Download Soil nutrient status determines how elephant utilize trees and shape

Journal of Animal Ecology 2011, 80, 875–883
doi: 10.1111/j.1365-2656.2011.01819.x
Soil nutrient status determines how elephant utilize
trees and shape environments
Yolanda Pretorius*1, Willem F. de Boer1, Cornelis van der Waal1, Henjo J. de Knegt1, Rina C.
Grant2, Nicky M. Knox3, Edward M. Kohi1, Emmanuel Mwakiwa1, Bruce R. Page4, Mike J. S.
Peel5, Andrew K. Skidmore3, Rob Slotow4, Sipke E. van Wieren1 and Herbert H. T. Prins1
1
Resource Ecology Group, Wageningen University, Droevendaalsesteeg 3a, 6708 PB Wageningen, The Netherlands;
Scientific Services, Kruger National Park, Private Bag X402, Skukuza 1350, South Africa; 3International Institute
for Geo-Information Science and Earth Observation, PO Box 6, 7500 AA, Enschede, The Netherlands; 4Amarula Elephant
Research Programme, School of Biological and Conservation Sciences, University of KwaZulu-Natal, Durban 4041, South
Africa; and 5Agricultural Research Council- Range and Forage Institute, PO Box 13054, Nelspruit 1200, South Africa
2
Summary
1. Elucidation of the mechanism determining the spatial scale of patch selection by herbivores has
been complicated by the way in which resource availability at a specific scale is measured and by
vigilance behaviour of the herbivores themselves. To reduce these complications, we studied patch
selection by an animal with negligible predation risk, the African elephant.
2. We introduce the concept of nutrient load as the product of patch size, number of patches and
local patch nutrient concentration. Nutrient load provides a novel spatially explicit expression of
the total available nutrients a herbivore can select from.
3. We hypothesized that elephant would select nutrient-rich patches, based on the nutrient load
per 2500 m2 down to the individual plant scale, and that this selection will depend on the nitrogen
and phosphorous contents of plants.
4. We predicted that elephant would cause more adverse impact to trees of lower value to them in
order to reach plant parts with higher nutrient concentrations such as bark and root. However,
elephant should maintain nutrient-rich trees by inducing coppicing of trees through re-utilization
of leaves.
5. Elephant patch selection was measured in a homogenous tree species stand by manipulating the
spatial distribution of soil nutrients in a large field experiment using NPK fertilizer.
6. Elephant were able to select nutrient-rich patches and utilized Colophospermum mopane trees
inside these patches more than outside, at scales ranging from 2500 down to 100 m2.
7. Although both nitrogen and phosphorus contents of leaves from C. mopane trees were higher
in fertilized and selected patches, patch choice correlated most strongly with nitrogen content. As
predicted, stripping of leaves occurred more in nutrient-rich patches, while adverse impact such as
uprooting of trees occurred more in nutrient-poor areas.
8. Our results emphasize the necessity of including scale-dependent selectivity in foraging studies
and how elephant foraging behaviour can be used as indicators of change in the availability of
nutrients.
Key-words: Colophospermum mopane, nitrogen, nutrient load, patch selection, reuse, spatial
scale
Introduction
Classical optimal foraging theory has neglected the issue of
scale (Prins & van Langevelde 2008), although it is well recognized that animals select forage resources at a range of
temporal and spatial scales (Spalinger & Hobbs 1992; Wallis*Correspondence author. E-mail: [email protected]
DeVries, Laca & Demment 1999; Ball, Danell & Sunesson
2000). Some studies have developed a theoretical framework
to explain the spatial scaling of foraging (Bailey et al.
1996; Ritchie & Olff 1999), but support from field research in
‘natural’ systems is scarce (Cromsigt & Olff 2006). With this
study, we aim to contribute to the knowledge of foraging
theory by conducting a field experiment that incorporates the
issues of spatial scale and nutrient heterogeneity.
2011 The Authors. Journal of Animal Ecology 2011 British Ecological Society
876 Y. Pretorius et al.
A herbivore that is selective and able to discriminate
between food of good or poor quality should have a selective
advantage (Fryxell 2008). However, herbivore food selection
is, unlike a carnivore, much less concentrated, being distributed as a nested hierarchy of aggregated resources varying
widely in nutrient composition and mass (Senft et al.1987;
Kotliar & Wiens 1990; Bailey et al. 1996; Searle, Hobbs &
Shipley 2005; Fryxell 2008). These aggregated resources can
be defined as patches that are discrete spatial units differing
from their surroundings in composition and ⁄ or appearance
and that cause changes in herbivore foraging behaviour
(Kotliar & Wiens 1990; Searle, Hobbs & Shipley 2005).
Furthermore, patch selection by herbivores depends on the
spatial scales at which the environment is perceived, where
‘grain’ is the smallest scale and ‘extent’ the largest scale, of
heterogeneity to which a herbivore responds to patch structure (Kotliar & Wiens 1990). Given that boundaries between
the subunits within different hierarchical scales should be
defined by the animals’ perceptions and foraging responses
(Senft et al. 1987), we tested whether a large herbivore is able
to select nutrient-rich patches at multiple scales. However,
within a specific spatial scale, patches are not simply distributed uniformly but vary in size, number and local nutrient
concentration (Bailey & Provenza 2008). Therefore, at each
scale, the resource value per grain should be expressed as the
total nutrient load, for example, areas with large nutrientrich patches, with many small nutrient-rich patches or with
patches with very high local nutrient concentrations. In our
study, spatial scales ranged from 2500 m2, where a herbivore
selects between areas with different total nutrient loads, to
selection between individual neighbouring plants of the same
size and species (Fig. 1).
Studying the mechanisms behind patch use is often confounded by the effects of predation risk. For example, the
marginal value theorem (MVT) predicts that foragers will
depart from a single patch when their instantaneous intake
rate drops below the average rate of intake attainable in all
patches (Charnov 1976). However, increased predation risk
should affect MVT in that time in the patch should decrease
(Brown 1999). Therefore, as a study animal, we chose the
largest terrestrial herbivore, the African elephant (Loxodonta
africana), for which predation risk in our study area is negligible. As elephant are able to select vegetation growing on
termite mounds at very small scales (Holdo & McDowell
2004), we predicted that elephant will be able to select nutrient-rich areas at scales ranging from 2500 m2 down to the
individual plant scale.
Many studies show that savannas are either limited by N
or P or co-limited by both (du Toit, Louw & Malan 1940;
Weir 1969; Ludwig et al. 2001; Snyman 2002; Augustine,
McNaughton & Frank 2003; Cech et al. 2008). Because nonruminants such as the elephant cannot make use of microbial
protein and have high food passage rates, they can be
expected to incur higher losses of N in the faeces (Foose
1982). Selection for N and P by elephant has been described
by Jachmann & Bell (1985), and from diet studies on elephant
at our study site in South Africa (Pretorius 2009) and else-
where (Woolley et al. 2009), we know that elephant maximize
N and P intake depending on the time of the year. Therefore,
we expect elephant to select patches and plants of the same
species depending on their N and P contents.
The impact African elephant have on trees has been a
topic of great controversy (Wiseman, Page & O’Connor
2004; De Beer et al. 2006; Lawes & Chapman 2006;
O’Connor, Goodman & Clegg 2007; Scholes & Mennell
2008; Chafota & Owen-Smith 2009). Elephant have been
implicated as one of the key factors maintaining low tree–
grass ratios in savannas (Van de Koppel & Prins 1998).
However, few studies have investigated the causal mechanisms of elephant impact, especially the interactions with
nutrient availability and distribution (Skarpe et al. 2004).
Adverse impact on trees where bark and roots are consumed
is not unique to elephant and has been described in voles
(Microtus spp.) (e.g. Sullivan et al. 2004), Sika deer (Cervus
nippon) (e.g. Yokoyama et al. 2001) or red deer (Cervus
elaphus) (e.g. Verheyden et al. 2006). However, the consumption of bark and roots has mostly been attributed to
the lack of good quality alternative food sources, especially
during winter, rather than to the high nutritional quality of
these plant parts (Servello 1984; Bucyanayandi et al. 1992;
Verheyden et al. 2006). Similarly, elephant impact on trees is
especially prevalent during the dry season when elephant
switch from a diet dominated by grass to browse because
grass quality decreases to below the animal’s maintenance
requirements during this time (Barnes 1982; Beekman &
Prins 1989; Pretorius 2009) and an increased consumption of
woody material indicates nutritional stress (O’Connor,
Goodman & Clegg 2007). We predict that elephant will have
a larger impact on trees that represent low-quality food
through, for example, increased utilization of roots, whereas
impact on trees growing in nutrient-rich patches will be less,
with leaves being utilized more.
To test these predictions, we experimentally manipulated
the spatial distribution of nutrients in a homogenous stand of
plant species dominated by Colophospermum mopane trees.
Materials and methods
In December 2004, we fertilized thirty 50 · 50 m plots in a 35-ha area
in the Timbavati Private Nature Reserve, South Africa (24̊14¢11¢¢S;
31̊22¢32¢¢E, at 380 m elevation), with NPK (3 : 2 : 1) fertilizer. The
area was re-fertilized 2 years later in an identical fashion. The experimental study site was dominated by a homogeneous layer of C.
mopane shrub veld on granitic soils. The herbaceous layer was dominated by Urochloa mosambicensis and Bothriochloa spp. (for a full
description of the vegetation, see Van der Waal 2010). The warm,
rainy season stretched from October to March, and during the study
period, the mean annual rainfall was 420 mm. Data were collected
from October 2005 to March 2008.
Surface water was not constraining plot use, as it was available
0Æ5 km to the north and south of the closest experimental plot. The
experimental layout followed a randomized block design, including
three replicates of nine different treatments and nine controls. The
fertilizer was applied at each 50 · 50 m plot in one of the three different spatial configurations: one large 50 · 50 m patch, five 10 · 10 m
2011 The Authors. Journal of Animal Ecology 2011 British Ecological Society, Journal of Animal Ecology, 80, 875–883
Soil nutrients & elephant tree utilization 877
Fig. 1. A schematic representation of the study set-up illustrating the spatial scales at which the experiment was conducted. To each spatial configuration, 50 · 50 m control plots were allocated, and at scales smaller than 2500 m2, unfertilized areas neighbouring fertilized patches were
used as controls (white blocks and white areas in blocks are representative of unfertilized plots and patches).
patches or 25 patches of 2 · 2 m, and using one of the three different
nitrogen concentrations (30Æ0, 6Æ0 and 1Æ2 g m)2). One 50 · 50 m
control plot was allotted to each of the three spatial configurations,
and this was replicated three times (Fig. 1). The lightest and heaviest
fertilizer concentrations were excluded, as these were expected to generate too faint a signal or a toxic effect. The result was seven combinations that also differed in their total nutrient load per 50 · 50 m
plot (0Æ6, 3, 15 kg N). Each replicate of these seven treatments,
together with their control plots made up a block, and the blocks represented different but contiguous topographical positions in a gently
undulating landscape.
Large herbivore species commonly found in the area include elephant (L. africana) at a mean density of 0Æ3 elephants km)2, buffalo
(Syncerus caffer), impala (Aepyceros melampus), warthog (Phacochoerus aethiopicus), duiker (Sylvicapra grimmia), zebra (Equus burchellii) and steenbok (Raphicerus campestris). C. mopane was the main
food plant for elephant in the area (Kos et al. 2007; Pretorius 2009).
Data collection on the type of nutrients selected by elephant and
the scale of nutrient selection consisted of annual measurements during the wet seasons from 2006 to 2008. We measured leaf nutrient
content and accumulated signs of elephant utilization on 600 marked
C. mopane trees within the field fertilization experiment and made
direct observations of elephant selecting between two C. mopane
trees of the same size within C. mopane-dominated areas surrounding
the experiment. Data on elephant use of trees, and the occurrence
and utilization of coppiced C. mopane trees in and outside fertilized
2011 The Authors. Journal of Animal Ecology 2011 British Ecological Society, Journal of Animal Ecology, 80, 875–883
878 Y. Pretorius et al.
patches, were obtained from an elephant utilization assessment on all
trees larger than 1Æ5 m within the experimental plots at the end of the
study.
ANNUAL MOPANE TREE MEASUREMENTS
Tree responses were monitored using 600 tagged C. mopane trees. In
the controls and in whole-plot fertilizer treatments, C. mopane trees
(> 1 m height) closest to twenty randomly generated points were
selected. In heterogeneous treatments (patch size either 10 · 10 or
2 · 2 m), ten trees with stems within 2-m distance of fertilized
patches were randomly selected, together with ten trees in the unfertilized plot area (> 2-m distance from fertilized patches). Five fully
expanded leaf samples were randomly collected from the canopies of
marked C. mopane trees during the 2006, 2007 and 2008 growing seasons. In the homogeneous treatments, two pooled samples were analysed per plot while in the heterogeneous treatments, samples were
pooled for leaves collected from the plants inside and outside of the
fertilized patches. This resulted in 60 samples per year. Prior to milling (through a 1-mm sieve), C. mopane leaves were dried to constant
weight at 60 C and weighed. All marked C. mopane trees were
assessed for elephant impact (percentage canopy volume reduction)
using the scale of Anderson & Walker (1974). For 2006, the height
and diameter of marked trees were calculated from digital photographs. At the end of the experiment in 2008, a selection of trees were
remeasured to determine tree height changes in relation to visual elephant impact scores. For analysis, as ten trees were measured per
patch, elephant utilization was also expressed as the proportion of
trees utilized per patch.
DIRECT OBSERVATIONS
All elephant observations made in areas dominated by C. mopane
surrounding the experiment were of randomly encountered solitary
adult bulls or bulls in small bachelor groups. All data were collected
during the wet season of 2006 and were spatially and temporally independent as only one paired sample was collected at each sighting of a
particular elephant, or group of elephant, on a particular day. Each
elephant was located using the extensive road network on the reserve
and observed from a vehicle for between 5 and 10 min (or until the
animal moved out of site) using binoculars. For an observation to be
accepted, an elephant had to be foraging in a homogeneous area of
C. mopane trees. Only once an elephant moved from the tree, it was
first observed feeding on to feed on the next tree, was the next tree
used for measurement. Measured trees were all C. mopane, between 3
and 6 m, and the elephant had to have taken at least five bites from
the canopy before the tree was sampled. For each of the measured
trees, a discarded paired control tree was selected. This discarded
tree had to be a C. mopane of similar height and canopy size, which
the elephant had walked past (within 2 m) and which was not
selected while moving towards tree it fed on. About 50 g of leaf
material was collected from various branches on each tree at the
same height range at which the elephant fed from. Leaves from both
the utilized tree and the discarded tree were stored in separate paper
bags, dried at 60 C for 24 h and ground through a 1-mm sieve for
later analysis.
CHEMICAL ANALYSIS OF LEAF SAMPLES
All dried leaf samples were analysed for dry matter, ash, neutral
detergent fibre, calcium, sodium, potassium, magnesium, nitrogen
and phosphorus contents at the laboratory of the Resource Ecology
Group, Wageningen University (the Netherlands). Total Ca, Na,
Mg, K, N and P contents were measured with a Skalar San-plus auto
analyzer, after destruction with a mixture of H2SO4, selenium and
salicylic acid. (Novozamsky et al. 1983). Neutral detergent fibre of
dry leaf was determined using the ANKOM filter bag procedure
(ANKOM Technology, Macedon, NY, USA) with omission of the
sodium sulphite and the heat-resistant a-amylase. A neutral detergent solution was prepared following Goering & van Soest (1970).
Condensed tannins were measured with the proanthocyanidin
method and total polyphenol with the Folin–Ciocalteu method
(Waterman & Mole 1994).
ELEPHANT UTILIZATION ASSESSMENT
During March 2008, we recorded the position within a 2 · 2 m grid,
of all trees taller than 1Æ5 m within 50 · 50 m plots containing five
fertilized patches of 10 · 10 m. Fertilized patches were marked at the
beginning of the study with iron stakes, to indicate whether a tree
occurred within a fertilized patch or not. We recorded tree species,
treatment within the 50 · 50 m plot, tree height and canopy width,
coppicing as a result of visible previous impact by elephant, percentage of each type of utilization, total percentage impact and estimation of the age of impact. The area on a branch impacted on by
elephant becomes grey after a year through a rainy season
(Ben-Shahar & Macdonald 2002). Therefore, the age of impact on a
tree was classified as fresh when scars had not yet turned grey. The
type of utilization was categorized into five classes: leaf stripping,
bark stripping, impact on branches, uprooting and breaking of the
main stem. The percentage of leaf stripping, bark stripping and
impact on branches was estimated in relation to the availability of the
plant part across the entire tree. Uprooting and breaking of the main
stem were recorded as 100% impact (i.e. the tree could die).
DATA ANALYSIS
All data were first tested for normality using the Kolmogorov–Smirnov test, and where proportions were used, data were first arcsine
transformed. For the fertilization experiment, block effects were
assumed to be negligible as each of the three blocks was a replication
containing a set of all treatments and controls. Two types of analyses
were used to test elephant selection for specific nutrients. First, we
used a one-way anova to test whether N, P, K, Mg, Ca and Na were
higher in leaves from trees in fertilized patches compared to unfertilized patches and whether elephant utilized these trees in fertilized
patches more than unfertilized patches. Second, we conducted a multiple linear regression analysis to test for effects of the concentrations
of different nutrients as independent variables on the proportion of
trees utilized by elephant per patch as dependent variable. Annual
elephant utilization measurements on marked C. mopane trees were
used in an anova to test whether elephant were able to select fertilized
patches at a 50 · 50, 10 · 10 and 2 · 2 m scale. Because of the set-up
of the experiment, and to compare the relevant unfertilized controls
with neighbouring fertilized areas, a separate model was used for
each of the three scales.
For analysis at the plant scale between neighbouring trees, paired
sample t-tests were used to compare nutrient levels in leaves between
eaten and uneaten trees. Because leaf stripping (%) and signs of elephant utilization on branches (%) were not normally distributed, the
nonparametric Mann–Whitney test was used to detect differences
between fertilized and unfertilized patches. Because the number of
coppicing C. mopane trees and C. mopane trees killed by elephant
are rare events resulting in many zero values in the data, a generalized
2011 The Authors. Journal of Animal Ecology 2011 British Ecological Society, Journal of Animal Ecology, 80, 875–883
Soil nutrients & elephant tree utilization 879
Table 1. Chemical analysis of Colophospermum mopane leaves inside and outside patches fertilized with NPK at three different scales
NPK fertilizer
Scale (m2)
N (%)
P (%)
Ca (%)
K (%)
Mg (%)
Na (%)
Tannin (mg g)1)
Absent
Present
Absent
Present
Absent
Present
4
4
100
100
2500
2500
1Æ92
2Æ04
1Æ95
2Æ22
1Æ93
2Æ11
0Æ12
0Æ13
0Æ12
0Æ14
0Æ12
0Æ13
1Æ49
1Æ51
1Æ36
1Æ25
1Æ55
1Æ51
1Æ02
1Æ08
1Æ03
1Æ05
1Æ06
1Æ05
0Æ22
0Æ23
0Æ22
0Æ23
0Æ23
0Æ22
0Æ00
0Æ00
0Æ01
0Æ01
0Æ01
0Æ00
1084Æ55
1064Æ12
1109Æ65
1021Æ57
1102Æ23
1030Æ03
linear model with a negative binomial error distribution was used to
test for differences of these variables inside and outside fertilized
patches (White 1996).
Results
The application of the NPK fertilizer significantly changed
the chemical composition of leaves of mopane trees at the
experimental site. Both nitrogen (F1,58 = 35Æ566,
P < 0Æ001) and phosphorus (F1,58 = 28Æ226, P < 0Æ001)
contents of tree leaves were significantly higher in fertilized
patches compared to unfertilized patches, while the tannin
content was significantly lower in fertilized patches
(F1,58 = 13Æ628, P < 0Æ001). However, there was no significant difference for K (even though the fertilizer contained K)
or for Ca, Mg, Na or NDF, concentration in leaves (Table 1).
The proportion of mopane trees utilized by elephant per
patch was significantly higher in fertilized patches vs. unfertilized patches, irrespective of patch size, (F1,58 = 10Æ95,
P = 0Æ002). The proportion of trees utilized by elephant per
patch increased significantly with increasing nitrogen concentration in leaves, but was not significantly affected by other
nutrients (linear regression model: R2 = 0Æ462, F7,52 =
6Æ368, P < 0Æ001) (Fig. 2).
Irrespective of patch size and local nutrient concentration,
elephant were able to select plots with the highest nutrient
loads at a 2500-m2 scale (F3,56 = 4Æ564, P = 0Æ006) (Fig. 3).
Fig. 2. The relationship between the proportion Colophospermum
mopane trees utilized by elephant per patch and the percentage nitrogen (on a dry matter basis) in tree leaves (n = 60).
Fig. 3. Proportion of Colophospermum mopane trees used by
elephant in unfertilized patches and fertilized patches with increasing
nutrient loads at 100-m2 scale (error bars show 95% confidence interval of the mean, and different letters in the figures denote significant
difference).
Although the nitrogen content of the leaves was significantly
higher in fertilized patches than unfertilized patches at the
100-m2 scale (F1,16 = 18Æ232, P = 0Æ001), this was not the
case for the 4-m2 scale (F1,10 = 3Æ727, P = 0Æ082). Hence,
the utilization of mopane trees was significantly higher in fertilized patches than in unfertilized patches at the 100-m2 scale
(F1,16 = 4Æ733, P = 0Æ045) but not at the 4-m2 scale
(F1,10 = 0Æ024, P = 0Æ88). At plant scale, N and P contents
from leaves of eaten trees were not significantly different
from leaves from neighbouring trees of the same species that
were not eaten (N: t14 = )1Æ242, P = 0Æ235, P: t14 = )1Æ61,
P = 0Æ13).
For the elephant utilization assessment at the 100-m2 scale,
utilization of tree leaves via leaf stripping occurred more
inside fertilized patches than outside (Z = )4Æ694, n = 786,
P < 0Æ001), whereas total impact on branches did not differ
between patches (Z = )0Æ946, n = 786, P = 0Æ344)
(Fig. 4a,b). Tree killing, through uprooting of trees or breaking main tree trunks, occurred more outside fertilized patches
than inside (Wald chi-square = 3Æ818, d.f. = 1, P = 0Æ05)
(Fig. 4c).
Tree coppicing tended to be more frequent inside fertilized patches than in the surrounding control areas, but this
was not significant (Wald chi-square = 3Æ181, d.f. = 1,
2011 The Authors. Journal of Animal Ecology 2011 British Ecological Society, Journal of Animal Ecology, 80, 875–883
880 Y. Pretorius et al.
(a)
(a)
(b)
(b)
Fig. 5. Differences in the proportion of coppicing trees in the presence or absence of fertilizer (a) and the proportion signs of fresh leaf
stripping on coppiced and noncoppiced trees (b) (error bars show
95% confidence interval of the mean).
(c)
Discussion
FORAGING AT DIFFERENT SPATIAL SCALES
Fig. 4. Differences in elephant utilization at a 100-m2 scale between
fertilized (n = 176) and unfertilized (n = 610) patches, distinguishing different use-categories: (a) leaf stripping, (b) impact on branches
and (c) killing of trees, respectively (error bars show 95% confidence
interval of the mean).
P = 0Æ075). As expected, coppiced trees had significantly
more signs of fresh leaf stripping than noncoppiced trees
(Wald chi-square = 5Æ398, d.f. = 1, P = 0Æ02) (Fig. 5).
Two decades ago, scales as the application of traditional optimal foraging theory were problematic, Senft et al. (1987)
introduced a hierarchy into herbivore foraging theory to integrate foraging decisions at different spatiotemporal. Since
then, many studies have illustrated how herbivores of various
body sizes are able to select nutrient-rich patches at a range
of spatial scales (WallisDeVries, Laca & Demment 1999;
Durant, Fritz & Duncan 2004; Cromsigt & Olff 2006). We
also found that elephant are able to select nutrient-rich
patches at scales ranging from 2500 to 100 m2.
Contradictory to the theoretical predictions of Ritchie &
Olff (1999) that larger species will not be able to detect food
patches at fine scales, we found that even the largest terrestrial mammal is able to select nutrient-rich plant parts at fine
scales within a homogeneous tree species stand. To our
knowledge, this has never been shown for elephant. At a
regional scale (100 km), in a study conducted in C. mopane
woodlands in Botswana, no relationship could be found
between elephant impact on C. mopane trees and leaf N con-
2011 The Authors. Journal of Animal Ecology 2011 British Ecological Society, Journal of Animal Ecology, 80, 875–883
Soil nutrients & elephant tree utilization 881
tent even though soil N levels and leaf N were related, and
leaf N differed significantly between sampling sites (BenShahar & Macdonald 2002). However, this latter study was
conducted at larger scales than our study, and more importantly, no analysis was carried out on the relationship
between the type of elephant impact and the nutrient status
of the plants. Soil and leaf samples were collected in the dry
season when elephant are more limited by energy (Pretorius
2009) and thus less selective of other nutrients such as N and
P.
NITROGEN SELECTION
Selection of plant species high in N has been described for elephant within their daily foraging ranges (Jachmann & Bell
1985), for barnacle geese (Branta leucopsis) in fertilizer experiments at 250-m2 scale (Ydenberg & Prins 1981), and at even
smaller scales, black colobus monkeys (Colobus satanas) in
the rain forests of Cameroon have been found to select plant
parts in relation to their nitrogen content (McKey et al.
1981). Some authors reason that switching between plant
species or plant parts is a behavioural adaptation to cope
with declines in N content (Mattson 1980) and that elephant
in particular switch between plant species and plant parts to
sequester the greatest amount of digestible protein per unit
time (O’Connor, Goodman & Clegg 2007). For example,
cambium in the bark and roots of plants contains relatively
high levels of N (Mattson 1980) and Hiscocks (1999) found
that cambium samples from bark and root that elephant utilized were higher in N than cambium from unutilized trees.
Therefore, uprooting of trees in nutrient-poor areas, such as
was found in our study, could be a result of low leaf N levels,
which encourages elephant to utilize other plant parts with
higher N content.
CONTRIBUTION TO OPTIMAL FORAGING THEORY
Irrefutably, the contribution of this study to the current body
of knowledge of optimal foraging theory is the concept of
patch selection by a herbivore based on the total nutrient
load within the grain size of a specific spatial scale. In situ
patch size does not simply increase uniformly, but patches
vary in size and can be scattered or aggregated depending on
the scale of observation. Moreover, the local nutrient concentration between patches can also vary widely (Fryxell 2008).
Thus, when studying patch selection by a herbivore at a particular scale, the available nutrients must be expressed as
total nutrient load per grain size, which is a product of the
number of patches, local nutrient concentration of patches
and patch sizes. We found that at a scale of 2500 m2, elephant
are able to select the patch combinations resulting in the
highest total nutrient loads, whether it was many small
patches of high local nutrient concentration per 2500 m2 or
few large patches of lower local nutrient concentration per
2500 m2. These findings may not only have implications for
the way patch selection by herbivores is interpreted at different scales but also offer a possible explanation why some
studies find, contrary to the MVT, that herbivores select
suboptimal patches or leave patches when there is still much
food left (Schaefer & Messier 1995). This dependence of
MVT predictions on the scale at which a herbivore perceives
patches, which is very difficult to determine, has also been
noted by Kotliar & Wiens (1990) and Searle, Hobbs &
Shipley (2005).
IMPACT OF KEYSTONE HERBIVORES
Furthermore, our study illustrates how elephant can possibly
facilitate other species by maintaining tree ‘islands of fertility’(Ludwig, de Kroon & Prins 2008; Treydte, Heitkönig &
Ludwig 2009) through the reuse of trees and dung deposition,
which may potentially initiate positive feedbacks into the
abiotic environment (van der Waal 2010). Jachmann & Bell
(1985) and subsequently Smallie & O’Connor (2000) hypothesized that, by continuously hedging C. mopane trees, elephants can maintain a supply of preferred coppice growth
through time. The increased proportion of coppiced mopane
trees because of previous breaking of branches by elephant
inside fertilized plots and the higher occurrence of leaf stripping on these trees indicate that the fertilizer in our experiment might have triggered a positive feedback response that
may persist through time. In a fenced off clipping experiment
conducted in northern Botswana, elephant broke into the
experimental site 3 years after initiation. The assessment of
elephant impact 1 month after the break-in revealed higher
utilization of trees subjected to simulated browsing 3 years
before compared to control trees (Makhabu & Skarpe 2006).
Often, the problem with studying keystone species under
natural conditions is that their effects on other organisms
only become apparent once they themselves have been
removed. This was demonstrated by the removal of impala
by disease followed by a subsequent increase in woodland
cover in East Africa (Prins & van der Jeugd 1993). The
advantage of an experimental approach such as ours is that
the mechanisms behind these effects become clearer without
the removal of the keystone species. Both soil fertility and elephant have been identified as major determinants of tree–
grass ratios in savannas (Scholes 1990; Fritz et al. 2002;
O’Connor, Goodman & Clegg 2007; Sankaran, Ratnam &
Hanan 2008). However, the interaction between these two
factors, and more specifically how soil nutrient status affects
the way in which elephant utilize their environments, was
until recently unclear (Olff, Ritchie & Prins 2002; Skarpe
et al. 2004). This study therefore contributed to a better
understanding of this mechanism, but more importantly the
results show how future studies should interpret patch selection by herbivores at different scales.
By concentrating limiting resources, patchiness may play a
critical role in maintaining ecosystem productivity (Aguiar &
Sala 1999) and herbivory has been shown to create spatial
heterogeneity and induce vegetation patterning through the
process of self-facilitation (Groen 2007). Recent research
using hyperspectral remote sensing in Kruger National Park
(Skidmore et al. 2009) and in saltmarshes (Schmidt & Skid-
2011 The Authors. Journal of Animal Ecology 2011 British Ecological Society, Journal of Animal Ecology, 80, 875–883
882 Y. Pretorius et al.
more 2001; Schmidt et al.2004) shows that foliar quality of
species within patches can be mapped and discriminated. In
other words, it is possible, at varying scales, to understand
foliar quality across space. Linking wildlife and livestock
observations to these maps may allow us to understand animal behaviour and survival.
Conclusion
We conclude that more emphasis should be placed on the
nutrient status of soils when trying to understand the impact
elephant have on trees (Fritz et al. 2002), as changes in elephant foraging behaviour can be used as indicators of
changes in the availability of nutrients.
Acknowledgements
We thank managers and owners of the Associated Private Nature Reserves for
allowing us access to the reserve to collect our data, all the co-workers on the
TEMBO programme for their support and the field staff for assisting us with
data collection in South Africa. We appreciate financial support from the WOTRO fund, Shell-SA and Omnia for helping us realize this study.
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Received 22 August 2010; accepted 25 January 2011
Handling Editor: Atle Mysterud
2011 The Authors. Journal of Animal Ecology 2011 British Ecological Society, Journal of Animal Ecology, 80, 875–883