diet traditions in wild orangutans

Diet Traditions in Wild Orangutans

Meredith L. Bastian,1,2 Nicole Zweifel,3 Erin R. Vogel,4 Serge A. Wich,5,6

and Carel P. van Schaik1,3*

1Department of Evolutionary Anthropology, Duke University, Durham, NC 27708-03832Gunung Palung Orangutan Project, Ketapang 78851, Kalimantan Barat, Indonesia3Anthropologisches Institut & Museum, Universitat Zurich, CH-8057 Zurich, Switzerland4Center for the Advanced Study of Hominid Paleobiology, Department of Anthropology,George Washington University, Washington, DC 200525Great Ape Trust of Iowa, Des Moines, IA 503206Behavioural Biology, Utrecht University, 3508 Utrecht, The Netherlands

KEY WORDS culture; food selection; geographic variation

ABSTRACT This study explores diet differencesbetween two populations of wild Bornean orangutans(Pongo pygmaeus wurmbii) to assess whether a signal ofsocial learning can be detected in the observed patterns.The populations live in close proximity and in similarhabitats but are separated by a river barrier that is im-passable to orangutans in the study region. We found a60% between-site difference in diet at the level of plantfood items (plant species–organ combinations). We alsofound that individuals at the same site were more likelyto eat the same food items than expected by chance.These results suggest the presence of diet (food selection)traditions. Detailed tests of three predictions of threemodels of diet acquisition allowed us to reject a modelbased on exclusive social learning but could not clearly

distinguish between the remaining two models: one pos-iting individual exploration and learning of food itemselection and the other one positing preferential sociallearning followed by individual fine tuning. We knowthat maturing orangutans acquire their initial dietthrough social learning and then supplement it by yearsof low-level, individual sampling. We, therefore, concludethat the preferential social learning model produces thebest fit to the geographic patterns observed in this study.However, the very same taxa that socially acquire theirdiets as infants and show evidence for innovation-basedtraditions in the wild paradoxically may have diets thatare not easily distinguished from those acquired exclu-sively through individual learning. Am J Phys Anthropol143:175–187, 2010. VVC 2010 Wiley-Liss, Inc.

Recent field studies of nonhuman primates haverevealed geographic patterns in population-specific forag-ing techniques and social signals that have been inter-preted in terms of socially learned innovations (Whitenet al., 1999; Boesch, 2003; Perry et al., 2003a,b; vanSchaik et al., 2003; but see Laland and Janik, 2006).These field studies receive strong support from experi-mental laboratory studies demonstrating the presence ofobservational forms of social learning needed to acquirethese innovations (e.g., Whiten et al., 2005) and fromobservational field studies that are consistent with theoperation of such processes (Lonsdorf et al., 2004; Perryand Ordonez, 2006; Jaeggi et al., 2010). Taken together,these studies have strongly suggested that culture, pre-viously considered as uniquely human (e.g., Kroeber andKluckhohn, 1963; but see Hallowell, 1963), is built ontraditions found among nonhuman animals (Whiten andvan Schaik, 2007).Experimental interspecific cross-fostering studies in

nature have suggested that simple forms of social learn-ing play a role in how individuals acquire the list of fooditems they select from among the many more potentiallyavailable to them (e.g., Rowley and Chapman, 1986;Slagsvold and Wiebe, 2007). These forms of social learn-ing may be due to mere gregariousness or due toenhancement, observational conditioning, or sociallyinduced affordance learning (sensu Whiten et al., 2004).Such experiments suggest the presence of dietary tradi-tions in a wide range of birds and mammals. Indeed, pri-

matologists have long speculated that geographic intra-specific variation in diet is traditional (Nishida et al.,1983; Chapman and Fedigan, 1990; Chapman andChapman, 2002; Panger et al., 2002; Ganas et al., 2004;Boesch et al., 2006; Russon et al., 2009). In addition,computer simulations suggest that gregariousness alonecan produce variable food traditions across groups inidentical habitats (van der Post and Hogeweg, 2006,2008). However, it is not clear to what extent this also

Additional Supporting Information may be found in the onlineversion of this article.

Grant sponsors: A.H. Schultz Foundation, American Society ofPrimatologists, Denver Zoological Society, Duke University Gradu-ate School, L.S.B. Leakey Foundation, National Geographic Society,and Netherlands Organization for Scientific Research (NWO); Grantsponsor: National Science Foundation; Grant numbers: 0452995 and0643122; Grant sponsor: Wenner-Gren Foundation for Anthropologi-cal Research; Grant number: 7330.

*Correspondence to: Carel P. van Schaik, AnthropologischesInstitut & Museum, Universitat Zurich, Winterthurerstrasse 190,CH-8057 Zurich, Switzerland. E-mail: [email protected]

Received 13 May 2009; accepted 2 February 2010

DOI 10.1002/ajpa.21304Published online 6 May 2010 in Wiley Online Library

(wileyonlinelibrary.com).

VVC 2010 WILEY-LISS, INC.

AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 143:175–187 (2010)

happens under natural conditions, because social learn-ing may merely serve to speed up the acquisition of spe-cies-specific diet choices or may get overruled by individ-ual experience (Galef and Whiskin, 2001). The latter isplausible because individuals may use a set of innaterules that evolved because food selection is critical forgrowth, survival, and reproduction (Stephens and Krebs,1986). Thus, whether or not geographic variation in pri-mate diets is traditional remains unclear, despite theevidence for much more complex skill traditions in thesame set of species.The goal of the present study is to document differen-

ces in diet (food selection) between two wild populationsof Bornean orangutans (Pongo pygmaeus wurmbii) andto determine whether the observed patterns are compati-ble with the observed modes of diet acquisition. Weselected a pair of nearby study sites that have similarhabitats but are separated by a wide, impassable river,allowing for social learning within sites but not betweenthem. Some orangutan studies have already suggestedthat variation in particular skilled feeding techniques,including the use of tools, reflects local traditions(van Schaik and Knott, 2001; van Schaik et al., 2003;Fox et al., 2004). Recent studies have also provided thefirst strong evidence that social learning, includingobservational learning, plays a critical role in the dietacquisition process of wild infant orangutans (Jaeggiet al., 2008, 2010).We aim to distinguish between three models (Table 1).

Under exclusive individual learning (Model 1), maturingindividuals acquire their diets independently becausethey do not have or ignore opportunities to learn socially.Under exclusive social learning (Model 2), they acquiretheir diets exclusively by adopting the food choices ofothers through social learning as immatures and thenretain their preferences due to reluctance or inability toexplore individually (orangutans: Rijksen, 1978; rats:Galef et al., 2008; chimpanzees: Hrubesch et al., 2009;Jaeggi et al., 2010). Under supplemented social learning(Model 3), they initially rely on social learning, as shownfor maturing orangutans (Jaeggi et al., 2010), but maysubsequently adjust diet composition through individualexperience (e.g., Galef and Whiskin, 2001).These models make different predictions concerning

within-population diet variation. Assume, as in Model 1,that individuals decide independently whether an unfa-miliar item should be included in the diet by sampling asmall portion and then assessing its quality through sen-sory feedback cues (Dominy et al., 2001) or through theinteraction between taste and the consequences of foodingestion (Provenza, 1996; Ueno, 2001). This strategyshould lead to within-site diet variation, because when-ever a food item provides little or no distinct sensoryfeedback (or even negative feedback but is nonethelessedible), is eaten in small amounts along with many

others, has low nutritional value or has some combina-tion of these properties, the animal cannot distinguishits profitability (cf. MacArthur and Pianka, 1966) fromthat of the many other food items eaten on a given day.These problems have been demonstrated empirically (seeextensive discussion in van der Post and Hogeweg,2006). In addition, any costs or risks to sampling shouldproduce dietary conservatism, leading animals not toswitch easily once they have made a choice. Individualswill, therefore, develop within-group differences in dietfor those items for which they fail to accurately assesstheir relative profitability. This is especially likely tohappen for two classes of items, which are not mutuallyexclusive: i) low-quality food items that are eaten whenpreferred foods are not available (fallback foods sensuMarshall and Wrangham, 2007, i.e., items of relativelypoor nutritional quality in high abundance eaten partic-ularly during periods when preferred foods are scarce),because assessment of their profitability is hampered bylow quality and small meal sizes; ii) items that are hardto obtain or require specialized processing techniques[e.g., inner bark, spiny rotan (Palmae spp.)], because ini-tial experience or chance may determine whether anindividual recognizes the item as profitable. In supportof this, Baritell et al. (2009) found that the least profita-ble food items in the diets of wild orangutans (i.e., innerbark, leaves, and vegetative material), measured byenergy gain per unit time, were characterized by lowpreference and shorter feeding bouts relative to moreprofitable food items (i.e., fruits and flowers). They alsoobserved a significant positive correlation between profit-ability and preference for all food items in the diets ofwild orangutans. Model 1, therefore, predicts that theaverage fallback food item will be eaten by a smallernumber of individuals than the average preferred itemwithin each site (Prediction 1).Alternatively, under Model 2 (exclusive social learn-

ing), we expect within-site homogeneity in diet providedthat all individuals are directly or indirectly linked in asocial learning network. Model 2 also does not predictany major difference in homogeneity between preferredand fallback foods, once possible artifacts due to insuffi-cient observation time are removed, so that there shouldbe no difference in the number of individuals eating apreferred or fallback food item. Under Model 3 (supple-mented social learning), individual learning may over-ride previous social learning when the feedback signalsare clear. However, this is most likely for highly profita-ble food items that were, for whatever reason, not ini-tially in the diet, and least likely for the fallback fooditems for which profitability is low and difficult to esti-mate (and which therefore will largely remain outsidethe diet). Thus, Model 3 would also predict that foodchoices of individuals converge and, therefore, thatwithin a site, the diets should be largely homogeneous,

TABLE 1. Expected patterns in diet between two sites according to different models of diet acquisition

Model Mechanism

Variation in diet

Between populations Within populations

1 Exclusive individual learning Homogeneous in theory,heterogeneous in practice,especially for fallback foods

Heterogeneous, especiallyfor fallback foods

2 Exclusive social learninga Equally heterogeneous for all items Homogeneous3 Supplemented social learninga Heterogeneous, more so for fallback foods Homogeneous

a Assuming individuals are equally connected within the social network.

176 M.L. BASTIAN ET AL.

American Journal of Physical Anthropology

regardless of whether they are preferred or fallbackfoods, although perhaps not as strictly as under Model 2.In sum, then, within-site diet variation among individu-als is expected to be greater for fallback than preferredfoods only if all food choices are based on individuallearning, as under Model 1 (Prediction 1) but not underModel 2, whereas Model 3 predicts an intermediate pat-tern but one closer to Model 2.Turning now to between-site diet differences, the mod-

els predict different patterns if the sites are ecologicallyidentical. Under Model 1 (exclusive individual learning),the two sites should produce identical diets, whereasunder Models 2 and 3 (exclusive or supplemented sociallearning, respectively), we expect differences betweenthe sites due to the social influences on food choice ateach site. However, because the sites are inevitably notecologically identical, there will be between-site diet dif-ferences under all models because differences in avail-ability of food items may create differences in the opti-mum diet (MacArthur and Pianka, 1966), and the sameplant species may also occasionally have food items ofdifferent chemical composition at different sites, and,thus, different food properties that may affect food selec-tion (Hladik, 1977; Glander, 1982). Model 1 predicts thatthe same randomness that characterized individual vari-ation in diet within sites will also play a role in diet dif-ferences between sites, and as a result, the choices oftop-ranked, preferred food items (mainly fruit) will showstrong between-site convergence, whereas those of fall-back foods will not to the same extent. Thus, Model 1predicts greater between-site diet differences for fallbackfoods just as it does at the level of individuals withinsites. Model 2 (exclusive social learning) predicts clearbetween-site differences, equal for all items; whereas,Model 3 predicts that the between-site diet differenceswill be most likely for the items whose profitability isdifficult to assess (i.e., fallback foods and items thatrequire processing innovations) for which local traditionscan more easily develop than for items for which it iseasy to assess profitability, such as preferred foods.Thus, both Models 1 and 3 predict that between-site dif-ferences in the diet list are greatest for fallback foods,unlike Model 2 (Prediction 2). We will test these predic-tions using estimates of between-site diet differences andcomparing differences for fallback and preferred fooditems.We can also develop a third prediction. If items that

are available at both the sites are eaten only at one site,then this suggests that they are not easily recognized asprofitable by orangutans (and thus are also more likelyto be fallback foods). Thus, if animals discover suchitems independently, then the number of individuals eat-ing them should be lower than the number eating themore obvious items shared between sites, even if werestrict ourselves to fallback foods (Model 1). On theother hand, if there is a major role for social learning,the number of individuals eating these unique fallbackitems should be as large as the number eating theshared food items (Model 2; Prediction 3), for the veryreason that these foods are hard to recognize individu-ally and social information should therefore overrideindividual feedback. Under Model 3, we expect an inter-mediate pattern but closer to Model 2 because the itemsindependently acquired are likely to be acquired by mostindividuals through positive feedback from these items.In this prediction, one needs to correct for extreme dif-ferences in abundance of items, if the reason that some

items are not obviously recognized as food and thusunique to one site is that they are extremely rare.Testing these predictions requires a clear definition of

fallback foods and a procedure for the estimation of thedietary difference. We follow Marshall and Wrangham’s(2007) definition of fallback foods as widely available,low-quality resources (based on energy/g dry weight, seeKnott, 1998; Baritell et al., 2009) that are consumed ininverse proportion to the availability of preferred foods(see also Knott, 1998; Vogel et al., 2008). Thus, we iden-tified fallback foods as a food item whose consumption isnegatively correlated with the availability of preferredfoods. Our procedure for estimating diets and diet differ-ences is laid out in Figure 1. First, we established thediets at each of the two sites by systematically collectinga large set of feeding records but excluding items thatwere only briefly sampled or eaten only once. We thenidentified those items available at both the sites (asrevealed by phenology or other records) but eaten atonly one site. This list is the first, maximum, estimate ofthe diet difference between the two populations. Toassess the validity of the between-site differences pro-duced in this list, we also corrected for two possible arti-ficial reasons for the absence of a food item from the dietat a particular site: i) insufficient observation time andii) low abundance of the item at that site. We used thesecorrections to produce a minimum estimate of thebetween-site diet differences.

METHODS

Study sites

Data were compiled from Tuanan (28 090 06.1"S; 1148260 26.3"E) and Sungai Lading (028 150 49"S; 1148 22043.1"E) located 12.6 km apart on either side of theKapuas River in Central Kalimantan, Indonesian Borneo(see Fig. 2). The Kapuas River is 100–150 m wide in the

Fig. 1. Systematic approach for comparison of dietary differ-ences of groups or populations based on increasingly conserva-tive criteria for inclusion of specific items in the comparison.

177DIET TRADITIONS IN WILD ORANGUTANS


study region, making it impossible for orangutans in onepopulation to cross the river or to observe the feedingbehavior of orangutans in the other population. Tuananis a well-established research area (see van Schaik et al.,2005) with a 950 ha trail system. Sungai Lading wasestablished especially for this study and covers 200 ha.Both the sites primarily consist of swamp forest lying

on shallow peat of varying thickness up to 2 m disturbedby intensive selective logging. Comparisons of vegetationplots at the two sites show very similar diversity of treespecies (Sungai Lading: 86 species from 29 families, 56genera, n 5 1,537 trees, x diameter at breast height(dbh) 5 17 cm; Tuanan: 98 species from 34 families, 64genera, n 5 1,612 trees, x dbh 5 16.4 cm) and high over-lap at all levels (family: 83%, genus: 75%, species: 70%).Sørenson’s index of similarity was well above the valuesnormally considered to represent high species similaritybetween communities (Mueller-Dombois and Ellenberg,1974) at all taxonomic levels (Table 2).Fluctuations in temperature and rainfall and in the

phenology of leaves, flowers, and fruit are virtually iden-tical, although the mean production rate of fruiting trees(�10 cm dbh) is significantly higher at Sungai Lading(Bastian, 2008). Both forests have high orangutan den-sities (Tuanan: 4.5 indiv/km2; Sungai Lading: 7.7indiv/km2) by Bornean standards (van Schaik et al.,2005; Bastian, 2008), especially at Sungai Lading,largely because the study area has recently becomehemmed in by burnt forest. The only major ecologicaldifference between the two sites is more regular floodingat Sungai Lading, due to its location within the freshwater flood zone of the Kapuas (Bastian, 2008).Genetic differences between populations may cause

diet differences through differences in food preferences.Large rivers serve as dispersal barriers for orangutansand may, therefore, cause genetic differentiation of the

populations living on opposite banks (Jalil et al., 2008).However, orangutans may be able to cross the KapuasRiver closer to the headwaters, and gradual migration ofgenes down its course may reduce genetic population dif-ferentiation between the opposite banks. Preliminaryresults of genetic research indicate clear overlap in themtDNA haplotype spectrum (M. Krutzen, personal com-munication). Thus, the Kapuas is more likely to act as abarrier to cultural transmission than to gene flow.

Study subjects

A total of 39 and 16 independent or semi-independent,individually recognized were encountered at Tuanan andSungai Lading, respectively. Only individuals followed atleast twice at each site for which we have a minimum of3 h of feeding data and whose identities were confirmedgenetically (Tuanan: 36; Sungai Lading: 13) were used inanalyses. During the full 4.5 years of data collection inTuanan (July 2003–February 2007) and 20 months inSungai Lading (July 2005–February 2007), a total of17,068 and 3,370 h of data were collected continuouslyat each site, respectively (Supporting Information Tables1 and 2). A total of 9,474 and 1,585 h of feeding datawere collected at Tuanan and Sungai Lading, respec-tively. Range overlap among adult females was between54–77% at Tuanan and 72–96% at Sungai Lading (see

Fig. 2. Location of Tuanan and Sungai Lading study areas.

TABLE 2. Summary of botanical composition similarity betweenTuanan and Sungai Lading at each taxonomic level

Taxonomic level Sørenson’s index Elenberg’s index

Family 0.81 0.93Genus 0.75 0.77Species 0.64 0.58



Bastian, 2008 for further details). Mean party size foradult females, a standardized method for summarizingthe overall sociality of an individual orangutan (vanSchaik, 1999), was 1.13 for Tuanan and 1.03 for SungaiLading. Individuals at Tuanan spent 15.1% (independentfemales: 12.4%; independent males: 24.6%) of the time inassociations with other individuals compared with the2.82% (independent females: 1.73%; independent males:5.70%) at Sungai Lading.

Sampling methods

We used standard focal animal sampling of orangutanbehavior using methods previously standardized acrossorangutan sites (www.aim.uzh.ch/orangutannetwork.html;see also van Schaik, 1999), allowing direct comparisonbetween the sites. All food items eaten by orangutanswere carefully recorded. A food item is defined here asthe specific combination of food type and plant species(e.g., the bark of Koompassia malaccensis) consumed byan animal. The following food types were distinguished:fruit (all stages of ripeness; including seeds), flowers,bark (all references to bark refer to inner bark, i.e.,phloem and cambium), leaves (young and mature), non-green vegetable matter (e.g., pith, soft wood, and roots),and insects (insects were not subdivided, as it is difficultto distinguish different species or estimate their avail-ability). Identification of plant species and categorizationof food items were extensively cross-checked to guaran-tee consistency. Our study was entirely observationaland complies with the Code of Ethics of the AmericanAssociation of Physical Anthropologists for the ethicaltreatment of research subjects.

Botanical surveys

To determine botanical composition of the two sites,data from a single large phenology plot (Tuanan: 2 ha;Sungai Lading: 1.5 ha) and 20 smaller (0.5 ha total) veg-etative plots were examined at each site. A phenologyplot was placed along transects in the center of each ofthe study areas. All trees with �10 cm dbh within 5 mfrom either side of the transect were monitored monthlyfor their fruit, flower, and young leaf phenology. Twentysmall vegetative plots (5 3 5 m2 each) were placed ran-domly throughout each site at trail crossings to gainadditional information about the presence or absence ofnontree species (e.g., lianas, epiphytes, and ground vege-tation) and trees \10 cm dbh or those too patchily dis-tributed to enter the phenology plots.Plants in the plots as well as food samples were regu-

larly collected for identification. Morphospecies (distinctplant species to which we may or may not have beenable to assign to a species as well as genus) were identi-fied using local names and regularly updated based onthe information from the phenology plots and from theother site. The scientific names of these morphospecieswere identified, mostly to the species level, using florasand in consultation with botanists from the WanarisetHerbarium (Balikpapan, East Kalimantan). Further-more, samples collected in the field were cross-checkedwith herbarium voucher specimens.

Diet composition

Diet composition was assessed for each site as thetotal number of distinct food items (see Supporting Infor-mation Tables 3 and 4 for details of monthly diet compo-

sition per individual orangutan). The following criteriawere used to establish the diet at a site. First, weexcluded items eaten only once or for\6 min throughoutthe duration of the entire study period to avoid theinclusion of foods only briefly sampled (when longer feed-ing bouts were usually possible) and to minimize sam-pling artifacts due to incorrect species identification byobservers (Fig. 1a). Second, only items eaten by inde-pendent or semi-independent individuals (orangutansregularly making and sleeping in night nests [50 mfrom their mother) were included; thus, infants wereexcluded. Third, items eaten exclusively by Sumi, afemale orangutan in the Tuanan population, were alsoexcluded, as she often foraged outside the transect sys-tem in open habitat with very different species composi-tion, where no other individuals ranged. A further justi-fication for this decision was that in no case was Sumithe only Tuanan orangutan to eat an item that was alsoeaten by orangutans at Sungai Lading.

Diet clustering

To assess the extent to which the consumption offood items present at both sites was distributed ran-domly among individuals regardless of site affiliation,or instead clustered according to site affiliation (whichwould indicate site-dependent diet choices and thus asocial learning signal), we calculated the expectednumber of items eaten at one site only as the

PiQi,

where

Qi ¼ ð1� PiÞNT þ ð1� PiÞNS ð1Þ

the probability that all individuals observed eating itemi happened to reside in a single site by chance, NT 5total number of individuals observed at Tuanan, NS 5total number of individuals observed at Sungai Lading,and Pi 5 the observed proportion of individuals eatingitem i for both sites combined (i.e., for NT 1 NS). In thisexpression, (1 2 Pi)

NT gives the probability that item i iseaten by none of the individuals at Tuanan (and thusentirely by animals at Sungai Lading only), (1 2 Pi)

NS

gives the equivalent for Sungai Lading, and the combi-nation, therefore, refers to the probability that an itemis eaten at only one site. The summed Qi over all items igives the expected number of items eaten at only onesite. Thus, the number was compared to the observednumber, and using the expected and observed number ofitems eaten at both sites, we performed a v2 goodness-of-fit test. This test was done on the full data set, and on areduced set, where only individuals followed between165 and 500 h were included. We used this criterionbecause 1) it maximized the number of individuals wecould include in the analyses at each site and 2) pro-vided a comparable number of individuals and observa-tion hours at each site.A cluster analysis was executed to determine

whether the differences in diet were at the level of theindividual or the population. Whether an item waseaten or not eaten by each individual was entered asbinary data into a matrix in which columns representitems (present at both the sites) and rows representindividuals. Only individuals with overlapping andcomparable observation hours at both the sites(between 165 and 500 h) were included to standardizethe comparison.



Preferred and fallback foods

To test Prediction 1, we needed a distinction betweenpreferred and fallback foods. We calculated Vanderploegand Scavia’s selectivity coefficient (Vanderploeg andScavia, 1979) for all food items (e.g., species and item ofthat species) consumed in the diet at each site in a givenmonth. Specifically, the following equation was used:

ri=piP

i

ðri=piÞ ð2Þ

where ri is the proportions of food item (i) of a given spe-cies in the diet and pi is the relative availability of thefood item of a given species in the environment. For ri,the percentage of time feeding on each item of each spe-cies was calculated for each month following the meth-ods outlined in Harrison et al. (2009). For pi, the relativeabundance of each species was calculated by taking thenumber of productive stems of species X per month for agiven species divided by the total number of producingstems in the phenology plots for that month. This indexwas calculated for each food item consumed in a givenmonth. To examine dietary preference for each food type(e.g., fruit, bark, leaves, and flower), the average prefer-ence was calculated for each food type from these itemsper month, and these monthly food type averages wereused in the analysis that compared preference amongfood types. Note that if a food type was not eaten in agiven month but was available, it had a zero for prefer-ence (see Supporting Information Table 5 for details ofindividual food item availability per month). Preferencesfor both the Tuanan and Sungai Lading populationswere calculated using data collected during the studyperiod when data were collected at Sungai Lading.

Number of individuals eating some classes offood items

Prediction 1 required testing the number of individu-als eating preferred and fallback food items at each site.It is obvious that an individual’s estimated diet willincrease with observation time, but it is not obvious howthis should bias the likelihood of including either pre-ferred or fallback food items into the diet. However, toexamine this possible bias, we repeated tests of this pre-diction with a subset of the data set using the samecriteria used for the cluster analysis.

Maximum estimate of dietary difference

Prediction 2 required that we estimate the dietary dif-ference between sites. Once a complete diet list was pro-duced for both sites, we determined the presence of allitems at each site. Following our logic (see Fig. 1), foodthat is present at both the sites but only eaten at one ofthem represents the maximum possible dietary differ-ence between the two populations. Presence was docu-mented on both species and item level, since a certainspecies could be highly abundant but never produce aparticular item during the observation period, so thatthis item was not available at a particular site for possi-ble consumption. We knew that a species was presentwhenever it occurred either on the phenology or vegeta-tive plots, when we observed the species elsewhere inthe study area or it was observed to be eaten by orangu-

tans. Leaves, bark, and other vegetative items were con-sidered available whenever the species was present.Fruits and flowers were only considered available if theywere recorded as present during the monthly phenologymonitoring, if they had been recorded as eaten, or if wehad a photograph or sample for positive identification.

Minimum estimates of dietary difference

A certain food could be recorded as eaten at one sitebut not at the other for artificial reasons. First, itemsthat were recorded as present but not eaten at a particu-lar site could in fact be eaten at such a low frequencythat they were missed due to insufficient observationtime. Second, an item may be so rare in the habitat ofthe site where it was not observed eaten that we missedits consumption by orangutans or that orangutans didnot eat it because they encountered it too rarely.To correct for the possible effects of differential follow

time, we used the Poisson distribution to calculate theprobability that an item should be eaten (where k 5 0denotes an item was not eaten and k � 1 denotes anitem was eaten at least once) at the site, say site A,where it was not observed to be eaten,

Pðk � 1Þ ¼ 1� Pðk ¼ 0Þ ¼ 1� e�m ð3Þ

We based the estimate of m on information from theother site (where the item was eaten), say site B, wheremB 5 pB 3 NB, pB is the probability that an individualeats item i per follow day at site B (i.e., proportion of fol-low days on which the item was eaten at least once) andNB is the number of follow days for this animal (pro-vided �6 h follow time was available for that day). Wethen calculated the mean pB over all individuals at siteB that had a nonzero value of pB. Then, for site A, thesite where the item was not seen to be eaten, we calcu-lated the mA 5 pA 3 NA, where NA is the actual followtime at site A (as number of days with �6 follow hours)and pA is the expected probability of use at site B [Eq.(3)]. When the expected probability of observing that theitem at site A was eaten exceeded 0.90, we assumed thatthe item could have been eaten at A but was not for rea-sons unrelated to limited follow time.To correct for the possible effect of reduced abundance

of the food item at the site where it was not eaten, weremoved all the food items from the dietary differencelist for which the item had a [10-fold higher abundanceat the site where it was eaten. To allow for the inclusionof those species consumed but not present in phenologyplots because they are rare, we assigned a value of 1 tothese species and also added 1 to the abundance of eachspecies within the plot (Tuanan 3.9% of food items; Sun-gai Lading 2.67% of food items). All species that wereunique to each site were encountered in the phenologyplots. On the basis of these sensitivity tests, we producedthree minimum estimates of the dietary difference(Fig. 1e).

Statistical analysis

Frequency data were analyzed using v2 or Fisher’sexact probability tests depending on sample sizes,whereas continuous data were analyzed using Mann-Whitney U (MWU) and Wilcoxon signed–ranks tests(WSRT) and Spearman’s rank order correlations. One-way analysis of variance (ANOVA) or the nonparametric



equivalent (WSRT) was used to examine the differencesin dietary selectivity coefficients. If the homogeneity ofvariance assumption was violated, we report a WelchANOVA statistic. If significance was detected, we usedTukey-Kramer HSD to determine pairwise differencesamong items.The cluster analysis was computed using R 2.50. All

other statistics were computed with JMP-SAS 6.0.3-7.0or StatView 5.0. The probability levels for all tests weretwo-tailed and a was set at 0.05.

RESULTS

Identification of fallback foods

To test predictions concerning the causes of within-and between-site diet differences, it was necessary tofirst identify fallback and preferred foods. WithinTuanan, there was significant individual variation inpreference among the major food types (Welch ANOVA,F4,98 5 58.87, P \ 0.0001). A post-hoc multiple meanscomparison revealed that fruit was preferred over allother food categories (Tukey-Kramer HSD, q* 5 2.73,P \ 0.05). Flowers were more preferred than leaves,inner bark, and nonleafy vegetable matter. Significantvariation in preference was also detected among themajor food types at Sungai Lading (Kruskal-Wallis: v2 555.68, df 5 4, P \ 0.0001). The post-hoc comparisonrevealed that at Sungai Lading, as at Tuanan, fruit waspreferred over leaves and vegetable matter, althoughthere was no statistical difference among fruit, flowers,and bark (Tukey-Kramer HSD, q 5 2.58, P \ 0.05),which may owe to small sample sizes and high variancein the bark and flower categories.Orangutans at both sites were primarily frugivorous,

spending a majority of their total foraging time feedingon fruits (overall monthly means: Tuanan: 71%; SungaiLading: 61%) whenever they were available. Duringperiods of fruit scarcity, orangutans at Tuanan fed pri-marily on flowers, whereas orangutans at Sungai Ladingfed mostly on bark, leaves, and other vegetative matter,a difference explained by the greater availability of flow-ers at Tuanan. Because the consumption of bark, leaves,and nonleafy vegetative matter correlated negativelywith the prevalence and consumption of preferred fruitsat both sites (Bastian, 2008; Vogel et al., 2008), all threefood item types were designated as fallback foods. Weleft flowers unclassified, since their relative abundancediffered so dramatically between sites but ran analysesboth with and without flowers included as fallback foods,considering only inner bark, leaves, and nonleafy vegeta-tion to be true fallback foods, to test the robustnessresults.

General patterns

Despite high floristic similarity between Tuanan andSungai Lading, we detected a difference between the twosites in orangutan diets. A total of 228 items from 140plant species were eaten (for �6 min per item) at thetwo sites combined (182 items eaten at Tuanan, 106 atSungai Lading). In addition, orangutans fed on 95 fooditems (Tuanan: 65; Sungai Lading: 50) for \6 min totalper item. These items were considered sampled only(because longer feeding times were generally possible)and were not included in the diet list. From the 228 fooditems eaten at either site, 150 items were present atboth sites and could potentially have been shared. Of

150 items, 90 were actually eaten at only one site or theother, yielding a maximum estimate of the between-sitediet difference of 60% between Tuanan and SungaiLading at the level of food items. The large size of thisdifference is consistent with a role for social learning indiet selection.A formal v2 test of the probability with which items

were observed versus expected to be eaten at one siterevealed that more individuals eating a particular itemhappened to live at the same site than expected if theinclusion of dietary items by individuals was independ-ent of the site where they lived (v2 5 73.33, df 5 1, P \0.0001). This result indicates that an individual orangu-tan’s food choice at a site is statistically dependent onthat of others, suggesting a possible role for social learn-ing to explain the observed patterns. If this same analy-sis is repeated only for fallback foods, the result indi-cates that fallbacks are also highly clustered by site(v2 5 41.11, df 5 1, P\ 0.0001).One might argue that false absences due to insuffi-

cient observation time, rather than social learning, cre-ated larger between-site diet differences than expectedby chance. The total number of food items in the diet perindividual does indeed showed a positive relationshipwith observed feeding time (Tuanan: Spearman’s q 50.923, P \ 0.0001, n 5 36; Sungai Lading: Spearman’sq 5 0.912, P 5 0.0004, n 5 13). We, therefore, repeatedthe v2 analysis including only individuals for whom wehad comparable and reasonably complete feeding obser-vations, that is, we took all individuals, regardless ofsite, for whom we had between 165 and 500 h of feedingtime. The outcome of this analysis, although based on amuch smaller data set, still showed a trend toward sta-tistical significance (v2 5 2.83, df 5 1, P 5 0.0925).Therefore, the between-site clustering of diets is not anartifact of differences in observation intensity betweenthe two sites.A cluster analysis of dietary overlap between dyads in

which both members had between 165 and 500 h of feed-ing observation time generated a dendrogram that splitindividual dietary profiles into two major groups (seeFig. 3). These groups sorted exactly according to popula-tion affiliation, illustrating the earlier result that thegreatest observed differences in diet are between popula-tions, rather than due to the idiosyncratic foragingbehaviors of a few individuals within one or both sites.Taken together, these results provide suggestive evi-dence of distinct diet traditions at the two sites.

Prediction 1: Within-site dietary variation

Exclusive individual learning (Model 1) predicts thatwithin each site, each individual preferred food item willbe consumed by a greater number of individual orangu-tans relative to each fallback item. Results of MWU testsconfirmed this prediction for Tuanan, regardless ofwhether flowers were or were not considered as fallbackfoods in the analysis (Table 3). For Sungai Lading, therewas a trend that preferred food items were consumed bya greater number of individuals if flowers were includedin fallback category, but this trend did not exist whenflowers were not considered fallback foods. However, it isimportant to note that the results for Sungai Lading,while not reaching statistical significance, were in thepredicted direction. No change was detected in either thepattern or statistical significance of these results aftercorrecting for observation time by using only data from



individuals within a similar range of follow hours (Table3). This pattern of results is more favorable to Models 1and perhaps 3 than Model 2.

Prediction 2: Between-site dietary variation

While Models 1 (exclusive individual learning) and 3(preferential social learning) predict greater between-site

differences in fallback compared with preferred foods,Model 2 (exclusive social learning) predicts no such dif-ference. The between-site difference in dietary itemspresent at both sites but eaten at only one site wasbased on the maximum estimate, as shown in Figure 4.This difference was smaller for the preferred food type,fruit (34.8% of 46 items), than for the fallback foods(leaves: 68.8% of 48; nonleafy vegetable matter: 70.6%

Fig. 3. The dendrogram resulting from a cluster analysis based on individual dietary profiles, indicating that individual orangu-tans cluster according to population affiliation; includes individuals from both sites with 10,000–30,000 min observation time duringoverlapping time periods; TU, Tuanan individual; SGL, Sungai Lading individual. Terminal ends represent individuals and verticaldistances between individuals reflect the magnitude of differences in dietary repertoire. Clustering specifications: manhattan met-ric, complete linkage.

TABLE 3. Number of individuals within each site eating preferred and fallback foods

Tuanan Sungai Lading

Before correctingfor observation

time

After correctingfor observation

time

Before correctingfor observation

time

After correctingfor observation

time

Preferred foods (fruit) vs. ‘‘true fallbacks’’(bark, leaves, nonleafyvegetative matter)

Mann-Whitney U 1544 1716 1015 996n1 (# preferred foods) 74 74 43 43n2 (# fallback foods) 73 73 54 54Median (Mean) # individuals eating

each preferred food item12.5 (14.72) 2 (2.32) 4 (4.77) 2 (2.33)

Median (Mean) # individuals eatingeach fallback food item

4 (8.14) 1 (1.12) 3.5 (4.13) 2 (2.04)

P value \0.0001 \0.0001 0.2820 0.2182Direction Preferred[ fallback Preferred[ fallback Preferred[ fallback Preferred[ fallback

Preferred foods (fruit)vs. all fallbacks(including flowers)

Mann-Whitney U 1966 2333 1081 1047n1 (# preferred foods) 74 74 43 43n2 (# fallback foods) 102 102 60 60Median (Mean) # individuals eating

each preferred food item12.5 (14.72) 2 (2.32) 4 (4.77) 2 (2.33)

Median (Mean) # individuals eatingeach fallback food item

4 (7.20) 1 (1.09) 3 (3.97) 2 (1.92)

P value \0.0001 \0.0001 0.1563 0.0956Direction Preferred[ fallback Preferred[ fallback Preferred[ fallback Preferred[ fallback



of 17; inner bark: 73.3% of 15) and for flowers (84.2% of19). A v2 test indicated that this distribution was hetero-geneous (v2 5 20.36, df 5 4, P \ 0.0001). A Fisher’sexact probability test indicated that the three fallbackfood types showed no variation in their between-site dietdifference (P 5 0.665), allowing us to combine them intoa single category. The difference between fruit and thesecombined fallback foods was highly significant (v2 514.79, df 5 1, P 5 0.0002). This result favors Models 1and 3 over Model 2. We submitted this result to two sen-sitivity tests, correcting for the rarity of food items andinsufficient observation time.

Correcting for variation in relative abundance offood items

To examine the possible effect of food item abundanceon inclusion in the diet, we compared the abundance ofan item not eaten at a site with its abundance at thesite where it was eaten. Of the 90 food items present atboth sites but eaten at only one, 65 belonged to tree spe-cies present on the phenology plot at the site where theywere not observed eaten. However, for these items, nosignificant differences were found between the overalldensities of items consumed versus those not consumed,within either Sungai Lading (WSRT: v2 5 0.01, n1 5 53,n2 5 58, p 5 0.919), or Tuanan (WSRT: v2 5 0.12, n1 598, n2 5 13, P 5 0.731), or for both sites combined(WSRT: v2 5 0.755, n1 5 156, n2 5 66, P 5 0.385; me-dian for eaten items 5 0.002, for items not eaten 50.001). These abundance effects were also assessed sepa-rately for each food type. No significant differences werefound between the abundances of consumed versus non-consumed food species-item combinations for fruit,leaves, bark, or vegetative items, although consumedflowers (median density 5 0.001) were more commonthan those not eaten (median \ 0.0001) across sites(WSRT: v2 5 5.34, n1 5 19, n2 5 15, P 5 0.021). Thissuggests that local differences in consumption of foodtypes do not reflect differences in item abundance, withthe possible exception of flowers. Hence, between-site dif-ferences in diet cannot be explained by variation in itemabundance.Nevertheless, we also examined the possibility that

extreme differences in abundance might affect inclusion

into the diet. After excluding the 19 items with a [10-fold higher abundance at the site where the items wereconsumed, we still found that fallback items accountedfor a greater proportion of the diet differences than didpreferred items (v2 5 4.29, df 5 1, P 5 0.05), confirmingthe first analysis.

Correcting for differences in observation time

It might be argued that the maximum difference listcontains false zeroes. If the between-site difference indiet composition were driven entirely by differences inobservation intensity, we would expect all 65 testable dif-ferences to disappear when we correct for observationtime. We found, however, that 36 items had a greaterthan 90% probability of being observed eaten by at leastone individual at the site where they were not observedeaten during the study period. Further, after removingthe likely false zeros due to insufficient observationtime, fallback foods still made up a statistically greaterproportion of dietary differences between sites than didpreferred items (v2 5 5.58, df 5 1, P 5 0.024). This sug-gests that correcting for differences in observation timedoes not affect the conclusion of the original analysis ofthe pattern in diet differences between sites.

Features of minimum between-site dietarydifferences

One way to distinguish between the power of each pro-posed model to account for the observed patternsreported in this article is to make qualitative observa-tions relating to specific food items on the most conserva-tive minimum differences list. This list (Table 4) includesonly items with differential consumption between sitesthat pass rigorous criteria, correcting for both differencesin observation time and in extreme differences in itemabundance. Out of the 15 items on this most conserva-tive dietary differences list, we identified four-item dif-ferences that are especially difficult to explain with anargument based on exclusive individual learning. Allfour of the following food items (species-item combina-tions) were present at both sites but consumed during aminimum of 23 different follow days at one site only, de-spite being more abundant at the site where they werenot consumed: ‘‘Manggis hutan’’ (two similar Garciniamorphospecies, difficult to distinguish) leaves (eaten byn 5 19 individuals), ‘‘Maruang’’ (Myristica lowiana) fruit(eaten by n 5 23 individuals), and ‘‘Tarantang’’ (Camp-nosperma coriaceum) fruit (eaten by n 5 21 individuals)were all consumed at Tuanan only and ‘‘Bengaris’’ (K.malaccensis) bark was consumed only at Sungai Lading(n 5 13 individuals). These examples are inconsistentwith Model 1 and are consistent with Models 2 and 3.

Prediction 3: Consumption of unique andshared foods

Model 1 predicts that within each site, unique fooditems (those eaten only at one site, despite being avail-able at both) will be eaten by a smaller number of indi-viduals than shared food items (eaten at both sites)because they are harder to recognize as food. Results ofMWU tests confirmed this prediction, reaching statisticalsignificance for both sites when the analysis was carriedout using the full diet list (Table 5). Results remainedhighly significant for Tuanan but, though in the samedirection, were no longer significant for Sungai Lading

Fig. 4. Percentage difference in dietary items present atboth sites but part of the diet at one site only (based on maxi-mum dietary differences between sites) is heterogeneous (v2 520.36, df 5 4, P < 0.0001). Abbreviations: bk, inner bark; fl,flowers; fr, fruit; lv, leaves; veg, non-leafy vegetable matter.



after excluding items with a greater than 10-fold higherabundance at the site where the items were consumed.When the analysis was performed only for fallbackitems, after correcting for extreme site differences initem abundance, no change was detected in either thepattern or statistical significance of the results whenflowers were included in the fallback category forTuanan. However, when flowers were excluded from thefallback food category, a strong trend remained in thepredicted direction (Table 5). For Sungai Lading, whilethe results did not reach statistical significance, therewas a trend that shared items were consumed by moreindividuals than unique items, regardless of whetherflowers were included in the fallback foods category.These results strongly favor Models 1 and perhaps 3over Model 2.

DISCUSSION

This study of intraspecific variation in diet is to ourknowledge the first to compare two physically separatedprimate populations, living in similar habitats butexchanging no migrants, to evaluate the possibility thatsocial learning is, at least partly, responsible for geo-graphic variation in diet, i.e., that there are local diettraditions. We also developed rigorous tests against thealternative that between-site diet differences are due toindividual exploration and learning of diet choices.Various patterns were examined to test predictions of

the scenarios presented in the introduction. First, wefound clear-cut and large differences in the diet ofthe two sites, with 60% (90) of the 150 potentiallyshared items actually eaten at only one site. Even after

TABLE 4. Dietary difference list correcting for observation time and extreme abundance differences

Species Local nameItem typeeatena

Site whereitem eaten

# Days itemeaten

Densityratiob Pr (K � 1)c

Licania splendens Bintan fr Tuanan 48 3.9318 0.9989Ilex cymosa Kambasira fr Tuanan 39 4.9147 0.9961Pouteria cf. malaccensis Lewang fl Tuanan 25 3.9318 0.9715Pouteria cf. malaccensis Lewang lv Tuanan 26 5.8976 0.9785Garcinia spp. Mangis hutan lv Tuanan 82 0.1695 1.0000Myristica lowiana Maruang fr Tuanan 23 0.1966 0.9671Antidesma cf. cuspidatum Nonang fr Tuanan 19 0.9829 0.9330Palaquium spp. Nyatoh lv Tuanan 60 9.9698 0.9998Campnosperma coriaceum Tarantang fr Tuanan 156 0.0756 1.0000Koompassia malaccensis Bengaris bk Sungai Lading 53 0.0391 1.0000Blumeodendron kurzii Karandau putih lv Sungai Lading 2 1.2717 1.0000Nephelium mangayi Piais lv Sungai Lading 4 1.0174 1.0000Diospyros siamang Pinding pandan fl Sungai Lading 2 0.0509 1.0000Xanthophyllum discolor Tabaras bk Sungai Lading 6 1.0174 1.0000Pometia pinnata Takasai fr Sungai Lading 4 0.2035 1.0000

a fr, fruit; lv, leaves; fl, flowers; bk, inner bark; veg, nonleafy vegetable matter.b Density ratio 5 density at site where item eaten/density at site where not eaten. Density calculated as # stems carrying itemX/total # stems on phenology plot.c Pr (K � 1) 5 Poisson probability (%) of a particular food item being observed eaten by at least one individual at the site wherethe item was not observed eaten during the study period.

TABLE 5. Number of individuals within each site eating shared and unique foods

Tuanan Sungai Lading

Before correctingfor abundance

After correctingfor abundance

Before correctingfor abundance

After correctingfor abundance

Full dietMann-Whitney U 946.50 540.5 461 151n1 (# shared foods) 60 59 60 59n2 (# unique foods) 67 39 23 6Median (mean) # individuals eatingeach shared food item

14 (16.05) 14 (15.80) 4 (4.87) 4 (4.76)

Median (mean) # individuals eatingeach unique food item

4 (6.76) 5 (6.36) 2 (3.33) 3 (4.33)

P value \0.0001 \0.0001 0.0198 0.5516Direction Shared[ unique Shared[ unique Shared[ unique Shared[ unique

‘‘True fallbacks’’ All fallbacks ‘‘True fallbacks’’ All fallbacks

Fallbacks only (after correcting for abundance)Mann-Whitney U 316.5 483.5 159 196n1 (# shared foods) 24 27 24 27n2 (# unique foods) 37 52 19 20Median (Mean) # individuals eatingeach shared food item

9.5 (10.75) 9 (10.52) 4 (4.67) 4 (4.44)

Median (Mean) # individuals eatingeach unique food item

4 (6.73) 4 (6.31) 2 (3.42) 2 (3.35)

P value 0.0580 0.0228 0.0875 0.1064Direction Shared[ unique Shared[ unique Shared[ unique Shared[ unique



excluding all those differences that could potentially bedue to local differences in item abundance or observationtime, a diet difference of 25% (16 of 65 testable differen-ces) nonetheless remained. Another general finding wasthat food items, including fallback foods, were not distrib-uted independently of site affiliation among the individu-als but instead showed significant clustering by site.These two findings provide prima facie support for themodels invoking social learning, supporting the numerousstudies that have shown such differences and interpretedthem as diet traditions (references in introduction).Tests of the three detailed predictions were not favor-

able to the model assuming exclusive social learning(Model 2), but could not distinguish unambiguouslybetween models of individual diet acquisition (Model 1)and social learning combined with individual explora-tion of diet post weaning (Model 3). We found that pre-ferred items were on average eaten by a greater num-ber of individuals at a site than were fallback items,consistent with Models 1 (exclusive individual learning)and 3 (supplemented social learning), that fallback fooditems showed greater dietary differences between thetwo sites than did preferred items, consistent withModels 1 and 3 (preferred, but not exclusive sociallearning), and that unique food items (in the diet atonly one site though available at both) were eaten byfewer individuals on average than shared food items,consistent with Models 1 and 3. Thus, these tests sup-port Models 1 and 3, suggesting that both individualand social learning play major roles in diet selectionamong orangutans, and may explain major diet differ-ences between sites.Differentiating between Models 1 and 3 based on the

observed diet patterns alone is difficult. Their predic-tions differ only quantitatively, leading to a lack of reso-lution. However, there are some reasons to believe thatModel 3 actually provides the closer fit. First, we canrely on behavioral observations of the actual modes oflearning to assess the importance of social learning.Thus, we know that infant orangutans at Tuanan ac-quire a core food set from their mothers via verticalsocial learning, much of which is very simple and classi-fiable as enhancement, such that the diet repertoires ofmother and infant are virtually identical at aroundweaning (Massen, 2004; Dunkel, 2006; Jaeggi et al.,2010). Other field studies of primates have suggestedvery similar patterns (Tarnaud, 2004; Rapaport andBrown, 2008). After weaning, young orangutans begin torange more independently not only continuing to copythe food choices of conspecifics with whom they regularlyassociate (van Noordwijk and van Schaik, 2005) but alsoindividually sampling potential food items. This couldexplain why the (independent, i.e., weaned) individualsin this study were observed to sample 95 food items. Sec-ond, we know that innovated feeding techniques, includ-ing some based on tool use differ between orangutanpopulations, leading to at least some between-site dietdifferences that are entirely cultural (van Schaik andKnott, 2001; van Schaik et al., 2003). We suspect thatthe extreme cases found in this study (Table 4) requireeither some innovative processing or, more plausibly, asocially induced override of the initial sensory feedbackprovided by these items. Finally, we may underestimatethe role of social learning in this study because theorangutans at the two sites are not gregarious enough towarrant local cultural uniformity (cf., Mitra Setia et al.2009).

Taken together, these considerations strongly suggestthat the results, while consistent with both Models 1and 3, are nonetheless most consistent with a combina-tion of initial social learning followed by some a continu-ous, low-level individual exploration and evaluation (i.e.,Model 3). However, having established that Model 3 pro-vides a somewhat better fit than Model 1, it should alsobe noted that their predictions are quite close. Thismeans that dietary traditions should be hard to detect inpermanently gregarious organisms, such as nonhumanprimates. The reason for this is individual sampling,which results in the convergence of diets between sites.Why does this convergence happen when it does notseem to work in the simulation models of van der Postand Hogeweg (2006)? We suspect that it is becauseorangutans start out with a profitable diet inheritedform their mothers and then, being long lived, havemany years to try out food items at low rates. Whensampling of food items is low level, not exceeding once aday, it may be possible for animals to develop some esti-mate of an item’s profitability, since it is the only thingdifferent compared to the diet the day before. This inter-pretation requires that sampling is low level; this predic-tion will be tested in subsequent work. An importanttheoretical reason to expect some continued low-level ex-ploration is that exclusive reliance on social learning(Model 2) would inevitably lead to an erosion of localdiet breadth. Thus, some sampling is required, but theolder immatures and adults that do the sampling maybe better able to avoid the costliest mistakes, and byadding items one by one to an existing diet they may bebetter able to evaluate each item’s profitability. Samplingalso allows individuals to buffer changes in their habitatby shifting or supplementing their initial core food set asnecessary (Russon, 2002).This process will tend to produce the patterns

observed in this study; whereas most of the diet isacquired socially, subsequent individual exploration andevaluation produces great convergence among individu-als and sites. Thus, initial social acquisition of the diet,followed by individual fine-tuning (Model 3) ends up pro-ducing nearly the same geographic pattern in diet aspurely individual exploration and learning (Model 1), atleast in long-lived organisms that have time to samplefood items not in the diet.Exceptions will remain for two kinds of food items.

First, whenever special processing is required to makethe item ingestible, those populations where the innova-tion did not take place or was not maintained throughsocial transmission would not have this food item intheir diet. This exception can account for the knowncases of tool-based exploitation of highly nutritious foodsources in some sites but not others (e.g., van Schaikand Knott, 2001) but probably for several more as wellthat are less striking because they do not involve tooluse. Second, when aversive sensory information from afood item strongly suggests low profitability or even tox-icity, when in fact they are profitable, this knowledgemay be discovered and transmitted socially in one popu-lation but not in another. This needs to be tested infuture work.In conclusion, these results suggest that comparisons

of diet patterns, even if done with great care to removepossible statistical artifacts and account for the inevita-ble ecological differences, will not yield strong evidencefor diet traditions, unlike the long-standing expectationin the literature (e.g., Nishida et al., 1983) and our own



intuition based on evidence for geographic variation insocially transmitted innovations (e.g., van Schaik et al.,2003, 2006). The mismatch between expectation andfinding may arise because those animals most likely tosocially acquire food choices, intelligent, long-lived ani-mals with long parent–offspring associations and thusnumerous opportunities for social learning, are the veryones that have numerous opportunities for subsequentadjustments of these choices through years of low-levelsampling, in a way that is neither too risky nor uninfor-mative. Diet differences between populations are, there-fore, only expected where novel food-processing techni-ques must be invented or where obvious negative feed-back (e.g., bad taste) wrongly suggests unpalatability.

ACKNOWLEDGMENTS

The authors thank the BOS Foundation for permissionto work at MAWAS; Universitas Nasional (UNAS) forsponsoring the Tuanan and Sungai Lading projects; theDirector General of the Departamen Kehutanan(PHKA), the Indonesian Institute of Sciences (LIPI), theDirektorat Fasilitasi Organisasi Politik dan Kemasyara-katan, Departamen Dalam Negri, and the BKSDAPalangkaraya for permission to work in Indonesia.The authors thank the numerous field staff, students,

and assistants who have participated in the projects andPak Ambriansyah and Pak Arbainsyiah of the WanirisetHerbarium, Pak Haji Achmad Ilas, Tono bin Guan, Idunbin Guan, Awan bin Daut, Pak Ihing, Pak K. Odom, PakNadi, Pak Linandi, Pak Rahmat, and Pak Kade Sidiasafor their help with the identification of botanical speci-mens. The authors thank Maria van Noordwijk for man-aging the Tuanan orangutan database and Karin Isler,Mark Harrison, Christine Drea, Ken Glander, TracyKivell, Josh Linder, and Cheryl Knott for fruitful discus-sion. The authors thank Willie Smits for his generous as-sistance with the Tuanan field station and providing uswith the satellite image. This study was conductedwithin the framework of a Memorandum of Understand-ing between UNAS and the Anthropological Institute(AIM) of the University of Zurich. The authors especiallythank Tatang Mitra Setia and Sri Suci Utami Atmokofor fruitful long-term collaboration.

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