Habitat Selection by African Buffalo (Syncerus caffer) in Response to Landscape-Level Fluctuations in Water Availability on Two Temporal Scales
Harris S (2014) Habitat Selection by African Buffalo (Syncerus caffer) in Response to Landscape-Level Fluctuations in Water
Availability on Two Temporal Scales. PLoS ONE 9(7): e101346. doi:10.1371/journal.pone.0101346
Habitat Selection by African Buffalo (Syncerus caffer ) in Response to Landscape-Level Fluctuations in Water Availability on Two Temporal Scales
Emily Bennitt 0
Mpaphi Casper Bonyongo 0
Stephen Harris 0
Sadie Jane Ryan, SUNY College of Environmental Science and Forestry, United States of America
0 1 School of Biological Sciences, University of Bristol , Bristol , United Kingdom , 2 Okavango Research Institute, University of Botswana , Maun , Botswana
Seasonal fluctuations in water availability cause predictable changes in the profitability of habitats in tropical ecosystems, and animals evolve adaptive behavioural and spatial responses to these fluctuations. However, stochastic changes in the distribution and abundance of surface water between years can alter resource availability at a landscape scale, causing shifts in animal behaviour. In the Okavango Delta, Botswana, a flood-pulsed ecosystem, the volume of water entering the system doubled between 2008 and 2009, creating a sudden change in the landscape. We used African buffalo (Syncerus caffer) to test the hypotheses that seasonal habitat selection would be related to water availability, that increased floodwater levels would decrease forage abundance and affect habitat selection, and that this would decrease buffalo resting time, reduce reproductive success and decrease body condition. Buffalo selected contrasting seasonal habitats, using habitats far from permanent water during the rainy season and seasonally-flooded habitats close to permanent water during the early and late flood seasons. The 2009 water increase reduced forage availability in seasonally-flooded habitats, removing a resource buffer used by the buffalo during the late flood season, when resources were most limited. In response, buffalo used drier habitats in 2009, although there was no significant change in the time spent moving or resting, or daily distance moved. While their reproductive success decreased in 2009, body condition increased. A protracted period of high water levels could prove detrimental to herbivores, especially to smaller-bodied species that require high quality forage. Stochastic annual fluctuations in water levels, predicted to increase as a result of anthropogenically-induced climate change, are likely to have substantial impacts on the functioning of water-driven tropical ecosystems, affecting environmental conditions within protected areas. Buffer zones around critical seasonal resources are essential to allow animals to engage in compensatory behavioural and spatial mechanisms in response to changing environmental conditions.
Funding: This work was funded by Jenny & Martin Bennitt, the Dulverton Trust (www.dulverton.org), Harry Ferguson, Ian Fuhr, Rodney Fuhr, Dane Hawk, Idea
Wild (www.ideawild.org), the North of England Zoological Society (www.chesterzoo.org/global/about-us/north-of-england-zoological-society), the Roberts Fund
and the Wilderness Safaris Wildlife Trust (www.wildernesstrust.com). The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
Temperate ecosystems are driven by changes in temperature,
whereas tropical ones are governed by fluctuations in water
availability . Highly seasonal rainfall in tropical regions
influences the spatial distribution of herbivores by causing
temporal variation in the availability of water and the productivity
of particular habitats . Water availability places spatial
constraints on herbivores during the dry season by forcing them
to occupy habitats close to permanent water sources , although
some species engage in long-distance central-place foraging,
regularly moving between permanent water sources and foraging
grounds several kilometres away . During the rainy season,
these spatial constraints are removed as temporary water holes are
filled by rainfall, which also promotes the growth of nutrient-rich
annual grasses .
Differences in soil type and nutrient concentration contribute to
variation in the nutrient content  and growth rates of seasonal
grasses, resulting in habitat types of disparate value . Soil type
influences the retention of ground water, which in turn affects
habitat productivity . Water availability interacts with soil type
and nutrient content to affect vegetation growth, and so the
distribution of particular habitat types at a landscape scale is
related to their proximity to permanent water sources . So
seasonal changes in resource availability cause temporal changes
in the profitability of a given habitat, resulting in marked seasonal
patterns of habitat selection by herbivores . Seasonal shifts in
habitat selection often result in geographically distinct seasonal
ranges, and can lead to long-distance migrations .
The profitability of particular habitat types can be affected by
annual variation in water availability as well as seasonal cycles
. Such variation can introduce fluctuations on a larger
temporal scale, and often cause sudden shifts in resource
availability . These stochastic effects can be detrimental for
animals adapted to existing conditions, particularly when they are
spatially-restricted by being confined to protected areas .
Sudden environmental changes cannot always be foreseen and,
while animals are likely to engage in compensatory behaviours
such as increased moving and feeding during periods of resource
deficiency , this may not balance the effects of stochastic
events . Anthropogenically-induced climate change means
that weather patterns are likely to become less predictable,
particularly in water-governed tropical systems . Annual
changes in water influx into a system may interfere with seasonal
cycles, affecting the behaviour of animals and potentially reducing
reproductive success, and hence population health [17,18].
The Okavango Delta in northern Botswana is a flood-pulsed
ecosystem that experiences substantial seasonal and annual
variations in water influx, both in terms of volume and distribution
. These fluctuations affect water availability, but can also alter
the characteristics of habitats prone to inundation , and hence
the spatial and temporal distribution of large herbivores. Future
plans for water extraction from the Okavango River, before it
enters the Delta, may compound climate-driven changes and
cause sudden fluctuations in water levels on a landscape scale, with
widespread implications for the ecosystem . So the Okavango
Delta is an ideal system to study the effects of regular and
stochastic fluctuations in water availability. The regional impacts
of climate change are difficult to predict, but quantifying the
responses of species to existing variation will allow a greater
understanding of future potential changes .
In 2009, the volume of water entering the Okavango Delta
system was almost double that in 2008 (Figure 1), thereby affecting
the productivity of key foraging habitats, in particular seasonal
floodplains. This could be particularly detrimental to herbivores
during the late flood season, when forage in most habitat types in
the Okavango Delta is at its least productive since many species
rely on floodplain grasses that grow after the floodwaters recede
. African buffalo (Syncerus caffer) are among the most numerous
herbivores in the Okavango Delta . Being large-bodied,
buffalo are capable of covering great distances in search of forage
and water. While they show seasonal variations in habitat selection
, they are water-dependent and require large quantities of
forage to maintain body condition. They are therefore an ideal
species to study behavioural responses to fluctuations in water
levels affecting habitat productivity. We used African buffalo to
test the hypotheses that (i) habitat selection varied seasonally, with
habitats close to permanent water selected during the flood seasons
and dry habitats further from permanent water selected during the
rainy season, (ii) an increase in water levels during the late flood
season affected forage availability and caused a shift in habitat
selection towards permanently dry habitats, and (iii) higher water
levels in 2009 reduced buffalo resting time, and caused a decrease
in reproductive success and body condition during the late flood
season. We use our results to examine broad issues of
environmental change at the landscape level and its impact on herbivore
populations in protected areas.
Materials and Methods
The Okavango Delta covers 15 000 km2 in northern Botswana,
between E 22.0uE 24.0u and S 18.5uS 20.5u . The extent of
the flooded area varies seasonally, from 3 000 to 5 000 km2 during
the driest part of the year, to 6 000 to 12 000 km2 during the
annual flooding event, which peaks between May and August .
The study area was located in the south-eastern part of the Delta
and included both flooded and dry regions, bounded by a
veterinary fence to the south-east (Figure 2). Changing water levels
were used to define three seasons: the rainy season (December to
March), when most rainfall occurred; the early flood season (April
to July), when flood waters were rising, and the late flood season
(August to November), when flood waters were receding. Six
habitat types were described, based on differences in woody and
herbaceous vegetation (Table 1). Grassland occurred throughout
the population range, but the other habitats were not distributed
evenly across the landscape: secondary floodplain, tertiary
floodplain and riparian woodland were close to permanent water
channels, whereas mopane woodland and mixed acacia woodland
were in areas that were dry outside the rainy season .
We produced a vector map of polygons delineating each habitat
patch in the study area using geo-referenced ortho-photographs
taken between 2001 and 2003 obtained from the Okavango
Research Institute and manually digitised in ArcGIS 10.0 (ESRI,
Redlands, CA) using a scale of 1:10 000. Some parts of the
blackand-white images had low levels of contrast, so colour images from
Google Earth (Google Inc., Mountain View, CA) were used to
support habitat identification. The vector map was converted to a
raster map with a pixel size of 50650 m to allow further analysis.
This reduced the resolution but maintained patch distribution and
accounted for errors associated with patch boundary definition.
To test the accuracy of this map, we recorded 796 ground-truthing
points, with a mean 6 SD of 132657 points per habitat type
(range 65224). The map represented the true habitat type 88% of
the time; accuracy was lowest for grassland (79%) and highest for
riparian woodland (96%).
Capture and Collaring
Fifteen buffalo cows in different herds were fitted with Tellus
Simplex 4D GPS-enabled satellite collars (Followit, Lindenberg,
Sweden) programmed to record one location per hour. Cows were
selected as they were more likely to retain their collars  and
formed the core of mixed-sex breeding herds, so data from cows
were representative of entire breeding herds. The collars weighed
1.8 kg, 0.4% of the weight of the smallest cow we collared
(450 kg). Weight was estimated from girth measurements using a
growth curve developed for buffalo in Botswana . There were
24 darting operations: 15 to collar animals, two to replace
malfunctioning collars and seven to remove collars. A helicopter
was used for 22 darting operations; a vehicle was used twice to
remove collars where the animals could be radio-tracked if visual
contact was lost after darting.
Drugs used to immobilise animals were either 8 mg of A3080,
reversed with Naltrexone (n = 13), or a combination of 10 mg
M99, 40 mg Azaperone and 5 000 i.u. Hyalase, reversed with
42 mg M5050 (n = 11). Mean total time 6 SD from darting to
recovery was 15:5967:28 minutes:seconds; mean time 6 SD from
darting to immobilisation was 4:1062:24; mean time 6 SD to
administering the reversal agent was 10:1166:17; and mean time
6 SD from administering the reversal agent to being fully mobile was 1:3860:55 minutes:seconds.
One of three experienced wildlife veterinarians registered with
the government of Botswana carried out each darting operation
under permit from the Department of Wildlife and National Parks.
All darted animals were adult females in good condition that were
not obviously pregnant or with a young calf. Every effort was
made to minimise the stress to darted buffalo and their herds. The
helicopter remained high unless a darting sweep was being made,
and we circled while waiting for the drugs to take effect so that we
could maintain visual contact without disturbing the herd. The
mean total time 6 SD for darting sweeps was 55614 seconds
(range 3184, n = 23). One individual had to be darted twice
because she showed no effects from the drugs 20 minutes after the
first dart, which had been plugged by skin as it pierced the
Figure 1. Water discharge from Okavango River between January 2008 and December 2009 at Mohembo. Redrawn with permission
from data collected by the Okavango Research Institute (www.okavangodata.ub.bw).
epidermis. Although the buffalo were running during the darting
sweeps, this equated to normal flight behaviour and did not cause
undue distress. When a vehicle was used, the herd was followed for
several hours to habituate the buffalo to our presence. They
showed no signs of distress and were relaxed enough for us to
approach within 60 m of the collared cow before darting. All
buffalo recovered quickly from the darting operations; no ill effects
were observed and they were all seen rejoining their herds.
Six collars dropped off and were recovered after the belting
failed, seven animals were darted to remove collars at the end of
the study, and two collars could not be recovered because they
failed suddenly and ceased to emit the VHF signals by which the
buffalo could be located. All capture and handling procedures
were approved by the University of Bristol Ethics Committee
(UB/08/034) and conformed to the American Society of
Mammalogists guidelines for the use of wild mammals in research
. All darting operations were carried out on
governmentowned protected land under control of the Department of Wildlife
and National Parks, after permission had been obtained from
concessionaires and all other relevant stake-holders. No protected
or endangered species were involved in the research.
Seasonal Minimum Convex Polygons (MCPs) for each
individual were computed using ArcGIS 10.0 (ESRI, Redlands, CA); the
first and last two weeks of GPS data from each season were
omitted to ensure a clear distinction between seasons. MCPs
define an animals maximum home range size based on a polygon
around its outermost known locations . While MCPs can
overestimate home range size , they identify the area, and hence
habitats, potentially available. Other methods, such as local
convex hull kernel methods, are useful in identifying unused areas
within a home range , but these may still be accessible to an
animal and so should be included in calculations of habitat
The adehabitatHR package  in R (R Development Core
Team, 2008) was used to calculate the seasonal utilisation
Figure 2. Location of study area in Botswana. The permanently flooded areas of the Okavango Delta are shown in blue in the right-hand image.
Mixed acacia woodland
Dominant woody species
Dominant grass species
distribution (UD) for each individual via the movement-based
kernel density estimation (MKDE) method . This uses
movement patterns derived from GPS fixes to calculate utilisation
distributions, which indicate the intensity of habitat use by animals
within their home ranges . The minimum distance threshold
(MDT), below which an animal was considered inactive, was
calculated from the mean location error of each collar. Prior to
deployment, each collar was hung at a height of 1 m for a
minimum of 100 hours. The mean position of the fixes taken
during this period was used as the reference position ; the
distance between this and each test fix was calculated using the
Point Distance tool in ArcGIS 10.0 (ESRI, Redlands, CA) and the
radius of the 95% circular error probability, defined as the area
containing 95% of fixes , was taken as the MDT. The time
threshold, above which successive relocations were no longer
correlated, was calculated by dividing the diameter of the MCP by
ten times the median hourly distance travelled . The minimum
smoothing parameter was defined for each individual as the MDT
plus 50 m, to account for the spread of the herd . When the
UDs included areas adjacent to the veterinary fence, this was
identified as a fixed boundary to prevent erroneous inclusion of
unavailable areas and resources .
Habitat selection ratios were calculated by dividing the
proportion of use by the proportion of availability for each habitat
, producing one value per season per habitat. Values were
significant if their 95% confidence intervals did not include 1;
those .1 indicated selection and those ,1 indicated avoidance
. To account for the effects of scale on resource availability
, both second and third order selection were assessed. Second
order habitat selection  was evaluated by comparing use in the
MCPs to availability in the seasonal range used by the entire
population as a design III analysis . Seasonal population-level
MCPs were calculated from the combined relocation data from all
the collared buffalo, but separate MCPs were produced for 2008
and 2009 because of the different flood levels. Third order habitat
selection  was evaluated by comparing UD-weighted use 
to availability in the MCPs as a design III analysis, with availability
defined for each individual . Using MCPs enabled us to define
habitat availability at a population level by combining ranges from
several individuals, as well as habitat availability in individual
home ranges, allowing meaningful comparisons between the two
datasets. Seasonal habitat selection ratios were subjected to
Multivariate Analyses of Variance (MANOVA) to determine
whether they varied significantly between seasons, and between
the 2008 and 2009 late flood seasons in response to the higher
water levels in 2009. Mahalanobis distances were used to check for
outliers, and Pillais trace test was used as it is robust to deviations
from multivariate normality and homogeneity of
variancecovariance matrices across groups . Habitats where selection
ratios had changed were identified using Analyses of Variance
We sampled sites in each of secondary floodplain, tertiary
floodplain, grassland and riparian woodland, the habitats most
utilised by buffalo during the late flood season. Locations were
stored on board the GPS collars and also sent via satellite to an
internet server in Sweden, which emailed them to us every
10 hours. These co-ordinates were entered into a vehicle-mounted
Garmin V GPS (Garmin, Schaffhausen, Switzerland) and we
drove to randomly selected sites in each habitat type not rendered
inaccessible by high water levels. We collected vegetation data
within a 50 m radius of the co-ordinates, which allowed for the
dispersion of the herd. We quantified grass biomass using a Disc
Pasture Meter (DPM) , dropped 50 times at 1 m intervals
along 5 randomly-placed 10 m transects. We avoided DPM drops
on woody plants and forbs and calculated biomass as: Y = 21633+
1791!X, where X is the mean settling height of 50 DPM drops and
Y is the biomass in kg/ha . When sites were flooded, we
calculated biomass from grass cut to just below the water surface,
dried in the sun and oven-dried at 60uC for 24 hours. We added
the dried weights from the four quadrats and multiplied them by
Body condition score
Ribs and pelvis
Fat rolls on neck
10 to convert biomass from g/m2 to kg/ha and used a generalized
linear model in R 3.0.1 to determine the effect of year on the log
biomass in each of the four habitat types.
Buffalo Movement Behaviour
We calculated the distances and turning angles between
consecutive fixes taken by the GPS collars using the Path, with
distances and bearings extension (http://www.jennessent.com/
downloads/Find_Path_online.pdf) in ArcView 3.2 (ESRI,
Redlands, CA). Fixes # MDT from the previous location were
designated as resting and fixes .MDT from the previous location
as active . We then grouped active fixes into movement states
based on their distances and turning angles using k-means cluster
analysis . This produced three clusters consistent with
movements at different spatial scales: grazing within a patch,
walking between patches, and relocating between ranges. We
assigned one of these behaviours to each GPS fix, then quantified
the proportion of time that buffalo allocated to each behaviour
during the 2008 and 2009 late flood seasons. This compositional
dataset was analysed using a multivariate analysis of variance
(MANOVA) after conversion into an acomp format using the
compositions package in R 3.0.1 . The movement data from
the GPS collars were used to calculate the total distance covered
each day by the collared buffalo during the 2008 and 2009 late
flood seasons. We ran a linear mixed model to determine the effect
of year on the log of the daily distance travelled, with individual
buffalo included as a random effect, using the nlme package in R
Demographic Composition and Body Condition
We recorded the demographic composition of all buffalo herds
encountered during field work, whether or not they contained
collared animals. To ensure that each buffalo was only assessed
once, demographic categories were recorded for a minimum of
50% of the herd as they walked past a fixed point. The horns,
genitals and body size were used to classify buffalo as adult,
subadult, juvenile and calf, with adults and sub-adults also classified as
males or females . Body condition of each animal was scored
using a system adapted from  based on the visibility of the ribs
and pelvis, and the presence of fat deposits on the neck and tail
base (Table 2). Although subjective, such visual assessments reflect
bone marrow fat content  and are widely used in ungulates
. While body condition may not be representative of an
animals health, for example if it is an asymptomatic disease
carrier, changes in general body condition reflect variations in
forage intake. There was no significant difference between the
body condition scores (BCS) of juveniles and calves , so these
were grouped as young, and gender only had an effect on BCS of
adults, so four categories, adult male, adult female, sub-adult and
young, were used for the analyses.
To assess reproductive success, generalized linear models with
binomial distributions were used in R 3.0.1 to compare
young:adult female and calf:adult female ratios in the two years. Adult
male buffalo leave breeding herds when their body condition falls
, so the ratios of adult males:adult females in the two years
were also compared.
The counts of individual buffalo in each BCS category were
analysed using a cumulative link mixed model with individual herd
included as a random effect  to determine whether the
different water levels in the 2008 and 2009 late flood seasons had
an effect on buffalo body condition. This is a form of ordinal
logistic regression that treats the rank order of BCS categories as a
linked set of binary response variables. To compare BCS in 2008
and 2009, the model calculated the difference in the likelihood that
a buffalo had a BCS of 2 rather than 1, 3 rather than 2, 4 rather
than 3 and 5 rather than 4.
Seasonal Habitat Selection
Seasonal UDs were produced for each collared buffalo, giving
11, 13 and 14 UDs, based on a mean 6 SD of 13786635,
14766627 and 14766406 GPS fixes for the early flood, late flood
and rainy seasons respectively (Figure 3). Variations in collar
efficiency and darting date resulted in unequal numbers of GPS
fixes from each buffalo (Table 3), but using UDs meant that GPS
locations were converted into intensity of habitat use, removing
any potential bias associated with differential sample sizes. The
MKDE method allowed use to be calculated from the GPS fixes,
but also enabled the estimation of movement paths between them,
and therefore provided a probabilistic measure of habitat use when
fixes were not acquired, as long as the period between consecutive
fixes was below the time threshold . The mean 6 SD values
for the MDT and the time threshold were 66.1620.3 m and
8.362.4 hours, respectively.
Overall tests of second order habitat selection showed that
habitat use was disproportionate to availability during the early
flood (X249 = 239.18, p,0.001), late flood (X263 = 184.84, p,
0.001) and rainy (X259 = 296.54, p,0.001) seasons. Third order
habitat selection was significant during the early (X248 = 75.14,
p = 0.007) and late flood (X259 = 173.80, p,0.001) seasons, but
not during the rainy season (X259 = 38.74, p = 0.98). Differences in
degrees of freedom were caused by the absence of some habitat
types in individual MCPs. Habitat selection varied seasonally:
buffalo selected mopane woodland and mixed acacia woodland
during the rainy season, and avoided secondary floodplain. They
selected tertiary floodplain during the late flood season, and
avoided mopane woodland and mixed acacia woodland; mixed
acacia woodland was also avoided during the early flood season
Figure 3. Example of a utilisation distribution produced using the Movement Density Kernel Estimation method. The figure is based
on 1754 GPS fixes from one collared buffalo, B5, during the rainy season of 2009.
Table 3. Number of GPS fixes and the parameters used to calculate the utilisation distributions each season for the 15 buffalo
Number of GPS fixes
MDT is the minimum distance threshold below which an animal was considered inactive, and was calculated from the mean location error of each collar. The time
threshold, above which successive relocations were no longer correlated, was calculated by dividing the diameter of the MCP by ten times the median hourly distance
MANOVAs showed significant differences between second
order selection in all seasons, caused by greater selection for
secondary and tertiary floodplains and riparian woodland,
together with greater avoidance of mopane woodland, during
the early and late flood seasons than during the rainy season
(Table 5). The only significant difference in third order selection
ratios was between the late flood and rainy seasons. Third order
selection for secondary and tertiary floodplains was significantly
greater during the late flood season, when mopane woodland and
mixed acacia woodland, which were further from permanent
water, were avoided.
Annual Habitat Selection and Biomass
Annual changes in habitat selection were assessed using data
from 13 buffalo, 6 collared in 2008 and 7 in 2009; two buffalo
were not collared during the late flood seasons. Overall tests of
Habitat selection ratios (95% confidence intervals)
(n = 11)
(n = 13)
(n = 14)
Ratios with 95% confidence intervals that did not include 1 indicated selection (.1) or avoidance (,1) of particular habitat types. Significant results are shown in bold.
Second order selection compared habitat use in individual MCP ranges to availability in the population range; third order selection compared habitat use in the
utilisation distributions to availability in the individual MCPs.
second order habitat selection showed that habitat use was
disproportionate to availability during the late flood season in
2008 (X229 = 117.31, p,0.001) and 2009 (X234 = 67.53, p,0.001).
Overall third order habitat selection was also significant during the
late flood season in 2008 (X225 = 93.73, p,0.001) and 2009
(X234 = 80.06, p,0.001). Differences in degrees of freedom were
Mixed acacia woodland
Early flood vs. late flood
Late flood vs. rainy
Rainy vs. early flood
Mixed acacia woodland
Mixed acacia woodland
Mixed acacia woodland
Pillai1,17 = 0.552, p = 0.020
F1,22 = 1.008, p = 0.326
F1,22 = 2.413, p = 0.135
F1,22 = 0.064, p = 0.803
F1,22 = 0.616, p = 0.441
F1,22 = 0.452, p = 0.509
F1,22 = 0.790, p = 0.384
Pillai1,20 = 0.467, p = 0.033
F1,25 = 8.922, p = 0.006
F1,25 = 2.911, p = 0.100
F1,25 = 0.248, p = 0.623
F1,25 = 5.397, p = 0.029
F1,25 = 17.547, p,0.001
F1,25 = 0.234, p = 0.633
Pillai1,18 = 0.513, p = 0.027
F1,23 = 15.755, p,0.001
F1,23 = 9.098, p = 0.006
F1,23 = 0.502, p = 0.486
F1,23 = 6.798, p = 0.158
F1,23 = 19.025, p,0.001
F1,23 = 1.024, p = 0.322
Pillai1,17 = 0.312, p = 0.317
F1,22 = 3.478, p = 0.076
F1,22 = 5.200, p = 0.033
F1,22 = 0.763, p = 0.392
F1,22 = 1.078, p = 0.310
F1,22 = 3.252, p = 0.085
F1,22 = 8.005, p = 0.010
Pillai1,20 = 0.742, p,0.001
F1,25 = 9.572, p = 0.005
F1,25 = 21.236, p,0.001
F1,25 = 2.124, p = 0.158
F1,25 = 3.824, p = 0.062
F1,25 = 20.426, p,0.001
F1,25 = 36.589, p,0.001
Pillai1,18 = 0.338, p = 0.224
F1,23 = 4.900, p = 0.037
F1,23 = 9.833, p = 0.005
F1,23 = 0.128, p = 0.724
F1,23 = 0.823, p = 0.374
F1,23 = 1.196, p = 0.285
F1,23 = 0.627, p = 0.436
Significant results are shown in bold. Second order selection compared habitat use in individual MCP ranges to availability in the population range; third order selection
compared habitat use in the utilisation distributions to availability in the individual MCPs.
Mixed acacia woodland
Habitat selection ratios (95% confidence intervals)
Ratios with 95% confidence intervals that did not include 1 indicated selection (.1) or avoidance (,1) of particular habitat types. Significant results are shown in bold.
Second order selection compared habitat use in individual MCP ranges to availability in the population range; third order selection compared habitat use in the
utilisation distributions to availability in the individual MCPs.
caused by the absence of some habitat types in individual MCPs.
Habitat selection ratios were calculated for the late flood season in
2008 and 2009 separately (Table 6). MANOVAs showed that in
2009 there was no significant change in second order selection
(Pillai1,6 = 0.543, p = 0.420) but there was a significant change in
third order selection (Pillai1,6 = 6.719, p = 0.018). ANOVAs
showed that there was a significant increase in the selection of
grassland (F1,11 = 16.202, p = 0.002) and riparian woodland
(F1,11 = 7.117, p = 0.022), although there were no differences in
the selection of secondary floodplain (F1,11 = 1.480, p = 0.249),
tertiary floodplain (F1,11,0.001, p = 0.983), mopane woodland
(F1,11 = 3.106, p = 0.106) or mixed acacia woodland (F1,11 = 1.170,
p = 0.303).
We estimated biomass at 157 and 101 sites in 2008 and 2009
respectively (Table 7). There was a significant interaction between
year and habitat type (Ddeviance3 = 5.53, p,0.001), which was
probably caused by higher biomass in secondary floodplain during
the 2008 late flood season (Figure 4). Biomass was lower in both
seasonally-flooded habitat types during the 2009 late flood season.
Annual Changes in Behaviour, Reproductive Success and
The clustering technique consistently identified similar distances
and turning angles for each behaviour category (Table 8). Within
categories, turning angles showed greater variation than distances
moved, but mean turning angle reduced progressively from resting
to relocating, confirming that movements over long distances were
less tortuous than those over short distances. Year had no
significant effect on the proportion of time spent in each behaviour
(Pillai1, 11 = 0.548, p = 0.133) (Table 9). Buffalo travelled a mean 6
SD of 725063551 m per day in 2008 (n = 6 buffalo, 392 days) and
776363821 m in 2009 (n = 7 buffalo, 483 days); the difference was
not significant (LR3 = 1.17, p = 0.279).
Mean 6 SD young:adult female ratios in 2008 (n = 18 herds)
and 2009 (n = 15 herds) were 0.47860.183 and 0.53560.154,
respectively; a quasibinomial distribution was used to account for
overdispersion but there was no significant difference between
years (t31 = 0.983, p = 0.333). Mean 6 SD calf:adult female ratios
in 2008 and 2009 were 0.19760.130 and 0.15660.107,
respectively; there was a significant difference between years (z31 = 3.027,
p = 0.002). Mean 6 SD adult male:adult female ratios in 2008 and
2009 were 0.43460.298 and 0.36860.252, respectively; a
quasibinomial distribution was used to account for overdispersion
and there was a significant difference between years (t31 = 22.087,
p = 0.045).
Model simplification showed that BCS was significantly affected
by demographic category (LR3 = 482.49, p,0.001) and year
(LR1 = 28.44, p,0.001). Mean BCS in all demographic categories
increased in 2009 (Figure 5).
Table 7. Number of sites sampled for biomass in the four habitats most utilised by buffalo during the 2008 and 2009 late flood
Climatic variability can have a substantial impact on resource
availability by altering growth patterns , distribution, and
relative abundance of plants  through species-specific
differences in response to changes in water availability and temperature
. Both seasonal and annual fluctuations in water levels cause
changes in resource availability at the landscape scale. The former
are more predictable, and animals adapt to seasonal resource
distribution. However, sudden annual changes can disrupt these
behaviour patterns by restricting access to critical habitats or by
altering productivity . We have shown that stochastic
environmental changes in the Okavango Delta cause substantial
changes in herbivore behaviour and spatial distribution. The rank
order of profitable habitats for buffalo varied temporally through
differential responses to seasonal and annual changes in water
availability. The proximity of water to particular habitat types was
directly related to rainfall and flood levels, which also caused
differential vegetation productivity, linked to between-habitat
differences in soil type and nutrient content . This combination
of water-driven factors resulted in seasonal and annual disparities
in habitat selection and associated space use by buffalo. Increased
water levels during the 2009 late flood season did not have a
significant effect on buffalo behaviour patterns, but reproductive
success decreased and body condition increased, highlighting the
range of effects caused by stochastic changes in environmental
Seasonal Changes in Habitat Selection
Our results supported the hypothesis that buffalo show seasonal
differences in habitat selection, with habitats in dry areas selected
during the rainy season and those close to permanent water
selected during the flood seasons. In the Okavango Delta, buffalo
selected habitats with optimal levels of forage biomass and quality
in relation to their energetic demands , which were higher
during the rainy season, when they gave birth and mated, than
during the late flood season, when resources were most limiting.
Seasonal selection of contrasting habitats enabled buffalo to take
advantage of differential profitability , while habitats that were
avoided benefited from a recovery period due to reduced grazing
pressure . The significance of the selection ratios varied
seasonally, reflecting changing environmental conditions.
During the rainy season, third order selection was not
significant, indicating that buffalo used habitats within their home
ranges in proportion to availability . These low selection levels
were probably linked to the abundant, high quality forage
prevalent across the landscape in the rainy season , which
reduced the benefit of selective foraging. Second order selection
for mopane woodland during the rainy season coincided with
increased productivity in that habitat due to the growth of annual
grasses . Mopane woodland occurred in dry parts of the
buffalos range, but rainfall in the rainy season created temporary
water holes, increasing the accessibility of mopane woodland by
removing the spatial constraints imposed by the daily water
dependency of buffalo. There were significant differences in
selection ratios between the rainy season and both flood seasons,
with an emphasis on dry habitats far from permanent water in the
former, and habitats close to water channels in the latter.
Overall habitat selection during the early flood season was
significant, but mixed acacia woodland, a dry habitat far from
permanent water, was the only habitat significantly avoided in
second order habitat selection. This was probably because early
flood home ranges had to be close to permanent water channels.
The three-month delay between the end of the rainy season and
vegetative dormancy  meant that most forage was still green
during the early flood season. Although spatially restricted by
water availability, the delayed onset of vegetation senescence
during the early flood season meant that buffalo did not need to be
as selective in their habitat use as they did during the late flood
season, explaining the lack of difference between rainy and early
flood season third order selection ratios.
Third order selection was strongest during the late flood season,
when vegetation was senescent in most habitats , but the
receding water caused grasses in both secondary and tertiary
floodplains to be at their most productive . The contrast
between the profitability of secondary and tertiary floodplains and
other habitats resulted in a clumped distribution of favourable
resources, and hence a strong selection pressure for those resources
. The significant differences between third order selection
ratios in the late flood and rainy seasons emphasized the contrast
between those two seasons in terms of the rank order of the most
favoured habitats. This highlights the strong dependence of buffalo
on secondary and tertiary floodplains, which appeared to be acting
as resource buffers during the most limiting season  by
providing access to relatively high quality forage in
heterogeneously distributed patches .
Seasonal changes in water availability alter the landscape
substantially, but animals have adapted to these changes so that
they can respond optimally to predictable spatial and temporal
fluctuations in resource availability. Such adaptive behaviour has
evolved over many generations, and populations may not be able
to respond quickly to sudden environmental change . Changes
in the timing of seasonal variation in resource availability, a
potential result of climate change, could reduce the capacity of
animals to identify the most temporally profitable areas and result
in e.g. sub-optimal birth periods  and altered migration
patterns . Access to critical seasonal resources may also be
restricted by spatial changes, such as the construction of fences
 and roads , or unusual inundation patterns .
Annual Changes in Habitat Selection
Our results support the hypothesis that changing water levels in
the Okavango Delta reduced forage availability in seasonal
floodplains, causing buffalo to switch from selecting secondary
and tertiary floodplains in 2008 to drier habitats further from
permanent water when the floodplains were inundated in 2009.
g t 3
in n 53 53 53 55 54 55 53 65 53 54 53 53 54
r ae ts
5 6 5 4 5 2 5 9 2 2 3 1 8
k 4 4 4 4 4 4 4 4 4 4 4 4 4
a 6 6 6 6 6 6 6 6 6 6 6 6 6
3 4 4 0 7 1 3 9 8 0 1 7 7
W 5 5 5 5 5 5 5 5 4 5 5 4 5
This demonstrated the impact of annual fluctuations in water
availability on habitat profitability and selection, which
counteracted to some extent the effects of seasonal water cycles. The time
scale of this study was too short for the habitat composition of the
range used by the buffalo to change substantially, so the habitat
map was valid for both years, but changes in water levels altered
the timing and abundance of floodplain forage growth, which was
associated with flood waters receding.
The water-dependency of buffalo meant that, in the late flood
season, home ranges had to be close to permanent water, which
explains the lack of a significant shift in second order selection
between the two years. However, the third order selection ratios
indicated that buffalo used the habitats within their home ranges
differently in 2008 and 2009, spending more time in grassland and
riparian woodland than on secondary and tertiary floodplains
when water levels were high. The increase in water levels in 2009
was a sudden environmental change, causing large, stochastic
variation in resource availability at a landscape scale. Since their
productivity was reduced in 2009, the buffering effects of the
floodplains were also reduced. To compensate, buffalo had to shift
their ranges towards drier habitats, even though these were at their
least productive during the late flood season.
Annual Changes in Buffalo Behaviour, Reproductive
Success and Body Condition
Being ruminants, buffalo cannot reduce their resting and
ruminating periods below the threshold that allows them to
process their forage intake , so this restricts their capacity to
change their behaviour patterns during periods of low resource
availability. While there was some indication that buffalo spent less
time resting and more time moving in 2009, when they travelled
slightly further on a daily basis, these differences were not
The ratio of young:adult females did not change in 2009, but
there was a significant reduction in the proportion of adult females
with calves, suggesting a decrease in reproductive success. Calves
are the most vulnerable demographic category , and would
have been the first to suffer mortality in stressful conditions.
Buffalo bulls are substantially larger than cows, so have different
optimal time budgets, particularly for feeding. Bulls leave breeding
herds when their condition falls, forming small temporary
bachelor herds in which they forage more intensively . So
the lower adult male:adult female ratio in breeding herds in 2009
indicated that environmental conditions were poor. Both these
demographic changes confirmed that the reduced abundance of
floodplain forage was causing environmental stress for the buffalo,
altering herd composition and potentially affecting population
However, contrary to our hypothesis, buffalo body condition
was significantly higher during the late flood season in 2009 than
in 2008; why is unclear. Buffalo utilised ranges further from
permanent water during the rest of the year, so the herbaceous
layer of areas closer to the channels would have had a lower
grazing pressure for most of the year , and may still have
provided adequate amounts of forage. In addition, there was an
unusual rainfall event in 2009, when 60 millimetres of rain fell
over June 1011. The effect on grass growth appears to have been
substantial: other herbivores in northern Botswana responded by
returning to their rainy season ranges , and so this atypical
event may have provided more abundant, higher quality forage
during the late flood season than was available the previous year.
Periods of low resource availability, such as drought, have a
delayed effect on herbivore mortality, only affecting animals in the
second year of a prolonged event . While there is little
information on the factors influencing fat storage in large
mammals , herbivores could increase their feeding in the first
year of such an event, depleting forage resources but potentially
increasing body condition. Delayed mortality would then be a
response to prolonged harsh conditions, exacerbated by forage
depletion in the previous year. Buffalo may therefore have
increased their feeding intake in response to lower forage
availability in the floodplains , leading to a temporary gain
in condition. The large body size of buffalo means that locomotive
costs are low, so carrying extra fat would not increase those costs
substantially, and any costs would be outweighed by the benefits of
having an energetic buffer against future periods of resource
deficiency . However, prolonged periods of high flooding in
years with typical rainfall patterns would have a detrimental effect
on the buffalo, with changes in behaviour, lowered reproductive
success, and reduced body condition.
Rising anthropogenic pressure, both through direct human
activities and the effects of climate change, renders environmental
conditions less predictable across the globe, and inevitably affects
resource availability within protected areas [14,7274]. Water is
one of the most important resources  and the future of highly
water-dependent riverine and lake ecosystems is uncertain,
particularly when several countries lay claim to water extraction
rights . The responses of regional ecosystems to environmental
change are difficult to predict , particularly tropical systems
since they are driven by water rather than temperature .
Long-term changes in water availability may alter forage
availability, thereby affecting the sizes of herbivore home ranges
 and population dynamics , possibly intensifying
densitydependent effects  and affecting survival and fertility rates
. Large-scale climatic fluctuations can also cause significant
shifts in habitat selection patterns, ultimately leading to changes in
species distributions, potentially causing them to leave protected
areas [79,80]. Substantial resource buffers around seasonally
important resources are necessary for herbivores to engage in
compensatory behaviours and movements in response to spatial
and temporal shifts in resource availability . However,
significant changes in environmental conditions may reduce the
capacity of animals to exploit these buffers . Since many
protected areas are surrounded by human developments , the
potential for expansion of protected areas is limited .
Unusually high water levels can have substantial detrimental
impacts , and so the management of water resources may be
key to the preservation of functioning ecosystems, particularly
where animal movements are restricted by barriers. Draining
seasonally critical habitats may become necessary to allow
herbivores access to productive forage during difficult times of
the year and maintain herbivore populations in existing protected
We thank the Botswana Ministry of Environment, Wildlife and Tourism
for permission to conduct this study (permit numbers EWT 3/3/8XXXVII
44 and EWT 8/36/4IV 62), Laura Atkinson, Janette Baarman, Roz Balen,
Shavaughn Davies and Jennifer Gilbert for assistance in the field, Dane
Hawk, Rob Jackson and Larry Patterson for veterinary support, Guy
Lobjoit and Duncan Rowles for logistical support, Innes Cuthill for
statistical advice, and Richard Fynn, Ian Johnson, Mario Melletti and an
anonymous reviewer for comments on the manuscript.
Conceived and designed the experiments: EB MCB SH. Analyzed the
data: EB. Contributed reagents/materials/analysis tools: MCB. Wrote the
paper: EB SH. Carried out the research: EB.
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