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Characterizing Hartmann’s mountain zebra resource selection and movement behavior within a large unprotected landscape in north-west Namibia

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Expanding human populations, combined with an increasingly variable climate, present challenges to the conservation of wide-ranging wildlife species, particularly for populations 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 farming areas of Namibia, has been scarce and may limit conservation effectiveness. Here, we investigated 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 movements and space use. HMZ movements averaged ~5 km d−1, and mean seasonal home range sizes were 681 and 256 km2 in the wet and dry season, respectively. HMZ selected for areas with high normalized difference vegetation index values (used as a proxy for primary production), particularly during the dry season, while avoiding areas further from water and closer to human settlements, 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 footprint to seek out valuable but ephemeral forage.
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Endang Species Res
Vol. 38: 159–170, 2019 Published March 28
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 ·
*Corresponding author:
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
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
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.
2.1. 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-
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).
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
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
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.
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.
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).
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,
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
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]).
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
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
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.
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Editorial responsibility: Matt Hayward,
Bangor, UK
Submitted: September 19, 2018; Accepted: January 7, 2019
Proofs received from author(s): March 7, 2019
... Rights reserved. Proportion of grassland cover 0-91% Zebras is variable grazer and use areas of grassland or in shrub communities with high biomass of grass (> 50% grass cover); therefore, we hypothesize they should use areas with relatively high grassland cover Hempson et al. (2015), Muntifering et al. (2019) Proportion of sandy soils Relative frequency (0-1) The foot morphology of zebras is not adapted to locomotion in soft sands. Therefore, we predict zebra distribution to be limited by soil amount of sand in the soil Zhang et al. (2015) Bruce Bennett (personal experience) ...
... Our findings are consistent with those found in NW Namibia, where zebras tended to select areas with higher forage quality and nearby water sources , which are key resources for this aridityadapted species (Gosling et al. 2019). However, opposite to the findings of Muntifering et al. (2019), our results show that zebra preferentially use areas with lower primary productivity. Hartmann's mountain zebra are typically found in rugged, broken mountainous and escarpment areas with a rich diversity of grass species and perennial water sources (Penzhorn 2013), but may expand into new, sometimes lowland, areas if water sources are available (Novellie et al. 2002;Wilson and Mittermeier 2011). ...
... Interestingly, our results conflict with the topographic preferences described for the species, as they were found to strictly avoid rugged areas at INP, where the highest primary productivity and number of natural springs are found. We suggest that the strong human disturbance occurring in the mountainous areas of INP are perceived as top-down constraints preventing zebras from occupying its preferred habitats (Wilson and Mittermeier 2011;Muntifering et al. 2019). The recent history and contemporary hunting practices in INP may have led to this scenario, as the persistence of these activities may shift the species' behaviour through learning (Crosmary et al. 2012). ...
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Deserts are typically governed by bottom-up forces and are predicted to be further depleted of their resources, exacerbating extinction risk for local wildlife populations. Additionally, human populations living in these ecosystems are predicted to increase, exposing wildlife to additional human-induced top-down constraints and intensifying human-wildlife conflicts. We aim to investigate how surface water availability, forage availability and other landscape factors shape the spatial arrangement of large herbivore populations in a desert region, and to explore wildlife-livestock co-occurrence patterns to inform coexistence strategies that maximize conservation outputs. We fitted Bayesian zero-inflated binomial N-mixture models (Kéry and Royle 2015) to group count data collected over a 4 year period in the northern Namib desert (Iona National Park, Angola), and found that Hartmann’s mountain zebra and gemsbok preferentially forage in suboptimal low productivity flat areas, away from human activities. Conversely, springbok preferentially occurred in more productive and relatively rugged terrain. We also found a reliance of Hartmann’s mountain zebra on natural water sources (βDistWater=-1.04±0.26\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{DistWater}=-1.04\pm 0.26$$\end{document} and βDistWater=-0.77±0.20,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{DistWater}=-0.77\pm 0.20,$$\end{document} for dry and wet seasons, respectively), and a weaker reliance by gemsbok (βDistWater=0.20±0.10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{DistWater}=0.20\pm 0.10$$\end{document} and βDistWater=-0.15±0.10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{DistWater}=-0.15\pm 0.10$$\end{document}, respectively for dry and wet seasons). Conversely, we found springbok to forage further from available water (βDistWater=0.43±0.05\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{DistWater}=0.43\pm 0.05$$\end{document} and βDistWater=0.26±0.06\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{DistWater}=0.26\pm 0.06$$\end{document}, for dry and wet seasons, respectively), suggesting this species may be able to balance hydric requirements from dietary water. Our results support that human activities (inc. livestock herding) induce broad scale top-down regulation in landscape use by our target species, which are then susceptible to resource-driven bottom-up forces at a finer scale. These constraints reflect differences between the realized and expected conservation value of Iona National Park, because human-occupied areas force wildlife to suboptimal habitats. Additionally, we found significant stretches of the landscape to be co-occupied by wildlife and livestock, increasing competition for already limited resources. Our results are useful for informing conservation actions, namely through protected area zonation. Securing exclusive access to key resources by wildlife could be of utmost importance to ensure the long-term survival of these species, and to foster sustained human-wildlife coexistence.
... It is mainly concerned with understanding the diversity of the perceptions and attitudes underlying conflicts (e.g., Bruskotter et al. 2019; Gosling et al. 2019). When explicitly tackling disagreements, most researchers explore means to avoid rather than to resolve them (e.g., Fang et al. 2019;Muntifering et al. 2019), thereby paralleling the attitude of many practitioners (Arpin 2019). Most studies addressing situations in which conflicts are unavoidable propose concrete solutions of local relevance (e.g., Dhungan et al. 2016;Oelrichs et al. 2016) or study opinions on the local relevance of specific solutions (Lute et al. 2018). ...
... However, limiting themselves to playing issue advocates would mean conservationists would give up any hope of influencing decisions, except in the presumably rare cases in which conservationist values have the upper hand. Studies in which conservation knowledge was used to identify ways to avoid wildlife-human conflicts (e.g., Muntifering et al. 2019) exemplify the work of honest brokers fostering conservation in more complex pluralist settings. ...
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Conservation decisions are typically made in complex, dynamic, and uncertain settings, where multiple actors raise diverse and potentially conflicting claims, champion different and sometimes contradictory values, and enjoy varying degrees of freedom and power to act and influence collective decisions. Therefore, effective conservation actions require conservation scientists and practitioners to take into account the complexity of multiactor settings. We devised a framework to help conservation biologists and practitioners in this task. Institutional economic theories, which are insufficiently cited in the conservation literature, contain useful insights for conservation. Among these theories, the economies of worth can significantly contribute to conservation because it can be used to classify the types of values peoples or groups refer to when they interact during the elaboration and implementation of conservation projects. Refining this approach, we designed a framework to help conservation professionals grasp the relevant differences among settings in which decisions related to conservation actions are to be made, so that they can adapt their approaches to the features of the settings they encounter. This framework distinguishes 6 types of agreements and disagreements that can occur between actors involved in a conservation project (harmony, stricto sensu arrangement, deliberated arrangement, unilateral and reciprocal compromise, and locked‐in), depending on whether they disagree on values or on their applications and on whether they can converge toward common values by working together. We identified key questions that conservationists should answer to adapt their strategy to the disagreements they encounter and identified relevant participatory processes to complete the adaptation.
... Large-bodied herbivores in particular are sensitive to anthropogenic activities (Selier et al., 2015), and black rhino space use is negatively affected by human disturbance in other small, fenced reserves , as well as in larger, open landscapes in Africa (Walpole et al., 2003;Gadiye & Koskei, 2016). Similarly, Hartmann's zebra in Namibia were found to strongly avoid areas of human activity (Muntifering et al., 2019), while Young et al. (2005) found plains zebra to avoid human activity areas associated with livestock. Conversely, the probability of use of bushbuck decreased with distance from the fence, which may be an effect of their low use of the medium-altitude vegetation that occurs in the centre of the reserve, rather than them preferring to be closer to the fence. ...
... Avoidance of the fence line is especially concerning for rhinoceros, as they use high-and medium-altitude woodland significantly less than other vegetation types, which further limits their available space. Largebodied herbivores are particularly sensitive to anthropogenic activities (Selier et al., 2015), with black rhinoceros similarly avoiding human disturbance in other small, fenced reserves (Odendaal-Holmes et al., 2014), as well as in larger, open landscapes (Gadiye & Koskei, 2016;Walpole et al., 2003) and zebra avoiding areas of human activity in Kenya (Young et al., 2005) and Namibia (Muntifering et al., 2019). The peculiar observation that bushbuck use areas closer to the fence more, may be an artefact of their avoidance of the mediumaltitude vegetation that occurs in the centre of the reserve, rather than a preference for the fence line. ...
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Understanding ecological-and management-related predictors of mammal space use within protected areas is critical for management planning. This is particularly true in small, fenced and isolated reserves where we hypothesised that management-related activities will influence large herbivore space use more than ecological characteristics. We used camera trap data to assess the space use patterns for 18 ungu-late species in the small and isolated Majete Wildlife Reserve (Majete) in the understudied Miombo woodland ecoregion of southern Malawi. In the 2018 dry season, the 691 km 2 reserve was systematically surveyed for ungulate presence at 140 camera trap locations. Over a period of 5456 camera days, the survey yielded 11 078 independent detections of 18 ungulate species and three predators. Using a single-species occupancy modelling framework, the probability of space use of ungulates was assessed in relation to five management (fire exposure, fire frequency , water availability, distance from fence and road) and five ecological (visi-bility, grass biomass, vegetation type, terrain ruggedness and predator abundance) space use covariates, while accounting for imperfect detection. Top-ranked models contained multiple covariates for 15 of the 16 species modelled, with only nyala's (Tragelaphus angasii) space use best predicted by vegetation type only. Distance to water, vegetation type, visibility and fire frequency were predictors having strong influences on six or more species each. More management-related covariates reflected in top models, but ecological covariates had more meaningful effect sizes making us reject the hypothesis. Importantly though, distance from the roads and fence were also identified as prominent predictors. Notably, black rhinoceros' (Diceros bicornis) probability of space use increased with distance from the reserve boundary. Beyond informing habitat management for ungulates in Majete, these results can form the basis for understanding species-specific space use patterns in other small reserves with similar characteristics and threats.
... If the configuration of resources in space constantly changes, then SSFs, which can only test one scale at a time, might have difficulty in detecting selection. Other studies across a number of taxa have used NDVI for SSFs (Canada lynx [Lynx canadensis]- [81]; pronghorn [Antilocapra americana]- [90]; Hartmann's mountain zebra [Equus zebra hartmannae]- [91]; African elephants [Loxodonta africana]- [92]), but no study indicated similar challenges with NDVI. All found selection for NDVI, although the time steps examined were much smaller than in our study (< = 4hrs). ...
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Nomadic movements are often a consequence of unpredictable resource dynamics. However, how nomadic ungulates select dynamic resources is still understudied. Here we examined resource selection of nomadic Mongolian gazelles ( Procapra gutturosa ) in the Eastern Steppe of Mongolia. We used daily GPS locations of 33 gazelles tracked up to 3.5 years. We examined selection for forage during the growing season using the Normalized Difference Vegetation Index (NDVI). In winter we examined selection for snow cover which mediates access to forage and drinking water. We studied selection at the population level using resource selection functions (RSFs) as well as on the individual level using step-selection functions (SSFs) at varying spatio-temporal scales from 1 to 10 days. Results from the population and the individual level analyses differed. At the population level we found selection for higher than average NDVI during the growing season. This may indicate selection for areas with more forage cover within the arid steppe landscape. In winter, gazelles selected for intermediate snow cover, which may indicate preference for areas which offer some snow for hydration but not so much as to hinder movement. At the individual level, in both seasons and across scales, we were not able to detect selection in the majority of individuals, but selection was similar to that seen in the RSFs for those individuals showing selection. Difficulty in finding selection with SSFs may indicate that Mongolian gazelles are using a random search strategy to find forage in a landscape with large, homogeneous areas of vegetation. The combination of random searches and landscape characteristics could therefore obscure results at the fine scale of SSFs. The significant results on the broader scale used for the population level RSF highlight that, although individuals show uncoordinated movement trajectories, they ultimately select for similar vegetation and snow cover.
Most animal habitats are affected by humans. While some species tolerate and even benefit from these changes, others suffer. Understanding when and how human-altered landscapes affect animal behavior, health, reproduction, and survival is essential to species management in a human-dominated world. Here we use 27 years of data on human-baboon encounters in a protected, semi-arid ecosystem in Kenya to: (i) identify spatial, environmental, and group-level predictors of baboon encounters with pastoralists; (ii) test whether human-built water sources alter baboon ranging patterns; and (iii) test if human encounters are linked to baboon survival, reproduction, and health. We find that the primary driver of human-baboon encounters is water availability. During dry periods, pastoralists migrate into baboon rangelands, leading to frequent human-baboon encounters, especially near water wells. Further, the baboons shift their ranges to encompass newly built wells and move away from abandoned, dried-up wells. Since 2006, a third of adult baboon deaths were linked to violent encounters with humans or their dogs. Human encounters were also linked to high infant mortality and parasite diversity in females (but this effect could not be disentangled from seasonal confounds). For wild baboons, life in protected, pastoralist conservancies presents a double-edged sword: human-built wells enable the baboons to access water during dry periods, but these wells lead to encounters with humans, which have become a common source of baboon mortality. Together, our results serve as a comprehensive case study of anthropogenic effects on wild primates, highlighting the complex interactions between humans and wildlife in protected areas.
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Microsites created by soil- disturbing animals are important landscape elements in arid environments. In the Pre- Namib, dust- bathing behavior of the near- endemic Hartmann's mountain zebra creates unique rolling pits that persist in the landscape. However, the ecohydrological characteristics and the effects of those microsites on the egetation and on organisms of higher trophic levels are still unknown. In our study, we characterized the soil grain size composition and infiltration properties of rolling pits and reference sites and recorded vegetation and arthropod assemblages during the rainy season of five consecutive years with different amounts of seasonal rainfall. We further used the excess green vegetation index derived from drone imagery to demonstrate the different green up and wilting of pits and references after a rainfall event. In contrast to the surrounding grassland, rolling pits had finer soil with higher nutrient content, collected runoff, showed a higher infiltration, and kept soil moisture longer. Vegetation in the rolling pits was denser, dominated by annual forbs and remained green for longer periods. The denser vegetation resulted in a slightly higher activity density of herbivorous arthropods, which in turn increased the activity density of omnivorous and predatory arthropods. In times of drought, the rolling pits could act as safe sites and refuges for forbs and arthropods. With their rolling pits, Hartmann's mountain zebras act as ecosystem engineers, contributing to the diversity of forb communities and heterogeneity of the landscape in the Pre- Namib.
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This subspecies is listed as Vulnerable (VU A3bcd) due to the probability of a future population reduction of at least 30% within the next 33 years (three generations) if further severe droughts occur. While the population has increased in recent years, Hartmann's Mountain Zebra (HMZ) remain at threat from another catastrophic drought as this would probably result in mortality across their range, but particularly of a high proportion of zebras on private farms and freehold conservancies. Over 3,500 HMZ are killed annually under license (data from 2008-2012, Shapi 2014, CITES trade statistics). At the moment this appears sustainable, but changing climatic conditions combined with over-harvesting could quickly cause this species to become threatened again. We have not been able to carry out a detailed analysis of the impact of the early 1980s drought but recommend that this should be done if sources can be identified. It is important to fully understand that the current population is more vulnerable than it may appear from its numbers alone and that we should learn the lessons of past droughts to plan conservation measures for a sustainable future. The text of this reassessment of the conservation status of Hartmann's mountain zebra is available on the IUCN website at:-
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Advances in tracking technology have led to an exponential increase in animal location data, greatly enhancing our ability to address interesting questions in movement ecology, but also presenting new challenges related to data management and analysis. Step-selection functions (SSFs) are commonly used to link environmental covariates to animal location data collected at fine temporal resolution. SSFs are estimated by comparing observed steps connecting successive animal locations to random steps, using a likelihood equivalent of a Cox proportional hazards model. By using common statistical distributions to model step length and turn angle distributions, and including habitat- and movement-related covariates (functions of distances between points, angular deviations), it is possible to make inference regarding habitat selection and movement processes or to control one process while investigating the other. The fitted model can also be used to estimate utilization distributions and mechanistic home ranges. Here, we present the R package amt (animal movement tools) that allows users to fit SSFs to data and to simulate space use of animals from fitted models. The amt package also provides tools for managing telemetry data. Using fisher (Pekania pennanti) data as a case study, we illustrate a four-step approach to the analysis of animal movement data, consisting of data management, exploratory data analysis, fitting of models, and simulating from fitted models. © 2019 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
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When predators are removed or suppressed for generations, prey populations tend to increase and when predators are re-introduced, prey densities should fall back to pre-control levels. In cases of apparent competition where there are alternate abundant and rare prey species, rare species may decline further than expected or disappear altogether. Recently, concern about the impact of recovering predator populations on wildlife in Laikipia County, Kenya, has led to questions of whether lions (Panthera leo, IUCN Red List Vulnerable) exert top-down pressure on Grevy’s zebra (Equus grevyi, IUCN Red List Endangered). We examined effects of lion predation on Plain’s zebra (E. quagga, IUCN Red List Near Threatened) and Grevy’s zebra populations in a 2,105 km² area defined by lion movements. We used line transect surveys to estimate density of Grevy’s (0.71/km²) and Plain’s (15.9/km²) zebras, and satellite telemetry to measure movements for lions and both zebras. We tracked lions to potential feeding sites to estimate predation rates on zebras. We compared field-based estimates of predation rates on both zebras to random gas models of encounters that result in predation to ask if lions prey preferentially on Grevy’s zebra at a sufficient rate to drive population declines. Lions preyed on Grevy’s zebra significantly less than expected in 15 of 16 (94%) scenarios considered and lions preyed on Plain’s zebras as expected or significantly less than expected in 15 of 16 scenarios. Population trend of Grevy’s zebra indicates that the Kenya population may be stabilizing. Recruitment rate to the population has tripled since 2004, making it unlikely that lions are having an impact on Grevy’s zebras. In Laikipia County, competitive displacement by livestock (Livestock: Grevy’s zebra ratio = 864:1) and interference competition for grass with Plain’s zebra (Plain’s zebra:Grevy’s zebra ratio = 22:1) are most likely the predominant threats to Grevy’s Zebra recovery.
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Migration is an important, but threatened ecological process. Conserving migration requires the maintenance of functional connectivity across sufficiently large areas. Therefore, we need to know if, where and why species migrate. Elephants are highly mobile and can travel long distances but we do not know if they migrate. Here, we analysed the movement trajectories of 139 savanna elephants (Loxodonta africana) within eight clusters of protected areas across southern Africa to determine if elephants migrate, and if so, where, how and why they migrate. Only 25 of these elephants migrated. Elephants are a facultative partially migratory species, where only some individuals in a population migrate opportunistically, and not every year. Elephants migrated between distinct seasonal ranges corresponding to southern Africa’s dry and wet seasons. The timing of wet season migrations was associated with the onset of rainfall and the subsequent greening up of forage. Conversely, the duration, distance, and the timing of dry season migrations varied idiosyncratically. The drivers of elephant migration are likely a complex interaction between individual traits, density, and the distribution and availability of resources. Despite most migrations crossing administrative boundaries, conservation networks provided functional space for elephants to migrate.
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Results from an 25 regional climate model simulations from the Coordinated Regional Downscaling Experiment (CORDEX) Africa initiative are used to assess the projected changes in temperature and precipitation over southern Africa at two Global Warming Levels (GWL), namely 1.5°C and 2.0°C, relative to pre-industrial values, under the Representative Concentration Pathway 8.5. The results show a robust increase in temperature compared to the control period (1971-2000) ranging from 0.5 to 1.5°C for the 1.5°C GWL and from 1.5 to 2.5°C, for the 2.0°C GWL. Areas in the south-western region of the subcontinent, covering South Africa and parts of Namibia and Botswana are projected to experience the largest increase in temperature, which are greater than the global mean warming, particularly during the September-October-November season. On the other hand, under 1.5°C GWL, models exhibit a robust reduction in precipitation of up to 0.4 mm/day (roughly 20% of the climatological values) over the Limpopo Basin and smaller areas of the Zambezi Basin in Zambia, and also parts of Western Cape, South Africa. Models project precipitation increase of up to 0.1 mm/day over central and western South Africa and in southern Namibia. Under 2.0°C GWL, a larger fraction of land is projected to face robust decreases between 0.2 and 0.4 mm/day (around 10-20% of the climatological values) over most of the central subcontinent and parts of western South Africa and northern Mozambique. Decreases in precipitation are accompanied by increases in the number of consecutive dry days and decreases in consecutive wet days over the region. The importance of achieving the Paris Agreement is imperative for southern Africa as the projected changes under both the 1.5°C, and more so, 2.0°C GWL imply significant potential risks to agricultural and economic productivity, human and ecological systems health and water resources with implied increase in regional water stresses.
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Pastures and rangelands underpin global meat and milk production and are a critical resource for millions of people dependent on livestock for food security1,2. Forage growth, which is highly climate dependent3,4, is potentially vulnerable to climate change, although precisely where and to what extent remains relatively unexplored. In this study, we assess climate-based threats to global pastures, with a specific focus on changes in within- and between-year precipitation variability (precipitation concentration index (PCI) and coefficient of variation of precipitation (CVP), respectively). Relating global satellite measures of vegetation greenness (such as the Normalized Difference Vegetation Index; NDVI) to key climatic factors reveals that CVP is a significant, yet often overlooked, constraint on vegetation productivity across global pastures. Using independent stocking data, we found that areas with high CVP support lower livestock densities than less-variable regions. Globally, pastures experience about a 25% greater year-to-year precipitation variation (CVP = 0.27) than the average global land surface area (0.21). Over the past century, CVP has generally increased across pasture areas, although both positive (49% of pasture area) and negative (31% of pasture area) trends exist. We identify regions in which livestock grazing is important for local food access and economies, and discuss the potential for pasture intensification in the context of long-term regional trends in precipitation variability.
Adaptive governance and network governance theory provide a useful conceptual framework to guide the conservation of threatened species in complex multi-actor, multijurisdictional social ecological systems. We use principles from this theory to assess strengths and weaknesses in (1) national legislation, and (2) the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Regulations applicable to the conservation of the Cape mountain zebra (Equus zebra zebra) (CMZ) in South Africa. A legislated conservation tool, Biodiversity Management Plans for Species (BMP-S), establishes a collaborative network of role players and facilitates the important principles of collaborative learning and adaptation. Effective governance of this network is critical to success, but challenging because of a mandate gap and limited capacity in government to provide essential network-level competencies. National regulations governing human use of CMZ (Threatened or Protected Species (TOPS) Regulations) accords with the principles of (1) being developed in consultation with stakeholders and (2) open to revision and adaptation. CITES Regulations also provide adequately for adaptation. Poor alignment of regulations between different regulatory authorities in South Africa and limited capacity for implementation of regulations seriously constrain learning and adaptation.
In our recent perspective article, we noted that most (approximately 0 percent) terrestrial large carnivore and large herbivore species are now threatened with extinction, and we offered a 13-point declaration designed to promote and guide actions to save these iconic mammalian megafauna (Ripple et al. 2016). Some may worry that a focus on saving megafauna might undermine efforts to conserve biodiversity more broadly. We believe that all dimensions of biodiversity are important and that efforts to conserve megafauna are not in themselves sufficient to halt the dispiriting trends of species and population losses in recent decades. From 1970 to 2012, a recent global analysis showed a 58 percent overall decline in vertebrate population abundance (WWF 2016). Bold and varied approaches are necessary to conserve what remains of Earth’s biodiversity, and our declaration in no way disputes the value of specific conservation initiatives targeting other taxa. Indeed, the evidence is clear that without massively scaling up conservation efforts for all species, we will fail to achieve internationally agreed-upon targets for biodiversity (Tittensor et al. 2014).
Anthropogenic activities have led to long-term range contraction in many species, creating isolated populations in ecologically marginal and suboptimal habitats. 'Refugee' species have a current distribution completely restricted to suboptimal habitat. However, it is likely that many species are partial refugees, where one or more populations are managed in ecologically unsuitable habitat. Here, we develop a framework to assess potential refugee populations in marginal habitats using a model species: the Cape mountain zebra. We assessed habitat quality by the abundance and palatability of grass and diet quality using proximate nutrient and element analysis. High grass abundance was associated with higher population growth rates and zebra density and less skewed adult sex ratios. Furthermore, faecal nutrient and dietary element quality was also positively associated with grass abundance. Our results show that poorly performing populations were characterised by suboptimal habitat, supporting the hypothesis that the Cape mountain zebra has refugee populations. In addition, we found more variance in sex ratio and population growth rates in smaller populations suggesting they may be more at risk for random stochastic effects, such as a biased sex ratio, compounding poor performance. We show how the 'ref-ugee' concept can be applied more generally when managing species with fragmented populations occurring across marginal habitats. More broadly, the results presented herein highlight the importance of recognizing the range of habitats historically occupied by a species when assessing ecological suitability. Identifying and mitigating against refugee, relict and gap populations is especially critical in the face of ongoing environmental change.