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ENDANGERED SPECIES RESEARCH
Endang Species Res
Vol. 38: 159–170, 2019
https://doi.org/10.3354/esr00941 Published March 28
1. INTRODUCTION
Understanding the environmental and anthro-
pogenic factors that influence animal movements
and resource use is fundamental for effective conser-
vation planning (Boyce 2006, Naidoo et al. 2014, Rip-
ple et al. 2016). It can be especially important for
threatened species that move considerable distances
across seasons, particularly outside of protected
areas, and must seek limited or variable resources
(Poor et al. 2012, Ripple et al. 2017, Purdon et al.
2018). Examining fine-scale space and habitat-use
patterns of individual animals from focal species can
help determine what landscape components are
essential for their future conservation, particularly as
landscapes change due to anthropogenic transforma-
© The authors 2019. Open Access under Creative Commons by
Attribution Licence. Use, distribution and reproduction are un -
restricted. Authors and original publication must be credited.
Publisher: Inter-Research · www.int-res.com
*Corresponding author: jmuntif@gmail.com
Hartmann’s mountain zebra resource selection
and movement behavior within a large unprotected
landscape in northwest Namibia
Jeff R. Muntifering1,*, Mark A. Ditmer1, 2, Seth Stapleton1, 2, Robin Naidoo3,
Tara H. Harris1,2
1Conservation Department, Minnesota Zoo, 13000 Zoo Boulevard, Apple Valley, MN 55124, USA
2Department of Fisheries, Wildlife and Conservation Biology, University of Minnesota, St. Paul, MN 55108, USA
3World Wildlife Fund, 1250 24th Street NW, Washington, DC 20090, USA
ABSTRACT: Expanding human populations, combined with an increasingly variable climate,
present challenges to the conservation of wide-ranging wildlife species, particularly for popula-
tions that persist in human-dominated landscapes. Although the movements and space use of
many equid species have been well studied, comparable research of the Hartmann’s mountain
zebra (HMZ) Equus zebra hartmannae, which primarily inhabits communal and commercial farm-
ing areas of Namibia, has been scarce and may limit conservation effectiveness. Here, we inves-
tigated the environmental and anthropogenic factors influencing HMZ movements and resource
use across a large area of their range in northwestern Namibia. We deployed 6 GPS collars on
HMZ during 2011 to 2013 and used integrated step selection functions to quantify HMZ move-
ments and space use. HMZ movements averaged ~5 km d−1, and mean seasonal home range sizes
were 681 and 256 km2in the wet and dry season, respectively. HMZ selected for areas with high
normalized difference vegetation index values (used as a proxy for primary production), particu-
larly during the dry season, while avoiding areas further from water and closer to human settle-
ments, although the effect was less apparent during the rainy season. Movement rates increased
when HMZ crossed roads and were closer to roadways, but rates were not impacted by proximity
to human activities. These results provide insights toward mitigating human−HMZ conflict. We
highlight the difficulty a changing and less predictable climate creates for grazing species living
in arid regions, as they must expend more energy and navigate dangers of a growing human foot-
print to seek out valuable but ephemeral forage.
KEY WORDS: Hartmann’s mountain zebra · Habitat · Resource selection · Integrated step
selection function · Movement · Seasonality · Namibia
O
PEN
PEN
A
CCESS
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Contribution to the Special ‘Biologging in conservation’
Endang Species Res 38: 159–170, 2019
tion and/or changing climates (Wasserman et al. 2012,
Zeller et al. 2012)
Wild and feral equids inhabit diverse grassland,
shrubland, and woodland environments around the
world and frequently display seasonal changes in
home range dimensions or use in response to shifts in
water and vegetation availability (Bartlam-Brooks et
al. 2013, Naidoo et al. 2014, Schoenecker et al. 2016).
Of the 7 extant species of wild equids recognized
by the IUCN Equid Specialist Group, all but the
kiang Equus kiang are considered threatened (Vul-
nerable, Endangered, or Critically Endangered) or
Near Threatened (IUCN 2017). Most of these equids
have experienced moderate to severe population
and/or range declines during the past 50 to 100 yr as
people and livestock have increasingly competed
with them for land and other resources, and as peo-
ple have targeted them for hunting — especially out-
side of protected areas (Moehlman et al. 2016, Parker
et al. 2017, O’Brien et al. 2018).
Considered vulnerable to extinction (Gosling et al.
2018), the mountain zebra E. zebra is a wild equid
native to southern Africa. Most nontaxonomic research
to date has focused on the Cape mountain zebra E. z.
zebra subspecies of South Africa, including studies of
behavior, ecology, and demography (Penzhorn 1982,
1988, Lloyd & Rasa 1989, Penzhorn & Novellie 1991,
Rasa & Lloyd 1994, Smith et al. 2007), effects of habi-
tat selection (Weel et al. 2015, Lea et al. 2016, 2018),
and management (Watson et al. 2005, Watson &
Chadwick 2007, Novellie et al. 2017) as well as dis-
ease and parasites (Krecek et al. 1994). Far less is
known about the Hartmann’s mountain zebra E. z.
hartmannae, the only other subspecies.
Classified as Vulnerable by the IUCN, Hartmann’s
mountain zebra (hereafter HMZ) are distributed pri-
marily along the western escarpment of Namibia,
stretching slightly into northwestern South Africa
and southwestern Angola (Gosling et al. 2018). The
largest population of free-roaming (i.e. not on pri-
vately owned and fenced commercial farms) HMZ
exists in northwestern Namibia, primarily on arid
unprotected communal lands. The start of the annual
summer rains, usually in November or December,
triggers a seasonal migration of large numbers of
HMZ (Joubert 1972). Anecdotal evidence suggests
these seasonal movements can sometimes span hun-
dreds of kilometers, and previous aerial and ground
surveys from this region suggest that ungulates
undertake seasonal movements in search of grazing
(i.e. green vegetation flushes) and water resources,
though data on HMZ were very limited (Leggett et
al. 2004). Since HMZ are presumed to move over
very large distances in response to spatial and tem-
poral variation in rainfall and primary production,
very large areas that are connected and support suit-
able habitat are needed if viable populations are to
survive. Recent climate projections suggest Namibia’s
northwest will become hotter and drier (Maure et al.
2018), potentially placing additional stress on HMZ,
both directly and indirectly through increased com-
petition with pastoralists for grazing. Thus, under-
standing how individuals within this key population
use the large formally unprotected landscape of
northwestern Namibia, especially as seasonal re -
source availability changes, is essential for effective
conservation.
We investigated seasonal changes in movement
patterns as well as the environmental and anthro-
pogenic factors influencing wild HMZ movements
and resource selection in unprotected areas of north-
west Namibia. We hypothesized that mountain zebra
will (1) seasonally move towards and be located in
areas with the greenest vegetation, (2) restrict their
movements to areas relatively close to water sources,
(3) avoid areas of heavy use by people and/or live-
stock, (4) have movements restricted by natural topo-
graphic features, and (5) alter their movement and
resource selection patterns when near or crossing an -
thropogenic features such as roadways as they seek
out forage. To test these hypotheses, we analyzed the
movements and resource use of GPS- collared HMZ
using integrated step selection functions (SSFs),
which allow for the quantification of factors that alter
animal movement and resource selection patterns
(Avgar et al. 2016). Our overall aim was to identify
the primary factors that drive HMZ space use
throughout the year in a landscape with an increas-
ing human influence and a changing climate.
2. MATERIALS AND METHODS
2.1. Study site
Our study area encompassed 14 227 km2of com-
munal land within the northwestern HMZ subpopu-
lation’s range in the Kunene region of Namibia;
7634 km2is categorized as communal conservancy
land, and 6593 km2is classified as state-administered
concession areas. The area averages 50 to 300 mm of
rainfall per annum, which primarily falls during the
rainy season (November−April), across an elevation
range from 242 to 1654 m on the largest flat-topped
Etendeka mountains (Mendelsohn et al. 2003, Munti -
fering et al. 2008). Geologically, the landscape is bro-
160
Muntifering et al.: Hartmann’s mountain zebra habitat use in Namibia
ken into a 132 million year old basalt deposit cover-
ing ~40% of the study area and granite hills which
support a relatively diverse assemblage of shrubs and
annual and perennial grasses. The land is bisected
by dry drainages dominated by mopane Colophos-
permum mopane and camelthorn trees Vachellia eri-
oloba interspersed with natural springs (Jacobson &
Jacobson 1995). In addition to the near-endemic
HMZ, other native ungulate species that may com-
pete with HMZ for resources include springbok Anti-
dorcas marsupialis and oryx Oryx gazella. Dominant
predators that are known to predate on HMZ are lion
Pathera leo, leopard Panthera pardus, cheetah Aci-
nonyx jubatus, and spotted hyaena Crocuta crocuta.
Human habitation and activity in the concession
areas are limited to ~100 permanent staff residing at
5 permanent tourism camps. About 6700 people
reside within the conservancy landscapes surround-
ing the concession areas, including Sesfontein, Ana -
beb, Omatendeka, and Ehirovipuka conservancies,
scattered across ~80 small settlements (NACSO 2014).
The dominant livelihood practice among these com-
munities is semi-nomadic pastoral farming. Small
herds of livestock including cattle, goats, and sheep
are maintained across the landscape and are man-
aged locally from each settlement (Muntifering et al.
2008). HMZ are utilized both nonconsumptively as a
major attraction for Namibia’s popular photographic
safari tourism industry (NACSO 2014) and consump-
tively, with an average of ~3500 hunted annually be -
tween 2008 and 2012 for commercial use of their
meat and skins (Gosling et al. 2018).
2.1.1. HMZ capture
We captured and fitted satellite tracking collars to 6
adult HMZ. In November 2011, 4 HMZ were cap-
tured within the Palmwag Concession (19° 53’ 12’’ S,
13° 56’ 13’’ E) and 1 in the neighboring Etendeka
Concession (19° 45’ 33’’ S, 13° 57’42’’E). Animals were
darted from a helicopter by a certified veterinarian
and were handled according to the protocols ap -
proved under research permit 1408/2009 from the
Ministry of Environment and Tourism in Namibia.
We outfitted these individuals (1 male stallion, 1
bachelor male, and 3 mares, all from different inde-
pendent groups) with GPS satellite collars (Africa
Wildlife Tracking) and monitored their movements
between November 2011 and March 2013. Collars
were programmed to attempt to collect a location
every 4 h. An additional bachelor male was captured
and collared using the same methods as above within
Etendeka Concession in July 2010 and was moni-
tored until September 2010 using a GPS-UHF collar
programmed to collect a location every half hour.
2.1.2. Spatial summary statistics
We calculated summary statistics of zebra move-
ment to highlight broad-scale differences among
individuals and seasons and for comparative pur-
poses with previous zebra studies using the program
R (R Development Core Team 2015). Metrics in -
cluded (1) daily movement distance, (2) net squared
displacement, (3) home range size using both the
95% minimum convex polygon and kernel density
estimates (KDEs; package adehabitatHR) (Calenge
2006), using an ad hoc method for choosing the
smoothing parameter), (4) estimates of kernel density
home range overlap (basic proportions using func-
tion kerneloverlaphr), (5) Euclidean distance (note:
all distance estimates in our analyses were Euclid-
ean) to natural water sources (km; see WaterDist,
Section 2.2.2), and (6) elevation ranges (m; see Elev,
Section 2.2.2). We calculated nonparametric boot-
strapped confidence intervals based on 10 000 sam-
ples from individual zebra sample mean values with
the package boot (Canty & Ripley 2017) using the
adjusted bootstrap percentile method. For any statis-
tic that compared values between seasons, we could
only use 5 of the 6 zebra because 1 zebra only col-
lected GPS locations during a single dry season.
2.1.3. Integrated SSFs
SSFs estimate resource selection at the data’s finest
spatiotemporal scales by comparing animal locations,
and their associated movement steps which connect
each set of sequential locations, to randomized move-
ments that can be drawn from empirical distributions
of the animal movements (Thurfjell et al. 2014).
Because the random locations incorporate the move-
ment characteristics of the individual animal, they
provide a more realistic comparison, relative to tradi-
tional resource selection functions, between actual
resource use and what was available at the same
moment in time given biologically feasible move-
ment distances. Integrated SSFs (iSSFs) not only esti-
mate resource use but can also provide insight into
how landscape features and resources shape animal
movement during the process of resource selection
by including movement characteristics in the same
model (Avgar et al. 2016).
161
Endang Species Res 38: 159–170, 2019
Using the package amt (Signer 2018, Signer et al.
2019), we regularized timesteps from the GPS loca-
tions of the 6 collared zebra. We created movement
bursts, which we defined as groups of GPS locations
that were no more than 4 h apart. Using these bursts,
we calculated step length (straight-line distance
between starting GPS location and the terminal loca-
tion of each step) and turning angles (derived from
the headings of 2 se quential steps) associated with
each movement step. Any movement burst without
adequate locations to calculate turning angles was
removed from the dataset, yielding a total of 10 835
used zebra steps for analyses. We then generated 15
random steps per used step. A large number of ran-
dom steps is not needed for estimating SSFs (Northrup
et al. 2013), so we chose a value similar to other stud-
ies with a similar fix frequency (Thurfjell et al. 2014).
To generate random steps, we fit gamma distribu-
tions to each individual’s step length values and a
von Mises distribution to individual turn angles. We
then used these distributions to draw the random
movement steps, with their associated step lengths
and turn angles, based on the derived terminal coor-
dinate values.
2.2. Model covariates
We calculated the log-transformed distance of step
length (StepLen) and the cosine of turning angle
(TurnAng). By taking the cosine of the turning angle,
values of TurnAng take values between −1 and 1,
where −1 indicates a complete 180° turn from the
previous heading and 1 represents moving in the
same direction as the previous heading (Benhamou
2006). In addition to step lengths and turn angles, we
calculated covariate values of both used and random
steps for temporal, natural resource, and human fac-
tors known or suspected to be important predictors of
HMZ habitat selection or movement.
2.2.1. Temporal patterns
To assess seasonal trends in movement and selec-
tion, we assigned all timestamps associated with
the terminal location of each step a season (rainy
[November− April] or dry [May−October]) based on
the precipitation patterns of this region of Namibia.
We used the function sunpos in the package map-
tools (Bivand & Lewin-Koh 2017) to assign the time-
stamp of each step’s terminal location a day period
(DayPeriod) based on the elevation of the sun (day:
>20, night: <−20, crepuscular: 20 to −20; location was
based on coordinates of terminal GPS location).
2.2.2. Natural resources
For each GPS location, we included terrain covari-
ates based on elevation (m; Elev) and slope (°; Slope)
extracted from the ASTER GDEM 2 digital elevation
model (30 m2resolution; NASA & METI 2011; ASTER
GDEM is a product of the Ministry of Economy,
Trade, and Industry [METI] of Japan and NASA). We
calculated the distances (km) to the nearest river or
other natural water source (WaterDist; e.g. spring,
wetland, ghorra). We uploaded our locations to
Move bank (Dodge et al. 2013) and extracted the nor-
malized difference vegetation index (NDVI; MODIS
Land Terra Vegetation Indices) associated with each
location and timestamp. Extracted NDVI values were
derived using inverse distance weighted interpola-
tion and were based on 250 m resolution that pro-
vides the estimate of NDVI using the highest quality
image over a 16 d period. Higher NDVI values are
associated with living green vegetation, while lower
values are suggestive of bare ground or sparse/dead
vegetation.
2.2.3. Human factors
Human factor covariates included the nearest (log-
transformed) distance to either a human settlement or
active well borehole (HumanSettle), log-transformed
distance to the nearest road (RoadDist), and whether
a movement step crossed a road (RoadX; 0 = no
crossing, 1 = crossing) using spatial data from the
Namibian atlas (Mendelsohn et al. 2003), ConINFO
(Environmental Information Service), and the Kunene
regional ecological assessment (Muntifering et al.
2008). We took the log-transformed distances of Hu-
manSettle and RoadDist because we hypothesized
that the effect might only occur when zebra were lo-
cated in close proximity. A veterinary exclusion fence,
constructed to isolate potential disease outbreaks and
protect livestock, runs the length of the study area
except in a few rugged mountainous areas. Based on
visualizations of zebra movements (Fig. 1), it was ap-
parent that the fence restricts zebra range, so we cal-
culated the number of times any actual or randomly
generated movement step crossed the fence (FenceX;
0 = no crossing, 1 = crossing). In visually examining
the steps with a fence crossing (FenceX = 1), we ob-
served that most used steps were likely the result of
162
Muntifering et al.: Hartmann’s mountain zebra habitat use in Namibia
the straight-line movement assumption between GPS
locations and the sinuosity of the fence (i.e. the zebra
likely did not cross but turned direction with the curve
of the fence). However, we did not change the covari-
ate value to reflect that it was likely an artificial fence
crossing (from FenceX = 1 to FenceX = 0), because we
had no way to verify, and there were relatively few in-
stances (relative to random steps where FenceX = 1).
2.3. Modelling process
Our modelling process generally follows Proko -
penko et al. (2017). We initially fitted 7 model formu-
lations to each of our 9 zebra-year (i.e. a full year of
movement data recorded per zebra) datasets individ-
ually (most zebra were collared for 2 yr; min. of 250
locations per year and season for a zebra-year to be
in cluded) using conditional logistic regression (pack-
age survival; Therneau 2018) with strata that associ-
ated the unique starting point ID of each used step
with the corresponding 15 random steps that share
the starting coordinates. We used zebra-year as our
sampling unit because changes in seasonal forage
availability among years and demographic status
(with or without foal) may influence zebra move-
ments from year to year. We created a null model that
only included movement characteristics (TurnAng,
StepLen) and a core model that included all temporal
and natural resource covariates (NDVI, Slope, Elev,
WaterDist) along with the movement
characteristics. The core model also
included an interaction between NDVI
and season be cause we expected
selection for green forage to be more
distinguishable during the dry season
and an interaction between StepLen
and DayPeriod to account for differing
levels of activity throughout the day.
The additional 5 models in cluded all
of the covariates from the core model
specification and each human factor
as an additional covariate (see Table 1
for all initial model formulations).
When testing for the influence of
covariates on selection, we used the
covariate values as sociated with the
terminal GPS coordinates of each step,
and we used the values associated
with the starting coordinates of each
step when assessing the influence on
movement. We did not include RoadX
and RoadDist in the same model to
avoid issues with collinearity.
For each model formulation, we calculated an
Akaike’s information criterion (AIC) (Anderson 2008)
value to determine which of the human factor vari-
ables (or combinations of variables) best de scribed
zebra selection and movement. The model formula-
tion with the lowest AIC value per zebra-year model
received a point. If multiple AIC scores for different
model formulations were within 2 AIC points, we
assigned fractions of points (e.g. if 2 models were
within 2 AIC points for a zebra-year, each received
0.5). We then tallied the points by model formulation.
We used the same system of accounting for the best-
fitting model within each zebra-year model as Proko -
penko et al. (2017) as a means of highlighting which
human factors ex plained the most additional (i.e. in
addition to the core model covariates) deviance with-
out reporting a large number of AIC values that con-
tain relative estimates of the deviance explained for
each zebra-year. Estimated beta coefficients from our
models are too narrow because they treat strata from
each zebra-year model as independent. To make
population-level inference, we bootstrapped the co -
efficient estimates from our individual zebra-year
models using the same process for bootstrapping as
de scribed in Section 2.1.2 but weighted the bootstrap
based on individual zebra ID. We also report the per-
centage of zebra-year models with the same coeffi-
cient directionality of the reported bootstrapped pop-
ulation sample mean.
163
Fig. 1. Movement pathways for 5 GPS-collared Hartmann’s mountain zebra
located in Namibia between November 2011 and March 2013
Endang Species Res 38: 159–170, 2019
2.4. Post hoc analyses
Because only 4 zebra-years had any used or ran-
dom steps with fence crossings, we could not include
FenceX in our initial model comparisons using all
zebra-year models. However, we believed the veteri-
nary fence may strongly influence the behavior of
zebra living near it. Thus, we tested 2 additional
models after examining our initial model results. We
used the most highly supported model formulation
(i.e. with the greatest point tally) and then included
the covariate FenceX and the interaction for FenceX×
StepLen to examine how the fence may influence
zebra movement.
We also found the weak response of zebra to
human settlements somewhat surprising, so we used
our top model formulation and included an interac-
tion between HumanSettle and season. We hypothe-
sized that zebra may show differing levels of toler-
ance for proximity to human settlements based on
the season because of differences in resources be -
tween the wet and dry seasons.
3. RESULTS
A total of 6 HMZ were collared and their daily
movement monitored between November 2011
and March 2013 (Fig. 1). Zebra average daily
movement rates were estimated to be around 5.4
km d−1 based on straight-line movements between
GPS locations (x[95% CI] = 5.39 [4.79, 5.98] km
d−1). Relative to the dry season, on average zebra
moved greater distances each day during the rainy
season (rain: x= 5.66 [4.9 6, 5.93]; dry: x= 5.14
[4.21, 6.31]). Plots of net squared displacement
indicated that for the 4 zebra with data throughout
both seasons, there was a clear shift in space use
between the dry and rainy seasons (see Fig. 2 for
an example). During the rainy season, zebra had
larger home ranges relative to the dry season
based on 95% KDEs (rain: x= 681.2 [287.9, 946.1];
dry: x= 255.8 [74.1, 419.5] km2) and 95%
minimum convex polygons (rain: x= 559.9 [245.5,
885.6]; dry: x= 223.1 [73.6−347.1] km2). Zebra
often shifted their home ranges between seasons
(% overlap of individual’s dry season 95% KDE
home range by rainy season home range: x= 24.8
[6.3, 74.8]; % rainy season home range covered by
dry season home range: x= 13.5 [6.0, 30.0]), but
there was considerable variability in home range
overlap among different individuals (rain: x= 11.6
range = 2.0−22.9% of 95% KDE rainy season esti-
mates; dry: x= 6.3% range = 0−15% of 95% KDE
dry season estimates). Within home ranges, zebra
were located closer to natural water sources
during the dry season (x= 1.76 [1.07, 2.13] km),
relative to the rainy season (x= 2.73 [1.72, 4.15]
km). Zebra utilized an average elevational gradient
of around 600 m within their home ranges (xmin.
elevation per zebra = 788 [686, 867] m; xmax. ele-
vation per zebra = 1374 [1241− 1507] m).
164
Model Model description Covariates Min. AIC tally
no. (zebra-years)
1 Influence of road crossings and distance to human Core + RoadX + RoadX×StepLen + HumanSettle 4.5
activity on zebra movement and selection (end pt.) + HumanSettle (start pt.)×StepLen
2 Influence of road crossings on zebra movement Core + RoadX + RoadX×StepLen 2.5
and selection
3 Influence of distance to roadways and distance to Core + RoadDist (end pt.) + RoadDist 1.5
human activity on zebra movement and selection (start pt.)×lnStepLen + HumanSettle (end pt.)
+ HumanSettle (start pt.)×StepLen
4 Influence of distance to roadways on zebra Core + RoadDist (end pt.) + RoadDist 0.5
movement and selection (start pt.)×StepLen
5 Influence of distance to human activity on zebra Core + HumanSettle (end pt.) + HumanSettle 0
movement and selection (start pt.)×StepLen
6 Core model; movement characteristics, temporal, Core (NDVI + NDVI:Season + Slope + Elev 0
terrain, and vegetation covariates + WaterDist + TurnAng + StepLen
+ StepLen×DayPeriod)
7 Influence of movement covariates Null (StepLen + TurnAng) 0
Table 1. Summary of model covariates and associated ranking. For each zebra-year, we fit all 7 model formulations. We con-
sidered the null model to only include step length and turning angle. Our core model (model no. 6) included all natural habitat
and geographic covariates. When considering human factor covariates, we added each to the core model formulation. The log-
transformed value of the covariates StepLen, HumanSettle, and RoadDist and the cosine of TurnAng were used in the model.
AIC: Akaike’s information criterion; NDVI: normalized difference vegetation index
Muntifering et al.: Hartmann’s mountain zebra habitat use in Namibia
3.1. Model ranking
The top model included covariates
that reflect the influence of road cross-
ings and distance to human activity
on zebra movement and selection
(Table 1). Models including the influ-
ence of road crossings were included
in the top models for 7 of 9 zebra-
years. Coefficient values from the co -
variates in the core and the top model
were similar, so all reporting of results
for the core covariates were taken
from the top model.
3.2. Core covariates
3.2.1. Core model: resource selection
Zebra selected for areas with high er
NDVI values (NDVI: x[95% CI] =
10.54 [4.76, 18.58]; 88.9% of models) based on boot-
strapped population means and 95% confidence
intervals, although the effect was far weaker during
the rainy season, when vegetation is more abundant
(NDVI × Season [rain]: x= −4.70 [−13.68, 0.003]; 75%
of models). Zebra avoided areas further from natural
water sources (WaterDist: x= −0.26 [−0.35, −0.092];
89% of models) and areas with higher elevations
(Elev: x= −1.62 [−4.48, −0.72]; 89% of models) but
were not influenced by slope (Slope: x= 0.00 [−0.014,
0.0039]).
3.2.2. Core model: movement
Overall, zebra did not consistently show directional
persistence in their movements (TurnAng: x= −0.023
[−0.028, 0.047]). Zebra movement rates were greatest
during the crepuscular times of day relative to day-
light (StepLen × DayPeriod[day]: x= −0.11 [−0.25,
−0.0009]; 67% of models) and especially when com-
pared to nighttime periods (StepLen× DayPeriod
[night]: x= −0.46 [−0.54, −0.37]; 100% of models).
3.3. Human factor covariates
3.3.1. Human factor model: resource selection
Seven of 9 zebra-year models indicated selection
for areas further away from human activity (Fig. 3A),
but the bootstrapped population 95% confidence
interval overlapped zero (HumanSettle: x= 0.38
[−0.28, 0.56]). Zebra commonly crossed roads (x% of
total movement steps with a crossing = 19.1% [13.4,
24.5%] by zebra-year) but less so than expected —
indicating avoidance (RoadX: x= −0.65 [−0.92,
−0.43]; 100% of models). Though the model formula-
tion including distance to the nearest road (RoadDist)
was only included in 22% of zebra-year top models
(Table 1), coefficient values indicate a consistent
selection for areas closer to roadways (RoadDist: x=
−0.041 [−0.13, −0.027]; 89% of models; Fig. 3A).
3.3.2. Human factor model: movement
Zebra movements were not consistently influenced
by proximity to areas of high human activity (Human -
Settle × StepLen: x= −0.16 [−0.28, 0.084]; Fig. 3B). Ze-
bra movement rates increased when crossing roads
(RoadX × StepLen: x= 0.63 [0.30, 0.73]; 100% of mod-
els) and when closer to roadways (RoadDist × StepLen:
x= −0.063 [−0.14, −0.043]; 100% of models).
3.4. Post hoc analysis using top model
3.4.1. Influence of fences
The veterinary fence influenced the movements of
the individual zebra that were located near it. The
165
Fig. 2. Net squared displacement of a GPS-collared Hartmann’s mountain
zebra (individual SAT170) in Namibia from April 2012 to June 2013. The gray
shaded areas represent the rainy season (November−April). We took the
square root of the raw net squared displacement for ease of interpretation
Endang Species Res 38: 159–170, 2019
top models from 3 of 4 zebra-year models that in -
cluded FenceX and FenceX × StepLen had large re -
ductions in their AIC scores (range of AIC reduction
from top model of each zebra-year model: 71−154),
suggesting very strong support for the inclusion of
FenceX and FenceX × StepLen. Fence crossings dur-
ing these 4 zebra-years occurred rarely (0.65 %;
[95% CI: 0.19, 2.69%] of movement steps), when
compared to the random movement steps generated
for the iSSF (4.85%; [95% CI: 1.31, 7.91 %]), resulting
in avoidance of fence crossings for all zebra-years
(FenceX β
ˆfor each zebra-year = −12.64, −3.70, −3.64,
−1.72). Fence crossings did not consistently influence
movement rates in these 4 zebra-year models
(StepLen × FenceX β
ˆ for each zebra-year = −0.35,
−0.27, 0.85, 1.04).
3.4.2. Seasonal influence of human activity
Including an interaction between season and dis-
tance to human activity reduced AIC values by >2 in
25% of models relative to the top model without the
interaction. In this model, zebra again selected for
areas further from human activity (HumanSettle: x=
0.61 [−0.14, 1.13]; positive in 63% of models) but less
so during the rainy season (HumanSettle × Season
[rain]: x= −0.50 [−0.95, 0.29]; negative in 75% of
models). Again, zebra movement rates were not
influenced by the distance to human activity (x=
−0.017 [−0.067, 0.13]).
4. DISCUSSION
Ensuring conservation action is effective in the
Anthropocene era (Caro et al. 2012, Ripple et al.
2015) will benefit from evidence-based studies that
seek to better understand how wild animals, particu-
larly wide-ranging species such as free-ranging
equids, behave in human-dominated landscapes and
respond to a changing climate, conditions which are
transforming wildlife habitat around the world (Sloat
et al. 2018). Our analysis detailed how HMZ move-
ments and resource use are closely tied to the sea-
sonality of NDVI at a fine scale, while demonstrating
how human components to the landscape may alter
their abilities to reach resources or change their
movement patterns when seeking them out. Prior to
this analysis, very few studies have provided any
detailed documentation on the ranging behavior of
the HMZ. Here, we provide one of the first quantita-
tive studies on home range, resource selection, and
movement as well as the effects of human develop-
ment on free-ranging HMZ in Namibia.
Our findings provide some important updated
basic knowledge on ranging behavior that could be
compared with other zebra and equid species. Free-
ranging HMZ were observed to cover approximately
166
Fig. 3. Individual coefficient estimates and bootstrapped
population mean and 95% confidence intervals from inte-
grated step selection function models of GPS-collared Hart-
mann’s mountain zebra in Namibia from 2010 to 2013. The
influence of 3 human factors on the landscape are shown
here for the effect on (A) zebra resource selection and (B) ze-
bra movement (interaction between the covariate and log-
transformed step length): (1) HumanSettle = distance to the
nearest village or man-made water source, (2) RoadX =
whether the movement step intersected with a roadway, and
(3) RoadDist = distance to the nearest roadway. For distance-
based covariates (HumanSettle, RoadDist), the distance to a
feature was based on the starting location of each movement
step when considering the influence of movement (B), and
the distance between the terminal location of each move-
ment step was used for estimating distances when consider-
ing selection (A)
Muntifering et al.: Hartmann’s mountain zebra habitat use in Namibia
5 km d−1 or 1825 km yr−1, with negligible differences
between seasons. However, estimated home range
size differed substantially between seasons and aver-
aged between 681 and 256 km2in the wet and dry
season, respectively, with a maximum of nearly
950 km2. This is more or less similar to other plains
zebra studied in large open landscapes such as the
Kruger National Park in South Africa (Smuts 1975),
Serengeti (Klingel 1969), and Botswana (Bartlam-
Brooks et al. 2013, Naidoo et al. 2014) and is typical
of equids ranging across arid, resource-limiting open
landscapes such as Przewalski horses in the Gobi
desert landscapes of both Mongolia (Kaczensky et al.
2008) and China (Chen 2008). However, it is surpris-
ingly less than Grevy’s zebra, which have been found
to range over 10 000 km2in parts of Kenya (Ruben-
stein et al. 2016). This could be due, in part, to our
sampling window occurring during and just follow-
ing an exceptionally wet period (2010−2011) in
northwestern Namibia with rainfall figures reaching
4 to 5 times above average. Although wet season
ranges are often longer than dry, an extended wet
period with high-quality forage easily available may
also reduce home range sizes temporarily. Some
HMZ individuals were also observed to have very lit-
tle home range overlap between dry and wet sea-
sons, but others exhibited high degrees of overlap.
This suggests that some HMZ groups may indeed
have smaller seasonal home ranges, like their close
relatives, Cape mountain zebra, in South Africa (Pen-
zhorn 1982, 1988), but ranges may vary between
groups (Owen-Smith 2013) or only a portion of the
population is migratory, as documented for other
zebra populations (Georgiadis et al. 2003). However,
our reported home range sizes here are significantly
larger than the 6−20 km2ranges previously reported
by Joubert (1972) for HMZ in western Etosha
National Park, Namibia.
Our results using an iSSF modelling technique
mostly confirmed what we hypothesized about HMZ
resource selection and effects of human-induced dis-
turbance. Many studies on equid species, and zebra
species specifically across Africa, have reported their
ranging behavior to be heavily influenced by rainfall
and associated high-quality resource availability
(Young et al. 2005, Schoenecker et al. 2016). Using
NDVI as a proxy for vegetation productivity, HMZ
demonstrated strong selection towards areas of high
primary productivity, especially during the dry sea-
son, confirming our first hypothesis that they would
seek out areas with high-quality grazing. This is con-
sistent with previous research that indicates moun-
tain zebra need consistent access to quality and con-
stant grazing (Penzhorn & Novellie 1991) and corrob-
orates with other regional studies of other zebra
habitat selection and ranging behavior (Bartlam-
Brooks et al. 2013, Naidoo et al. 2014).
In addition to selecting areas of high NDVI, HMZ
were found to select areas exceptionally close to per-
manent water and lower elevation. Monitored zebra
rarely moved beyond 4 km from water sources, aver-
aging less than 2 km in the dry season, confirming
our second hypothesis that HMZ would not be
located in areas far from a permanent water source.
These results further confirm their water depend-
ency (Joubert 1972) and are similar to those found for
Grevy’s zebra, which rarely range beyond 10 km
from permanent water (Hostens 2009). This finding
emphasizes the importance of ensuring zebra have
sufficient access to permanent water to maintain
functionally connected landscapes. Even relatively
small increases in NDVI variability or reductions in
access to high-NDVI forage, from either direct human
alterations to the landscape or climatic change, may
negatively impact reproduction (Stoner et al. 2016).
Most notably, our iSSF analysis demonstrates the
negative effect of human activity on resource selec-
tion and the negative impact of roads on HMZ move-
ment. Our results indicate that zebra select areas fur-
ther from human settlement. This is not surprising,
considering nearly every settlement in the area is
dominated by livestock, creating competition for
grazing closer to settlements. However, the effect of
human settlement on movement was not substantial.
This would likely be due to the relatively small dis-
tance threshold of human impact from settlements.
For example, previous social surveys across 136 set-
tlements within and surrounding our study area doc-
umented that livestock rarely moved beyond 4 to 6 km
from their kraals at each settlement (Muntifering et
al. 2008). Thus, any direct competition from livestock,
especially cattle, would be the same at any distance
beyond 6 km from settlements. Given the very low
human population density in the region at less than 1
km−2 (Steytler 2014), it is not surprising that if zebra
are already avoiding human settlements, their finer-
scale movement decisions would not be influenced
much by human activity. The strong avoidance of
human activity areas has been well documented for
other zebra in Kenya (Young et al. 2005) and for
Grevy’s (Hostens 2009) and other equid species,
especially kiang or Tibetan wild ass (Sharma 2004),
whose ranges are heavily restricted by livestock dis-
tribution. Even other related species within the peris-
sodactyl order (odd-toed ungulates), such as free-
ranging black rhino populations in the Masai Mara
167
Endang Species Res 38: 159–170, 2019
and northwestern Namibia, have been found to show
strong avoidance towards humans and livestock
(Walpole et al. 2003, Muntifering et al. 2008).
HMZ also slightly selected for areas closer to roads
but increased their movement rates substantially in
close proximity to roads and when crossing them.
This has been found elsewhere for other migrating
ungulates such as elk (Prokopenko et al. 2017) and
moose (Berger 2007). Although we did not test the
related effects on demographic rates and population
performance, these results suggest that areas near
roads may have resources sought out by HMZ but
that crossing these foreign linear landscape features
alters the movements of this wide-ranging species as
they attempt to reach critical resources.
In addition to roads, 3 of our collared zebra clearly
demonstrated the negative barrier effects of fences
by spending significant time moving alongside the
veterinary fence which bisects Namibia, but not
crossing it. Although inference is somewhat limited
by the small sample size, this suggests critical re -
sources were likely being sought out along or on the
other side of the fence, but the zebra failed to find a
way to cross although some locations appeared to
cross the fence by a few meters due to GPS collar
error. The same fence had devastating effects on
HMZ in the 1980− 1982 drought in the area where
hundreds of emaciated zebra carcasses were found
lying along the fenceline (Gosling et al. 2018).
Research in neighboring Botswana recently discov-
ered that following the removal of the same veteri-
nary fence, over 15 000 plains zebra began migrating
to what has been suggested as ancient migratory
areas after 50 yr (Bartlam-Brooks et al. 2013). We
acknowledge that the intervals between time steps
(i.e. 4 h) somewhat limit inferences associated with
our movement results and thus suggest some caution
in interpretation.
Last, our analysis provides useful insights towards
informing human development planning for the
landscape as well as designing future management-
oriented research to fill key knowledge gaps. Two
key trends emerging in northwestern Namibia that
may significantly affect HMZ conservation are a rel-
atively small but persistent positive growth in the
human population and the tourism industry. These
trends offer opportunities for conservation, especially
the tourism industry, yet they also create challenges
for management. The expanding human populations
consistently seek areas to graze their livestock and
demand infrastructure improvements such as roads
to enhance mobility and accessibility. Tourism devel-
opment also requires similar infrastructure expan-
sion with ever-expanding lodges to cater to the
increasing numbers of tourists, exerting greater pres-
sure on already low water levels. Under the most
recent climate projections, the area is likely to expe-
rience more frequent and intense drought conditions.
As drought conditions worsen, farmers will become
more desperate to find grazing areas for their live-
stock, and the negative impacts of tourism develop-
ment may become more relevant, likely increasing
competition with persisting HMZ. The evidence
compiled here on the tendency of HMZ to avoid
human development and activity extenuates the im -
portance of ensuring that future development plan-
ning takes into account impacts on wide-ranging vul-
nerable and valuable species such as HMZ and
works to minimize impacts.
Acknowledgements. We thank Namibia’s Ministry of Envi-
ronment and Tourism as well as Palmwag and Etendeka
concessionaires for permission to conduct this research. We
thank both Dr. Mark Jago and Dr. Axel Hartmann for lead-
ing the capture and collaring. We are grateful for funding
received from The Nature Conservancy for the GPS collars
and Disney’s Animal Kingdom, Minnesota Zoo’s Ulysses S.
Seal Conservation Grant Program and Volunteer Activity
Program, Wilderness Wildlife Trust, Oregon Zoo’s Future for
Wildlife Conservation Fund, Roger Williams Park Zoo’s
Sophie Danforth Conservation Biology Fund, NEW Zoologi-
cal Society’s Conservation Fund, and the B. Bryan Preserve
for logistical support.
LITERATURE CITED
Anderson DR (2008) Model based inference in the life sci-
ences. Springer, New York, NY
Avgar T, Potts JR, Lewis MA, Boyce MS (2016) Integrated
step selection analysis: bridging the gap between
resource selection and animal movement. Methods Ecol
Evol 7: 619−630
Bartlam-Brooks HLA, Beck PSA, Bohrer G, Harris S (2013)
In search of greener pastures: using satellite images to
predict the effects of environmental change on zebra
migration. J Geophys Res Biogeosci 118: 1427−1437
Benhamou S (2006) Detecting an orientation component in
animal paths when the preferred direction is individual-
dependent. Ecology 87: 518−528
Berger J (2007) Fear, human shields and the redistribution of
prey and predators in protected areas. Biol Lett 3:
620−623
Bivand R, Lewin-Koh N (2017) Maptools: tools for reading
and handling spatial objects. R package version 0.9-2.
https: //CRAN.R-project.org/package=maptools
Boyce MS (2006) Scale for resource selection functions.
Divers Distrib 12: 269−276
Calenge C (2006) The package ‘adehabitat’ for the R soft-
ware: a tool for the analysis of space and habitat use by
animals. Ecol Modell 197: 516−519
Canty A, Ripley B (2017) Bootstrap R (S-Plus) functions. R
package version 1.3-20. https: //CRAN.R-project.org/
package=boot
168
Muntifering et al.: Hartmann’s mountain zebra habitat use in Namibia
Caro TIM, Darwin J, Forrester T, Ledoux-Bloom C, Wells C
(2012) Conservation in the Anthropocene. Conserv Biol
26: 185−188
Chen J (2008) Utilization of food, water and space by
released Przewalski horse with reference to survival
strategies analysis. PhD thesis, Beijing Forestry Univer-
sity, Beijing
Dodge S, Bohrer G, Weinzierl R, Davidson SC and others
(2013) The environmental-data automated track annota-
tion (Env-DATA) system: linking animal tracks with
environmental data. Mov Ecol 1: 1−14
Georgiadis N, Hack M, Turpin K (2003) The influence of
rainfall on zebra population dynamics: implications for
management. J Appl Ecol 40: 125−136
Gosling LM, Muntifering JR, Kolberg H, Uiseb K, King SRB
(2018) Equus zebra ssp. hartmannae. The IUCN Red List
of Threatened Species 2018. IUCN, Gland
Hostens E (2009) Modelling the migration of Grevy’s zebra
in function of habitat type using remote sensing. Univer-
sity of Gent, Gent
IUCN (2017) The IUCN Red List of Threatened Species. Ver-
sion 2017-3. IUCN, Gland
Jacobson P, Jacobson KMS (1995) Ephemeral rivers and
their catchments: sustaining people and development in
western Namibia. Desert Research Foundation of
Namibia, Windhoek
Joubert E (1972) The social organization and associated
behaviour in the Hartmann zebra (Equus zebra hartman-
nae). Madoqua 6: 17−55
Kaczensky P, Ganbaatar O, Von Wehrden H, Walzer C
(2008) Resource selection by sympatric wild equids in the
Mongolian Gobi. J Appl Ecol 45: 1762−1769
Klingel H (1969) The social organisation and population
ecology of the plains zebra (Equus quagga). Zool
Africana 4: 249−263
Krecek NJP, Horak IG, Malan FS (1994) Helminth parasites
of Cape mountain zebras from Cape Province, South
Africa. J Wildl Dis 30: 277−280
Lea J, Ih G, Hrabar H, Barry TJ, Shultz S (2016) Recogni-
tion and management of ecological refugees: a case
study of the Cape mountain zebra. Biol Conserv 203:
207−215
Lea JMD, Walker SL, Kerley GIH, Jackson J, Matevich SC,
Shultz S (2018) Non-invasive physiological markers
demonstrate link between habitat quality, adult sex ratio
and poor population growth rate in a vulnerable species,
the Cape mountain zebra. Funct Ecol 32: 300−312
Leggett K, Fennessy J, Schneider S (2004) A study of animal
movement in the Hoanib River catchment, northwestern
Namibia. Afr Zool 39: 1−11
Lloyd PH, Rasa OAE (1989) Status, reproductive success and
fitness in Cape mountain zebra (Equus zebra zebra).
Behav Ecol Sociobiol 25: 411−420
Maure G, Pinto I, Ndebele-Murisa M, Muthige M and others
(2018) The southern African climate under 1.5°C and 2°C
of global warming as simulated by CORDEX regional cli-
mate models. Environ Res Lett 13
Mendelsohn J, Jarvis A, Roberts C, Robertson T (2003) Atlas
of Namibia: a portrait of the land and its people. David
Philip Publishers, Cape Town
Moehlman PD, King SRB, Kebede F (2016) Status and con-
servation of threatened equids. In: Ransom J, Kaczen-
sky P (eds) Ecology, management and conservation of
wild equids. John Hopkins University, Baltimore, MD,
p 167−186
Muntifering JR, Lockhart CJ, Tingey R, Griggs J, Heine-
meyer K, Lalley J, Sizemore D (2008) The Kunene
regional ecological assessment. Round River Conserva-
tion Studies, Salt Lake City, UT
NACSO (Namibia Association of CBNRM Support Organi-
sations) (ed) (2014) The state of community conservation
in Namibia: a review of communal conservancies, com-
munity forests and other CBNRM initiatives (2013 annual
report). NACSO, Windhoek
Naidoo R, Chase MJ, Beytell P, Du Preez P, Landen K, Stu-
art-Hill G, Taylor R (2014) A newly discovered wildlife
migration in Namibia and Botswana is the longest in
Africa. Oryx 50: 1−9
NASA (National Aeronautics and Space Administration),
METI (Ministry of Economy, Trade, and Industry) (2011)
ASTGTM: ASTER Global Digital Elevation Model.
NASA EOSDIS Land Processes DAAC, USGS Earth Re -
sources Observation and Science (EROS) Center, Sioux
Falls, SD. https://lpdaac.usgs.gov (accessed December
14, 2017)
Northrup JM, Hooten MB, Anderson CR, Wittemyer G
(2013) Practical guidance on characterizing availability
in resource selection functions under a use−availability
design. Ecology 94: 1456−1463
Novellie P, Birss C, Cowell C, Kerley GIH and others (2017)
Adaptive governance of Cape mountain zebra, Can it
work? Afr J Wildl Res 47: 79−91
O’Brien TG, Kinnaird MF, Ekwanga S, Wilmers C and others
(2018) Resolving a conservation dilemma: vulnerable
lions eating endangered zebras. PLOS ONE 13: e0201983
Owen-Smith N (2013) Daily movement responses by African
savanna ungulates as an indicator of seasonal and
annual food stress. Wildl Res 40: 232−240
Parker GE, Davidson Z, Low B, Lalampaa PR, Sundaresan
SR, Fischer M (2017) Can pastoral communities offer
solutions for conserving the Endangered Grevy’s zebra
(Equus grevyi) at the periphery of its range. Oryx 51:
517−526
Penzhorn BLI (1982) Home range sizes of Cape mountain
zebras in the Mountain Zebra National Park. Koedoe 25:
103−108
Penzhorn BLI (1988) Equus zebra. Mamm Species 314: 1−7
Penzhorn BLI, Novellie PA (1991) Some behavioural traits of
Cape mountain zebras (Equus zebra zebra) and their
implications for the management of a small conservation
area. Appl Anim Behav Sci 29: 293−299
Poor EE, Loucks C, Jakes A, Urban DL (2012) Comparing
habitat suitability and connectivity modeling methods for
conserving pronghorn migrations. PLOS ONE 7: e49390
Prokopenko CM, Boyce MS, Avgar T (2017) Characterizing
wildlife behavioural responses to roads using integrated
step selection analysis. J Appl Ecol 54: 470−479
Purdon A, Mole MA, Chase MJ, van Aarde RJ (2018) Partial
migration in savanna elephant populations distributed
across southern Africa. Sci Rep 8: 11331
R Development Core Team (2015) R: a language and envi-
ronment for statistical computing. R Foundation for Sta-
tistical Computing, Vienna. https: //www.r-project.org/
Rasa OA, Lloyd PH (1994) Incest avoidance and attainment
of dominance by females in a Cape mountain zebra pop-
ulation. Behaviour 128: 3−4
Ripple WJ, Newsome TM, Wolf C, Dirzo R and others (2015)
Collapse of the world’s largest herbivores. Sci Adv 1:
e1400103
Ripple WJ, Chapron G, López-Bao JV, Durant SM and oth-
169
Endang Species Res 38: 159–170, 2019
ers (2016) Saving the world’s terrestrial megafauna. Bio-
science 66: 807−812
Ripple WJ, Chapron G, López-Bao JV, Durant SM and oth-
ers (2017) Conserving the world’s megafauna and biodi-
versity: the fierce urgency of now. Bioscience 67: 197−200
Rubenstein D, Low Mackey B, Davidson ZD, Kebede F, King
SRB (2016) Equus grevyi. The IUCN Red List of Threat-
ened Species 2016: e.T7950A89624491. http:// dx. doi.
org/ 10. 2305/ IUCN. UK. 2016-3. RLTS. T7950 A89624491. en
Schoenecker K, King S, Nordquist M, Nandintsetseg D, Cao
Q (2016) Habitat and diet of equids. In: Ransom J,
Kaczensky P (eds) Wild equids: ecology, management
and conservation. John Hopkins University, Baltimore,
MD, p 41−57
Sharma B (2004) Mapping Equus kiang (Tibetan wild ass)
habitat in Surkhang, Upper Mustang, Nepal. Mt Res Dev
24: 149−156
Signer J (2018) amt: animal movement tools. R package ver-
sion 0.0.5.0. https: //CRAN.R-project.org/package=amt
Signer J, Fieberg J, Avgar T (2019) Animal movement tools
(amt): R package for managing tracking data and con-
ducting habitat selection analyses. Ecol Evol 9: 880– 890
Sloat LL, Gerber JS, Samberg LH, Smith WK and others (2018)
Increasing importance of precipitation variability on glo -
bal livestock grazing lands. Nat Clim Chang 8: 214−218
Smith RK, Marais A, Chadwick P, Lloyd PH, Hill A (2007)
Monitoring and management of the endangered Cape
mountain zebra Equus zebra zebra in the Western Cape,
South Africa. Afr J Ecol 46: 207−213
Smuts GL (1975) Home range sizes for Burchell’s zebra
Equus burchelli antiquorum from the Kruger National
Park. Koedoe 18: 139−146
Steytler J (2014) Namibia population projections. Windhoek
Stoner DC, Sexton JO, Nagol J, Bernales HH, Edwards TC
Jr (2016) Ungulate reproductive parameters track satel -
lite observations of plant phenology across latitude and
climatological regimes. PLOS ONE 11:e0148780
Therneau TM (2018) Survival analysis. Version 2.42-6.
https:// cran. r-project. org/ web/ packages/ survival/ index.
html
Thurfjell H, Ciuti S, Boyce MS (2014) Applications of step-
selection functions in ecology and conservation. Mov
Ecol 2: 4
Walpole M, Karanja G, Sitati NW, Leader-Williams N (2003)
In: Walpole M, Karanja G, Sitati NW, Leader-Williams N
(eds) Wildlife and people: conflict and conservation in
Masai Mara, Kenya International Institute for Environ-
ment and Development, London
Wasserman TN, Cushman S, Shirk A, Landguth EL, Littell
JS (2012) Simulating the effects of climate change on
population connectivity of American marten (Martes
americana) in the northern Rocky Mountains, USA.
Landsc Ecol 27: 211−225
Watson LH, Chadwick P (2007) Management of Cape moun-
tain zebra in the Kammanassie Nature Reserve, South
Africa. S Afr J Sci 37: 31−39
Watson LH, Odendaal HE, Barry TJ, Pietersen J (2005) Pop-
ulation viability of Cape mountain zebra in Gamka
Mountain Nature Reserve, South Africa: the influence of
habitat and fire. Biol Conserv 122: 173−180
Weel S, Watson LH, Weel J, Venter JA (2015) Cape moun-
tain zebra in the Baviaanskloof Nature Reserve, South
Africa: resource use reveals limitations to zebra perform-
ance in a dystrophic mountainous ecosystem. Afr J Ecol
53: 428−438
Young TP, Palmer TM, Gadd ME (2005) Competition and
compensation among cattle, zebras, and elephants in a
semi-arid savanna in Laikipia, Kenya. Biol Conserv 122:
351−359
Zeller KA, McGarigal K, Whiteley AR (2012) Estimating
landscape resistance to movement: a review. Landsc Ecol
27: 777−797
170
Editorial responsibility: Matt Hayward,
Bangor, UK
Submitted: September 19, 2018; Accepted: January 7, 2019
Proofs received from author(s): March 7, 2019
