American Journal of Primatology 70:927–938 (2008)

RESEARCH ARTICLE

Food Preferences of Wild Mountain Gorillas

JESSICA GANAS1�, SYLVIA ORTMANN2, AND MARTHA M. ROBBINS1

1Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany2Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany

Determining the nutritional and phenolic basis of food preference is important for understanding thenutritional requirements of animals. Preference is a measure of which foods would be consumed by ananimal if there was no variation in availability among food items. From September 2004 to August2005, we measured the food preferences of four wild mountain gorilla groups that consume foliage andfruit in Bwindi Impenetrable National Park, Uganda, to determine what nutrients and phenols arepreferred and/or avoided. To do so, we asked the following questions: (1) Which plant species do thegorillas prefer? (2) Considering the different plant parts consumed of these preferred species, whatnutrients and/or phenols characterize them? (3) Do the nutritional and phenolic characteristics ofpreferred foods differ among gorilla groups? We found that although some species were preferred andothers were not, of those species found in common among the different group home ranges, the sameones were generally preferred by all groups. Second, all groups preferred leaves with relatively highprotein content and relatively low fiber content. Third, three out of four groups preferred leaves withrelatively 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 highcondensed tannin and fiber/sugar contents, whereas the other group’s preferred fruits were notcharacterized by any particular nutrient/phenol. Overall, there were no differences among gorillagroups in nutritional and phenolic preferences. Our results indicate that protein and sugar areimportant in the diets of gorillas, and that the gorillas fulfil these nutritional requirements through acombination of different plant parts, shedding new light on how gorillas balance their diets in a variableenvironment. Am. J. Primatol. 70:927–938, 2008. r 2008 Wiley-Liss, Inc.

Key words: nutritional ecology; foraging strategy; protein; sugar; Bwindi Impenetrable NationalPark

INTRODUCTION

Animal foraging strategies are based on acomplex suite of variables including nutritionalrequirements, spatial and temporal availability offood, and the amount of energy and time needed tolocate and consume food resources [Schoener, 1971;Stephens & Krebs, 1986; Westoby, 1974]. Under-standing a species’ foraging strategy includes anexamination of food preference, choice, and selectiv-ity. Food preference is a measure of food consump-tion with the assumption that there is no variation inavailability among food items in the animal’s diet[Chesson, 1983; Johnson, 1980]. Many authors claimto measure preference; however, often these calcula-tions do not take into consideration the (equal)availability of dietary items [Calvert, 1985; Haywardet al., 2006; Norscia et al., 2006]. Food preferencediffers from food choice because although food choiceinvestigates how the attributes of each food species(their differing availabilities and nutrient composi-tions) may influence the decision of what an animalconsumes, preference controls for differences in

availability and then calculates which species wouldbe chosen over another. Another measure of foragingbehavior, selectivity, measures why certain foods arenot consumed by comparing the nutritional contentsof foods eaten with those not eaten.

Investigating food preference is importantbecause it can lend insight into the nutritionalrequirements of an animal, which is vital to repro-duction, fitness, and survival [Altmann, 1998; Orians& Wittenberger, 1991; Schoener, 1983]. Additionally,information on which nutrients and foods arepreferred by an animal can tell us which food species

Published online 19 June 2008 in Wiley InterScience (www.interscience.wiley.com).

DOI 10.1002/ajp.20584

Received 5 August 2007; revised 28 February 2008; revisionaccepted 19 May 2008

Contract grant sponsors: Max Planck Society; Berrgorilla &Regenwald Direkthilfe; The John Ball Zoo; Leakey Foundation.

�Correspondence to: Jessica Ganas, Royal Society for theProtection of Birds and the Gola Forest Programme, Kenema,Sierra Leone. E-mail: jessica.ganas@rspb.org.uk

rr 2008 Wiley-Liss, Inc.

may influence feeding competition and habitatutilization, and which food species and habitatsshould be considered in management and conserva-tion efforts. Because the availability of a particularfood may influence whether it is consumed or not(independent of its nutritional content), determiningwhat nutrients are required by analyses of foodchoice and/or selectivity may not give a truerepresentation of an animal’s nutritional require-ments. For example, during periods of low foodavailability, animals may eat poor quality, but highlyavailable foods to subsist during lean times. Thus,preference is the most accurate method of investigat-ing the nutritional requirements of animals.

Although measures of food preference can beeasily conducted in captivity, many studies usuallyonly offer a few food options per experiment, whichdoes not represent what a wild animal experiences,especially for those species with a large dietaryrepertoire [Benz et al., 1992; Laska et al., 2000;Remis, 2002]. Furthermore, captive studies differfrom wild studies in that captive studies do notcontrol for the diet of the animals outside of the trials(which can influence what is preferred), whereasthose in the wild can take into consideration theentire diet. Conversely, food preference can bedifficult to measure in the wild because there isvariation in the availability among most food species,whereas preference controls for availability. How-ever, it is possible to estimate food preferences ofwild animals using indices that quantify diet choicesbased on relative equal availability [Ivlev, 1961;Johnson, 1980]. Although these are not measures ofchoices made by animals among equally availablefoods as with experiments, the index is the closestreflection of preference that is possible for wildanimals [Chesson, 1983; Johnson, 1980].

Gorillas (Western: Gorilla gorilla and Eastern:Gorilla beringei) are the largest extant primatespecies; they have an enlarged and highly ciliatedhindgut, which facilitates processing of some plantfiber for energy [Milton, 1984; Remis, 2000]. Theyconsume both foliage (nonreproductive plant partsfrom herbs, shrubs, and trees) and fruit to varyingdegrees depending on food availability [Ganas et al.,2004; Rogers et al., 2004; Watts, 1984]. Additionally,gorillas generally consume a particular part of aplant (i.e. leaves, pith, or bark) and not the entireplant, suggesting that they selectively consume theseparts for specific nutritional reasons.

Our knowledge of gorilla food preferences islimited. Food preference experiments on westerngorillas conducted in zoos (with fruits and vegeta-bles) found that preferred foods were relatively highin sugar and energy with moderate levels of tannins,with avoided foods having a relatively high proteincontent [Remis, 2002; Remis & Kerr, 2002]. Howeverthese studies did not take into account the regulardiet of the gorillas, which can influence which foods

are preferred during the trials. Furthermore, nopreference trials were conducted with foliage, astaple of gorilla diets. In a preference study of wildmountain gorillas in Rwanda, Vedder [1989] foundno correlation between food preference and levels ofprotein although research on another gorilla groupin the same population found that protein anddigestibility positively influenced food choice [Watts,1983]. To date, we lack information on the foodpreferences of wild gorilla groups that consume bothfruit and foliage and on the nutritional and phenolicattributes that are associated with these preferences.

Mountain gorillas (Gorilla beringei beringei) inBwindi Impenetrable National Park, Uganda, con-sume both foliage and fruit from a diversity of plantspecies and experience differences in food availabilitywithin and between locations in the park withcorresponding diet variability [Ganas et al., 2004].A study on food choice, which examined howconsumption was influenced by both the differingavailability of food and food nutritional content,found that Bwindi gorillas chose individual foodspecies based on their relatively high abundance,relatively high sugar contents, and relatively lowdigestion inhibitor contents [Ganas et al., 2008].

The goal of this study was to measure one facetof Bwindi gorilla foraging strategy, food preference[other aspects of their foraging behavior are treatedelsewhere; Ganas et al., 2008]. We asked thefollowing questions considering four gorilla groupsat two separate locations: (1) Which plant species dothe gorillas prefer? (2) Given the different plantparts consumed of these preferred species, whatnutrients and/or phenols characterize them? (3)Were there differences among groups in preference?We predicted that gorillas would prefer leaves andpith with relatively high protein and sugar contents,fruit with relatively high sugar, energy and/or fatcontents while avoiding fibers (cellulose, hemicellu-lose, lignin) and phenols (total phenols, total tannins,condensed tannins) in all food types. As animalnutritional requirements should be the same withina species, we also predicted that preferences wouldnot differ among groups, despite the fact that foodavailability differed among groups’ home ranges.

METHODS

Study Groups

Data on diet were collected from four habituatedgorilla groups from September 2004 to August 2005.Three groups, Mubare, Habinyanja, and Rusheguraranged around Buhoma (1,450–1,800 m). Becausegroups are used for an ecotourism program, theUganda Wildlife Authority limits direct contact withthese groups and we were not able to conduct directobservations. The fourth group, Kyagurilo, rangesnear Ruhija (2,100–2,500 m), and is habituated forresearch. Although direct observations are possible

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here, we used indirect methods on all groups forcomparative purposes. For details of the study sitesee McNeilage et al. [2001]. All research adhered tothe protocols and legal requirements in Uganda.

DietAll weaned individuals of a group make nests in

close proximity to one another to form a nest siteevery night. During the day, the gorillas move andfeed between each night’s nest location. During thistime, the gorillas create obvious trails by tramplingvegetation, discarding food, and defecating, whichfacilitates documentation of the animals’ daily diet.To quantify the frequency of herbs in the gorillas’diet, we followed each groups’ main trail on a dailybasis and recorded observations of each plant speciesremains left behind and plant part consumed (i.e.leaf, pith, peel [peel is the outer layer of an herb’sstem]). These trails and feeding spots are easy tofollow and easy to distinguish from other animalswith the assistance of experienced trackers.Although this method does not document the actualamount of food ingested, it is the best approach whenindirect means are necessary and is commonly usedin dietary studies of gorillas [Calvert, 1985; McNei-lage, 1995]. The monthly frequency of each plantspecies found on these trails was then calculated torepresent the relative percentage of foods in a diet[Frequency of species A 5 ] of feeding spots ofSpecies A/total number of feeding spots� 100%;following Calvert, 1985; McNeilage, 1995]. Wedefined ‘‘important’’ herb species as those occurringin Z1% frequency in any month. Although thegorillas eat other plant parts such as flowers andbark, these foods were relatively infrequently eaten.Therefore, these foods are likely not ‘‘important’’ inreference to macronutrient and phenol contents, thefocus of this study, and were not included in ouranalyses. Food species that were consumed infre-quently were likely eaten for other reasons such asmineral content or medicinal purposes and forstudies that focus on these components, it may beimportant to consider all foods eaten and usedifferent methodologies [Huffman, 1997; Rothmanet al., 2006]. Additionally, for each group there were0–3 plant species for which peel was important in thegorillas’ diet but owing to the small sample sizes, wewere unable to do additional analysis to determinewhich nutrients they preferred in peel. On average,we analyzed trail signs 19 days per month pergroup (Mubare monthly range 5 14–26; Habinyanjamonthly range 5 15–23; Rushegura range 5 17–25;Kyagurilo range 5 14–19).

To determine the frequency and species of fruitconsumed, we collected fecal samples from eachgroups’ night nests, and recorded whether thesample (based on size) was from a silverback, anadult 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 througha 1 mm sieve and seed species were identified.Important fruit species were defined as thoseoccurring in Z1% of samples per group in anymonth [modified from Ganas et al., 2004; Remis,1997]. Because gorillas in Bwindi have not been seento spit out seeds in over 8 years of observation andalso because the vast majority of fruits consumedduring this study period were relatively small andconsumed in their entirety [mean width of fruit 5

7.7 mm, Ganas, unpublished data; Robbins, personalobservation] we assumed that if the gorillas ate fruit,it would be detected in the fecal samples. There wereno differences in the frequency of fruit consumptionbetween age and sex classes (using a w2 test) and thusonly adult female samples were used in the analysis.Considering all groups, we collected an averageof 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; Rush-egura mean 5 23.8, monthly range 18–30; Kyagurilomean 5 26.9, monthly range 12–30).

Food Availability

TemporalWe measured the temporal biomass availability

of 20 herb species (Buhoma 5 18, Ruhija 5 11)considered important to the gorillas (see above) in89 1 m2 permanent plots (Buhoma 5 51, Ruhija 5 38)in the forest [Ganas et al., in press]. Plots wereestablished in areas of high herb density withinvarious areas of the gorillas’ range. An average of16.8 individual plants per species per month weremonitored in Buhoma (range 2.8–37.9, SD 5 11.5),whereas in Ruhija, an average of 24.2 individuals perspecies was monitored (range 8.5–60.1, SD 5 15.3).

To estimate the biomass of herbs, at approxi-mately the same time every month, for eachpermanent plot, we first took measurements ofparticular plant parts. Next, we harvested 40individuals of each species of varying lengths fromoutside the plots. For each plant, we measured thelength of the plant stem or counted the number ofleaves on the individual plant and recorded the wetweight of the part eaten by the gorillas. We thendried the plant parts in sheds with charcoal stoves.After they were dry, we again recorded the weight ofeach individual. To determine whether there was asignificant relationship between the length of thestem or the number of leaves and weight, we plottedlength/number of leaves against the weight (one testeach for wet and dry weight) and calculated a linearregression that was forced through the origin [Zar,1999]. We found a significant relationship betweenthese variables, and regression equations wereproduced that were used to calculate the biomass of

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each herb species in the plots for each month. Thisalso allowed us to calculate the monthly changes (%)in their temporal availability. For further details, seeGanas et al. [in press].

To calculate the temporal availability of fruit, ona biweekly basis within each location, we monitored397 trees and herbs of 40 species, 13 of which werefound 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 abundanceof ripe fruit in the crown scoring between zero andfour (0 5 0%, 1 5 1–25%, 2 5 26–55%, 3 5 51–75%,and 4 5 76–100% of crown covered) following Sunet al. [1996].

The fruits of Trichilia sp. were not previouslyknown to be consumed by gorillas and we didnot record phenological data on them and thisspecies was excluded from the analysis. Overall,Trichilia sp. was of low to medium importance forthe two groups analyzed (annual % frequency eaten:Mubare: 2.2%, Habinyanja 9.4%). Additionally, themajority of Ficus spp. (excluding F. capensis) wasstrangler figs and a fruit availability index (FAI)could not be calculated.

Spatial availabilityTo determine the spatial distribution of herbs

and fruit, we cut and measured 102 and 54 transectsof 200 m in length at Buhoma and Ruhija, respec-tively, placing one transect each within a 500 m2 gridoverlaid onto a map of each study location [Greig-Smith, 1983]. For each transect, we placed nestedquadrats (1 and 10 m2) on alternate sides in intervalsof 20 m for 10 quadrats (total transectlength 5 200 m) per transect. In these quadrats wedocumented herb biomass (using the same methodsfrom the permanent plots, length/] leaves) and tree(density and diameter at breast height) availability.

To then calculate the total biomass of each herbspecies in each home range per month, we appliedthe biomass estimates (regression equations) and thecorresponding monthly changes in biomass (%)recorded from the permanent plots to the measure-ments from the transects.

To determine fruit availability at each locationfor each biweekly period, a score of fruit abundancewas calculated using an FAI [following Nkurunungiet al., 2004; calculated as the product of the meanDBH (of phenology trees of each fruit species eatenby the gorillas), density of each species at eachlocation (recorded from the transects), and theirmean biweekly abundance score value from thephenology study]. To get a value for total fruitavailability for each location, we summed theindividual FAI scores for each biweekly period.

Nutritional Sampling

We collected 42 important food species (fruit,leaves, pith, peel) consumed by the gorillas at bothlocations. Fruit crops produced by the herb Rubus sp.were very small and samples could not be obtainedfor nutritional analysis (this fruit accounted for 1%yearly frequency in diet, and it was not consumed byevery group) All food items collected for analyseswere processed in a way that mimicked which partsthe gorillas consumed. For example, if the gorillasate the pith from a particular species, we collectedonly pith. Because indirect observations were madeor entire plants eaten, often it was difficult to collectplant parts from the exact tree or herb the part wasconsumed from. Nonetheless, every attempt wasmade to sample food items from the specific areaswhere the gorillas fed. We also made multiplecollections of plant species when possible by collect-ing them from different areas of the gorillas’ homerange (where they had been feeding) and collectedthem during two different wet seasons, mixingsamples before analysis. Owing to the difficulty ofcollecting fruit from tall canopy trees, most fruitsamples came from a single tree or location. Becauseof the short-term availability of fruits in Bwindi,fruit was sampled once when available.

We stored samples in cryo tubes, froze them inliquid nitrogen, and then freeze dried them. Driedsamples were then stored in a cool, dry place untilthey were sent to the Institute of Zoo and WildlifeResearch for nutritional analysis and the Universityof Hohenheim for phenolic determination.

Phytochemistry Analyses

All samples were ground before analysis using a1 mm screen. Dry matter (DM) content was deter-mined by drying a portion of the sample at 1051Covernight. All data are given as % DM. Samples wereanalyzed for the following macronutrients usingstandard techniques: Nitrogen was determined bycomplete combustion (Dumas combustion) at hightemperature (about 9501C) in pure oxygen, using aRapid N III analyzer (Elementar Analyser SystemeGmbH, Hanau, Germany) and a factor of 6.25 wasused for conversion into protein (crude protein(%DM) 5 6.25�N (%DM)). Starch, D-glucose, D-fruc-tose, and sucrose were determined with commercia-lized enzymatic tests (UV method; R-Biopharm AG,Darmstadt, Germany). Lipids were extracted withethyl ether using a fully automatic Soxhlett system(Soxtherm; Gerhardt Laboratory Systems, Konigs-winter, Germany), and gross energy was determinedby burning a sample of DM in pure oxygen atmo-sphere in a bomb calorimeter (C5003 bomb calori-meter; IKA-Werke GmbH & Co. KG, Staufen,Germany). The heat produced is measured in kJ/gDM. Detergent Fiber Analysis was performed follow-ing van Soest [1991] with neutral detergent fiber

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(NDF), acid detergent fiber (ADF), and acid deter-gent lignin (ADL) being determined sequentiallyfrom each sample using an Ankom Fiber Analyser220 (Ankom Technology, Macedon, NY). Hemicellu-lose (NDF–ADF) and cellulose (ADF–ADL) werecalculated by weight difference. Total phenols weredetermined according to Makkar et al. [1993].Determination of condensed tannins followed Porteret al. [1986].

Statistical Analyses

To reduce the large number of macronutrientand phenolic variables tested per herb and fruitspecies (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, totalphenols, total tannins, condensed tannins), we firstinspected correlations between them and in the caseof two variables being highly correlated to oneanother (absolute correlation coefficient40.75) weremoved one of them (they included starch, fructose,glucose, lignin, ADF, energy, total tannins). The fruitand herb data sets were treated separately as thenutritional and phenolic components significantlydiffered between the two food groups [Ganas et al., inpreparation]. The individual remaining variableswere then subjected to a principal componentsanalysis (PCA). Reducing variables by using correla-tion tests and PCAs decreases the possible instabilityin results of subsequent analyses of data sets thatconsist of large number of correlating variables. Italso removes redundant information resulting in amore stable result. Even if some of the variables thatare put into the PCA (and group onto the sameprincipal component) are related in some way (i.e. fatcontent and energy content) putting these variablesinto a PCA is valid [Field, 2005]. In the case of aprincipal component where only a single variable hadits highest loading, we reran the PCA without thatvariable and included the variable directly intosubsequent analyses. Both the fruit and herb PCAswere justified [Kaiser–Maier–Olkin measure of sam-pling adequacy: herbs: 0.48; fruit: 0.64; Bartlett’stest 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 fruitprincipal components (with eigenvalues in excess ofone), which together explained 71.2% (herb) and68.2% (fruit) of the total variance. The loadings forthe three components for herbs were (a) NDF,hemicellulose, cellulose, condensed tannin, (b) pro-tein, fat, total phenols, and (c) WSC and sucrose(Table I(A)). The loadings for the two components forfruit 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 thanas a component because it grouped on its own inthe PCA.

We calculated preference scores for each foodspecies and part consumed (fruit, leaves, and pith)using Ivlev’s electivity index, which is a measure offoraging behavior in relation to food availability[Ivlev, 1961]. Traditionally it is used to determinewhether a food species is consumed proportionally toits availability. It can also be used as a relativemeasure of preference by considering all foodspecies eaten and then ranking them according toboth frequency in the diet and their correspondingavailability [Johnson, 1980]. By ranking foods ratherthan using absolute values, it circumvents theproblem that arises from accurately measuring rarefoods [Johnson, 1980; Lechowicz, 1982]. Althoughpreference measures should control for relativeavailability, owing to the nature of this index,availability still somewhat influences preferencescores. For example, species that have a relativelyhigh abundance may not ever be considered highlypreferred regardless of how much is consumed, andvery rare foods will often be considered highlypreferred [Johnson, 1980; Maitland, 1965].Lechowicz [1982] reviewed a variety of preferenceindices, examining the pros and cons of eachand determined that the majority provided usefulmeasures of feeding preference, including the oneused here. Despite some limitations, we choseIvlev’s electivity index because it works best withlarge sample sizes, it has been used often byother authors [Vedder, 1989; Watts, 1984], andit is one of the most appropriate indices availablefor wild animals [Chesson, 1983; Johnson, 1980;Lechowicz, 1982].

To calculate the index for each biweekly (fruit)or monthly (herb) period, a rank was assigned forboth diet frequency and food availability of eachspecies, resulting in 12 (herb) or 24 (fruit) preferencescores for each species. Ranks were between 1 andthe highest number of fruit or herb species available(considering only those that were consumed) in thetime period. The greater the diet frequency/avail-ability, the higher the rank score was. For speciesthat shared the same availability or diet frequencyscore, tied ranks were assigned.

Based on these ranks, we then calculatedpreference using the formula: Ivlev’s electivityindex 5 (rd�na)/(rd1na), where rd 5 rank of food itemin diet na 5 rank of food item in the home range.Scores between �1 and 0 indicated that a food wasnot preferred, a score of 0 signified neutrality, and ascore between 0 and 1 indicated that it waspreferred. Rather than creating categories of ‘‘highlypreferred’’ or ‘‘medium preferred’’’ that are notrooted in biological meaning, food preference scoresshould simply be viewed on a relative continuumwhere one is regarded as more preferred than

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another based on its preference score. For most fruitspecies, there were periods when they were unavail-able and thus no preference score was calculated forthese time periods. Because we were only able toobtain both nutritional and availability data for 40%of the fruit species eaten by Rushegura and becauseduring the study period Kyagurilo group only ate 1–2species of fruit per time period, we did not analyzethese groups’ fruit preferences.

To determine what nutritional and phenolicattributes characterized preferred foods, we corre-lated each species preference scores for each timeperiod with its nutrient and phenolic composition(the PCA factor scores; each plant part has the samePCA factor score during each biweekly/monthlyperiod as we sampled each plant species once, butthe diet frequency values differ per period) using aPearson’s correlation test. A correlation coefficientbetween 0 and 1 indicated preference for a particularnutrient/phenol, and a coefficient between �1 and 0indicated that it was not preferred. We then used aone-sample t-test to determine whether thesemonthly correlation coefficients on average, equaledzero (a significant result means that they were notand thus a significant correlation). We controlled formultiple testing by using a Fisher’s Omnibus test;

one test for the herb preferences (leaves, pith) andone for fruit. We used two separate tests owing to thetwo different sets of PCA analyses. Analyses wereperformed using SPSS 13.0.

RESULTS

Plant Species Preferences

Herb foods that had relatively high preferencescores (40.35) were Aframomum angustifoliapith, Aframomum sanguinum pith, Basella albaleaves, Desmodium repandum leaves, Ipomea wightiileaves, Mormodica calantha leaves, and Palisotamannii pith. Fruit that had relatively highpreference scores (40.20) were Ficus capensis andPrunus africana.

Species that were highly abundant, which mayhave resulted in low preference scores, includedCassine aethiopica, Mimulopsis solmsii, and Mimu-lopsis arborescens. There were no major differencesamong groups in these scores. For individual foodspecies preference scores, see Tables II and III.

Nutritional and Phenolic Characteristics ofPreferred Foods

A Fisher’s Omnibus test confirmed that thefollowing results are not simply owing to chance(w2 5 148.4, df 5 24, Po0.001).

LeavesAll groups significantly preferred leaves rela-

tively 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; Rushegurat 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 avoidedfiber (Mubare t 5�12.6, df 5 10, Po0.001; Habi-nyanja t 5�23.2, df 5 10, Po0.001; Rushegurat 5 16.8, df 5 10, Po0.001; Kyagurilo t 5�20.9,df 5 11, Po0.001).

Habinyanja, Rushegura, and Kyagurilo pre-ferred leaves relatively high in sugar, whereasMubare 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; Kyagurilot 5 2.9, df 5 11, P 5 0.02).

PithMubare preferred pith relatively high in protein,

whereas the other groups did not (Table IV; Mubaret 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; Habi-nyanja t 5 3.6, df 5 10, P 5 0.005, Rushegura t 5 4.8,df 5 10, P 5 0.001).

TABLE I. Results of the Principal ComponentsAnalyses on the (A) herb and (B) fruit nutrient andphenolic values

TraitComponent

1Component

2Component

3

(A)Protein 0.135 0.618 �0.597WSC �0.324 0.195 0.773Sucrose 0.002 �0.067 0.719NDF 0.837 �0.412 �0.091Hemicellulose 0.549 0.071 �0.184Lignin 0.766 0.269 �0.418Fat �0.156 0.816 0.299Total phenols 0.172 0.857 �0.152Condensed tannins 0.732 0.139 0.458Eigenvalue 2.77 2.09 1.55% variance explained 30.8 23.2 17.2

(B)Sucrose 0.160 �0.755WSC �0.857 �0.157Fat 0.441 0.762Energy 0.345 0.636Lignin 0.596 0.581NDF 0.862 0.069Eigenvalue 3.03 1.06% variance explained 50.5 17.8

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 componentsderived. Bold values indicate the largest absolute loading per variable.

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TABLE II. For each group, the diet frequency, the corresponding yearly average preference rank, foodavailability, its corresponding average yearly rank, and preference scores of each herb species used in thepreference analysis

Group Plant speciesParteaten

Yearly dietfreq. (%)

Avg. dietrank (SD)

Medianavail. g/m2

Avg. avail.rank (SD) ] mo.

Avg. pref.score

MubarePalisota mannii Pith 5.1 13.1(4.7) 0.02 4.9(1.9) 11 0.45Desmodium repandum Leaves 0.3 3.5(2.6) 0.003 1.1(0.4) 11 0.43Ipomea wightii Leaves 4.2 12.8(4.0) 0.05 5.5(2.4) 11 0.41Basella alba Leaves 13.5 17.9(7.0) 0.10 8.4(3.7) 11 0.37Aframomum angustifolia Pith 1.7 7.9(3.2) 0.01 3.3(1.2) 12 0.36Mormodica calantha Leaves 1.7 8.9(3.7) 0.05 5.2(2.6) 11 0.26Rubus sp. Leaves 0.3 4.6(2.2) 0.08 2.8(1.4) 11 0.21Laportea aestuans Peel 6.6 15.0(4.5) 0.12 11.4(3.5) 11 0.13Mimulopsis arborescens Pith 12.4 17.5(5.2) 1.9 16.9(4.9) 11 0.02Urera sp. Peel 7.4 15.1(4.6) 0.22 14.5(4.3) 11 0.02Triumfetta sp. Leaves 9.9 16.7(5.1) 5.4 17.2(5.1) 11 �0.01Aframomum sp. Pith 0.9 6.2(3.0) 0.04 7.5(2.2) 12 �0.13Mimulopsis solmsii Leaves 3.0 11.0(3.5) 4.5 18.2(5.5) 11 �0.25Pennisetum purpureum Pith 0.9 4.8(4.1) 0.04 7.2(3.8) 11 �0.25Ipomea sp. Leaves 1.8 8.8(5.1) 2.4 15.5(7.2) 11 �0.30Laportea aestuans Leaves 0.9 5.9(3.3) 0.12 11.4(3.5) 11 �0.35Gouania longispicata Leaves 0.7 4.8(3.2) 0.31 9.4(4.5) 11 �0.36Urera sp. Leaves 0.8 5.9(3.2) 0.22 14.5(4.3) 11 �0.45Mormodica foetida Leaves 0.9 4.2(2.9) 0.48 11.2(3.8) 11 �0.48Piper capense Pith 0.7 — — — 11 —Aframomum sanguinum Pith 1.5 — Not detected — 12 —

HabinyanjaAframomum angustifolia Pith 0.7 6.8(3.1) 0.003 1.4(0.7) 12 0.59Ipomea wightii Leaves 3.7 13.3(4.3) 0.10 5.0(2.2) 11 0.46Basella alba Leaves 10.0 17.5(5.1) 0.03 7.0(2.5) 11 0.43Mormodica calantha Leaves 2.1 11.0(4.0) 0.02 5.0(2.2) 11 0.39Desmodium repandum Leaves 0.4 5.3(3.6) 0.003 1.8(1.0) 11 0.38Palisota mannii Pith 6.0 15.0(4.9) 0.03 8.2(2.8) 11 0.29Rubus sp. Leaves 0.3 4.8(3.4) 0.05 3.8(1.5) 11 0.03Urera sp. Peel 5.4 14.6(4.4) 0.10 13.7(4.4) 11 0.03Mimulopsis arborescens Pith 16.0 18.3(5.4) 2.4 17.9(5.2) 11 0.01Mormodica foetida Leaves 1.2 7.2(3.8) 0.12 6.8(3.0) 11 0.01Laportea aestuans Peel 5.4 14.3(4.8) 0.20 14.1(4.3) 11 0Gouania longispicata Leaves 0.7 7.8(4.1) 0.16 9.0(4.2) 11 �0.09Triumfetta sp. Leaves 5.0 13.8(4.3) 4.8 18.0(5.4) 11 �0.13Aframomum sp. Pith 1.1 7.5(3.7) 0.05 9.8(2.6) 12 �0.18Mimulopsis solmsii Leaves 2.3 8.1(3.2) 3.2 17.5(4.4) 11 �0.21Laportea aestuans Leaves 0.8 6.8(3.2) 0.2 14.1(4.3) 11 �0.37Pennisetum purpureum Pith 0.3 3.5(3.6) 0.02 7.0(3.2) 11 �0.39Urera sp. Leaves 0.6 5.4(2.7) 0.10 13.7(4.4) 11 �0.45Ipomea sp. Leaves 0.7 4.8(3.0) 1.01 14.8(6.8) 11 �0.53Ipomea sp. Peel 0.7 4.1(3.1) 1.01 14.8(6.8) 11 �0.60Aframomum sanguinum Pith 1.2 — Not detected — 12 —Piper capense Pith 1.4 — — — 11 —

RusheguraAframomum sanguinum Pith 0.9 5.0(4.3) 0.002 1(0) 12 0.54Mormodica calantha Leaves 2.6 9.9(4.2) 0.03 2.9(1.9) 11 0.50Basella alba Leaves 10.5 15.5(5.0) 0.13 7.3(2.7) 11 0.36Aframomum angustifolia Pith 1.0 6.0(2.8) 0.01 2.9(0.9) 12 0.29Ipomea wightii Leaves 4.7 11.9(4.6) 0.16 8.5(3.4) 11 0.16Palisota mannii Pith 4.4 12.5(4.4) 0.07 10.3(3.7) 11 0.10Laportea aestuans Peel 7.4 14.4(5.0) 0.16 12.4(3.9) 11 0.07Rubus sp. Leaves 0.2 4.1(2.9) 0.17 4.0(1.6) 11 0.05Urera sp. Peel 7.2 13.3(4.3) 0.06 12.3(4.5) 11 0.05Aframomum sp. Pith 1.8 7.1(4.6) 0.03 5.7(1.5) 12 0.04Mimulopsis arborescens Pith 22.8 17.2(5.1) 4.2 16.9(4.9) 11 0.01Ipomea sp. Leaves 0.7 6.1(3.2) 0.16 6.8(4.4) 11 �0.02

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Gorilla Food Preferences / 933

FruitA Fisher’s Omnibus test confirmed that the

following results were not owing to chance (w2 5 51.7df 5 16, Pr0.001).

For the two groups tested, the Mubare grouppreferred fruits with a relatively large NDF andlignin/WSC ratio, whereas the Habinyanja group didnot (Table IV; t 5 2.8, df 5 19, P 5 0.011; Habinyanjat 5�1.6, df 5 19, P 5 0.13). Neither group signifi-cantly preferred fat and energy/sucrose in fruit overthe year (Table IV). Furthermore, periods in whichthere was a positive correlation between fruitpreference and fat and energy/sucrose were mostlyduring the times that NDF and lignin/WSC was notpreferred. This could indicate that this grouppreferred some type of energy source every month.

The Mubare group preferred fruits with con-densed tannin, whereas the Habinyanja group didnot (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 gorillanutritional requirements, found that although thegorillas preferred some plant species over others,

these preferences were related to particular nutrientsand phenols. Groups generally preferred leaves withrelatively high levels of protein, fat, phenols andsugar and low amounts of fiber, and pith withrelatively high amounts of sugar. Fruit preferenceswere less clear, and the result that one group ofgorillas preferred digestion inhibitors (condensedtannins and fiber) was possibly owing to simulta-neously ingesting relatively high amounts of sugar.Despite differences in spatial and temporal variabilityin food availability among gorilla home ranges, therewere no large differences in either preference for aparticular plant species or preference for particularnutrients and phenols among gorilla groups.

All groups preferred leaves with relatively highamounts of protein, fat, and phenols and relativelylow amounts of fiber and condensed tannins, con-curring with other studies of primate herbivores,which found that the protein/fiber ratio is animportant component of their foraging strategy[Chapman et al., 2004; Ganzhorn, 1992; Milton,1979; Oates et al., 1990]. Curiously, these resultsdiffer from similar work conducted on mountaingorilla preference in Rwanda (using the samemethodology as this study), which found no relation-ship between preference and protein [Vedder, 1989].

TABLE II. Continued

Group Plant speciesParteaten

Yearly dietfreq. (%)

Avg. dietrank (SD)

Medianavail. g/m2

Avg. avail.rank (SD) ] mo.

Avg. pref.score

Mormodica foetida Leaves 1.0 6.0(3.1) 0.26 7.1(3.7) 11 �0.08Triumfetta sp. Leaves 5.8 13.0(4.5) 6.8 15.5(4.6) 11 �0.09Gouania longispicata Leaves 0.4 4.7(3.1) 0.22 7.0(3.6) 11 �0.22Ipomea sp. Peel 0.5 3.9(3.6) 0.16 6.8(4.4) 11 �0.29Urera sp. Leaves 0.7 5.7(2.5) 0.06 12.3(4.5) 11 �0.41Laportea aestuans Leaves 0.5 4.5(2.2) 0.16 12.4(3.9) 11 �0.48Desmodium repandum Leaves 0.4 — Not detected — — —Pennisetum purpureum Pith 0.9 — Not detected — — —Piper capense Pith 0.9 — — — 11 —

KyaguriloMormodica calantha Leaves 2.6 12.3(1.3) 0.2 1.8(0.9) 12 0.75Basella alba Leaves 7.5 8.3(1.8) 0.24 3.1(0.9) 12 0.45Cardus sp. Stalk/pith 1.6 3.6(1.3) 0.08 3.0(2.7) 12 0.20Rubus sp. Leaves 5.3 7.0(1.5) 1.0 5.2(0.8) 12 0.14Ipomea sp. Leaves 8.9 9.7(2.2) 0.15 7.4(0.8) 12 0.12Urera sp. Peel 11.2 11.3(2.2) 0.2 9.3(0.6) 12 0.08Mimulopsis arborescens Pith 14.6 12.0(2.8) 1.2 11.3(0.5) 12 0.01Mimulopsis solmsii Bark 11.1 11.4(1.9) 6.2 13.5(0) 12 �0.09Piper capense Pith 0.8 2.9(0.9) 0.91 4.0(1.9) 12 �0.11Triumfetta sp. Leaves 7.8 9.3(1.8) 2.1 11.7(0.5) 12 �0.12Mimulopsis solmsii Leaves 7.1 8.6(2.4) 6.2 13.5(0) 12 �0.24Urera sp. Leaves 2.3 4.5(1.2) 0.2 9.3(0.6) 12 �0.36Ipomea sp. Peel 1.2 3.0(3.1) 0.15 7.4(0.8) 12 �0.44Mormodica foetida Leaves 0.1 1.2(0.4) 0.8 4.4(1.4) 12 �0.57

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, themedian availability for L. aestuans peel includes the biomass from the leaves and peel, and vice versa. ‘‘Not detected’’ means that during the measurementsof 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 ratherthan 12, and was thus considered available every month of the study.

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Because the same methods were used in both studies,it is unknown why these differences occurred. Onepossibility could be that because mountain gorillas in

Rwanda consume only a small number of highlyabundant species, the limitations of Ivlev’s electivityindex could have contributed to this result.

TABLE III. For each group, the diet frequency, the corresponding yearly average preference rank, foodavailability, its corresponding average yearly rank, and preference scores of each fruit species used in thepreference analysis

Group Plant species Yearly diet freq. (%) Avg. diet rank (SD) Avg. avail. Avg. avail. rank ] mo. Avg. pref. score

MubarePrunus africana 1.8 3.0(1.0) 0.22 1.0(0.3) 1.5 0.47Ficus capensis 5.1 2.0(0.9) 10.5 1.2(0.4) 12 0.20Myrianthus holstii 29.5 2.6(1.1) 400.2 2.9(0.9) 12 �0.06Syzygium guineense 19.6 1.8(1.0) 52.0 2.0(1.1) 5 �0.08Cassine aethiopica 34.6 2.7 (1.0) 996.1 3.4(0.9) 9.5 �0.13

HabinyanjaFicus capensis 2.4 2.3(0.9) 8.5 1.1(0.4) 12 0.29Prunus africana 20.5 2.5(1.0) 1.0 2.0(0.7) 1.5 0.10Syzygium guineense 20.8 3.4(1.9) 46.9 3.1(1.6) 5 0.02Maesa lanceolata 1.0 1.7 (0.6) 23.3 2.0(0.6) 10.5 �0.03Cassine aethiopica 35.4 3.6(1.2) 1156.8 4.3(1.1) 9.5 �0.08Myrianthus holstii 33.3 3.2(1.3) 392.7 3.8(0.8) 12 �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 ofthe mean DBH (of phenology trees of fruits eaten by the gorillas), density of those species in the gorillas’ range, and their mean monthly abundance scorevalue from the phenology for each species). The average preference score is the average of the biweekly periods that fruit was available. ] mo 5 number ofmonths fruit species was available during the study period.

TABLE IV. Nutritional and phenolic attributes of preferred foods/plant parts (individual species preferencescores [per plant part] compared with their nutrient compositions)

Plant part Group PCA Component Avg Rho SD Range

Leaf Mubare Protein, fat, total phenols 0.30 0.20 0.04–0.60Leaf Habinyanja Protein, fat, total phenols 0.34 0.23 0.07–0.68Leaf Rushegura Protein, fat, total phenols 0.45 0.29 0.06–0.82Leaf Kyagurilo Protein, fat, total phenols 0.36 0.20 0.14–0.57Leaf Mubare WSC and sucrose 0.03 0.12 �0.19–0.32Leaf Habinyanja WSC and sucrose 0.17 0.14 �0.02–0.35Leaf Rushegura WSC and sucrose 0.18 0.21 �0.22–0.77Leaf Kyagurilo WSC and sucrose 0.09 0.09 �0.07–0.21Leaf Mubare NDF, hemicellulose, cellulose, CT �0.35 0.19 �0.47–0.16Leaf Habinyanja NDF, hemicellulose, cellulose, CT �0.68 0.36 �0.82–0.54Leaf Rushegura NDF, hemicellulose, cellulose, CT �0.58 0.30 �0.7–0.43Leaf Kyagurilo NDF, hemicellulose, cellulose, CT �0.72 0.38 �0.89–0.50Pith Mubare Protein, fat, total phenols 0.07 0.06 �0.03–0.17Pith Habinyanja Protein, fat, total phenols 0.02 0.14 �0.24–0.27Pith Rushegura Protein, fat, total phenols 0.07 0.14 �0.04–0.33Pith Mubare WSC and sucrose 0.14 0.01 0.04–0.30Pith Habinyanja WSC and sucrose 0.20 0.16 0.06–0.43Pith Rushegura WSC and sucrose 0.36 0.25 �0.24–0.59Pith Mubare NDF, hemicellulose, cellulose, CT 0.14 0.16 �0.1–0.5Pith Habinyanja NDF, hemicellulose, cellulose, CT 0.10 0.13 �0.2–0.5Pith Rushegura NDF, hemicellulose, cellulose, CT �0.05 0.28 �0.89–0.5Fruit Mubare NDF and lignin/WSC 0.28 0.44 �0.32–0.74Fruit Habinyanja NDF and lignin/WSC �0.17 0.46 �0.63–0.46Fruit Mubare Fat and energy/sucrose 0.05 0.47 �0.8–0.74Fruit Habinyanja Fat and energy/sucrose 0.15 0.51 �0.5–1Fruit Mubare Condensed tannin 0.28 0.44 �0.32–0.74Fruit Habinyanja Condensed tannin 0.18 0.46 �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 andrange 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 witheach biweekly or monthly period correlation coefficient as a data point) is indicated in bold. WSC, water soluble carbohydrates; NDF, neutral detergentfiber.

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Gorilla Food Preferences / 935

One of the most interesting and novel results ofthis study was that the gorillas preferred leaves andpith of herbs that contained relatively high amountsof sugar. Similarly, sugar also influenced the choiceof herbs [Ganas et al., 2008]. Although the averagesugar content of leaves is relatively low comparedwith other plant parts (0.2–1.3% DM; Table V), asleaves constitute the greatest amount of wet massintake to the diet, at least for the Kyagurilo group[Rothman et al., 2007]; sugar intake from leavescould be substantial. For herbivores that live inhabitats where fruit availability is low or nonexis-tent, sugar in nonfruit plant parts may provide arequired nutrient previously not associated with thistype of vegetation [Danish et al., 2006]. Sugar indifferent foliage parts, as well as fruit, may enablethe gorillas to be flexible and to exploit a variety offoods and habitat types when trying to fulfil nutri-tional requirements.

Surprisingly, the Mubare group preferred fruitwith relatively high condensed tannin amounts.However, this appears to be largely driven by thefruit of Myrianthus holstii, which contains muchgreater amounts of condensed tannin that otherfruits [Ganas et al., in preparation]. Perhaps con-suming fruit that contains a relatively high amountof condensed tannin is sometimes necessary to ingestsubstantial amounts of sugar. Other studies havealso found that some animals tolerate relatively hightannin amounts in food if that food is consumedinfrequently or the food’s consumption allowed aconcurrent ingestion of a relatively high nutrientamount [Oates et al., 1980; Remis and Kerr, 2002].To better understand the importance of sugar infruit to Bwindi gorillas, we compared the sugar

contents of the top five fruits consumed with fivehighly available, but avoided fruits (selectivity) andfound that consumed fruits contained significantlymore sugar than those not consumed [Ganas et al., inpreparation]. This example highlights the impor-tance of examining different measures of an animal’sforaging behavior to fully understand their foragingstrategy.

Owing to the nature of Ivlev’s electivity index(foods relatively high in availability can rarely scorehigh preference scores or rare foods usually scorehigh preference scores), the preference scores forsome highly abundant foods may be biased comparedwith experimental studies of food preference. Forexample, widely abundant foods such as C. aethiopi-ca (fruit) and M. solmsii (foliage) scored as avoided orneutral, despite their high frequency in the diet(Tables II and III). Therefore, it is important toindividually examine preference scores of speciesthat are of high availability (or rare) to determinewhether their preference scores could be simplyowing to this inherent limitation of this index. Forexample, C. aethiopica fruits were not preferred; yettheir nutrient profile typifies what a high-qualityfruit contains (relatively high amounts of sugar),suggesting that these fruits may be consumed fortheir sugar contents.

Our preference results differed in some aspectsfrom our concurrent research on food choice, whichmeasured how the fluctuating availability of foods aswell as the nutrient and phenolic composition influ-enced what foods were eaten. In that study, the year-round availability of herbs high in protein led to theresult that protein did not influence the choice of herbfoods [unlike this study, which showed that protein is

TABLE V. The average and standard deviations (represented in parenthesis after the average) of the nutritionaland phenolic contents (% dry matter) of important plant parts used in the preference tests

Buhoma Ruhija

Leaves n 5 11 Pith n 5 7 Peel n 5 2 Fruit n 5 5 Leaves n 5 8 Pith n 5 3 Peel n 5 2 Fruit n 5 4

PT 26.6 (3.8) 10.4 (2.2) 13.0 (0.6) 8.1(2.8) 24.7 (2.9) 7.8 (2.3) 12.5 (5.6) 9.4(3.4)ST 2.6 (2.1) 1.8 (2.6) 0.7 (0.5) 6.3(12.3) 3.6 (2.0) 2.4 (3.8) 0.5 (0.2) 3.0(5.6)FC 1.1 (0.9) 9.5 (5.2) 0.4 (0.1) 7.5(8.6) 1.3 (1.3) 1.2 (1.0) 1.0 (0.5) 10.2(5.7)GC 0.9 (0.7) 9.2 (3.8) 0.3 (0.1) 7.1(7.9) 1.2 (1.4) 1.8 (1.4) 0.5 (0.3) 6.3(2.7)SC 0.3 (0.2) 0.9 (0.3) 0.1 (0.1) 1.5(2.5) 0.2 (0.4) 0.2 (0.1) 0.3 (0.5) 0.05(0.1)NDF 35.3 (8.1) 38.3 (6.5) 61.6 (0.8) 32.7(13.0) 41.4 (10.0) 41.4 (15.7) 55.1 (8.6) 25.1(9.2)ADF 17.3 (5.6) 23.1 (3.4) 52.3 (0.8) 20.1(8.3) 16.7 (4.7) 31.2 (12.9) 37.0 (2.1) 16.8(7.8)LN 4.4 (2.6) 1.5 (1.4) 8.1 (7.0) 7.5(4.8) 4.4 (2.6) 4.8 (5.9) 8.1 (5.4) 7.0(3.4)CL 12.9 (3.3) 21.6 (2.3) 44.1 (7.8) 12.7(4.4) 12.3 (3.1) 26.3 (7.0) 28.9 (7.5) 9.8(5.1)HC 18.0 (5.7) 15.2 (4.9) 9.3 (2.6) 12.6(8.9) 24.6 (7.9) 10.3 (2.9) 18.1 (6.5) 8.3(2.5)FT 1.9 (0.7) 1.3 (0.7) 0.6 (0.4) 6.1(7.2) 0.9 (0.6) 2.7 (3.3) 2.1 (2.0) 7.5(11.0)EN 19.6 (1.3) 15.1 (0.8) 16.3 (0.5) 18.8(2.1) 18.7 (1.2) 13.6 (1.3) 18.1 (1.7) 20.2(2.1)TP 4.4 (3.3) 0.7 (0.4) 1.0 (0.1) 4.2(3.6) 3.5 (4.1) 1.2 (0.4) 1.0 (0.5) 4.0(4.7)TT 3.3 (3.0) 0.3 (0.4) 0.7 (0.01) 3.6(3.7) 2.7 (3.8) 0.6 (0.1) 0.5 (0.3) 2.7(2.9)CT 1.0 (1.9) 0.4 (0.5) 0.7 (0.2) 4.4(4.5) 0.3 (0.7) 0.1 (0.001) 0.03 (0.01) 5.1(7.2)

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.

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preferred, and thus is nutritionally important; Ganaset al., 2008, in press]. Together, these results showthat protein is important to Bwindi gorillas, but thatthey do not need to specifically seek it out owing to itshigh availability. These studies demonstrate theimportance of calculating both food preference (todetermine nutritional requirements) and food choice(to determine which factors influence the consumptionof particular foods in a variable environment) to betterunderstand an animals’ foraging strategy.

From a broader perspective, although preferredfood species are found in various habitat types [i.e.open, mixed, regenerating, swamp forests; Nkuru-nungi et al., 2004], which likely plays a role in gorillahabitat use, open forest at both study locations inBwindi contributed the greatest proportion of herbbiomass [considering gorilla food; Ganas et al., inpress], in both locations and contained manypreferred foods [Basella alba, Ipomea spp. Mormo-dica calantha, etc.; Ganas et al., in press]. Therefore,in terms of mountain gorilla conservation, openforests can be considered a habitat that should beprioritized for conservation efforts.

Overall, our results on food preference are notatypical of what we would expect for gorillas.However, the ways in which the gorillas fulfil theserequirements, through a combination of differentplant parts, shed new light on how gorillas canbalance their diet in a variable environment. Theseresults, together with our concurrent study of foodchoice, tell us the following information about theBwindi gorilla foraging strategy: First, Bwindi gor-illas need protein in their diets, but owing to the year-round high availability of herbs high in protein, it iseasy to fulfil this requirement. Second, sugar is alsoimportant to the gorillas’ diet, and gorillas can eatfruit, pith, and/or leaves to obtain this nutrient.These studies underscore the importance of investi-gating the different facets of feeding behavior,nutrition, and food availability to get a comprehen-sive understanding of an animals’ foraging strategy.

ACKNOWLEDGMENTS

We thank the Uganda Wildlife Authority and theUganda National Council of Science and Technologyfor permission to conduct this research. All researchcomplied with animal care regulations and nationallaws. We appreciate the work of our field assistantswho are too numerous to name. Further thanks toBosco Nkurunungi, Alastair McNeilage, RobertBarigira, the Institute of Tropical Forest Conserva-tion, Paul Kakende and the Biochemistry depart-ment at Makerere University, Heidrun Barleben,and the lab of Dr. Klaus Becker. This articlebenefited from statistical assistance from RogerMundry as well as comments to previous versionsby Oliver Schulke, Joanna Lambert, Shelly Masi,Colin Chapman, and four anonymous reviewers.

This research was funded by the Max Planck Society,Berrgorilla & Regenwald Direkthilfe, The John BallZoo, and the Leakey Foundation.

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