Animal Perception of Seasonal Thresholds: Changes in Elephant Movement in Relation to Rainfall Patterns

Abstract

Background: The identification of temporal thresholds or shifts in animal movement informs ecologists of changes in an animal's behaviour, which contributes to an understanding of species' responses in different environments. In African savannas, rainfall, temperature and primary productivity influence the movements of large herbivores and drive changes at different scales. Here, we developed a novel approach to define seasonal shifts in movement behaviour by examining the movements of a highly mobile herbivore (elephant; Loxodonta africana), in relation to local and regional rainfall patterns. Methodology/Principal Findings: We used speed to determine movement changes of between 8 and 14 GPS-collared elephant cows, grouped into five spatial clusters, in Kruger National Park, South Africa. To detect broad-scale patterns of movement, we ran a three-year daily time-series model for each individual (2007–2009). Piecewise regression models provided the best fit for elephant movement, which exhibited a segmented, waveform pattern over time. Major breakpoints in speed occurred at the end of the dry and wet seasons of each year. During the dry season, female elephant are constrained by limited forage and thus the distances they cover are shorter and less variable. Despite the inter-annual variability of rainfall, speed breakpoints were strongly correlated with both local and regional rainfall breakpoints across all three years. Thus, at a multi-year scale, rainfall patterns significantly affect the movements of elephant. The variability of both speed and rainfall breakpoints across different years highlights the need for an objective definition of seasonal boundaries. Conclusions/Significance: By using objective criteria to determine behavioural shifts, we identified a biologically meaningful indicator of major changes in animal behaviour in different years. We recommend the use of such criteria, from an animal's perspective, for delineating seasons or other extrinsic shifts in ecological studies, rather than arbitrarily fixed definitions based on convention or common practice.

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Animal Perception of Seasonal Thresholds: Changes in
Elephant Movement in Relation to Rainfall Patterns
Patricia J. Birkett1*, Abi T. Vanak1¤, Vito M. R. Muggeo2, Salamon M. Ferreira1,3, Rob Slotow1
1Amarula Elephant Research Programme, School of Life Sciences, University of Kwazulu-Natal, Durban, South Africa, 2Dipartimento di Scienze Statistiche e Matematiche
‘Vianelli’, Universita ` di Palermo, Palermo, Italy, 3Scientific Services, Kruger National Park, Skukuza, South Africa
Abstract
Background: The identification of temporal thresholds or shifts in animal movement informs ecologists of changes in an
animal’s behaviour, which contributes to an understanding of species’ responses in different environments. In African
savannas, rainfall, temperature and primary productivity influence the movements of large herbivores and drive changes at
different scales. Here, we developed a novel approach to define seasonal shifts in movement behaviour by examining the
movements of a highly mobile herbivore (elephant; Loxodonta africana), in relation to local and regional rainfall patterns.
Methodology/Principal Findings: We used speed to determine movement changes of between 8 and 14 GPS-collared
elephant cows, grouped into five spatial clusters, in Kruger National Park, South Africa. To detect broad-scale patterns of
movement, we ran a three-year daily time-series model for each individual (2007–2009). Piecewise regression models
provided the best fit for elephant movement, which exhibited a segmented, waveform pattern over time. Major breakpoints
in speed occurred at the end of the dry and wet seasons of each year. During the dry season, female elephant are
constrained by limited forage and thus the distances they cover are shorter and less variable. Despite the inter-annual
variability of rainfall, speed breakpoints were strongly correlated with both local and regional rainfall breakpoints across all
three years. Thus, at a multi-year scale, rainfall patterns significantly affect the movements of elephant. The variability of
both speed and rainfall breakpoints across different years highlights the need for an objective definition of seasonal
boundaries.
Conclusions/Significance: By using objective criteria to determine behavioural shifts, we identified a biologically
meaningful indicator of major changes in animal behaviour in different years. We recommend the use of such criteria, from
an animal’s perspective, for delineating seasons or other extrinsic shifts in ecological studies, rather than arbitrarily fixed
definitions based on convention or common practice.
Citation: Birkett PJ, Vanak AT, Muggeo VMR, Ferreira SM, Slotow R (2012) Animal Perception of Seasonal Thresholds: Changes in Elephant Movement in Relation
to Rainfall Patterns. PLoS ONE 7(6): e38363. doi:10.1371/journal.pone.0038363
Editor: Wayne M. Getz, University of California, Berkeley, United States of America
Received October 10, 2011; Accepted May 4, 2012; Published June 27, 2012
Copyright: ? 2012 Birkett et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Funding for this study was received through a donation from the Amarula Trust (http://www.amarulatrust.com/). UKZN and The National Research
Foundation, South Africa, provided funding for PJB (GUN: 61470 to RS) (http://www.nrf.ac.za/) and a post-doctoral fellowship to ATV. There are no constraints
from the funders on use or dissemination of the data, or influence on the results or conclusions. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Competing Interests: Dr. Sam Ferreira is a research manager and employee of SANParks Scientific Services, Kruger National Park. There are no patents, products
in development or marketed products to declare. This does not alter the authors’ adherence to all the PLoS ONE policies on sharing data and materials.
* E-mail: birkett.patricia@gmail.com
¤ Current address: Ashoka Trust for Research in Ecology and the Environment. Bangalore, India
Introduction
The study of animal movement patterns allows ecologists to
determine the distribution of species both in space and time, and
the factors that influence their movements in different environ-
ments [1]. Spatial variation in the landscape results in a
heterogeneous distribution of resources such as habitats, water,
and forage patches [2,3]. However, the time-frames over which
these resources are available to an individual also vary, and are
influenced by abiotic factors such as rainfall and temperature [4].
For example, in African savanna systems, forage resources may
vary according to seasonal changes in rainfall [5] and animals
respond to these conditions by altering or shifting their patterns of
movement over time [6]. Such variations in responses impose a
range of challenges when conservationists seek specific outcomes.
Decision makers can thus be better informed by defining
appropriate temporal scales over which shifts in species movement
patterns occur.
Recent advances in animal-mounted GPS technology has
increased the availability of fine-scale animal movement data,
thus enhancing our ability to better understand patterns in animal
movement behaviour [7,8]. Several studies have investigated the
fine-scale ranging behaviour of large mammals, including elk
(Cervus elaphus) [6], moose (Alces alces) [9], caribou (Rangifer tarandus)
[2] and African elephant (Loxodonta africana) [10,11]. Many of these
studies focussed on defining the movement ‘modes’ or types of
movement of animals across seasons, and over various scales of
resolution. In each case, seasons were designated based on climatic
proxies such as temperature, rainfall or snowfall. Although these
variables may have a direct influence on the movement behaviour
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of the animal, this method imposes seasonal boundaries which
may not necessarily reflect the natural variation in movement
patterns of the animal within the ecosystem, i.e. the responses of
the study animal itself. Furthermore, ecological changes, including
changes in both abiotic and biotic factors across temporal
boundaries such as seasons, often impose a constraint or release
on an animal’s behaviour [12]. We suggest that a process which
allows the movement patterns of an animal to define these
temporal boundaries may be more informative (e.g. [13]).
Large herbivores are highly mobile and able to cover large
distances across the landscape. Since they encounter forage
resources at various levels of spatial and temporal scale [5], it is
important to consider the effect of scale in studies of these animals
(see [14]). Patterns of herbivore movement over time also vary in
scale; for example, fine-scale patterns may include short periods of
foraging, searching for food, and resting, while extended periods of
exploration may span more than a day [15,16]. At broader scales;
monthly, seasonal, annual and even inter-annual patterns of
movements may be detected [6,15].
In this study, we focus on defining broad-scale temporal changes
in the movement behaviour of female elephant. The African
elephant is considered to be a keystone species in African savanna
systems, since elephant foraging behaviour affects various ecosys-
tem processes [17]. Although there is extensive literature on the
seasonal space use or home range dynamics of elephant from
many areas in southern Africa [18–25], these studies have pre-
defined ‘wet’ and ‘dry’ periods, usually inferred from regional
rainfall records, and arbitrarily designated according to calendar
months (e.g.: ‘wet’ period: October – March). Here, we apply a
different approach by considering animal perception of seasonal
change: we examine movement behaviour in order to identify
ecologically significant thresholds (i.e. breakpoints) over time.
Thus, we aim to define an unbiased temporal scale over which
changes in the movement patterns of elephant can be detected.
We then examine whether these shifts in elephant movement can
be related to rainfall patterns at local and regional scales.
Because of the broad-scale effects of rainfall on both water
availability and vegetation phenology and biomass [4], we
hypothesised that, in general, changes in elephant movements
over time would be affected by rainfall patterns [25]. We expected
major breakpoints in elephant movement to occur with the onset
of the first rains of the season in each year. We also predicted that
elephant would be more strongly affected by local rather than
broader regional rainfall patterns across all years. We expected
additional minor breakpoints to occur at other points during the
year, in which elephant may be responding to factors other than
rainfall. However, our focus was to define the major seasonal
breakpoints, in particular, the breakpoint at the end of the dry
season. During this period, elephant will most likely respond to an
increase in rainfall, coupled with an increase in forage quality and
biomass, resulting in a release from constrained movement
behaviour, typical of late dry-season conditions [12].
Materials and Methods
Ethics Statement
Elephant capture & handling was conducted in strict accor-
dance with ethical standards. Specific approval for this particular
research project was obtained through the University of KwaZulu-
Natal Animal Ethics sub-committee (Ref. 009/10/Animal). This
research also forms part of a registered and approved SANParks
project, in association with Kruger National Park and Scientific
Services (Ref: BIRPJ743).
The Kruger National Park (KNP) and associated private
reserves along the western boundary (Sabie Sand, Klaserie,
Timbavati, Umbabat and Manyaleti), extend across an area of
approximately 21,281 km2, in the north-eastern Lowveld region of
the South Africa. Our study area covers the southern, central and
western regions of KNP and includes the associated private
reserves, since elephant are able to move freely between these
areas (Fig. 1). Vegetation in this region is primarily classified as
semi-arid to arid wooded savanna [26].
The elephant population in KNP was estimated to be , 14,000
individuals during 2010 (SANParks, unpublished data). From 2006
to 2010, we collected geographical location data, downloaded
from GPS/GSM Collars (Africa Wildlife Tracking cc., South
Africa), fitted to 17 elephant cows from different herds. To ensure
the independence of sampling, a single female in each herd was
selected and collared. The movements of these collared females
are thus assumed to represent the movement behaviour of the
breeding herd to which they belong [27,28]. The females were
categorised according to five ‘clusters’, based on the area in which
they were collared: Orpen-Skukuza, Satara-Nhlanguleni-Muzan-
duzi, Lower Sabie, Satara and Skukuza (Fig. 1). The Lower Sabie
cluster included four collars, however three of these malfunc-
tioned. Thus we were only able to use a single collar for this cluster
and 14 collars in total. All herds were distributed between
225.37uS in the south and 223.75uS in the north; 32.00uE in the
east and 30.99uE in the west. To maintain data capture at a
relatively high temporal resolution, the collars were set to record
locations at 30 min intervals. We obtained PDOP (Positional
dilution of precision) values from six of the collars. PDOP is a 3-D
measure of the quality of GPS data, where lower values usually
indicate higher location accuracy [29]. The average value
obtained was 1.82 with a variance of 0.76, indicating a low error
in position estimation. We assumed other collars to have similar
levels of error.
To examine the temporal scale over which elephant movement
behaviour changed, we calculated the mean daily speed (km/h)
and the variance (standard deviation) associated with the speed.
To calculate these variables, we computed step-lengths at 30 min
intervals using Hawth’s Tools [30] in ArcGIS 9.3 (Environmental
Systems Research Institute, Redlands, CA, USA) and then
converted this to speed (km/hr). To minimise the effect of
acquisition errors, we used only those step-length values recorded
within the interval 27.5–32.5 min. We checked for errors where
step-lengths appeared to be either unusually long or abnormal, by
converting points to paths using Hawth’s tools [30]. Cases of
obvious errors were either corrected where possible, or removed
from the dataset. Because of data errors, we were not able to use
91 days of data across all collars in all years (2007: average 1.9
days per collar; 2008:1.8 days per collar: 2009:6.1 days per collar).
We were able to use full-year datasets from eight collars in 2007,
14 collars in 2008 and eight collars in 2009.
We analysed the daily time series for each elephant separately
using a piece-wise linear or segmented regression model (hereafter
PRM), since this provides a useful method for determining
ecological thresholds [31]. We estimated breakpoints via the
algorithm described in [32] and implemented in R 2.12 [33] using
the package ‘segmented’, version 0.2–7.2 [34]. Due to the positive
support of the response variable and the observed ‘waveform’
temporal patterns, we assumed a generalised linear model
(hereafter GLM) with a log-link function (Poisson distribution)
and an identity variance function, with multiple breakpoints.
Namely, for each elephant:
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log E½Yt?~b0zb1Ztzb2(Zt{y1)z... zbKz1(Zt{y1)
where Yt is the random variable representing the elephant
movement at day t, E[Yt] is its expected value to be expressed as
a segmented function of day with parameters y and b. More
specifically, y represents the breakpoints, i.e. the days where the
elephant movement changes, and b regulates the slopes in the
different time periods. Zt is the generic numeric covariate
representing the time index, which has a piecewise linear
relationship with the response variable, Yt.
We ensured that breakpoints were valid by checking the
corresponding gap coefficient and its t-value, (breakpoint accepted
when t,2, [35]). In order to select the most appropriate model, we
compared Bayesian Information Criteria (hereafter BIC) values
(see [32,36]) for the two models (GLM, PRM). We also calculated
the bootstrapped 95% confidence intervals for the breakpoints in
the PRM’s.
To calculate a measure of rainfall, we applied two methods. For
a regional measure of rainfall, we used rainfall values averaged
from ten stations (see Fig. 1) within the combined spatial area used
by all herds. To obtain rainfall values at a finer scale, we averaged
values from between one and three stations located within the
spatial range of individual herds. These represented ‘local’ rainfall
for individual herds in separate years. We chose local stations by
visually inspecting individual location points in relation to the
position of a station.
Following the methods used to model step length variables, we
fitted GLM’s and PRM’s to the rainfall data and obtained
breakpoints by using the algorithm described in [32]. We then
compared BIC values to determine the best fit for the rainfall
models.
We ran 1-tailed bivariate correlations to identify the relationship
between local rainfall and elephant movement, and regional
rainfall and elephant movement. To check whether ‘year and
‘cluster’ were confounding variables, we also ran 1-tailed partial
correlations using: all breakpoints, and then upper and lower
breakpoints separately. In each case, we controlled for the
variables together, and then separately. Statistical analyses were
conducted in PASW Statistics 18, (SPSS Inc., 2009, Chicago IL).
Results
The analysis of elephant movement patterns revealed that in all
cases, the Piecewise Regression Model (PRM) provided a better fit
than the Generalised Linear Model (GLM). The movement
patterns of female elephant exhibited a distinct waveform trend
over the three-year period, with behavioural changes occurring at
the transition between both the wet and dry season, and the dry
and wet season. Thus we obtained dry-wet season breakpoints at
the troughs within the model, (hereafter referred to as ‘lower’
speed breakpoints) and wet-dry season breakpoints at the peaks
(hereafter referred to as ‘upper’ speed breakpoints) (see Appendix
S1). Although we detected 2 additional breakpoints for collar
AM99 in 2008, and an additional breakpoint for AM108 in 2008,
these were considered minor breakpoints (based on BIC values),
and were removed from the correlation analysis. The major
breakpoints were of primary interest, since the aim of the study
was to define broad-scale shifts in elephant movement over time.
In all years, the relationship between both speed and variance of
speed, and day of year was negative during the period between the
wet and dry season, followed by a breakpoint at the dry-wet season
boundary. Beyond this, as the wet season commenced, the
relationship was positive, up until a breakpoint at the end of the
wet season (example AM93, Fig. 2). In one case, (AM108 in 2008),
an initial lower breakpoint occurred at approximately day 50 (mid-
February) in 2008. Between the 14thof June and the 7thof July,
this elephant increased her average speed on 11 of the 23 days
(.0.5km/h). Examination of AM108’s movement pattern during
this period indicated that she was in close association with AM106
within the south of Kruger from the 15thuntil the 19thof June,
where after she began moving rapidly and directly, across Sabi
Sand Reserve, and into the central region of Kruger, covering a
distance of approx. 50 km in 3 days (19th–21stJune). From this
point, she moved further into the northern section of her range.
AM108’s increased movements may be the result of a disturbance
event [37], although it is more likely that she is moving in order to
access forage, possibly as a result of limited resources in the
southern areas of Kruger during this dry period, or because of
competitive exclusion by AM106 (e.g. see [10]). AM108’s speed
breakpoint at day 50 occurred because of the increase in average
speed over this period in June-July. As a result of this ‘peak’ in
speed, the breakpoint at 551 weeks is an ‘upper’ breakpoint, when
it would usually be a ‘lower’ breakpoint. Thus for AM108 in 2008,
the shape of the curve, and by implication, the movement pattern,
appears inverted. We have removed this outlier ‘upper’ break-
point, along with corresponding local and regional rainfall
breakpoints for AM108 in 2008, from the correlation analysis.
Individual collar breakpoints in speed varied between years,
with lower breakpoints occurring, on average, on day 249 (approx.
6thSeptember) in 2007 (95% CI , 128 days), day 614 (approx. 5th
September) in 2008 (95% CI , 160 days), and day 950 (approx.
7thAugust) in 2009 (%95 CI , 138 days) (Appendix S2). Upper
breakpoints occurred, on average, on day 320 (approx. 16th
November) in 2007 (95% CI , 41 days). There were only 2
breakpoints in 2008, for collars AM306 and AM307) (day 717;
approx. 17thDecember) (95% CI , 1 day). In 2009, breakpoints
occurred on day 754 (approx. 23rd January) (95% CI , 16 days)
(Appendix S2).
For rainfall patterns, the PRM’s provided a better fit, and the
models also identified a distinct waveform relationship, between
both local and regional rainfall, and day of year. Thus, as with
speed breakpoints, we also obtained dry-wet season breakpoints at
the troughs within the local and regional rainfall models, (hereafter
referred to as ‘lower’ rainfall breakpoints) and wet-dry season
breakpoints at the peaks (hereafter referred to as ‘upper’ rainfall
breakpoints) (see Appendix S1). Major local rainfall breakpoints
varied between years, with lower breakpoints occurring, on
average, on day 225 (approx. 13thAugust) in 2007 (95% CI ,
93 days), day 543 (approx. 26thJune) in 2008 (95% CI , 115
days), and day 956 (approx.13thAugust) in 2009 (95% CI , 121
days) (Appendix S2). Local rainfall (upper) breakpoints occurred
Figure 1. The distribution of elephant clusters in The Kruger National Park and associated private reserves. The KNP boundary is
outlined in grey, with the contiguous private reserves shaded in grey. The five clusters are shown as minimum convex polygons (MCP’s), which have
been clipped according to the boundaries of Kruger and other Private Reserves. These clusters are based on the distribution of collared female
elephant over the entire study period. MCP’s were calculated for three collars in the Orpen-Skukuza cluster, four collars in the Satara-Nhlanguleni-
Muzanduzi cluster, one collar in the Lower Sabie cluster, four collars in the Satara cluster and three collars in the Skukuza cluster. The 10 rainfall
stations used are abbreviated: HOU– Houtboschrand, SAT – Satara, NWA – Nwanetsi, TSH – Tshokwane, OSA – Lower Sabie, SKZ – Skukuza, PRE –
Pretoriuskop, NHL – Nlhanguleni, TAL – Talamati, KFT – Kingfisherspruit.
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on average, on day 343 (approx. 9thDecember) in 2007 (95% CI
, 90 days). There was only one breakpoint in 2008, for the collar
AM306 (day 696; approx. 26thNovember). In 2009, breakpoints
occurred on day 764 (approx. 2ndFebruary) (95% CI , 41 days)
(see Appendix S2).
Major regional rainfall breakpoints (lower) occurred, on
average, on day 222 (approx. 10thAugust) in 2007 (95% CI ,
102 days); day 561 (approx. 14thJuly) in 2008 (95% CI , 43 days);
and day 962 (approx. 19thAugust) in 2009 (95% CI , 60 days),
(Appendix S2). Major regional rainfall breakpoints (upper)
occurred, on average, on day 330 (approx. 26thNovember) in
2007 (95% CI , 83 days), no upper breakpoints for regional
rainfall occurred in 2008; and in 2009, breakpoints occurred on
day 742 (approx. 11thJanuary) (95% CI , 8 days) (Appendix S2).
We found that, overall, 35% of elephant increased their speed
before the rainfall breakpoint, while 65% increased their speed
after this breakpoint. When viewed separately, upper and lower
breakpoints showed different trends: Lower breakpoints (dry to
wet season transition): 29% of elephant increased their speed
before lower rainfall breakpoints, 71% after (see Fig. 3); Upper
breakpoints (wet to dry season transition): 63% of elephant
increased their speed before upper rainfall breakpoints, 37% after.
In general, values for mean speed, and the variance in mean
speed, were at their highest during summer months when rainfall
reached a peak; and at their lowest during the driest winter
months.
Bivariate correlations revealed a strong positive relationship
between speed, and both local (Pearson Correlation: r=0.973;
P,0.01) and regional (r=0.982; P,0.01) rainfall breakpoints.
However, when we used ‘year’ and ‘cluster’ as controls in a partial
regression analysis, we found slightly lower, but still significant
correlations: speed and local rainfall breakpoints; r=0.752,
P,0.01 and speed and regional rainfall breakpoints; r=0.828,
P,0.01. For upper breakpoints (wet to dry season transition), we
found very little effect of either ‘year’ or ‘cluster’: speed and local
rainfall: r=0.913; speed and regional rainfall: r=0.972. However,
for lower breakpoints (dry to wet season transition), ‘year’ had a
confounding effect on speed and rainfall breakpoints (speed and
local rainfall: r=0.04; P=0.421; speed and regional rainfall:
r=20.003; P=0.495). ‘Cluster’, as a second control, did not
appear to affect the breakpoints (speed and local rainfall: r=0.973;
speed and regional rainfall: r=0.981).
Discussion
It is widely acknowledged that, within savanna environments,
elephant movements are affected by seasonal changes in rainfall
[11,24,38]. However, no prior studies have used these movement
patterns to discern temporal breakpoints or shifts in behaviour
across seasons. By examining variation in elephant speed across
broad scales (multiple years), we have allowed the behaviour of
individual elephant to reveal distinct seasonal shifts, rather than
Figure 2. Multiyear piecewise regression models for Collar AM93, within the period 2007–2009, in Kruger National Park. Variables
modelled include: average speed in the upper row (red line), average local rainfall in the middle row (green line), and average regional rainfall in the
lower row (blue line). The columns (separated by dashed-lines) represent different years: 2007, 2008 and 2009 from left to right. The X axis represents
‘day of year’. Breakpoints are given (in associated colours), together with the 95% confidence interval for each breakpoint (represented by horizontal
bars).
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prescribing arbitrarily defined seasons. In this way, the timing of
our seasons is biologically relevant to the species, and does not
depend on coarse-scale measures of an external variable which
may not reflect the true variation in the species’ behaviour.
Examination of elephant movement patterns in conjunction with
rainfall patterns indicated that all herds changed their behaviour at
two distinct thresholds: at the end of the dry season before the first
rains commenced, and at the end of the wet season, during the
period of the highest average daily rainfall. These changes
coincided with the major rainfall breakpoints, which signal a
seasonal transition between wet and dry, and dry and wet periods.
In general, elephant in KNP increased their speed during the
wet season (summer months) up until a maximum threshold point
at the onset of the dry season, following which their speed reduced,
until a minimum threshold point at the end of the dry season.
Beyond this point, at the onset of the wet season, their speed
increased once more (see Fig. 2). The lower breakpoints indicate
that a change from dry to wet season conditions triggered a release
from constrained movement behaviour, which is consistent with
the ‘dry season bottleneck’ theory proposed for herbivores in
environments where there are markedly different seasons [12].
Herbivores are restricted during dry periods, since both forage
quality and quantity are reduced; however, there is a release from
these constraints once the wet season commences [12]. Female
elephant in KNP are most restricted in the dimension associated
with rainfall and herbaceous biomass [14]. During the dry season,
females, and in particular weaned calves, are susceptible to stress
as a result of the decreased nutritional value of forage [39]. Thus,
in order to conserve energy, female elephant in KNP are likely to
restrict their movement at drier times of the year when forage
quantity and quality is lower. The decrease in speed and lower
variance in speed during the dry season also suggests that elephant
in KNP may be using smaller areas more intensively. Since past
research has revealed that elephant use riparian vegetation and
low-lying thickets during the dry season [40–42], these habitats
Figure 3. The effect of local rainfall on the average speed of elephant during the dry to wet season transition in Kruger National
Park. Lower speed breakpoints are plotted against lower rainfall breakpoints for 8 collars in 2007 (blue), 14 collars in 2008 (purple) and 7 collars in
2009 (orange). Points above the line (y=x) represent speed breakpoints that occur after rainfall breakpoints; points below the line (y=x) represent
speed breakpoints that occur before rainfall breakpoints. Rainfall and speed breakpoints that fall exactly on the line are equal to each other i.e. the
breakpoints occur simultaneously. Vertical lines for each year represent the range in speed breakpoints (from Day 159 to 287 in 2007; Day 142 to 302
in 2008 and Day 142 to 280 in 2009). Horizontal lines represent the range in local rainfall (from Day 196 to 289 in 2007; Day 135 to 250 in 2008 and
Day 158 to 279 in 2009).
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with available browse may be more intensively used, and are thus
vulnerable to higher levels of impact.
There is a strong inter-annual variability in rainfall patterns in
savannas, where cycles of above- and below-average rainfall occur
in different years [43]. This variability is likely to affect the timing
of the rainfall breakpoints in different years, and thus confound the
relationship between elephant speed and rainfall. Between
different years, the relationship between rainfall and speed
breakpoints in the dry-wet period (lower breakpoints) appears to
be highly variable: differences between average speed and local
rainfall breakpoints range from 24 days in 2007, to 71 days in
2008 and 7 days in 2009 (Appendix S2). Monthly rainfall data
from KNP indicate that in 2008, conditions were drier, with
below-average rainfall measurements at the majority of stations for
eight of the 12 months (SANParks unpublished data). This may
explain the differences in the timing of speed and rainfall
breakpoints during this year.
Rainfall directly affects primary productivity in savanna systems
[44]. In north-western and south-eastern regions in South Africa,
both forage quality and abundance within savannas is positively
correlated with rainfall, and forage quality gradually increases
after mid-August [22]. Since the lower breakpoints (dry to wet
season transition) in elephant speed (in 2007, 2008 and 2009)
occurred from early August up until early September (Appendix
S2), it is likely that this increase in movement was a response by
elephant to increased forage quality. The appearance of leaves in
certain savanna tree species have been shown to precede rainfall
events, (e.g. [45,46]), an occurrence which is likely to be caused by
changes in day length [47]. Thus, female elephant and calves may
rely on early flushes in certain tree species prior to rainfall events
[48]. In this study, female elephant increased their movements at
the end of the dry season, which indicates that they may have
tracked these early flushes and moved into new habitats or areas
where browse was available. The reason that elephant decreased
their speed during the wet-dry season threshold, during a period of
the highest average daily rainfall, is less clear. Elephant in Kruger
demonstrate a switch from a 50% inclusion of grass in their diet
during the wet months, to a 10% inclusion during the dry months
[49]. Thus, since areas of palatable grass are more widely spread
during the early-to-mid wet season [43,49], we could hypothesise
that elephant moved more during this period (i.e. increased their
speed) to access these areas. Later in the season, certain areas
become less palatable (e.g. sourveld) [43], resulting in reduced
availability of graze and slower movements by elephant. An
investigation of elephant habitat-use over this period would be
necessary to verify these hypotheses.
Although abiotic factors such as rainfall, snowfall or tempera-
ture may be informative when examined alongside movement
patterns (e.g. [2,9]), in isolation, they may bias the estimation of
time frames which become arbitrary to the species in question.
Thus it is necessary to identify biologically relevant breakpoints
over time, in order to assess changes in behaviour relating to the
spatial use of habitats by elephant and other large herbivores in
different environments. This in turn allows for a better under-
standing of animal responses to seasonally available resources, and
variable environmental conditions [13]. In terrestrial systems
where large herbivores play a vital role as ecosystem drivers [50–
52], it is important to elucidate not only where individuals are
spatially distributed, but when behavioural changes occur and
which factors play a key role in determining these shifts. This
allows for the implementation of more effective management
protocols for threatened species and habitats.
This study provides a unique method for identifying appropriate
temporal breakpoints over broad scales, which can be applied to
other species, in particular large mammals capable of carrying
GPS collars. Within this framework, variables that may influence
the movement of an animal (e.g. rainfall, snowfall, temperature)
can be examined alongside movement patterns, in order to
examine potential interactions. Although we have used a broad-
scale approach, other temporal scales (yearly, monthly, weekly), or
a multi-scale approach may be applied. Thus the methods used in
this study provide useful tools that can be used to extract a
comprehensive description of the animal’s movement behaviour,
in association with important variables.
Supporting Information
Appendix S1
Table 1 Results for speed, local rainfall and regional
rainfall breakpoints from all collars, obtained using
multi-year piecewise regression models.
(DOC)
Appendix S2
Table 1 Average speed, local rainfall and regional rainfall
breakpoints for all collars, obtained using multiyear
piecewise regression models.
(DOC)
Acknowledgments
We are grateful for the contribution of two anonymous reviewers to the
paper. We thank the Kruger National Park Veterinary Services for
collaring of the elephant. We also thank A. Delsink for data management,
G. Lagendijk for comments on an earlier draft of the manuscript, M.
Thaker for assistance and comments on statistical analyses, and A. Smith
for ArcGIS mapping advice and expertise.
Author Contributions
Analyzed the data: PJB ATV VMRM. Wrote the paper: PJB. Conceived
and designed the research questions and analysis: PJB ATV VMRM SMF
RS. Provided supervisory inputs and support: RS SMF. Provided inputs to
manuscript: ATV VMRM SMF RS.
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