108
Figure 3.3—Mammalian species frequency of detection result from camera trap survey
within The Tuli Wilderness Area, Botswana, 2011.
109
Table 3.16—Results for mammalian species mean latency to detection (LTD) with associated
standard errors in parentheses, total number of sites detected and overall frequency of
detection at camera sites from camera trap surveys within The Tuli Wilderness Area,
Botswana, 2011.
Species Name
Number of
sites
LTD (Days) detected
Proteles cristatus
Civetticis civetta
Panthera pardus pardus
Panthera leo
Lycaon pictus
Felis silvestris lybica
Mungos mungo
Otcyon megalotis
Canis mesomelas
Parahyaena brunnea
Caracal caracal
Mellivora capensis
Genetta maculataenet
Galerella flavescenson
Crocuta crocuta
Ichneumia albicauda
5(-)
5.21(0.93)
5.9(1.34)
11.33(4.98)
21(-)
8(2.32)
9.8(4.2)
3(0)
4.4(0.86)
3.83(0.44)
6(-)
6(3.08)
6.67(1.20)
5.89(1.97)
3.6(0.56)
2(-)
1
14
13
3
1
9
5
4
22
32
1
4
9
10
13
1
0.03
0.36
0.33
0.08
0.03
0.23
0.13
0.10
0.56
0.82
0.03
0.10
0.23
0.26
0.33
0.03
Artiodactyla
Blue Wildebeest
Bushbuck
Bushpig
Common Duiker
Common Eland
Common Warthog
Giraffe
Greater Kudu
Impala
Connochaetes taurinus
Tragelas scriptus
Potamochoerus larvatus
Sylvicapra grimmia
Tragelaphus oryx
Phacochoerus africanus
Giraffa camelepardalis
Tragelaphus strepsiceros
Aepyceros melampus
4.4(0.58)
5(1.53)
3.4(0.4)
4.43(1.23)
6.44(1.28)
3.5(0.43)
7(1)
4.16(0.67)
3(0.37)
11
4
6
6
12
24
2
21
27
0.28
0.10
0.15
0.15
0.31
0.62
0.05
0.54
0.69
Artiodactyla
Klipspringer
Steenbok
Waterbuck
Orertragus oreotragus
Raphicerus campestris
Kobus ellipsiprymnus
2(-)
6.38(1.31)
9(5)
2
8
4
0.05
0.21
0.10
Common Name
Carnivora
Aardwolf
African Civet
African Leopard
African Lion
African Wild Dog
African Wildcat
Banded Mongoose
Bat-eared Fox
Black-Backed Jackal
Brown Hyena
Caracal
Honey Badger
Large-Spotted Genet
Slender Mongoose
Spotted Hyena
White-tailed Mongoose
110
Frequency of
Detection
Table 3.16—Continued
Species Name
LTD (Days)
Number of
sites
detected
Hystrix africaeaustralis
Paraxerus cepapi
Pedetes capensis
7(3.04)
4.5(1.5)
4.57(0.85)
8
2
5
0.21
0.05
0.31
Proboscidea
African Elephant
Loxodonta africana
4.5(0.73)
21
0.54
Perissodactyla
Plains Zebra
Equus quagga
5.21(1.18)
14
0.36
Hyracoidea
Rock Hyrax
Procavia capensis
6(1)
2
0.05
Primates
Chacma Baboon
Vervet Monkey
Papio hamadryas ursinus
Chlorocebus pygerythrus
3.67(0.82)
3.5(0.5)
9
2
0.23
0.05
Lagomorpha
Scrub Hare
Lepus saxatilis
11.8(2.38)
12
0.13
Common Name
Rodentia
Cape Porcupine
Smith’s Brush Squirrel
Spring Hare
Frequency of
Detection
Tubulidentata
Ardvark
Orycteropus afer
9.75(4.87)
4
0.10
(-) Represents lack of standard errors for species that were only detected at one camera site.
111
INCLUSIVE LITERATURE CITED
AUGUSTINE, D. J.
AND
S. J. MCNAUGHTON. 1998. Ungulate effects on the functional species
composition of plant communities: herbivore selectivity and plant tolerance. Journal of
Wildlife Management 62(4):1165-1183.
BAKKER, E. S., M. E. RITCHIE, H. OLFF, D. G. MILCHUNAS,
AND
J. M. H. KNOPS. 2006.
Herbivore impact on grassland plant diversity depends on habitat productivity and herbivore
size. Ecology Letters 9: 780-788.
BALME, G., L. HUNTER,
AND
R. SLOTOW. 2007. Feeding habitat selection by hunting lopards
Panthera pardus in a woodland savanna: prey catchability versus abundance. Animal
Behaviour 74:589-598.
BALME, G. A., L. T. B. HUNTER,
AND
R. SLOTOW. 2009. Evaluating methods for counting
cryptic carnivores. Journal of Wildlife Management 73(3):433-441.
BALTANAS, A. 1992. On the use of some methods for the estimation of species richness. Oikos
5(3):484-492.
BENNETT, A. F. 1990. Habitat corridors and the conservation of small mammals in fragmented
forest environment. 4:109-122.
112
BOULINIER, T., J. D. NICHOLS, J. R. SAUER, J. E. HINES,
AND K.
H. POLLOCK. 1998.
Estimating species richness: the importance of heterogeneity in species detectability. Ecology
79(3):1018-1028.
BOWEKETT, A. E., N. R. LUNT,
AND
A. B. PLOWMAN.. 2006.. How do you monitor rare and
elusive mammals? Counting duikers in Kenya, Tanzania and Zimbabwe Pp. 21-28 in
Animals, zoos, and conservation (Ewa Zgrabczynska, Piotr Cwiertnia, and Joanna Ziomek)
The Zoological Garden, Poznan, Poland.
BURKEY, T. A. 1998. Extinction in nature reserves: the effect of fragmentation and the
importance of migration between reserve fragments. Oikos 55(1):75-81.
BURNHAM, K . P.,
AND
D. R. ANDERSON. 2002. Model selection and multimodel inference: a
practical information-theoretic approach. Second edition. Springer-Verlag, New York, New
York. 488 pp.
CARO, T. 2008. Decline of large mammals in the Katavi-Rukwa ecosystem of western Tanzania.
African Zoology 43(1):99-116.
CARO, T. M. 1999. Densities of mammals in partially protected areas: the Katavi ecosystem of
western Tanzania. Journal of Applied Ecology 36:205-217.
113
CARO, T. M. 2001. Species richness and abundance of small mammals inside and outside an
African national park. Biological Conservation 98:251-257.
CEBALLOS, G.,
AND
J. H. BROWN. 1995. Global patterns of mammalian diversity, endemism,
and endangerment. Conservation Biology 9:559-568.
CEBALLOS, G., P. RODRIGUEZ,
AND
R. A. MEDELLIN. 1998. Assessing conservation priorities
in magadiverse Mexico: mammalian diversity, endemicity, and endangerment. Ecological
Applications 8(1):8-17.
CHANG, K. T. 2012. Introduction to Geographic Information Systems. 6th ed. McGraw-Hill,
New York, NY.
COLLIER, B. A., R. A. MCCEERY, K. W. CALHOUN, K. G. ROQUES,
AND A.
MONADJEM.
2011. Detection probabilities of ungulates in the eastern Swaziland lowveld. Journal of South
African Wildlife Research 41(1):61-67.
DAVIS, A.
AND J.
R. WAGNER. 2003. Who knows? On the importance of identifying “experts”
when researching local ecological knowledge. Human Ecology 31(3):463-489.
114
DE VOS, A. 1964. Range changes of mammals in the Great Lakes Region. American Naturalist
71(1):210-231.
DONALD, P. F.
AND
A. D. EVANS. 2006. Habitat connectivity and matrix restoration: The wider
implications of agri-environment schemes. Journal of Applied Ecology 43:209-218.
ESRI 2011. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research
Institute.
ESTES, R. D. 1991. The Behavior Guide to African Mammals. University of California Press,
Ltd. Oxford, England: 366-369.
FINLEY, D. J., G. C. WHITE,
AND J.
P. FITZGERALD. 2005. Estimation of swift fox population
size and occupancy rates in eastern Colorado. Journal of Wildlife Management 69(3):861873.
FJELDSA, J., D. D. BURGESS, S. BLYTH, AND H. M. DEKLERK. 2004. Where are the major gaps
in the reserve network for Africa’s mammals? Oryx 38(1):17-25.
FORESMAN, K. R. AND D. E. PEARSON 1998. Comparison of proposed survey procedures for
detection of forest carnivores. The Journal of Wildlife Management 62(4):1217-1226.
115
GAVASHELISHVILI, A.,
AND
V. LUKAREVSKIY. 2008. Modeling the habitat requirements of
leopard Panthera pardus in west and central Asia." Journal of Applied Ecology 45.2
(2008):579-588.
GELDERBLOM, C. M., G. N. BRONNER, A. T. LOMBARD,
AND
P. J. TAYLOR. 1995. Patterns of
distribution and current protection status of the Carnivora, Chiroptera and Insectivora in
South Africa. South African Journal of Zoology 30(3):103-114.
GOMPPER, M. E., R. W. KAYS, J. C. RAY, S. D. LAPOINT, D. A.. BOGAN,
AND
J. R.
CRYAN. 2006. A comparison of noninvasive techniques to survey carnivore communities in
northeastern North America. Wildlife Society Bulletin 34(4):1142-1151.
HACKEL, J. D. 1999. Community conservation and the future of Africa’s wildlife. Conservation
Biology 13(4):726-734.
HADDAD, N. M., D. R. BOWNE, A. CUNNINGHAM, B. J. DANIELSON, D. J. LEVEY, S.
SARGENT,
AND T.
SPIRA. 2003. Corridor use by diverse taxa. Ecology 84(3):609-615.
HAYWARD, M. W., P. HENSCHEL, J. O’BRIEN, M. HOFMEYR, G. BALME,
AND
G. I. H.
KERLEY. 2006. Prey preferences of the leopard (Panthera pardus). Journal of Zoology
270:298-313.
116
HAYWARD, M. W., R. M. WHITE, K. M. MABANDLA,
AND
P. PAKAMA. 2005. Mammalian
fauna of indigenous forest in the Transkei region of South Africa: an overdue survey. South
African Journal of Wildlife Research 35(2):117-124.
HARRIS, G. M., G. J. RUSSELL, R. I.
VANAARDE, AND S.
L. PIMM. 2008. Rules of habitat use
by elephants Loxodonta africana in southern Africa: insights for regional management. Oryx
42(1):66-75.
HARRIS, JOHN E.,
AND
CHESTER V. OGAN., EDS. 1997. Mesocarnivores of Northern
California: Biology, Management, and Survey Techniques, Workshop Manual. August 1215, 1997, Humboldt State Univ., Arcata, CA. The Wildlife Society, California North Coast
Chapter, Arcata, CA. 127 p.
HILLEBRAND, H. AND T. BLENCKNER. 2002. Regional and local impact on species diversity-from
pattern to process. Oecologia 132:479-491.
HINES, J. E. (2006). PRESENCE2-Software to estimate patch occupancy and relates
parameters. USGS-PWRC. http://www.mbr-pwrc.usgs.gov/software/presence.html.
HOARE, R. E. 1999. Determinants of human-elephant conflict in a land-use mosaic. Journal of
Applied Ecology 36:689-700.
117
Holekamp, K. E., and S. M Dloniak. 2010. Intraspecific variation in the behavioral ecology
of a tropical carnivore, the spotted hyena. Advances in the Study of Behavior 42:189-229.
HUGHES, D. M. 2005. Third nature: Making space and time in the great Limpopo Conservation
Area. Cultural Anthropology 20(2):157-184.
IUCN 2012. IUCN Red List of Threatened Species. Version 2012.2. <www.iucnredlist.org>.
Downloaded on 05 February 2013.
JABERG, C.
AND
A. GUISAN. 2001. Modeling the distribution of bats in relation to landscape
structure in a temperate mountain environment. Journal of Applied Ecology 38(6):11691181.
JACKSON, R. M., J. D. ROE, R. WANGCHUK,
AND
D. O. HUNTER. 2005. Surveying snow
leopard populations with emphasis on camera trapping: a handbook. The Snow Leopard
Conservancy, Sonoma, California (73 pages)
JOHNSON, L. B. 1990. Analyzing spatial and temporal phenomena using geographical
information systems. Landscape Ecology 4(1):31-43.
118
KARANTH, K. U.
AND
J. D. NICHOLS. 2010. Non-invasive survey methods for assessing tiger
populations. Pp. 241-261 in Tigers of the world: the science, politics, and conservation of
Panthera tigris (R. Tilson and P. J. Nyhus). Academic Press, London, United Kingdom.
KARANTH, K. K., J. D. NICHOLS, J. E. HINES, K. U. KARANTH,
AND
N. L. CHRISTENSEN.
2009. Patterns and determinants of mammal species occurrence in India. Journal of Applied
Ecology 46(6):1189-1200.
KARANTH, K. K., J. D. NICHOLS, K. U. KARANTH, J. E. HINES,
AND
N. L. CHRISTENSEN JR.
2010. The shrinking ark: patterns of large mammal extinctions in India. Proceedings of The
Royle Society 277:1971-1979.
KAUFFMAN, M. J., M. SANJAYAN, J. LOWENSTEIN, A. NELSON, R. M. JEO,
AND K.
R.
CROOKS. 2007. Remote camera-trap methods and analyses reveal impacts of rangeland
management on Namibian carnivore communities. Oryx 41(1):70-78.
KAUFFMAN, M. J., N. VARLEY, D. W. SMITH, D. R. STAHLER, D. R. MACNULTY,
AND M.
S. BOYCE. 2007. Landscape heterogeneity shapes predation in a newly restored predator-prey
system. Ecology Letters 10:690–700.
119
KAYS, R.
AND K.
SLAUSON. 2008.. Remote Cameras Pp. 110-140 in Noninvasive survey
methods for carnivores (Robert Long, Paula MacKay, Willian Zielinski, and Justina Ray).
Island Press, Washington DC.
KELLY, K. J.
AND
E. L. HOLUB. 2008. Camera trapping of carnivores: trap success among
camera types and across species, and habitat selection by species, on Salt Pond Mountain,
Giles County, Virginia. Northeastern Naturalist 15(2):249-262.
KHOROZYAN, I. G., A. G. MALKHASYAN, AND A. V. ABRAMOV. 2008. Presence-absence
surveys of prey and their use in predicting leopard (Panthera pardus) densities: a case study
from Armenia. Integrative Zoology 3:322-332.
KRUESS, A.
AND
T. TSCHARNTKE. 1994. Habitat fragmentation, species loss, and biological
control. Science 264:1581-1584.
KUHN, B. F. 2012. Bone accumulations of spotted hyaenas (Crocuta crocuta, Erxleben, 1777) as
indicators of diet and human conflict; Mashatu, Botswana. International Journal of Ecology
2012:1-6
KWASI FOSU, A.
AND G.
MWABU. 2010. Human Development in Africa. World Institute for
Development Economic Research, United Nations University, Helsinki, Finland.
120
LANGHOLZ, J. 1996. Economics, objectives, and success of private nature reserves in subSaharan Africa and Latin America. Conservation Biology 10(1):271-280.
LOMBARD, A. T. 1995 Introduction to an evaluation of the protection status of South African’s
vertebrates. South African Journal of Zoology 30(3):63-70.
LOMBARDI, L., N. FERNANDEZ, S. MORENO,
AND R.
VILLAFUERTE. 2003. Habitat-related
differences in rabbit (Oryctolagus cuniculus) abundance, distribution, and activity. Journal of
Mammalogy 84(1):26-36.
LONG, R. A., T. M. DONOVAN, P. MACKAY, W. J. ZIELINSKI,
AND
J. S. BUZAS. [online]
2011. Predicting carnivore occurrence with noninvasive surveys and occupancy modeling.
Landscape Ecology 26:327-340
LOVERRIDGE, A. J., S. W. WANG, L. G. FRANK,
AND
J SEIDENSTICKER. 2010. Biology and
conservation of wild felids. Pp 161-195 People and wild felids: conservation of cats and
management of conflicts (MacDonald, D. W. and A. J. Loverridge). Oxford University
Press, United States, NY.
LYRA-JORGE, M. C.
AND
G. CIOCHETI. 2008. Comparing methods for sampling large-and
medium sized mammals: camera traps and track plots. European Journal of Wildlife
Research 54:739-744
121
MACKENZIE, D. I., J. D. NICHOLS, J. A. ROYLE, K. H. POLLOCK, L. L. BAILY,
AND J. E.
HINES. 2006. Occupancy estimation and modeling. Academic Press, Burlington,
Massachusetts
MACKEZIE, D. I., J. D. NICHOLS, N. SUTTON, K. KAWANISHI,
AND
L. L. BAILEY. 2005
Improving inferences in population studies of rare species that are detected imperfectly.
Ecology 86(5):1101-1113.
MACKENZIE, D. I.
AND
L. L. BAILEY. 2004. Assessing the fit of site-occupancy models.
Journal of Agricultural, Biological and Environmental Statistics 9(3):300-318.
MACKENZIE, D. I., J. D. NICHOLS, G. B. LACHMAN, S. DROEGE, J. A. ROYLE,
AND
C. A.
LANGTIMM. 2002. Estimating site occupancy rates when detection probabilities are less than
one. Ecology 83(8):2248-2255.
MACKEY, B. G.
AND D.
B. LINDENMAYER. 2001. Towards a hierarchical framework for
modeling the spatial distribution of animals. Journal of Biogeography 28(9):1147-1166.
MARNEWICK, K. A., J. DU P. BOTHMA,
AND
G. H. VERDOORN. 2006. Using camera-trapping
to investigate the use of a tree as a scent-marking post by cheetahs in the Thabazimbi district.
South African Journal of Wildlife Research 36(2):139-145.
122
MCKENZIE, A.A. 1990. Co-operative hunting in the black-backed jackal (Canis mesomelas, Schreber).
PhD Thesis, University of Pretoria, Pretoria, South Africa.
MILLS, L. S., M. E. SOULE,
AND
D. F. DOAK. 1993.The keystone-species concept in ecology
and conservation. BioScience 43(4):219-224.
MORUZZI, T. L., T. K. FULLER, R. M. DEGRAFF, R. T. BROOKS,
AND
W. LI. 2002. Assessing
remotely triggered cameras for surveying carnivore distribution. Wildlife Society Bulletin
30(2):380-386.
NEGROES, N., P. SARMENTO, J. CRUZ, C. EIRA, E. REVILLA, C. FONSECA, R. SOLLMANN, N.
M. TORRES, M. M. FURTADO, A. T. A. JACOMO,
AND
L. SILVEIRA. 2010. Use of camera-
trapping to estimate puma density and influencing factors in central Brazil. Journal of
Wildlife Management 74(6):1195-1203.
NOWAK, R. M. 1999. Walker’s mammals of the world. Vol. 1 & 2. The Johns Hopkins
University Press, Baltimore, Maryland.
O'NEILL, R. V., J. R. KRUMMEL, R. H. GARDNER, G. SUGIHARA, B. JACKSON, D. L.
DEANGELIS, B. T. MILNE, M. G. TURNER, B. ZYGMUNT, S. CHRISTENSEN, V. H. DALE,
AND R.
L. GRAHAM. 1988. Indices of landscape pattern. Landscape Ecology 1(3):153-162.
123
OSBORNE, P. E., J. C. ALONSO,
AND R.
G. BRYANT. 2001. Modeling landscape-scale habitat
using GIS and remote sensing: A case study with great bustards. Journal of Applied Ecology
38:458-471.
ORIANS, G.
AND M. GROOM.
2006. Global biodiversity patterns and processes. Pp 27-62 in
Principles of Conservation Biology (M. J. Groom,, G. K. Meffe, C. R. Carroll, and
Contributors. Sinauer Associates, Inc. Third Edition.
PICKETT, S. T. A.
AND P.
S. WHITE. 1985. The ecology of natural disturbance and patch
dynamics. Academic Press, New York.
PRINS, H. H. T.
AND
H. P. VAN DER JEUGD. 1993. Herbivore population crashes and woodland
structure in East Africa. Journal of Ecology 81:305-314.
RAMAKRISHNAN, U., R. G. COSS,
AND N.
W. PELKEY. 1999. Tiger decline caused by the
reduction of large ungulate prey: evidence from a study of leopard diets in southern India.
Biological Conservation 89:113-120.
RAY, J. C.
ET AL.
2005. Large carnivores and the conservation of biodiversity. Pp. 34-59 Large
carnivorous animals as tools for conserving biodiversity: Assumptions and uncertainties
(Justina C. Ray). Island Press, Washington, DC.
124
RODRIGUEZ-ARROYO, V.
AND
PA. D. DIAS. 2009. Effects of habitat fragmentation and
disturbance on howler monkeys: A review. American Journal of Primatology 71(1):1-16.
ROEMER, G. W., M. E. GOMPPER,
AND
B. VAN VALKENBURGH. 2009. The ecological role of
the mammalian mesocarnivore. BioScience 9(2):165-173.
ROWCLIFFE, J. M.,
AND C.
CARBONE. 2008. Surveys using camera traps: are we looking to a
brighter future? Animal Conservation 11:185-186.
RYAN, J. M. 2011. Mammalogy techniques manual. 2nd ed. Lulu, Raleigh, NC.
SHURIN, J. B.
AND
E. G. ALLEN. 2001. Effects of competition, predation, and dispersal, on
species richness at local and regional scales. The American Naturalist 158(6):624-637.
SELIER, S. A. J. 2007. The social structure, distribution and demographic status of the African
elephant population in the Central Limpopo River Valley of Botswana, Zimbabwe and South
Africa. Centre of Wildlife Management, Faculty of Natural and Agricultural Sciences,
University of Pretoria, Dissertation.
125
SIKES, R. S., W. L. GANNON,
AND THE ANIMAL CARE AND USE COMMITTEE OF THE AMERICAN
SOCIETY OF MAMMALOGISTS. 2011. Guidelines of the American Society of Mammalogists
for the use of wild animals in research. Journal of Mammalogy 92(1):235-253.
SRBEK-ARAUJO, A. C.
AND A.
G. CHIARELLO. 2005. Is camera-trapping an efficient method for
surveying mammals in Neotropical forests? A case study in south-eastern Brazil. Journal of
Tropical Ecology 21:121-125.
STAPP, P. 2007. Rodent communities in active and inactive colonies of black-tailed prairie dogs
in shortgrass steppe. Journal of Mammalogy 88(1):241-249.
STYLES, C. V.
AND J.
D. SKINNER. 2000. The influence of large mammalian herbivores on
growth form and utilization of mopane trees, Colophosperum mopane, in Botswana’s
Northern Tuli Game Reserve. African Journal of Ecology 38:95-101.
TEWKSBURY, J. J., D. J. LEVEY, N. M. HADDAD, S. SARGENT, J. L. ORROCK, A. WELDON,
B. J. DANIELSON, J. BRINKERHOFF, E. I. DAMSCHEN,
AND P.
TOWNSEND. 2002. Corridors
affect plants, animals, and their interactions in fragmented landscapes. Proceedings of the
National Academy of Sciences of the United States of America 99(20):12923-12926.
126
THORN, M., M. GREEN, P. W. BATEMAN, S. WAITE,
AND
D. M. SCOTT. 2011. Brown
hyaenas on roads: Estimating carnivore occupancy and abundance using spatially autocorrelated sign survey replicates. Biological Conservation (In press).
THORN, M., D. M. SCOTT, M. GREEN, P. W. BATEMAN,
AND
E. Z. CAMERON. 2009.
Estimating brown hyena occupancy using baited camera traps. South Africa Journal of
Wildlife Research 39(1):1-10.
TILSON, R.
AND P.
J. NYHUS. 2010. Tigers of the World. Pp. 241-261 Non-invasive Survey
Method for assessing tiger populations (K. Ullas Karanth and J. D. Nichols). Elsevier, San
Diego, CA.
TROLLE M.
AND
M. KERY. 2003. Estimation of ocelot density in the pantanal using capture-
recapture analysis of camera-trapping data. Journal of Mammalogy 84(2):607-614.
VERBOOM, B.
AND
R. VAN APELDOORN. 1990. Effects of habitat fragmentation on the red
squirrel, Sciurus vulgaris L. Landscape Ecology 4:171-176.
WALKER, B. H., R. H. EMSLIE, R. N. OWEN-SMITH,
AND R.
J. SCHOLES. 1987. To cull or not
to cull: lessons from a Southern African drought. Journal of Applied Ecology 24(2):381-401.
127
WEBER, D., U. HINTERMANN,
AND A.
ZANGGER. 2004. Scale and trends in species richness:
considerations for monitoring biological diversity for political purposes. Global Ecology and
Biogeography 13:97-104.
WEGGE P, C. P. D. POKHERAL,
AND S.
R. JNAWALI. 2004. Effects of trapping effort and trap
shyness on estimates of tiger abundance from camera trap studies. Animal Conservation
7:251–256.
WESTERN, D.,
AND
D. MAITUMO. 2004. Woodland loss and restoration in a savanna park: A
20-year experiment. African Journal of Ecology 42:111-121.
WOODROFFE, R. AND SILLERO-ZUBIRI, C. 2012. Lycaon pictus. In: IUCN 2012. IUCN Red List
of Threatened Species. Version 2012.2. <www.iucnredlist.org>. Downloaded on 06
November 2012.
WOODROFFE, R. 2000. Predators and people: Using human densities to interpret declines of large
carnivores. Animal Conservation 3:165-173.
WOODROFFE, R.
AND J.
R. GINSBERG. 1998. Edge effects and the extinction of populations
inside protected areas. Science 280:2126-2128.
128
VAN DE BILDT, M. W. G., T. KUIKEN, A. M. VISEE, S. LEMA, T. R. FITZJOHN,
AND A.
D.
M. E. OSTERHUS. 2002. Distemper outbreak and its effect on African wild dog conservation.
Emerging Infectious Diseases 8(2):211-213.
VAN DE KOPPEL, J.
AND
H. H. T. PRINS. 1998. The importance of herbivore interactions for the
dynamics of African savanna woodlands: an hypothesis. Journal of Tropical Ecology 14:565576.
129
APPENDICES
APPENDIX A
Biology and Earth Science WC Morris 306
Warrensburg, MO 64093
Office 660-543-4933
FAX 660-543-4355
May 13, 2011
Dr. Victoria Jackson
Department of Biology and Earth Science, WCM 306
University of Central Missouri
Dear Dr. Jackson,
Congratulations! Your animal use protocol entitled, Predicting occupancy from community survey of
medium to large-sized mammalian carnivores and herbivores in the Tuli Game Reserve of Botswana, has
been reviewed and approved by the University of Central Missouri Institutional Animal Care and Use
Committee (IACUC). Upon receipt of this letter, implementation of described research procedures may
begin. Please remember that a statement of any modification to this animal use protocol, including
personnel and procedural modifications, must be submitted to the IACUC prior to implementation of said
modifications. Likewise, animal use training of all personnel must be completed before work may begin.
Approval by this committee does not imply that equipment or facilities are available. Please contact
animal facility managers to make specific arrangements.
Your approved protocol has been issued a protocol number and an expiration date listed below. Please
keep this information in your records, as you may need it for granting and publication purposes. Please
reference your protocol number on correspondence concerning this animal use protocol. This protocol is
approved for three years; however, every protocol must be reviewed by the IACUC once a year. If you
intend to use animals purchased under this protocol number after the expiration date, you must resubmit
the protocol as a new initial submission.
Animal use protocol #: 11-3217
Protocol expiration Date: 5/2/2014
If you have further questions and/or concerns regarding the use of animals in research or the classroom at
the University of Central Missouri, please notify:
Dr. Scott Lankford ([email protected]) –Institutional Animal Care and Use Committee Chair Dan
Metcalf, M.S. – Institutional Animal Care and Use Committee Liaison
Sincerely,
Scott Lankford, PhD
Institutional Animal Care and Use Committee Chair
Dept. of Biology and Earth Science
WCM 303, University of Central Missouri
[email protected]
130