American Journal of Primatology 70:927–938 (2008)
RESEARCH ARTICLE
Food Preferences of Wild Mountain Gorillas
JESSICA GANAS1, SYLVIA ORTMANN2, AND MARTHA M. ROBBINS1
1
Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
2
Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany
Determining the nutritional and phenolic basis of food preference is important for understanding the
nutritional requirements of animals. Preference is a measure of which foods would be consumed by an
animal if there was no variation in availability among food items. From September 2004 to August
2005, we measured the food preferences of four wild mountain gorilla groups that consume foliage and
fruit in Bwindi Impenetrable National Park, Uganda, to determine what nutrients and phenols are
preferred and/or avoided. To do so, we asked the following questions: (1) Which plant species do the
gorillas prefer? (2) Considering the different plant parts consumed of these preferred species, what
nutrients and/or phenols characterize them? (3) Do the nutritional and phenolic characteristics of
preferred foods differ among gorilla groups? We found that although some species were preferred and
others were not, of those species found in common among the different group home ranges, the same
ones were generally preferred by all groups. Second, all groups preferred leaves with relatively high
protein content and relatively low fiber content. Third, three out of four groups preferred leaves with
relatively high sugar amounts. Fourth, all groups preferred pith with relatively high sugar content.
Finally, of the two groups tested, we found that the preferred fruits of one group had relatively high
condensed tannin and fiber/sugar contents, whereas the other group’s preferred fruits were not
characterized by any particular nutrient/phenol. Overall, there were no differences among gorilla
groups in nutritional and phenolic preferences. Our results indicate that protein and sugar are
important in the diets of gorillas, and that the gorillas fulfil these nutritional requirements through a
combination of different plant parts, shedding new light on how gorillas balance their diets in a variable
environment. Am. J. Primatol. 70:927–938, 2008.
r 2008 Wiley-Liss, Inc.
Key words: nutritional ecology; foraging strategy; protein; sugar; Bwindi Impenetrable National
Park
INTRODUCTION
Animal foraging strategies are based on a
complex suite of variables including nutritional
requirements, spatial and temporal availability of
food, and the amount of energy and time needed to
locate and consume food resources [Schoener, 1971;
Stephens & Krebs, 1986; Westoby, 1974]. Understanding a species’ foraging strategy includes an
examination of food preference, choice, and selectivity. Food preference is a measure of food consumption with the assumption that there is no variation in
availability among food items in the animal’s diet
[Chesson, 1983; Johnson, 1980]. Many authors claim
to measure preference; however, often these calculations do not take into consideration the (equal)
availability of dietary items [Calvert, 1985; Hayward
et al., 2006; Norscia et al., 2006]. Food preference
differs from food choice because although food choice
investigates how the attributes of each food species
(their differing availabilities and nutrient compositions) may influence the decision of what an animal
consumes, preference controls for differences in
r 2008 Wiley-Liss, Inc.
availability and then calculates which species would
be chosen over another. Another measure of foraging
behavior, selectivity, measures why certain foods are
not consumed by comparing the nutritional contents
of foods eaten with those not eaten.
Investigating food preference is important
because it can lend insight into the nutritional
requirements of an animal, which is vital to reproduction, fitness, and survival [Altmann, 1998; Orians
& Wittenberger, 1991; Schoener, 1983]. Additionally,
information on which nutrients and foods are
preferred by an animal can tell us which food species
Contract grant sponsors: Max Planck Society; Berrgorilla &
Regenwald Direkthilfe; The John Ball Zoo; Leakey Foundation.
Correspondence to: Jessica Ganas, Royal Society for the
Protection of Birds and the Gola Forest Programme, Kenema,
Sierra Leone. E-mail: [email protected]
Received 5 August 2007; revised 28 February 2008; revision
accepted 19 May 2008
DOI 10.1002/ajp.20584
Published online 19 June 2008 in Wiley InterScience (www.
interscience.wiley.com).
928 / Ganas et al.
may influence feeding competition and habitat
utilization, and which food species and habitats
should be considered in management and conservation efforts. Because the availability of a particular
food may influence whether it is consumed or not
(independent of its nutritional content), determining
what nutrients are required by analyses of food
choice and/or selectivity may not give a true
representation of an animal’s nutritional requirements. For example, during periods of low food
availability, animals may eat poor quality, but highly
available foods to subsist during lean times. Thus,
preference is the most accurate method of investigating the nutritional requirements of animals.
Although measures of food preference can be
easily conducted in captivity, many studies usually
only offer a few food options per experiment, which
does not represent what a wild animal experiences,
especially for those species with a large dietary
repertoire [Benz et al., 1992; Laska et al., 2000;
Remis, 2002]. Furthermore, captive studies differ
from wild studies in that captive studies do not
control for the diet of the animals outside of the trials
(which can influence what is preferred), whereas
those in the wild can take into consideration the
entire diet. Conversely, food preference can be
difficult to measure in the wild because there is
variation in the availability among most food species,
whereas preference controls for availability. However, it is possible to estimate food preferences of
wild animals using indices that quantify diet choices
based on relative equal availability [Ivlev, 1961;
Johnson, 1980]. Although these are not measures of
choices made by animals among equally available
foods as with experiments, the index is the closest
reflection of preference that is possible for wild
animals [Chesson, 1983; Johnson, 1980].
Gorillas (Western: Gorilla gorilla and Eastern:
Gorilla beringei) are the largest extant primate
species; they have an enlarged and highly ciliated
hindgut, which facilitates processing of some plant
fiber for energy [Milton, 1984; Remis, 2000]. They
consume both foliage (nonreproductive plant parts
from herbs, shrubs, and trees) and fruit to varying
degrees depending on food availability [Ganas et al.,
2004; Rogers et al., 2004; Watts, 1984]. Additionally,
gorillas generally consume a particular part of a
plant (i.e. leaves, pith, or bark) and not the entire
plant, suggesting that they selectively consume these
parts for specific nutritional reasons.
Our knowledge of gorilla food preferences is
limited. Food preference experiments on western
gorillas conducted in zoos (with fruits and vegetables) found that preferred foods were relatively high
in sugar and energy with moderate levels of tannins,
with avoided foods having a relatively high protein
content [Remis, 2002; Remis & Kerr, 2002]. However
these studies did not take into account the regular
diet of the gorillas, which can influence which foods
Am. J. Primatol.
are preferred during the trials. Furthermore, no
preference trials were conducted with foliage, a
staple of gorilla diets. In a preference study of wild
mountain gorillas in Rwanda, Vedder [1989] found
no correlation between food preference and levels of
protein although research on another gorilla group
in the same population found that protein and
digestibility positively influenced food choice [Watts,
1983]. To date, we lack information on the food
preferences of wild gorilla groups that consume both
fruit and foliage and on the nutritional and phenolic
attributes that are associated with these preferences.
Mountain gorillas (Gorilla beringei beringei) in
Bwindi Impenetrable National Park, Uganda, consume both foliage and fruit from a diversity of plant
species and experience differences in food availability
within and between locations in the park with
corresponding diet variability [Ganas et al., 2004].
A study on food choice, which examined how
consumption was influenced by both the differing
availability of food and food nutritional content,
found that Bwindi gorillas chose individual food
species based on their relatively high abundance,
relatively high sugar contents, and relatively low
digestion inhibitor contents [Ganas et al., 2008].
The goal of this study was to measure one facet
of Bwindi gorilla foraging strategy, food preference
[other aspects of their foraging behavior are treated
elsewhere; Ganas et al., 2008]. We asked the
following questions considering four gorilla groups
at two separate locations: (1) Which plant species do
the gorillas prefer? (2) Given the different plant
parts consumed of these preferred species, what
nutrients and/or phenols characterize them? (3)
Were there differences among groups in preference?
We predicted that gorillas would prefer leaves and
pith with relatively high protein and sugar contents,
fruit with relatively high sugar, energy and/or fat
contents while avoiding fibers (cellulose, hemicellulose, lignin) and phenols (total phenols, total tannins,
condensed tannins) in all food types. As animal
nutritional requirements should be the same within
a species, we also predicted that preferences would
not differ among groups, despite the fact that food
availability differed among groups’ home ranges.
METHODS
Study Groups
Data on diet were collected from four habituated
gorilla groups from September 2004 to August 2005.
Three groups, Mubare, Habinyanja, and Rushegura
ranged around Buhoma (1,450–1,800 m). Because
groups are used for an ecotourism program, the
Uganda Wildlife Authority limits direct contact with
these groups and we were not able to conduct direct
observations. The fourth group, Kyagurilo, ranges
near Ruhija (2,100–2,500 m), and is habituated for
research. Although direct observations are possible
Gorilla Food Preferences / 929
here, we used indirect methods on all groups for
comparative purposes. For details of the study site
see McNeilage et al. [2001]. All research adhered to
the protocols and legal requirements in Uganda.
Diet
All weaned individuals of a group make nests in
close proximity to one another to form a nest site
every night. During the day, the gorillas move and
feed between each night’s nest location. During this
time, the gorillas create obvious trails by trampling
vegetation, discarding food, and defecating, which
facilitates documentation of the animals’ daily diet.
To quantify the frequency of herbs in the gorillas’
diet, we followed each groups’ main trail on a daily
basis and recorded observations of each plant species
remains left behind and plant part consumed (i.e.
leaf, pith, peel [peel is the outer layer of an herb’s
stem]). These trails and feeding spots are easy to
follow and easy to distinguish from other animals
with the assistance of experienced trackers.
Although this method does not document the actual
amount of food ingested, it is the best approach when
indirect means are necessary and is commonly used
in dietary studies of gorillas [Calvert, 1985; McNeilage, 1995]. The monthly frequency of each plant
species found on these trails was then calculated to
represent the relative percentage of foods in a diet
[Frequency of species A 5 ] of feeding spots of
Species A/total number of feeding spots 100%;
following Calvert, 1985; McNeilage, 1995]. We
defined ‘‘important’’ herb species as those occurring
in Z1% frequency in any month. Although the
gorillas eat other plant parts such as flowers and
bark, these foods were relatively infrequently eaten.
Therefore, these foods are likely not ‘‘important’’ in
reference to macronutrient and phenol contents, the
focus of this study, and were not included in our
analyses. Food species that were consumed infrequently were likely eaten for other reasons such as
mineral content or medicinal purposes and for
studies that focus on these components, it may be
important to consider all foods eaten and use
different methodologies [Huffman, 1997; Rothman
et al., 2006]. Additionally, for each group there were
0–3 plant species for which peel was important in the
gorillas’ diet but owing to the small sample sizes, we
were unable to do additional analysis to determine
which nutrients they preferred in peel. On average,
we analyzed trail signs 19 days per month per
group (Mubare monthly range 5 14–26; Habinyanja
monthly range 5 15–23; Rushegura range 5 17–25;
Kyagurilo range 5 14–19).
To determine the frequency and species of fruit
consumed, we collected fecal samples from each
groups’ night nests, and recorded whether the
sample (based on size) was from a silverback, an
adult female/blackback (indistinguishable), and a
juvenile (defined as sleeps in his/her own nest,
sexually immature) nest each day (Schaller, 1963).
After collection, fecal samples were washed through
a 1 mm sieve and seed species were identified.
Important fruit species were defined as those
occurring in Z1% of samples per group in any
month [modified from Ganas et al., 2004; Remis,
1997]. Because gorillas in Bwindi have not been seen
to spit out seeds in over 8 years of observation and
also because the vast majority of fruits consumed
during this study period were relatively small and
consumed in their entirety [mean width of fruit 5
7.7 mm, Ganas, unpublished data; Robbins, personal
observation] we assumed that if the gorillas ate fruit,
it would be detected in the fecal samples. There were
no differences in the frequency of fruit consumption
between age and sex classes (using a w2 test) and thus
only adult female samples were used in the analysis.
Considering all groups, we collected an average
of 25 (range 15–26) samples per group per month
(Mubare mean 5 23.3, monthly range 5 17–26;
Habinyanja mean 5 24, monthly range 17–29; Rushegura mean 5 23.8, monthly range 18–30; Kyagurilo
mean 5 26.9, monthly range 12–30).
Food Availability
Temporal
We measured the temporal biomass availability
of 20 herb species (Buhoma 5 18, Ruhija 5 11)
considered important to the gorillas (see above) in
89 1 m2 permanent plots (Buhoma 5 51, Ruhija 5 38)
in the forest [Ganas et al., in press]. Plots were
established in areas of high herb density within
various areas of the gorillas’ range. An average of
16.8 individual plants per species per month were
monitored in Buhoma (range 2.8–37.9, SD 5 11.5),
whereas in Ruhija, an average of 24.2 individuals per
species was monitored (range 8.5–60.1, SD 5 15.3).
To estimate the biomass of herbs, at approximately the same time every month, for each
permanent plot, we first took measurements of
particular plant parts. Next, we harvested 40
individuals of each species of varying lengths from
outside the plots. For each plant, we measured the
length of the plant stem or counted the number of
leaves on the individual plant and recorded the wet
weight of the part eaten by the gorillas. We then
dried the plant parts in sheds with charcoal stoves.
After they were dry, we again recorded the weight of
each individual. To determine whether there was a
significant relationship between the length of the
stem or the number of leaves and weight, we plotted
length/number of leaves against the weight (one test
each for wet and dry weight) and calculated a linear
regression that was forced through the origin [Zar,
1999]. We found a significant relationship between
these variables, and regression equations were
produced that were used to calculate the biomass of
Am. J. Primatol.
930 / Ganas et al.
each herb species in the plots for each month. This
also allowed us to calculate the monthly changes (%)
in their temporal availability. For further details, see
Ganas et al. [in press].
To calculate the temporal availability of fruit, on
a biweekly basis within each location, we monitored
397 trees and herbs of 40 species, 13 of which were
found at both sites (211 [mean ] per species 5 7.3,
SD 5 4.6] and 186 [mean ] per species 5 7.8,
SD 5 4.1] at Ruhija and Buhoma, respectively),
which have been known to provide fruits for gorillas.
For each species, we recorded the percent abundance
of ripe fruit in the crown scoring between zero and
four (0 5 0%, 1 5 1–25%, 2 5 26–55%, 3 5 51–75%,
and 4 5 76–100% of crown covered) following Sun
et al. [1996].
The fruits of Trichilia sp. were not previously
known to be consumed by gorillas and we did
not record phenological data on them and this
species was excluded from the analysis. Overall,
Trichilia sp. was of low to medium importance for
the two groups analyzed (annual % frequency eaten:
Mubare: 2.2%, Habinyanja 9.4%). Additionally, the
majority of Ficus spp. (excluding F. capensis) was
strangler figs and a fruit availability index (FAI)
could not be calculated.
Spatial availability
To determine the spatial distribution of herbs
and fruit, we cut and measured 102 and 54 transects
of 200 m in length at Buhoma and Ruhija, respectively, placing one transect each within a 500 m2 grid
overlaid onto a map of each study location [GreigSmith, 1983]. For each transect, we placed nested
quadrats (1 and 10 m2) on alternate sides in intervals
of 20 m for 10 quadrats (total transect
length 5 200 m) per transect. In these quadrats we
documented herb biomass (using the same methods
from the permanent plots, length/] leaves) and tree
(density and diameter at breast height) availability.
To then calculate the total biomass of each herb
species in each home range per month, we applied
the biomass estimates (regression equations) and the
corresponding monthly changes in biomass (%)
recorded from the permanent plots to the measurements from the transects.
To determine fruit availability at each location
for each biweekly period, a score of fruit abundance
was calculated using an FAI [following Nkurunungi
et al., 2004; calculated as the product of the mean
DBH (of phenology trees of each fruit species eaten
by the gorillas), density of each species at each
location (recorded from the transects), and their
mean biweekly abundance score value from the
phenology study]. To get a value for total fruit
availability for each location, we summed the
individual FAI scores for each biweekly period.
Am. J. Primatol.
Nutritional Sampling
We collected 42 important food species (fruit,
leaves, pith, peel) consumed by the gorillas at both
locations. Fruit crops produced by the herb Rubus sp.
were very small and samples could not be obtained
for nutritional analysis (this fruit accounted for 1%
yearly frequency in diet, and it was not consumed by
every group) All food items collected for analyses
were processed in a way that mimicked which parts
the gorillas consumed. For example, if the gorillas
ate the pith from a particular species, we collected
only pith. Because indirect observations were made
or entire plants eaten, often it was difficult to collect
plant parts from the exact tree or herb the part was
consumed from. Nonetheless, every attempt was
made to sample food items from the specific areas
where the gorillas fed. We also made multiple
collections of plant species when possible by collecting them from different areas of the gorillas’ home
range (where they had been feeding) and collected
them during two different wet seasons, mixing
samples before analysis. Owing to the difficulty of
collecting fruit from tall canopy trees, most fruit
samples came from a single tree or location. Because
of the short-term availability of fruits in Bwindi,
fruit was sampled once when available.
We stored samples in cryo tubes, froze them in
liquid nitrogen, and then freeze dried them. Dried
samples were then stored in a cool, dry place until
they were sent to the Institute of Zoo and Wildlife
Research for nutritional analysis and the University
of Hohenheim for phenolic determination.
Phytochemistry Analyses
All samples were ground before analysis using a
1 mm screen. Dry matter (DM) content was determined by drying a portion of the sample at 1051C
overnight. All data are given as % DM. Samples were
analyzed for the following macronutrients using
standard techniques: Nitrogen was determined by
complete combustion (Dumas combustion) at high
temperature (about 9501C) in pure oxygen, using a
Rapid N III analyzer (Elementar Analyser Systeme
GmbH, Hanau, Germany) and a factor of 6.25 was
used for conversion into protein (crude protein
(%DM) 5 6.25 N (%DM)). Starch, D-glucose, D-fructose, and sucrose were determined with commercialized enzymatic tests (UV method; R-Biopharm AG,
Darmstadt, Germany). Lipids were extracted with
ethyl ether using a fully automatic Soxhlett system
(Soxtherm; Gerhardt Laboratory Systems, Königswinter, Germany), and gross energy was determined
by burning a sample of DM in pure oxygen atmosphere in a bomb calorimeter (C5003 bomb calorimeter; IKA-Werke GmbH & Co. KG, Staufen,
Germany). The heat produced is measured in kJ/g
DM. Detergent Fiber Analysis was performed following van Soest [1991] with neutral detergent fiber
Gorilla Food Preferences / 931
(NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL) being determined sequentially
from each sample using an Ankom Fiber Analyser
220 (Ankom Technology, Macedon, NY). Hemicellulose (NDF–ADF) and cellulose (ADF–ADL) were
calculated by weight difference. Total phenols were
determined according to Makkar et al. [1993].
Determination of condensed tannins followed Porter
et al. [1986].
Statistical Analyses
To reduce the large number of macronutrient
and phenolic variables tested per herb and fruit
species (protein, starch, glucose, fructose, sucrose,
water soluble carbohydrates (WSC; sum of glucose,
fructose, sucrose), cellulose, hemicellulose, lignin,
NDF (sum of hemicellulose, cellulose, lignin), ADF
(sum of cellulose and lignin), fat, energy, total
phenols, total tannins, condensed tannins), we first
inspected correlations between them and in the case
of two variables being highly correlated to one
another (absolute correlation coefficient40.75) we
removed one of them (they included starch, fructose,
glucose, lignin, ADF, energy, total tannins). The fruit
and herb data sets were treated separately as the
nutritional and phenolic components significantly
differed between the two food groups [Ganas et al., in
preparation]. The individual remaining variables
were then subjected to a principal components
analysis (PCA). Reducing variables by using correlation tests and PCAs decreases the possible instability
in results of subsequent analyses of data sets that
consist of large number of correlating variables. It
also removes redundant information resulting in a
more stable result. Even if some of the variables that
are put into the PCA (and group onto the same
principal component) are related in some way (i.e. fat
content and energy content) putting these variables
into a PCA is valid [Field, 2005]. In the case of a
principal component where only a single variable had
its highest loading, we reran the PCA without that
variable and included the variable directly into
subsequent analyses. Both the fruit and herb PCAs
were justified [Kaiser–Maier–Olkin measure of sampling adequacy: herbs: 0.48; fruit: 0.64; Bartlett’s
test of sphericity: herbs: w2 5 117.2, df 5 36,
Po0.001; fruit: w2 5 23.3, df 5 15, Po0.08; Field,
2005]. The PCAs revealed three herb and two fruit
principal components (with eigenvalues in excess of
one), which together explained 71.2% (herb) and
68.2% (fruit) of the total variance. The loadings for
the three components for herbs were (a) NDF,
hemicellulose, cellulose, condensed tannin, (b) protein, fat, total phenols, and (c) WSC and sucrose
(Table I(A)). The loadings for the two components for
fruit were (a) NDF and lignin (1)/WSC ( ) and (b)
fat and energy (1)/sucrose ( ) (Table I(B)).
Condensed tannin is included in the fruit preference
analysis as its own variable rather than
as a component because it grouped on its own in
the PCA.
We calculated preference scores for each food
species and part consumed (fruit, leaves, and pith)
using Ivlev’s electivity index, which is a measure of
foraging behavior in relation to food availability
[Ivlev, 1961]. Traditionally it is used to determine
whether a food species is consumed proportionally to
its availability. It can also be used as a relative
measure of preference by considering all food
species eaten and then ranking them according to
both frequency in the diet and their corresponding
availability [Johnson, 1980]. By ranking foods rather
than using absolute values, it circumvents the
problem that arises from accurately measuring rare
foods [Johnson, 1980; Lechowicz, 1982]. Although
preference measures should control for relative
availability, owing to the nature of this index,
availability still somewhat influences preference
scores. For example, species that have a relatively
high abundance may not ever be considered highly
preferred regardless of how much is consumed, and
very rare foods will often be considered highly
preferred [Johnson, 1980; Maitland, 1965].
Lechowicz [1982] reviewed a variety of preference
indices, examining the pros and cons of each
and determined that the majority provided useful
measures of feeding preference, including the one
used here. Despite some limitations, we chose
Ivlev’s electivity index because it works best with
large sample sizes, it has been used often by
other authors [Vedder, 1989; Watts, 1984], and
it is one of the most appropriate indices available
for wild animals [Chesson, 1983; Johnson, 1980;
Lechowicz, 1982].
To calculate the index for each biweekly (fruit)
or monthly (herb) period, a rank was assigned for
both diet frequency and food availability of each
species, resulting in 12 (herb) or 24 (fruit) preference
scores for each species. Ranks were between 1 and
the highest number of fruit or herb species available
(considering only those that were consumed) in the
time period. The greater the diet frequency/availability, the higher the rank score was. For species
that shared the same availability or diet frequency
score, tied ranks were assigned.
Based on these ranks, we then calculated
preference using the formula: Ivlev’s electivity
index 5 (rd na)/(rd1na), where rd 5 rank of food item
in diet na 5 rank of food item in the home range.
Scores between 1 and 0 indicated that a food was
not preferred, a score of 0 signified neutrality, and a
score between 0 and 1 indicated that it was
preferred. Rather than creating categories of ‘‘highly
preferred’’ or ‘‘medium preferred’’’ that are not
rooted in biological meaning, food preference scores
should simply be viewed on a relative continuum
where one is regarded as more preferred than
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932 / Ganas et al.
TABLE I. Results of the Principal Components
Analyses on the (A) herb and (B) fruit nutrient and
phenolic values
Trait
Component Component Component
1
2
3
RESULTS
(A)
Protein
WSC
Sucrose
NDF
Hemicellulose
Lignin
Fat
Total phenols
Condensed tannins
Eigenvalue
% variance explained
0.135
0.324
0.002
0.837
0.549
0.766
0.156
0.172
0.732
2.77
30.8
0.618
0.195
0.067
0.412
0.071
0.269
0.816
0.857
0.139
2.09
23.2
(B)
Sucrose
WSC
Fat
Energy
Lignin
NDF
Eigenvalue
% variance explained
0.160
0.857
0.441
0.345
0.596
0.862
3.03
50.5
0.755
0.157
0.762
0.636
0.581
0.069
1.06
17.8
0.597
0.773
0.719
0.091
0.184
0.418
0.299
0.152
0.458
1.55
17.2
WSC, water soluble carbohydrates (sum of glucose, fructose, sucrose);
NDF, neutral detergent fiber (sum of cellulose, hemicellulose, lignin).
Indicated are loadings of the variables on the principal components
derived. Bold values indicate the largest absolute loading per variable.
another based on its preference score. For most fruit
species, there were periods when they were unavailable and thus no preference score was calculated for
these time periods. Because we were only able to
obtain both nutritional and availability data for 40%
of the fruit species eaten by Rushegura and because
during the study period Kyagurilo group only ate 1–2
species of fruit per time period, we did not analyze
these groups’ fruit preferences.
To determine what nutritional and phenolic
attributes characterized preferred foods, we correlated each species preference scores for each time
period with its nutrient and phenolic composition
(the PCA factor scores; each plant part has the same
PCA factor score during each biweekly/monthly
period as we sampled each plant species once, but
the diet frequency values differ per period) using a
Pearson’s correlation test. A correlation coefficient
between 0 and 1 indicated preference for a particular
nutrient/phenol, and a coefficient between 1 and 0
indicated that it was not preferred. We then used a
one-sample t-test to determine whether these
monthly correlation coefficients on average, equaled
zero (a significant result means that they were not
and thus a significant correlation). We controlled for
multiple testing by using a Fisher’s Omnibus test;
Am. J. Primatol.
one test for the herb preferences (leaves, pith) and
one for fruit. We used two separate tests owing to the
two different sets of PCA analyses. Analyses were
performed using SPSS 13.0.
Plant Species Preferences
Herb foods that had relatively high preference
scores (40.35) were Aframomum angustifolia
pith, Aframomum sanguinum pith, Basella alba
leaves, Desmodium repandum leaves, Ipomea wightii
leaves, Mormodica calantha leaves, and Palisota
mannii pith. Fruit that had relatively high
preference scores (40.20) were Ficus capensis and
Prunus africana.
Species that were highly abundant, which may
have resulted in low preference scores, included
Cassine aethiopica, Mimulopsis solmsii, and Mimulopsis arborescens. There were no major differences
among groups in these scores. For individual food
species preference scores, see Tables II and III.
Nutritional and Phenolic Characteristics of
Preferred Foods
A Fisher’s Omnibus test confirmed that the
following results are not simply owing to chance
(w2 5 148.4, df 5 24, Po0.001).
Leaves
All groups significantly preferred leaves relatively high in protein, fat, and phenols (Table IV;
Mubare one sample t-test t 5 5.6, df 5 10, Po0.001;
Habinyanja t 5 5.2, df 5 10, Po0.001; Rushegura
t 5 6.1, df 5 10, Po0.001; t 5 11.9, df 5 11, Po0.001;
Kyagurilo t 5 5.8, df 5 11, Po0.001) and avoided
fiber (Mubare t 5 12.6, df 5 10, Po0.001; Habinyanja t 5 23.2, df 5 10, Po0.001; Rushegura
t 5 16.8, df 5 10, Po0.001; Kyagurilo t 5 20.9,
df 5 11, Po0.001).
Habinyanja, Rushegura, and Kyagurilo preferred leaves relatively high in sugar, whereas
Mubare did not (Table IV; Mubare t 5 0.6, df 5 10,
P 5 0.57; Habinyanja t 5 3.4, df 5 10, P 5 0.006;
Rushegura t 5 2.6, df 5 10, P 5 0.03; Kyagurilo
t 5 2.9, df 5 11, P 5 0.02).
Pith
Mubare preferred pith relatively high in protein,
whereas the other groups did not (Table IV; Mubare
t 5 3.8, df 5 10, P 5 0.003; Habinyanja t 5 0.3,
df 5 10, P 5 0.78; Rushegura t 5 1.2, df 5 10,
P 5 0.25).
All groups preferred pith high in sugar
(Table IV; Mubare t 5 5.0, df 5 10, P 5 0.001; Habinyanja t 5 3.6, df 5 10, P 5 0.005, Rushegura t 5 4.8,
df 5 10, P 5 0.001).
Gorilla Food Preferences / 933
TABLE II. For each group, the diet frequency, the corresponding yearly average preference rank, food
availability, its corresponding average yearly rank, and preference scores of each herb species used in the
preference analysis
Group
Plant species
Yearly diet
freq. (%)
Avg. diet
rank (SD)
Median
avail. g/m2
Avg. avail.
rank (SD)
] mo.
Pith
Leaves
Leaves
Leaves
Pith
Leaves
Leaves
Peel
Pith
Peel
Leaves
Pith
Leaves
Pith
Leaves
Leaves
Leaves
Leaves
Leaves
Pith
Pith
5.1
0.3
4.2
13.5
1.7
1.7
0.3
6.6
12.4
7.4
9.9
0.9
3.0
0.9
1.8
0.9
0.7
0.8
0.9
0.7
1.5
13.1(4.7)
3.5(2.6)
12.8(4.0)
17.9(7.0)
7.9(3.2)
8.9(3.7)
4.6(2.2)
15.0(4.5)
17.5(5.2)
15.1(4.6)
16.7(5.1)
6.2(3.0)
11.0(3.5)
4.8(4.1)
8.8(5.1)
5.9(3.3)
4.8(3.2)
5.9(3.2)
4.2(2.9)
—
—
0.02
0.003
0.05
0.10
0.01
0.05
0.08
0.12
1.9
0.22
5.4
0.04
4.5
0.04
2.4
0.12
0.31
0.22
0.48
—
Not detected
4.9(1.9)
1.1(0.4)
5.5(2.4)
8.4(3.7)
3.3(1.2)
5.2(2.6)
2.8(1.4)
11.4(3.5)
16.9(4.9)
14.5(4.3)
17.2(5.1)
7.5(2.2)
18.2(5.5)
7.2(3.8)
15.5(7.2)
11.4(3.5)
9.4(4.5)
14.5(4.3)
11.2(3.8)
—
—
11
11
11
11
12
11
11
11
11
11
11
12
11
11
11
11
11
11
11
11
12
0.45
0.43
0.41
0.37
0.36
0.26
0.21
0.13
0.02
0.02
0.01
0.13
0.25
0.25
0.30
0.35
0.36
0.45
0.48
—
—
Pith
Leaves
Leaves
Leaves
Leaves
Pith
Leaves
Peel
Pith
Leaves
Peel
Leaves
Leaves
Pith
Leaves
Leaves
Pith
Leaves
Leaves
Peel
Pith
Pith
0.7
3.7
10.0
2.1
0.4
6.0
0.3
5.4
16.0
1.2
5.4
0.7
5.0
1.1
2.3
0.8
0.3
0.6
0.7
0.7
1.2
1.4
6.8(3.1)
13.3(4.3)
17.5(5.1)
11.0(4.0)
5.3(3.6)
15.0(4.9)
4.8(3.4)
14.6(4.4)
18.3(5.4)
7.2(3.8)
14.3(4.8)
7.8(4.1)
13.8(4.3)
7.5(3.7)
8.1(3.2)
6.8(3.2)
3.5(3.6)
5.4(2.7)
4.8(3.0)
4.1(3.1)
—
—
0.003
0.10
0.03
0.02
0.003
0.03
0.05
0.10
2.4
0.12
0.20
0.16
4.8
0.05
3.2
0.2
0.02
0.10
1.01
1.01
Not detected
—
1.4(0.7)
5.0(2.2)
7.0(2.5)
5.0(2.2)
1.8(1.0)
8.2(2.8)
3.8(1.5)
13.7(4.4)
17.9(5.2)
6.8(3.0)
14.1(4.3)
9.0(4.2)
18.0(5.4)
9.8(2.6)
17.5(4.4)
14.1(4.3)
7.0(3.2)
13.7(4.4)
14.8(6.8)
14.8(6.8)
—
—
12
11
11
11
11
11
11
11
11
11
11
11
11
12
11
11
11
11
11
11
12
11
0.59
0.46
0.43
0.39
0.38
0.29
0.03
0.03
0.01
0.01
0
0.09
0.13
0.18
0.21
0.37
0.39
0.45
0.53
0.60
—
—
Pith
Leaves
Leaves
Pith
Leaves
Pith
Peel
Leaves
Peel
Pith
Pith
Leaves
0.9
2.6
10.5
1.0
4.7
4.4
7.4
0.2
7.2
1.8
22.8
0.7
5.0(4.3)
9.9(4.2)
15.5(5.0)
6.0(2.8)
11.9(4.6)
12.5(4.4)
14.4(5.0)
4.1(2.9)
13.3(4.3)
7.1(4.6)
17.2(5.1)
6.1(3.2)
0.002
0.03
0.13
0.01
0.16
0.07
0.16
0.17
0.06
0.03
4.2
0.16
1(0)
2.9(1.9)
7.3(2.7)
2.9(0.9)
8.5(3.4)
10.3(3.7)
12.4(3.9)
4.0(1.6)
12.3(4.5)
5.7(1.5)
16.9(4.9)
6.8(4.4)
12
11
11
12
11
11
11
11
11
12
11
11
0.54
0.50
0.36
0.29
0.16
0.10
0.07
0.05
0.05
0.04
0.01
0.02
Part
eaten
Avg. pref.
score
Mubare
Palisota mannii
Desmodium repandum
Ipomea wightii
Basella alba
Aframomum angustifolia
Mormodica calantha
Rubus sp.
Laportea aestuans
Mimulopsis arborescens
Urera sp.
Triumfetta sp.
Aframomum sp.
Mimulopsis solmsii
Pennisetum purpureum
Ipomea sp.
Laportea aestuans
Gouania longispicata
Urera sp.
Mormodica foetida
Piper capense
Aframomum sanguinum
Habinyanja
Aframomum angustifolia
Ipomea wightii
Basella alba
Mormodica calantha
Desmodium repandum
Palisota mannii
Rubus sp.
Urera sp.
Mimulopsis arborescens
Mormodica foetida
Laportea aestuans
Gouania longispicata
Triumfetta sp.
Aframomum sp.
Mimulopsis solmsii
Laportea aestuans
Pennisetum purpureum
Urera sp.
Ipomea sp.
Ipomea sp.
Aframomum sanguinum
Piper capense
Rushegura
Aframomum sanguinum
Mormodica calantha
Basella alba
Aframomum angustifolia
Ipomea wightii
Palisota mannii
Laportea aestuans
Rubus sp.
Urera sp.
Aframomum sp.
Mimulopsis arborescens
Ipomea sp.
Am. J. Primatol.
934 / Ganas et al.
TABLE II. Continued
Group
Plant species
Mormodica foetida
Triumfetta sp.
Gouania longispicata
Ipomea sp.
Urera sp.
Laportea aestuans
Desmodium repandum
Pennisetum purpureum
Piper capense
Kyagurilo
Mormodica calantha
Basella alba
Cardus sp.
Rubus sp.
Ipomea sp.
Urera sp.
Mimulopsis arborescens
Mimulopsis solmsii
Piper capense
Triumfetta sp.
Mimulopsis solmsii
Urera sp.
Ipomea sp.
Mormodica foetida
Part
eaten
Leaves
Leaves
Leaves
Peel
Leaves
Leaves
Leaves
Pith
Pith
Leaves
Leaves
Stalk/pith
Leaves
Leaves
Peel
Pith
Bark
Pith
Leaves
Leaves
Leaves
Peel
Leaves
Yearly diet
freq. (%)
Avg. diet
rank (SD)
Median
avail. g/m2
Avg. avail.
rank (SD)
] mo.
1.0
5.8
0.4
0.5
0.7
0.5
0.4
0.9
0.9
6.0(3.1)
13.0(4.5)
4.7(3.1)
3.9(3.6)
5.7(2.5)
4.5(2.2)
—
—
—
0.26
6.8
0.22
0.16
0.06
0.16
Not detected
Not detected
—
7.1(3.7)
15.5(4.6)
7.0(3.6)
6.8(4.4)
12.3(4.5)
12.4(3.9)
—
—
—
11
11
11
11
11
11
—
—
11
0.08
0.09
0.22
0.29
0.41
0.48
—
—
—
2.6
7.5
1.6
5.3
8.9
11.2
14.6
11.1
0.8
7.8
7.1
2.3
1.2
0.1
12.3(1.3)
8.3(1.8)
3.6(1.3)
7.0(1.5)
9.7(2.2)
11.3(2.2)
12.0(2.8)
11.4(1.9)
2.9(0.9)
9.3(1.8)
8.6(2.4)
4.5(1.2)
3.0(3.1)
1.2(0.4)
0.2
0.24
0.08
1.0
0.15
0.2
1.2
6.2
0.91
2.1
6.2
0.2
0.15
0.8
1.8(0.9)
3.1(0.9)
3.0(2.7)
5.2(0.8)
7.4(0.8)
9.3(0.6)
11.3(0.5)
13.5(0)
4.0(1.9)
11.7(0.5)
13.5(0)
9.3(0.6)
7.4(0.8)
4.4(1.4)
12
12
12
12
12
12
12
12
12
12
12
12
12
12
0.75
0.45
0.20
0.14
0.12
0.08
0.01
0.09
0.11
0.12
0.24
0.36
0.44
0.57
Avg. pref.
score
Food species are ranked from largest to smallest preference score. The average preference score is the average of the monthly preference scores. SD,
standard deviation. When two parts are eaten from the same plant species (i.e. Laportea aestuans), the biomass of the two parts is added together. Thus, the
median availability for L. aestuans peel includes the biomass from the leaves and peel, and vice versa. ‘‘Not detected’’ means that during the measurements
of plant spatial availability, we did not encounter these plants in this particular home range, indicating that these species were at an extremely low density.
For the majority of species in Buhoma, monitoring of their biomass began 1 month after the beginning of the study, thus availability is 11 months rather
than 12, and was thus considered available every month of the study.
Fruit
A Fisher’s Omnibus test confirmed that the
following results were not owing to chance (w2 5 51.7
df 5 16, Pr0.001).
For the two groups tested, the Mubare group
preferred fruits with a relatively large NDF and
lignin/WSC ratio, whereas the Habinyanja group did
not (Table IV; t 5 2.8, df 5 19, P 5 0.011; Habinyanja
t 5 1.6, df 5 19, P 5 0.13). Neither group significantly preferred fat and energy/sucrose in fruit over
the year (Table IV). Furthermore, periods in which
there was a positive correlation between fruit
preference and fat and energy/sucrose were mostly
during the times that NDF and lignin/WSC was not
preferred. This could indicate that this group
preferred some type of energy source every month.
The Mubare group preferred fruits with condensed tannin, whereas the Habinyanja group did
not (Table IV; Mubare t 5 2.8, df 5 19, P 5 0.01;
Habinyanja t 5 1.7, df 5 19, P 5 0.11).
DISCUSSION
Our research, which gives an insight into gorilla
nutritional requirements, found that although the
gorillas preferred some plant species over others,
Am. J. Primatol.
these preferences were related to particular nutrients
and phenols. Groups generally preferred leaves with
relatively high levels of protein, fat, phenols and
sugar and low amounts of fiber, and pith with
relatively high amounts of sugar. Fruit preferences
were less clear, and the result that one group of
gorillas preferred digestion inhibitors (condensed
tannins and fiber) was possibly owing to simultaneously ingesting relatively high amounts of sugar.
Despite differences in spatial and temporal variability
in food availability among gorilla home ranges, there
were no large differences in either preference for a
particular plant species or preference for particular
nutrients and phenols among gorilla groups.
All groups preferred leaves with relatively high
amounts of protein, fat, and phenols and relatively
low amounts of fiber and condensed tannins, concurring with other studies of primate herbivores,
which found that the protein/fiber ratio is an
important component of their foraging strategy
[Chapman et al., 2004; Ganzhorn, 1992; Milton,
1979; Oates et al., 1990]. Curiously, these results
differ from similar work conducted on mountain
gorilla preference in Rwanda (using the same
methodology as this study), which found no relationship between preference and protein [Vedder, 1989].
Gorilla Food Preferences / 935
TABLE III. For each group, the diet frequency, the corresponding yearly average preference rank, food
availability, its corresponding average yearly rank, and preference scores of each fruit species used in the
preference analysis
Group
Plant species
Yearly diet freq. (%) Avg. diet rank (SD) Avg. avail. Avg. avail. rank ] mo. Avg. pref. score
Mubare
Prunus africana
Ficus capensis
Myrianthus holstii
Syzygium guineense
Cassine aethiopica
Habinyanja
Ficus capensis
Prunus africana
Syzygium guineense
Maesa lanceolata
Cassine aethiopica
Myrianthus holstii
1.8
5.1
29.5
19.6
34.6
3.0(1.0)
2.0(0.9)
2.6(1.1)
1.8(1.0)
2.7 (1.0)
2.4
20.5
20.8
1.0
35.4
33.3
2.3(0.9)
2.5(1.0)
3.4(1.9)
1.7 (0.6)
3.6(1.2)
3.2(1.3)
0.22
10.5
400.2
52.0
996.1
8.5
1.0
46.9
23.3
1156.8
392.7
1.0(0.3)
1.2(0.4)
2.9(0.9)
2.0(1.1)
3.4(0.9)
1.5
12
12
5
9.5
0.47
0.20
0.06
0.08
0.13
1.1(0.4)
2.0(0.7)
3.1(1.6)
2.0(0.6)
4.3(1.1)
3.8(0.8)
12
1.5
5
10.5
9.5
12
0.29
0.10
0.02
0.03
0.08
0.09
Food species are ranked from largest to smallest preference score. Availability was calculated using the FAI (fruit availability index), which is the product of
the mean DBH (of phenology trees of fruits eaten by the gorillas), density of those species in the gorillas’ range, and their mean monthly abundance score
value from the phenology for each species). The average preference score is the average of the biweekly periods that fruit was available. ] mo 5 number of
months fruit species was available during the study period.
TABLE IV. Nutritional and phenolic attributes of preferred foods/plant parts (individual species preference
scores [per plant part] compared with their nutrient compositions)
Plant part
Leaf
Leaf
Leaf
Leaf
Leaf
Leaf
Leaf
Leaf
Leaf
Leaf
Leaf
Leaf
Pith
Pith
Pith
Pith
Pith
Pith
Pith
Pith
Pith
Fruit
Fruit
Fruit
Fruit
Fruit
Fruit
Group
Mubare
Habinyanja
Rushegura
Kyagurilo
Mubare
Habinyanja
Rushegura
Kyagurilo
Mubare
Habinyanja
Rushegura
Kyagurilo
Mubare
Habinyanja
Rushegura
Mubare
Habinyanja
Rushegura
Mubare
Habinyanja
Rushegura
Mubare
Habinyanja
Mubare
Habinyanja
Mubare
Habinyanja
PCA Component
Avg Rho
Protein, fat, total phenols
Protein, fat, total phenols
Protein, fat, total phenols
Protein, fat, total phenols
WSC and sucrose
WSC and sucrose
WSC and sucrose
WSC and sucrose
NDF, hemicellulose, cellulose,
NDF, hemicellulose, cellulose,
NDF, hemicellulose, cellulose,
NDF, hemicellulose, cellulose,
Protein, fat, total phenols
Protein, fat, total phenols
Protein, fat, total phenols
WSC and sucrose
WSC and sucrose
WSC and sucrose
NDF, hemicellulose, cellulose, CT
NDF, hemicellulose, cellulose, CT
NDF, hemicellulose, cellulose, CT
NDF and lignin/WSC
NDF and lignin/WSC
Fat and energy/sucrose
Fat and energy/sucrose
Condensed tannin
Condensed tannin
CT
CT
CT
CT
0.30
0.34
0.45
0.36
0.03
0.17
0.18
0.09
0.35
0.68
0.58
0.72
0.07
0.02
0.07
0.14
0.20
0.36
0.14
0.10
0.05
0.28
0.17
0.05
0.15
0.28
0.18
SD
0.20
0.23
0.29
0.20
0.12
0.14
0.21
0.09
0.19
0.36
0.30
0.38
0.06
0.14
0.14
0.01
0.16
0.25
0.16
0.13
0.28
0.44
0.46
0.47
0.51
0.44
0.46
Range
0.04–0.60
0.07–0.68
0.06–0.82
0.14–0.57
0.19–0.32
0.02–0.35
0.22–0.77
0.07–0.21
0.47–0.16
0.82–0.54
0.7–0.43
0.89–0.50
0.03–0.17
0.24–0.27
0.04–0.33
0.04–0.30
0.06–0.43
0.24–0.59
0.1–0.5
0.2–0.5
0.89–0.5
0.32–0.74
0.63–0.46
0.8–0.74
0.5–1
0.32–0.74
0.87–0.62
Nutritional composition of plant parts was represented by principal component analysis scores. The results are displayed as the yearly average, SD and
range of the biweekly/monthly correlation coefficients for each plant part (Avg Rho) A significant correlation for the year (tested via a one sample t-test with
each biweekly or monthly period correlation coefficient as a data point) is indicated in bold. WSC, water soluble carbohydrates; NDF, neutral detergent
fiber.
Because the same methods were used in both studies,
it is unknown why these differences occurred. One
possibility could be that because mountain gorillas in
Rwanda consume only a small number of highly
abundant species, the limitations of Ivlev’s electivity
index could have contributed to this result.
Am. J. Primatol.
936 / Ganas et al.
TABLE V. The average and standard deviations (represented in parenthesis after the average) of the nutritional
and phenolic contents (% dry matter) of important plant parts used in the preference tests
Buhoma
Leaves n 5 11
PT
ST
FC
GC
SC
NDF
ADF
LN
CL
HC
FT
EN
TP
TT
CT
26.6
2.6
1.1
0.9
0.3
35.3
17.3
4.4
12.9
18.0
1.9
19.6
4.4
3.3
1.0
(3.8)
(2.1)
(0.9)
(0.7)
(0.2)
(8.1)
(5.6)
(2.6)
(3.3)
(5.7)
(0.7)
(1.3)
(3.3)
(3.0)
(1.9)
Ruhija
Pith n 5 7
Peel n 5 2
Fruit n 5 5
10.4
1.8
9.5
9.2
0.9
38.3
23.1
1.5
21.6
15.2
1.3
15.1
0.7
0.3
0.4
13.0
0.7
0.4
0.3
0.1
61.6
52.3
8.1
44.1
9.3
0.6
16.3
1.0
0.7
0.7
8.1(2.8)
6.3(12.3)
7.5(8.6)
7.1(7.9)
1.5(2.5)
32.7(13.0)
20.1(8.3)
7.5(4.8)
12.7(4.4)
12.6(8.9)
6.1(7.2)
18.8(2.1)
4.2(3.6)
3.6(3.7)
4.4(4.5)
(2.2)
(2.6)
(5.2)
(3.8)
(0.3)
(6.5)
(3.4)
(1.4)
(2.3)
(4.9)
(0.7)
(0.8)
(0.4)
(0.4)
(0.5)
(0.6)
(0.5)
(0.1)
(0.1)
(0.1)
(0.8)
(0.8)
(7.0)
(7.8)
(2.6)
(0.4)
(0.5)
(0.1)
(0.01)
(0.2)
Leaves n 5 8
24.7
3.6
1.3
1.2
0.2
41.4
16.7
4.4
12.3
24.6
0.9
18.7
3.5
2.7
0.3
(2.9)
(2.0)
(1.3)
(1.4)
(0.4)
(10.0)
(4.7)
(2.6)
(3.1)
(7.9)
(0.6)
(1.2)
(4.1)
(3.8)
(0.7)
Pith n 5 3
7.8
2.4
1.2
1.8
0.2
41.4
31.2
4.8
26.3
10.3
2.7
13.6
1.2
0.6
0.1
(2.3)
(3.8)
(1.0)
(1.4)
(0.1)
(15.7)
(12.9)
(5.9)
(7.0)
(2.9)
(3.3)
(1.3)
(0.4)
(0.1)
(0.001)
Peel n 5 2
Fruit n 5 4
12.5
0.5
1.0
0.5
0.3
55.1
37.0
8.1
28.9
18.1
2.1
18.1
1.0
0.5
0.03
9.4(3.4)
3.0(5.6)
10.2(5.7)
6.3(2.7)
0.05(0.1)
25.1(9.2)
16.8(7.8)
7.0(3.4)
9.8(5.1)
8.3(2.5)
7.5(11.0)
20.2(2.1)
4.0(4.7)
2.7(2.9)
5.1(7.2)
(5.6)
(0.2)
(0.5)
(0.3)
(0.5)
(8.6)
(2.1)
(5.4)
(7.5)
(6.5)
(2.0)
(1.7)
(0.5)
(0.3)
(0.01)
PT, protein; ST, starch; FC, fructose; GC, glucose; SC, sucrose; NDF, neutral detergent fiber; ADF, acid detergent fiber; LN, lignin; CL, cellulose; HC,
hemicellulose; FT, fat; EN, energy; TP, total phenols; TT, total tannins; CT, condensed tannins.
One of the most interesting and novel results of
this study was that the gorillas preferred leaves and
pith of herbs that contained relatively high amounts
of sugar. Similarly, sugar also influenced the choice
of herbs [Ganas et al., 2008]. Although the average
sugar content of leaves is relatively low compared
with other plant parts (0.2–1.3% DM; Table V), as
leaves constitute the greatest amount of wet mass
intake to the diet, at least for the Kyagurilo group
[Rothman et al., 2007]; sugar intake from leaves
could be substantial. For herbivores that live in
habitats where fruit availability is low or nonexistent, sugar in nonfruit plant parts may provide a
required nutrient previously not associated with this
type of vegetation [Danish et al., 2006]. Sugar in
different foliage parts, as well as fruit, may enable
the gorillas to be flexible and to exploit a variety of
foods and habitat types when trying to fulfil nutritional requirements.
Surprisingly, the Mubare group preferred fruit
with relatively high condensed tannin amounts.
However, this appears to be largely driven by the
fruit of Myrianthus holstii, which contains much
greater amounts of condensed tannin that other
fruits [Ganas et al., in preparation]. Perhaps consuming fruit that contains a relatively high amount
of condensed tannin is sometimes necessary to ingest
substantial amounts of sugar. Other studies have
also found that some animals tolerate relatively high
tannin amounts in food if that food is consumed
infrequently or the food’s consumption allowed a
concurrent ingestion of a relatively high nutrient
amount [Oates et al., 1980; Remis and Kerr, 2002].
To better understand the importance of sugar in
fruit to Bwindi gorillas, we compared the sugar
Am. J. Primatol.
contents of the top five fruits consumed with five
highly available, but avoided fruits (selectivity) and
found that consumed fruits contained significantly
more sugar than those not consumed [Ganas et al., in
preparation]. This example highlights the importance of examining different measures of an animal’s
foraging behavior to fully understand their foraging
strategy.
Owing to the nature of Ivlev’s electivity index
(foods relatively high in availability can rarely score
high preference scores or rare foods usually score
high preference scores), the preference scores for
some highly abundant foods may be biased compared
with experimental studies of food preference. For
example, widely abundant foods such as C. aethiopica (fruit) and M. solmsii (foliage) scored as avoided or
neutral, despite their high frequency in the diet
(Tables II and III). Therefore, it is important to
individually examine preference scores of species
that are of high availability (or rare) to determine
whether their preference scores could be simply
owing to this inherent limitation of this index. For
example, C. aethiopica fruits were not preferred; yet
their nutrient profile typifies what a high-quality
fruit contains (relatively high amounts of sugar),
suggesting that these fruits may be consumed for
their sugar contents.
Our preference results differed in some aspects
from our concurrent research on food choice, which
measured how the fluctuating availability of foods as
well as the nutrient and phenolic composition influenced what foods were eaten. In that study, the yearround availability of herbs high in protein led to the
result that protein did not influence the choice of herb
foods [unlike this study, which showed that protein is
Gorilla Food Preferences / 937
preferred, and thus is nutritionally important; Ganas
et al., 2008, in press]. Together, these results show
that protein is important to Bwindi gorillas, but that
they do not need to specifically seek it out owing to its
high availability. These studies demonstrate the
importance of calculating both food preference (to
determine nutritional requirements) and food choice
(to determine which factors influence the consumption
of particular foods in a variable environment) to better
understand an animals’ foraging strategy.
From a broader perspective, although preferred
food species are found in various habitat types [i.e.
open, mixed, regenerating, swamp forests; Nkurunungi et al., 2004], which likely plays a role in gorilla
habitat use, open forest at both study locations in
Bwindi contributed the greatest proportion of herb
biomass [considering gorilla food; Ganas et al., in
press], in both locations and contained many
preferred foods [Basella alba, Ipomea spp. Mormodica calantha, etc.; Ganas et al., in press]. Therefore,
in terms of mountain gorilla conservation, open
forests can be considered a habitat that should be
prioritized for conservation efforts.
Overall, our results on food preference are not
atypical of what we would expect for gorillas.
However, the ways in which the gorillas fulfil these
requirements, through a combination of different
plant parts, shed new light on how gorillas can
balance their diet in a variable environment. These
results, together with our concurrent study of food
choice, tell us the following information about the
Bwindi gorilla foraging strategy: First, Bwindi gorillas need protein in their diets, but owing to the yearround high availability of herbs high in protein, it is
easy to fulfil this requirement. Second, sugar is also
important to the gorillas’ diet, and gorillas can eat
fruit, pith, and/or leaves to obtain this nutrient.
These studies underscore the importance of investigating the different facets of feeding behavior,
nutrition, and food availability to get a comprehensive understanding of an animals’ foraging strategy.
ACKNOWLEDGMENTS
We thank the Uganda Wildlife Authority and the
Uganda National Council of Science and Technology
for permission to conduct this research. All research
complied with animal care regulations and national
laws. We appreciate the work of our field assistants
who are too numerous to name. Further thanks to
Bosco Nkurunungi, Alastair McNeilage, Robert
Barigira, the Institute of Tropical Forest Conservation, Paul Kakende and the Biochemistry department at Makerere University, Heidrun Barleben,
and the lab of Dr. Klaus Becker. This article
benefited from statistical assistance from Roger
Mundry as well as comments to previous versions
by Oliver Schülke, Joanna Lambert, Shelly Masi,
Colin Chapman, and four anonymous reviewers.
This research was funded by the Max Planck Society,
Berrgorilla & Regenwald Direkthilfe, The John Ball
Zoo, and the Leakey Foundation.
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