This project, conducted under the supervision of Mduduzi Ndlovu Ph.D., was conducted within the Kruger National Park and looked at foraging behavior of waterbirds in response to time of day. Our results present evidence that time of day mediates foraging activity and supports the optimal foraging theory for waterbirds found in the Kruger National Park.

1. Time of day influences foraging behavior of waterbirds in Kruger National Park,
South Africa
Category: Long-Term Research Project
Participants: Luana Deng, Kelly Fowler, Joe Galaske
Mduduzi Ndlovu
Site: Kruger National Park, Mpumalanga Province, South Africa
Key words: disturbance, foraging, optimal foraging theory, temperature fluctuation,
waterbird ecology
Abstract
The optimal foraging theory is widely accepted as a comprehensive approach towards
understanding animal foraging behavior. It states that an animal will forage to maximize its net
rate of energy intake, while minimizing energy expenditure. We tested the relevance of this
theory in Kruger National Park, an extremely water limited environment, at two different sites:
Nyamundwa dam and Phabeni dam. Our study analyzed the fine scale foraging behavior of
waterbirds, an important bioindicator of wetland ecosystems, in response to environmental
fluctuations over different periods of the day. We found that the number of non-foraging
waterbirds significantly increased as the day progressed. Additionally, the proportion of foraging
waterbirds was significantly greater at one site than the other. The proportion of non-foraging
birds, with respect to feeding guild, was not significantly different as the day progressed. Lastly,
only ducks were significantly correlated to hippopotami and crocodiles. Our results present
evidence that “time of day” mediates foraging activity and supports the optimal foraging theory
for waterbirds found in the Kruger National Park.
Introduction
Waterbirds are important bioindicators of wetland ecosystems because they can quickly respond
to any changes in vegetation composition and water level fluctuations compared to other species
(Rajpar and Zakaria 2011; Cumming et al. 2012; Wenny et al. 2011; Lameed 2012). Many
ecosystem services that waterbirds provide result from foraging activity (Wenny et al. 2011).
Through their foraging, waterbirds act as mobile links that transfer energy both within and
among ecosystems, and thus contribute to ecosystem function and resilience (Wenny et al. 2011).
Despite their ecological importance, the foraging behavior of waterbirds in freshwater
wetland ecosystems is not well studied (Ntiamoa-Baidu et al. 1998). In particular, there is little
knowledge on waterbird communities in tropical environments (Cumming et al. 2012; Ntiamoa-
Baidu et al. 1998). In the Northern hemisphere, long-term data sets from temperate freshwater
ecosystems and intensive studies on waterbird responses to spatial drivers of biodiversity,
including water pH, water flow, water surface temperature, etc., have created a solid scientific

2. basis to guide conservation and management (Cumming et al. 2012; Ntiamoa-Baidu et al. 1998).
In contrast, relatively little is known about the waterbird communities of freshwater ecosystems
in arid regions of the Southern hemisphere (Cumming et al. 2012). In addition, studies analyzing
temporal drivers of biodiversity, including daily temperature fluctuations, are markedly absent.
Our study examines waterbird foraging behavior in response to time of day. Foraging
behavior is governed by the principles outlined in the optimal foraging theory, which
hypothesizes that an animal’s food search pattern will be such that its net rate of energy intake is
maximized, with energy expenditure minimized to ensure maximum fitness (Pyke et al. 1977;
Pyke 1984). According to the predictions made in optimal foraging theory, waterbirds will
forage when daily temperatures are at their lowest, i.e. morning and evening. Therefore, they
would maximize their energy intake, while also minimizing their energy expenditure.
Given that waterbirds forage to maximize net rate of energy intake to obtain maximum
fitness, and that energy expenditure for waterbirds is lowest in the morning and evening, we
hypothesized that the foraging behavior of waterbirds would be strongly related to the time of
day. There are three possible outcomes of this analysis. Firstly, time of day may have no effect
on waterbird foraging behavior, implying that other costs to foraging in the early
morning/evening, such as disturbance, are more influential on foraging behavior in waterbirds
(Ntiamoa-Baidu et al. 1998; Morrison 1980). It may also suggest that while time of day may not
directly influence waterbird foraging, it may influence other environmental variables, including
ambient temperature, water temperature, pH, daylight visibility, prey activity and external
disturbance (Cumming et al. 2012; Cumming et al. 2013; Ntiamoa-Baidu et al. 1998; Rivers-
Moore et al. 2004; Rajpar and Zakaria 2011). Secondly, time of day would have an effect on
waterbird foraging behavior. These results would support our hypothesis, and demonstrate that
waterbirds have the highest net rate of energy intake in the early morning/late evening regardless
of any energy expended during these times. Lastly, location, rather than time of day, has a
greater effect on the foraging behavior of waterbirds. This would suggest that perhaps
characteristics of a particular habitat, such as pH, water temperature, water level, vegetation
composition etc., have a greater effect on waterbird foraging behavior than time of day (Rivers-
Moore et al. 2004). Each of these possible outcomes has different implications for understanding
the foraging behavior of waterbirds in freshwater ecosystems.
We used a semi-arid environment within the Kruger National Park in South Africa to test
these predictions. We considered Kruger to be a resource-limited environment for waterbirds.
Therefore, we assume that foraging behavior would strongly be influenced by a need to
minimize energy expenditure, which changes throughout the day. This fine scale analysis can
also inform bird conservation and management practices in similar environments.
Methods
Study Sites
This study was conducted in Kruger National Park, Mpumalanga Province, South Africa. Kruger
can be described as a semi-arid African savanna, and is currently experiencing a prolonged

3. drought. Kruger, a protected area, has a mean annual rainfall of 553 mm (SAN Parks 2006).
Mean summer temperature is 26.4°C (SAN Parks 2006). We selected two study sites, A
(25.02422° S, 31.33401° E) and B (25.0162° S, 31.25824° E) located in the southern section of
Kruger. The sites were 20.78km apart. Data was collected on three consecutive days during the
wet summer season, 12 – 14 February 2016.
Located on Nyamundwa Dam, site A has an area of ~2 hectares. The site is lined with
woody vegetation, leafy forbes, short grasses and rocks. The water is surrounded by higher,
sloping ground. Site A is exposed to disturbances including tourists, hippopotami
(Hippopotamus amphibius), crocodiles (Crocodylus niloticus) and herbivores such as Cape
buffalo (Syncerus caffer), waterbucks (Kobus ellipsiprymnus), wildebeest (Connochaetes
taurinas) and impala (Aepyceros melampus).
Site B is located on Phabeni dam with an area of ~3.14 hectares. Large rocks cover site
B on one side of the shore, while another side consists of wet clay soil. This site is not accessible
by the public and is also exposed to animal disturbances.
Experimental Design and Protocol
We collected pH, surface water temperature, waterbird counts, foraging activity and
disturbance data at each site during four set intervals each day for three days. Each interval
period was three hours, and fieldwork was carried out during the daytime hours (06h00-18h00).
The time slots were: early morning (06h00-09h00), late morning (09h00-12h00), early afternoon
(12h00-15h00) and late afternoon (15h00-18h00). We determined the number of foraging and
non-foraging birds by identifying, counting and labeling each individual’s behavior. We define
foraging as searching for or eating food. Non-foraging is defined as partaking in other actions
that do not involve food. One person recorded, while others scanned the landscape for birds with
pairs of binoculars (25x40). Species were tallied separately. To standardize the counts, we
counted birds for 10 minutes. Before we began counting, we waited five minutes to ensure that
the birds were acclimated to our presence. If an individual’s behavior changed from idle to
foraging (or vice versa) during the 10-minute count period, the bird’s final behavior was
switched to (or maintained as) foraging.
Water pH and surface temperature were recorded in each count interval slot for two days.
Water quality measurements were taken after counting to ensure minimal disturbance. Water pH
was measured using a Hanna Instruments pHep+ pocket pH tester. We measured surface
temperature in degrees Celsius with a remote laser temperature gauge. Disturbance was
measured by counting the number of animals (excluding birds) present at the site. Three
categories, crocodiles, hippopotami and other, were used to tally the number of individuals. The
category labeled “other” includes herbivores such as impala, Cape buffalo, waterbuck, and
wildebeest. We recorded this data before we began counting waterbirds, thereby maximizing the
10-minute counting period. This data was collected during each interval for three days.
Statistical Analyses

4. A Mann-Whitney U test was used to determine if there was a significant difference
between foraging and non-foraging waterbirds. Multiple two-way ANOVAs were calculated to
assess differences between sites, foraging activity and within different time slots. We set
significance at p = 0.05.
To simplify our data, we grouped bird species into four different guilds: small waders,
large waders, blacksmith lapwings, and ducks (Cumming et al. 2012). We used multiple one-
way ANOVAs to determine differences in foraging activity numbers per time slot according to
waterbird guild. Spearman’s test for correlation was used to determine any relationship of
foraging activity to environmental variables (i.e. pH, surface temperature, time of day,
hippopotami, crocodiles, other). We used Statistica 10 for the Spearman’s test for correlation and
MinitabExpress (Version 1.3.0) to calculate all ANOVA’s.
Results
In total, we counted 26 different waterbird species (Table 1). Site A had 21 waterbird species
while site B had 18 waterbird species. Major disturbances were hippopotami and crocodiles,
however there were also disturbances from buffalos, waterbucks, wildebeest, and impalas. Site B
had more disturbances counts than site A. Maximum number of hippopotami and crocodiles
were 65 and 16 at site B compared to only 23 hippopotami and one crocodiles at site A.
Waterbird species were classified based on size and feeding patterns into four guilds:
small waders, large waders, ducks, and blacksmiths (Table 1). Species were excluded if they did
not fit into one of the four categorized guilds.

5. Table 1. Total waterbird species identified at two sites. Species were further classified into
guilds. Presence of species at a site is denoted by a ().
Guilds Common Name Scientific Name Site A Site B
Small Waders
Common Greenshanks Tringa nebularia  
Three-Banded Plovers Charadrius tricollaris  
Blackwing Stilts Himantopus himantopus 
Common Sandpipers Actitis hypoleucos  
Wood Sandpipers Tringa glareola  
African Jacanas Actophilornis africanus  
Kittlitz’s Plovers Charadrius pecuarius 
Large Waders
Grey Herons Ardea cinerea  
Squacco Herons Ardeola ralloides 
Saddle-Billed Storks Ephippiorhynchus senegalensis 
Little Egrets Egretta garzetta  
Ducks
Spur-Winged Geese Plectropterus gambensis 
Egyptian Geese Alopochen aegyptiaca  
White-Faced Ducks Dendrocygna viduata 
Blacksmiths Blacksmith Lapwings Vanellus armatus  
Ruffs Philomachus pugnax  
Bateleur Eagles Terathopius ecaudatus 
African Fish Eagles Haliaeetus vocifer  
White-Backed Vultures Gyps africanus 
Water Thick-Knees Burhinus vermiculatus  
Grey-Headed Gulls Chroicocephalus cirrocephalus 
Hadeda Ibis Bostrychia hagedash 
Pied Kingfisher Ceryle rudis 
African Pied Wagtails Motacilla aguimp  
Turtle Dove Streptopelia turtur 
The mean number of non-foraging birds significantly increased as the day progressed
between different time slots (F = 3.49, p = 0.05, df = 3). Simultaneously, although the mean
number of foraging birds did not differ significantly between time slots (F = 1.34, p = 0.307, df =
3), there was a negative trend in the mean number of foraging birds as the day progressed (Figure
1). The mean number of foraging (F = 7.95, p = 0.006, df = 2) and non-foraging (F = 5.48, p =
0.020, df = 2) birds between days was also significantly different. There was no interaction
between time and day for foraging and non-foraging waterbirds (foraging, F = 1.65, p = 0.218, df
= 6; non-foraging, F = 1.45, p = 0.274, df = 6).

6. Figure 1. Mean numbers of foraging and non-foraging waterbirds (± standard error) during four
different time slots.
The mean number of foraging birds was significantly higher at site A (30.833 ± 2.912)
than at site B (22.083 ± 2.912) (Figure 2). The mean number of non-foraging birds however was
not significantly different between the sites (site A = 35.333 ± 2.912 and site B = 39.500 ±
3.693). Foraging effort across the days was also significantly different (F=7.09, p < 0.05, df = 2),
but non-foraging was not significant (F = 3.46, p = 0.053, df = 2). There was no interaction
between site and day for foraging and non-foraging waterbirds (foraging, F = 0.28, p = 0.758, df
= 2; non-foraging, F = 0.53, p = 0.597, df = 2).
0
10
20
30
40
50
60
6:00-9:00 9:00-12:00 12:00-15:00 15:00-18:00
Meannumbersofwaterbirds
Time of Day (hours)
Non-foraging
Foraging

7. Figure 2. Observed mean number of foraging and non-foraging waterbirds (± standard error) at
site A and B.
There was no significant differences in mean proportional numbers of foraging waterbird
guilds across time slots (small waders, F = 0.57, p = 0.651, df = 3; large waders, F = 0.03, p =
0.991, df = 3; ducks, F = 2.17, p = 0.170, df = 3; and blacksmiths, F = 0.30, p = 0.828, df = 3;
Figure 3). Proportions of foraging waterbirds were calculated by total number of foraging
waterbirds in each guild divided by total number of waterbirds observed in each guild. Figure 3c
suggests a negative trend in duck foraging and time of day. As time of day progressed, there was
a decrease in duck foraging activity.
0
5
10
15
20
25
30
35
40
45
50
Site A Site B
Observedmeannumberofwaterbirds
Non-foraging
Foraging

8. Figure 3. Mean proportional numbers of waterbirds foraging (± standard error) at different times
of the day. (a) Represents small waders, (b) large waders, (c) ducks, and (d) blacksmiths.
There were no significant correlations between water pH, time of day, and the
proportional number of foraging waterbird guilds at site A. Surface temperature was positively
correlated with pH (rs = 0.965, p < 0.001, df = 7; Table 2). There was also a strong emerging
positive correlation between ducks and hippopotami (rs = 0.789, df = 10), and surface
temperature and large waders (rs = 0.782, df = 6).
Table 2. A Spearman’s correlation test matrix for foraging guilds, disturbance variables, and
water quality at site A, Nyamundwa dam. Figures represent Spearman correlation coefficient rs
and red values are significant.
Time of Day Hippopotami Crocodiles Surface Temperature
pH 0.340 ------- ------- 0.965
Time of Day ------- 0.348 -0.009 0.382
Small Waders -0.067 0.409 0.400 0.275
Large Waders 0.454 0.116 0.024 0.782
Ducks -0.303 0.789 0.158 0.036
Blacksmiths 0.158 0.434 -0.094 -0.124
0
0.2
0.4
0.6
0.8
1
ProportionofSmall
WadersForaging
Time of Day
Small
Waders
0
0.1
0.2
0.3
0.4
0.5
ProportionofLarge
WadersForaging
Time of Day
Large
Waders
0
0.2
0.4
0.6
0.8
1
ProportionofDucks
Foraging
Time of Day
Ducks 0
0.2
0.4
0.6
0.8
1
Proportionof
BlacksmithsForaging
Time of Day
Blacksmith
s
(a) (b)
(c) (d)
(d)(c)

9. There were also no significant correlations between water pH, time of day, and the
proportional number of foraging waterbird guilds at site B. With the exception of ducks, the
presence of hippopotami and crocodiles showed no significant effects on other bird guilds.
Hippopotamus had a significant relationship with ducks (rs = 0.789, p = 0.006, df = 10) and
crocodiles also had a significant relationship with ducks (rs = 0.670, p = 0.034, df = 10). Similar
to site A, surface temperature was positively correlated to water pH (rs = 0.962, p < 0.001, df =
7).
Table 3. A Spearman’s correlation test matrix for foraging guilds, disturbance variables and
water quality at site B, Phabeni dam. Figures represent Spearman correlation coefficient rs and
red values are significant.
Time of Day Hippopotami Crocodiles Surface Temperature
pH 0.365 ------- ------- 0.962
Time of Day ------- 0.220 -0.144 0.365
Small Waders 0.152 0.220 0.039 -0.115
Large Waders 0.131 0.076 0.467 0.303
Ducks -0.106 0.789 0.670 0.525
Blacksmiths 0.338 -0.138 0.344 0.363
Discussion
Results suggest that non-foraging activity decreased as the day progressed, with the highest
numbers of non-foraging waterbirds in the last time slot (03h00 - 18:00; Figure 1). This leads us
to support our hypothesis that time of day influences waterbird foraging behavior. Previous
studies have already shown that spatial divers, such as water pH, local water flow, water depth
and water surface temperature influence foraging activity in waterbirds (Cumming et al. 2012;
Cumming et al. 2013; Ntiamoa-Baidu et al. 1998; Rivers-Moore et al. 2004; Rajpar and Zakaria
2011). Our study now demonstrates that time of day, as a temporal driver, also influences
waterbird foraging activity (Figure 1). In accordance with the optimal forage theory, waterbird
foraging behavior maximized net rate of energy intake and minimized energy expenditure.
We attribute differences in foraging activity between sites A and B to environmental
factors, such as disturbance and water depth. There was a difference in the maximum observed
counts of both hippopotami and crocodiles between sites, although there were no significant
correlations. Given these observed differences, we infer that disturbances from hippopotami and
crocodiles played a role in the waterbird foraging activity. Previous studies by Morrison (1980)
have also found reduced foraging activity in response to disturbance. Additionally, studies have
found that local water depth also plays a role in foraging activity (Ntiamoa-Baidu et al. 1998;
Rajpar and Zakaria 2011). Water depth influences variables such as prey availability and
accessibility, as well as the presence and availability of safe roosting/breeding sites (Rajpar and
Zakaria 2011). Therefore, differences in foraging activity could be attributed to differing water
depths between site A and B.

10. Some waterbird species feed throughout the day, while others show peak foraging at
different points in the day (Ntiamoa-Baidu et al. 1998). It is not surprising, then, that foraging
activity in feeding guild showed no significant differences throughout the day (Figure 3). We
suspect that patterns in waterbird foraging activity may not be species specific, but rather depend
on feeding ground conditions. It’s interesting to note that, though not significant, there was an
emerging trend in decreasing foraging activity of ducks as the day progressed. Therefore, ducks
may be a key species in driving the overall increase in non-foraging behavior of waterbirds
throughout the day.
Contrary to previous studies that have suggested a negative relationship between foraging
activity and disturbance (Ntiamoa-Baidu et al. 1998; Morrison 1980), we found a positive
correlation between duck foraging behavior and disturbance (Table 3). While the reasons for our
results are unclear, we speculate that hippopotami displace sub-surface vegetation, making
vegetation more accessible for foraging ducks. Therefore, more hippopotami would explain
higher duck foraging activity.
This study was conducted on a fine scale, which may account for the lack of significance
between foraging and time of day. Future studies should be conducted on a larger scale, over a
longer period of time with more sites, to include more data points. In agreement with previous
studies, future studies should look at seasonal differences in waterbird foraging behavior
(Ntiamoa-Baidu et al. 1998; Cumming et al. 2012). Because Kruger is amidst a drought, few
freshwater sites are available for study, especially flowing water sites. Rivers-Moore et al. (2004)
found significant environmental differences between flowing and standing freshwater bodies.
Future studies would need to also look at foraging activity of waterbirds in a flowing water
system, and compare these results with standing water sources.
Understanding waterbird foraging behavior is essential for efficient management
techniques. Not only can our results inform conservation practices within Kruger, but they can
be applied to similar environments. Our study demonstrates the influence of temporal drivers on
foraging activity. It is our hope that the results of this study will inform ecologists on the role of
temporal drivers on waterbird biodiversity, as well as provide a foundation for future studies to
look at foraging behavior of waterbirds.
Acknowledgements
This research project was led by Dr. Mduduzi Ndlovu. We would like to thank him for
his time, advice and guidance. We also thank SAN Parks and the Organization for Tropical
Studies for the opportunity to conduct our experiment at the Kruger National Park. Additionally,
we thank Mr. Renson Thethe for protection in the field. Finally, we thank David and the SAN
Parks canine unit for the use of their facility.

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