Robert Walker,Kim Hill,Hillard Kaplan& Garnett McMillanDepartment of Anthropology,University of New Mexico,Albuquerque, New Mexico87131, U.S.A. E-mail:robwal@unm.edu

Received 23 March 2001Revision received15 October 2001 andaccepted 19 December 2001

Keywords: Ache hunters,strength and skill, humanlife history evolution.

Age-dependency in hunting ability amongthe Ache of Eastern Paraguay

This paper examines changes in hunting ability across the lifespan forthe Ache of eastern Paraguay. Hunting ability is decomposed into twocomponents—finding prey and probability of kill upon encounter—and analyzed for important prey species. Results support theargument that skill acquisition is an important aspect of the humanforaging niche with hunting outcome variables reaching peakssurprisingly late in life, significantly after peaks in strength. Theimplications of this study are important for modeling the role of thehuman foraging niche in the co-evolution of various outstandinghuman life history characteristics such as large brains, long lifespans,and extended juvenile periods.

� 2002 Elsevier Science Ltd. All rights reserved.

Journal of Human Evolution (2002) 42, 639–657doi:10.1006/jhev.2001.0541Available online at http://www.idealibrary.com on

Introduction

The human foraging niche, and hunting inparticular, is often interpreted as playing acentral role in the evolution of humanbehavior and life history (e.g., Dart, 1953;White, 1959; Washburn & Lancaster,1968; Isaac, 1978; Hill, 1982; Lancaster &Lancaster, 1983; Foley & Lee, 1991; Foley,1992). Several studies have quantitativelyexamined age-dependent food productionability in traditional societies. These includeGinjingali foragers (Meehan, 1982; Altman,1987), mixed economies in Botswana(Bock, 1995), children’s subsistence activi-ties on Mer, Torres Strait (Bleige Bird et al.,1995), Gidra (Kawabe, 1983; Ohtsuka,1983, 1989) and Etolo (Dwyer, 1983)hunters in Papua New Guinea, Hadza for-agers (Blurton Jones et al., 1989, 1997;Hawkes et al., 1989, 1995; Marlowe, 2000;Blurton Jones & Marlowe, 2001), Acheand Hiwi foragers (Kaplan et al., 2000),and Machiguenga and Piro horticultural-foragers (Kaplan, 1994; Gurven & Kaplan,n.d.). While these studies are useful inunderstanding some of the diversity in age-dependent foraging ability, none explicitly

0047–2484/02/060639+19$35.00/0

models strength and skill as determinants ofhunting ability. This paper contrasts ageschedules of Ache hunting ability with thoseof physical performance, models strengthand skill effects on ability, and decomposeshunting ability into some of its constituentparts—e.g., finding game, killing game uponencounter, and archery ability.

While many foraging activities may becomplicated, hunting is potentially themost skill- and strength-intensive foragingactivity. This is indicated by the fact thathunting return rates peak later in lifethan most other food acquisition activities.Hunting return rate curves peak in the early30s for the Hiwi (Kaplan et al., 2000), 40for the Machiguenga and Piro (Gurven &Kaplan, n.d.), early or mid-40s to mid-50sfor the Etolo (Dwyer, 1983), 35–45 forthe Gidra (Ohtsuka, 1989), 45–50 for theHadza (Marlowe, 2000) and 37–42 forthe Ache (this study). That some extractivegathering activities, such as mongongo nutprocessing (Bock, 1995, 2001), Gidjingalishellfish collecting (Meehan, 1982), andHadza (Blurton Jones & Marlowe, 2001)and Hiwi root digging (Kaplan et al., 2000),have return rate schedules with similar

� 2002 Elsevier Science Ltd. All rights reserved.

640 . ET AL.

shapes as hunting curves. This probablyindicates that success is based on learnedskills rather than strength.

Many ethnographers have noted the age-dependence of hunting ability. For example,Lee (1979) states that the Ju/’hoansi aresuperb trackers who are able to identify thename of a person based on their footprintalone, yet ‘‘tracking is a skill cultivated overa lifetime, that builds on literally tens ofthousands of observations’’ (Lee, 1979:47).Liebenberg (1990), who worked with the!Xo, concludes that a hunter’s career peaksbetween the ages of 30 and 45 with ‘‘anoptimum combination of physical fitness,skill, wisdom, and experience’’ (Liebenberg,1990:70). No anthropologist has reportedbeing able to hunt at the same ability levelas their study group members, and mostethnographers usually only make kills ifa native hunter brings them to the game(Kaplan et al., 2000). In contrast, anthro-pologists and acculturated natives appear togather fruits and plant resources with a rateof return similar to more experienced indi-viduals (see Blurton Jones et al., 1994 fordata on acculturated !Kung).

Understanding the causes of this age pro-file of food acquisition has implications forthe evolution of large brains, extended juv-enile periods, and long lifespans in humans.If proficiency in the human foraging nicherequires extended periods of time in skillacquisition, then large brains, extended juv-enile periods and a long lifespan may beco-evolutionary responses to a dietary shift(Kaplan et al., 2000). However, a debatecurrently exists among human behavioralecologists concerning the importance of skillvs. strength components in hunting andgathering and the implications for humanlife history evolution. Some argue that for-aging activities do not require long learningperiods and have demonstrated that foragerchildren can produce a significant percent-age of their daily caloric allowance (BleigeBird et al., 1995; Blurton Jones et al., 1997).

Experimental studies with the Hadza havedemonstrated that activities like Baobab treeclimbing, digging tubers, and archery eithershow no increases with age or that theincreases are better explained by augmentedstrength as opposed to skill and that lostpractice time does not adversely affect per-formance (Blurton Jones & Marlowe, 2001).Results from these studies have been used toinfer that the complexity of human foragingdoes not explain the selection for largebrains or delayed maturity during the courseof human evolution. The juvenile ‘‘waiting’’period is then seen as a result of slow growthdue to low levels of adult mortality and theextension of this period is therefore epiphe-nomenal with respect to increased learning(Hawkes et al., 1998, 2000; Blurton Joneset al., 1999).

On the other hand, Kaplan et al. (2000)present evidence showing that rates ofreturn on difficult foraging tasks, such ashunting and extractive foraging, among theAche, Hiwi and Hadza, peak at older agesthan would be expected by strength andendurance effects. It is interesting that chil-dren’s activities respond to environmentalvariation and that they can produce a signifi-cant percentage of their food in certainenvironments where foraging is relativelysafe, for example Hadza (Blurton Joneset al., 1997) and Mer (Bleige Bird et al.,1995) vs. !Kung (Blurton Jones et al., 1994)and Ache (Hurtado et al., 1985) ecologiesand in seasons of the year when easily-accessible foods such as fruits are avail-able (Blurton Jones et al., 1989; Kaplan,1997). However, in terms of total caloriesand macronutrients provided to a hunter-gatherer diet, difficult-to-acquire extractedresources appear to be much more import-ant than easily acquired foods (Kaplan et al.,2000; Cordain et al., 2002).

While some foraging activities maydepend more on strength than skill, thisdoes not vitiate the argument that subsist-ence task learning is an integral part of the

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juvenile and early adult behavior. Forexample, young Hadza boys ‘‘entertainthemselves by trying to shoot birds andsmall animals’’ (Blurton Jones et al.,1989:379). This activity probably results ina sacrifice of short-term foraging returns thatHadza girls accrue in the harvest of plantproducts. The boys then are potentiallyinvesting in skill acquisition in order todevelop adult proficiency (though theauthors conclude otherwise, Blurton Joneset al., 1997). Indeed, Marlowe (2000) seesexperience as being important for theobserved age pattern of Hadza huntingbecause the peak in hunting performanceand reputation occurs after peak physicalperformance, as we also conclude for theAche data presented here. We hypothesizethat some important foraging activitiesdo require extended periods of skillacquisition in order to reach proficiencyand that a skill-intensive human forag-ing niche has potentially important impli-cations for the evolution of the human lifehistory.

In this paper we attempt to disaggregatethe effects of strength and skill on Achehunting success. We conceptualize skill asspecific motor performance that includescognitive and memory functions. This isdistinguished from strength and endurance,conceptualized as general motor perform-ance and as the energetic capacity toperform work. Strength is considered agrowth-based form of organized somatictissue or embodied capital (sensu Kaplan,1996), whereas skill is considered anexperience-based form of embodied capital(Kaplan, 1996; Bock, 2001). Both strengthand skill are expected to have positive func-tional impacts on resource harvesting rates.Skill acquisition is likely comprised of bothphysical and cognitive abilities that mayrequire appropriate exposure and practiceduring developmental windows (Bock,2001) as well as long periods of adultexperiential learning.

Physical strength is primarily a function ofbody size, which should peak around adult-hood. Cross-sectional analyses of physicalperformance measures across the lifespanfor Ache hunters demonstrate that individ-uals tend to be the strongest around age 24and senesce thereafter (Walker & Hill, n.d.).This pattern is seen in body size, gripstrength, push-ups, pull-ups and chin-ups.Individuals in their early 20s also show thehighest absolute VO2 max estimates, con-sidered a valid measure of the functionalcapacity of the cardiorespiratory system, andthey are also the fastest in the 50 m dash (seemethods and graphs in Walker & Hill, n.d.).Studies of modern populations find peaks incardiorespiratory (Shvartz & Reibold, 1990)and muscular performance in the late teens(Backman et al., 1995). Performance peaksin the 20s have been found for cardio-respiratory and muscular performance in anInuit population (Rode & Shephard, 1971)and in grip strength for the Gidra (Ohtsukaet al., 1987) and a Zapotec-speaking com-munity in Mexico (Malina et al., 1982).Based on numerous studies of muscle func-tion in modern societies, Aoyagi & Shepherd(1992) conclude that physical strengthremains rather constant from maturity toaround age 45 and then declines at anaccelerating rate. Backman and associates(1995) study in Sweden of eight differentmuscle groups found both men and womento be strongest at the age of about 17–18, astrength that remains nearly constant until40 and then declines. However, while iso-metric muscle strength in these studies mayremain relatively constant for some timeafter maturity, this does not appear to holdfor either the traditional studies listed aboveor for more general performance measureslike VO2 max or running speed in eithermodern or traditional populations. Dynamicand cardiorespiratory measures are likely tobe better overall indicators of physicalactivity that is applicable to most huntingactivities.

642 . ET AL.

This paper examines age-dependenttrends in archery and hunting data amongthe Ache to test the hypothesis that reachingproficiency in hunting ability necessitateslong periods of learning prior to and afterphysical maturity. Hunting data are decom-posed into the two important components ofhunting return rate—finding game and theprobability of a successful pursuit whengame is encountered. Multivariate statisticalmodels separate strength effects from skilleffects on ability. Hunting outcome vari-ables are examined across the lifespan tocompare differences between age of peakstrength and age of peak hunting ability. Wepresent preliminary results from a projectdesigned to measure the effects of exper-ience on the hunting success of naivehunters. In addition, patterns of humanhunting are contrasted to documentedpatterns of chimpanzee hunting.

Study groupFood acquisition data have been collectedsince the early 1980s among the NorthernAche of eastern Paraguay, shortly afterpeaceful contact. All ethnographic evidencesuggests that the Northern Ache werehunter-gatherers without horticulture beforecontact in the 1970s (Hill & Hurtado, 1996;Clastres, 1998). Since then the Ache fre-quently trek into the forest with familygroups and return to a permanent reser-vation settlement after several days to amonth. The Ache have exclusive use rightsto the Mbaracayu Reserve where they areallowed to hunt with hands, machetes, andbows and arrows but not using firearmsor dogs. In 1998, the Ache at the ArroyoBandera settlement, where most of the datain this study were collected, spent 14% of allperson days (range 0–50% for individuals)on trek (McMillan, 2001).

Ache men harvest on average 4 kg ofundressed meat per foraging day. There are,however, real and consistent differences inability between adult men (Kaplan & Hill,

1985; Hill & Hurtado, 1996), on the orderof a ten-fold difference (Hill & Hawkes,1983). Moreover, controlling for age, hunt-ing ability is correlated with both increasedfertility and survivorship of offspring (Hill &Hurtado, 1996) making it an importantaspect of biological fitness.

Game animals comprise up to 80% of theAche diet in the forest (see Kaplan et al.,2000). In order of decreasing importance(in terms of biomass harvested) theseinclude the nine-banded armadillo (Dasypusnovemcinctus), paca (Agouti paca), browncapuchin monkey (Cebus apella), tapir(Tapirus terrestris), white-lipped peccary(Tayassu pecari), coati (Nasua nasua), redand grey brocket deer (Mazama sp.), col-lared peccary (Tayassu tajacu), and tegulizard (Tupinambis marianae) (Hill et al.,1997; Hill & Padwe, 2000). The majority ofthese prey animals weigh less than 10 kgwith the exception of the white-lipped pec-cary (mean adult weight 24·9 kg), collaredpeccary (16·3 kg), deer (25·8 kg), and tapir(177·0 kg) (Hill & Padwe, 2000). Manyhunts are cooperative ventures between sev-eral men, and the Ache utilize a dynamic setof often dangerous techniques to harvestthese prey species (see Hill & Hawkes, 1983for descriptions).

Methods

Arrow shooting contestsFifteen arrow shooting contests at twocolonies were conducted with the formatchosen by the Ache—two with womenincluding several teenagers, one with womenand both sex youths 10–16 years of age, andthe other 12 with men 17 years of age orolder. During the course of a contest, eachindividual shot ten times at a wad of strawwrapped in a burlap sack (target circumfer-ence 25·0 cm) propped above the top of atree with a 4 m pole, The average straight-line distance from the shooter to the targetwas 8·8 m for women and youths and

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14·3 m for men. The contests simulate anextremely difficult monkey shot because ofthe shallowness of the angle (33–34�). Thecontestants chose whether or not to partici-pate in any one event creating a participantbias with those deciding to shoot also beingthe more confident shooters. Nonetheless,young men who often do not own bows wereencouraged to participate by the authors andby other Ache. The contestants split intotwo teams for each event with the winningteam receiving the equivalent of US$15 tosplit among 6–10 people.

Young hunter’s notebook dataDuring the pre-contact period (i.e., prior tothe mid 1970s), hunters spent almost everyhealthy day of their lives hunting, with theexception of bad weather days. On thereservation today, school and then horticul-tural activities and wage labor compete withhunting for a young man’s time. Thus, bythe year 2000, most young men had verylittle hunting experience. In May 2000, aselected group of 11 men, ages 14–37, whowere relatively inexperienced at hunting,agreed to participate in a hunting experi-ment. They are paid the equivalent of US$3per day for their efforts, slightly less thanexpected from wage labor but with theadded benefit of obtaining meat that isotherwise expensive to buy. They keep adaily log in a notebook of their huntingactivities. This log includes foraging timestart and end and a description of everyresource encounter, i.e., who found theresource, who participates in the pursuit,and if the resource is harvested andby whom. After running the experimentfor several weeks, their notebooks wereinspected for completeness, and after severalclarifications their recording has becomeremarkably detailed.

The 11 young hunters have documented595 hunting days (3,796 hours) betweenMay 2000 and June 2001. This method-ology supplements time-consuming focal

follow data, avoids confounding factorscaused by an anthropologist’s presence (seeMcMillan, 2001), allows cross-validationbetween hunters’ records, and will eventu-ally allow us to analytically track theirimprovement over time starting from ameasured base return rate. The 11 youngmen’s notebooks have thus far proved to beinternally consistent. That is, because co-operative hunting is the norm, all involved ina pursuit generally report the same finder,caller, and killer, though they occasionallyfill out their notebooks cooperatively. Thereare 64 man-days of overlap between focalfollow and interview data collected by aresearcher and data collected by the youngmen in their notebooks. This overlap allowsfor a reliability test. The number of findsper day for nine prey species (no tegulizards were found) recorded in the note-books matches up closely with those docu-mented by the researcher over the 64 days(555 matches, 21 mismatches, Cronbach’sreliability alpha 0·8915). Information re-garding who kills what is 100% reliable overthe sample.

The hunting sampleHunting data are taken from nine foresttreks in 1981–82, 12 in 1997–98, and fivein 2000 and are combined with the younghunter’s notebook data. Trekking datainclude information gained from a combi-nation of both focal follows of individualhunters, end-of-day interviews of the day’sactivities, and game weights of all animalskilled on a particular day. The ten mostimportant prey species in the Ache diet (theabove list plus the agouti, a small rodent) arethe focus of finding rates and probabilities ofkill upon encounter in this study. In order tomake a kill, a hunter must first find or becalled to game and then, if the decision topursue is made, attempt a kill. We analyzeoutcomes of the search and pursuit modesseparately as two integral components ofhunting ability.

644 . ET AL.

The sample composition broken down byage groups and decade of data collection arepresented in Table 1. The age distributionof hunters has shifted over the last twodecades. In the trekking data from 1981–82,40% of the hunters in the sample wereunder the age of 25 (lower bound age 12).In recent years, 1997–98 and 2000, thisnumber has fallen to 16%, though this agegroup currently constitutes 36% of the malepopulation of hunting age (i.e., 12 or older)at Arroyo Bandera.

The number of days spent hunting forArroyo Bandera men from 1995–1999 wasrecorded by a native Ache informant (Hillet al., 1997; McMillan, 2001). The percentof days spent hunting for the young data-collecting men before the experiment(August 1995–December 1999) is only 2%,but during the experiment (15 May 2000–30 June 2001) has risen to 13%. Several ofthe men did not reside at Arroyo Banderafor sample period before the experimentstarted, but they are unlikely to have doneany hunting elsewhere as other Ache settle-ments rely less on foraging. Two young meneach spent a year in military service.

Because of the secular trend of decreasingtime allocations to traditional hunting, theeffects of age on hunting ability (with bodysize controlled) presented in this paper arelikely results of both acculturation-drivencohort effects and true age effects. Bothsupport the hypothesis that hunting requires

skill investment, but it is possible that therole of experience becomes much moreimportant as the range of skill variation inthe population increases with acculturation.

Table 1 Sample population sizes (n) for threeage groups separated by decade of data collection

Age group Decade n

<25 80s 2390s/00 6 (0)

25-40 80s 2190s/00 16 (11)

>40 80s 1390s/00 18 (8)

Numbers in parentheses represents the number ofindividuals represented in both decades.

Data analysisData analyses are conducted in SAS usingthe GLIMMIX (general linear mixedmodels) macro. This macro allows bothfixed and random effects. A random effectvariable representing an individual is usedthroughout this paper. A random effect par-ameter estimate is constructed for each indi-vidual that accounts for individual variationthat may exist independently of strengthand skill measures (i.e., unmeasured hetero-geneity) and is thus preferable to using eachindividual as a single data point, especiallysince most individuals enter the sample atvarious ages and body sizes. The individualrandom effect also controls for the lack ofindependence inherent in using variousnumbers of multiple measurements on anyone individual without making the assump-tion of a homogenous population and bias-ing the results towards those individualswho are over-represented in the data set(Verbeke & Molenberghs, 1997).

The general equation for the modelsis Yji =�0 +�1AGEji +�2AGE2

ji +�3BODYSIZEji+�i where Yji is the outcome variablefor event j (e.g., prey encounter or day ofhunting) for individual i. �1–�3 are fixedeffect parameters that relate age and bodysize (kg) to Yji. �i is a random effect esti-mated for each individual i in the sample.Finding rate models include an additionalterm that adjusts for methodological differ-ences (focal follow, interview and note-book), and the pursuit models include anadditional term for differences in decadeand another random effect that accounts forvarious prey types. Finding rates with gameare expected to follow a Poisson distribution(Stephens & Krebs, 1986) so counts of preyfound per day are statistically modeledassuming Poisson errors. The link function

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is adjusted by total foraging time to makeeach day comparable. Probability of killupon encounter and probability of shoot-ing a successful arrow are modeled asa probability using logistic regression.Unfortunately there are no accepted globalgoodness of fit tests for GLIMMIX models.However, each model fit can be compared tonon-parametric curve fits (e.g., LOWESS)in plots of dependent variable by age. Thosecomparisons suggest that the models pro-duce curves that very closely approximatethe nonparametric fits. These comparisonsare more valid for models where the effect ofbody size is insignificant.

Results

14

12

8

4

010 15 40

Age50 65

Sh

ooti

ng

Su

cces

s (%

)

6

10

2

20 25 30 35 45 55 60

Figure 1. Percentage of shots individual men stuck into the target by age with a LOWESS fit using SPSSsoftware. The sample is 2004 shots by 57 different men.

Arrow shootingThe overall success, or arrows stuck into thetarget, was 2 in 344 (0·6%) for all women, 0out of 70 for the male youths, and 81 out of1934 (4·2%) for the men. Assuming astraight line shot with constant velocity, an

arrow more than about one degree off

target is a miss. Considering the additionaladjustments necessary to account forgravitational force and wind speed, it isremarkable that the target was ever hit.

We analyze all arrow shoots for adult menand male youths because the target was ofsimilar size, angle, and distance (thoughadjusted closer for the youths) for each ofthe ten contests (2004 shots by 57 differentmen). We use a logistic regression modelwith hit (=1) or miss (=0) as the dependentvariable and individual as a random effect.Both age and grip strength (or body size)are significant predictors of success withsimilar magnitudes up to age 40 (AGE par-ameter estimate=0·1142, P=0·0170; GRIPparameter estimate=0·1198, P=0·0269)with no significant effect thereafter (AGEP=0·3352, GRIP P=0·1757). The ageeffect is illustrated in Figure 1 where successrate increases to around age 40 and thenlevels off. There are large differencesbetween men as seen both in the graph and

646 . ET AL.

in the significant individual random effect inthe model (P=0·0266 for all ages). Becausethe archery data are cross-sectional, we can-not separate the true age effects from apotential cohort effect on these results, butwe tentatively conclude that strength andskill are important determinants of shootingability.

Rates of finding and killing preyAche hunting follows an ordered sequence.First, hunters generally enter search modespread apart from other hunters, out of sightbut within earshot. If a resource is found, ahunter can decide to ignore it, to call others,or to pursue it alone. If the resource is notignored, the hunter spends some time inpursuit and then goes back to searching,thereby repeating the sequence. In order tomake a kill, a hunter must first find or becalled to game and then, if the decision topursue is made, attempt a kill. We analyzeoutcomes of the search and pursuit modesseparately as two integral components ofhunting ability.

Figures 2(a) and (b) shows the age profileof hunting return rate (kilograms of preykilled per hour) and demonstrates that kill-ing rates continue to increase well afterfull-adult strength is obtained. Overall hunt-ing ability peaks at approximately age 37 inthe 1980s but has shifted slightly to aroundage 42 in the 1990s, probably due to lesspractice time in the later sample. Note howsome good hunters are successful into their50s, though the sample size at older ages isvery small. Figure 2(b) demonstrates ratesof increase of individual hunters sampled inboth decades. These longitudinal data sug-gest that the age profile of hunting abilityis real, as opposed to an artifact of accultur-ation. Of the eight men first sampled in theirlate teens or early 20s and then again intheir 30s or 40s, four hunters increased intheir ability at an average rate approximateto that of the LOWESS curves in Figure2(a), two hunters had exceptional increases,

and two hunters had small increases.Patterns of senescence in Figure 2(b) aremuch more variable as the end points tendto be for only a few individuals with loweramounts of hunting time, yet some huntersdo remain quite productive into their 50s.

Counts of prey found per day are statisti-cally modeled in Table 2. To make differentman-days comparable, time spent out ofcamp is entered in the model as an offset.Table 2 analyzes rates of finding the tenimportant prey items. Direct independenteffects in the model include age, age-squared and body size (in kg). Data areanalyzed separately for the 1980s and the1990s/2000 sample.

The analyses indicate that finding ratesincrease with age and then decrease. Theage effect on finding game is positive and theage-squared term is negative. Both are sig-nificant and models of each decade haveparameter estimates of similar magnitude.Given the parameter estimates from Table2, prey finding rates peak at age 39 and 38for the first and second study periods,respectively. Figure 3 graphs rates of findingprey across the lifespan for the 1990s only(because of better methodological hom-ogeneity) for armadillos (finding rate peaksat age 38), pacas (age 36), and monkeys (age49). These performance peaks are surpris-ingly late in life, significantly later than agewhen physical strength peaks.

Because strength peaks at the age whenpeak body size is attained, body size is usedas a proxy for overall physical strength in themodel; the positive linear age effect can thenbe interpreted as a measure of skill. Thenegative parameters for the age-squaredterm (Table 2) probably reflects both dimin-ishing returns to knowledge and decreasedstamina resulting in the eventual decline infinding rates. Body size is significant forthe finding rate model for the 1980s inTable 2, but not for the 1990s/2000 sample.Interestingly, Ohtsuka (1989) found thissimilar pattern to be true for the Gidra

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1.50

1.25

(a)

0.75

0.50

0.0010 15 40 50 70

1.00

0.25

20 25 30 35 45 55 60 65

1.50

1.25

(b)

0.75

0.50

0.0010 15 40

Age50 70

Ret

urn

Rat

e (k

g/h

r)

1.00

0.25

20 25 30 35 45 55 60 65

1990s/2000

1980s

Figure 2. (a) LOWESS curves fitted to return rates for 78 individual hunters (1392 hunter-days) acrossthe lifespan separated by decade. The denominator for return rates is total time out of camp. The returnrate data use focal follow and interview data for 1981–82 and notebook, focal follow, and interview datafor 1997–98 and 2000. (b) Graph is the same as (a) except hunters that were sampled in both decadeshave their data points connected with a line to show longitudinal trends.

Papuans—a higher and significant corre-lation between hunting return rate and gripstrength in 1971–72 than in 1981 where

there was a nonsignificant correlation.Perhaps strength becomes less of animportant determinant of hunting ability as

648 . ET AL.

Table 2 GLIMMIX models of rates of finding all ten prey per hour separated by decade

Dependent Intercept Age Age�age Body size Individual Interview Notebook

1981–82 finding rate model (824 hunter-days; 57 hunters)All 10 prey �6·4899 0·0931 �0·0011 0·0555 0·1215 �1·0605

(<0·0001) (0·0155) (0·0204) (0·0004) (0·0149) (<0·0001)

1997–98/2000 finding rate model (512 hunter-days; 40 hunters)All 10 prey �3·0949 0·0874 �0·0011 0·0056 0·0121 �0·1706 �0·7346

(<0·0001) (0·0010) (0·0020) (0·4307) (0·3097) (0·1813) (<0·0001)

Estimates are given with their associated P-values in parentheses. Individual ID is entered as a random effectwith the cumulative individual effect given. Interview and Notebook represent methodology as compared to thebaseline of focal follow data.

0.10

0.08

0.04

0.0010 15 40

Age50 70

0.06

0.02

20 25 30 35 45 55 60 65

Armadillo

Paca

Monkey

Figure 3. LOWESS curves of finding rates per hour with armadillos, pacas, and monkeys across thelifespan (other prey too rare to display) for the 1990s/2000 sample.

a population undergoes acculturation andthe range of variation in skill within thepopulation increases. Nonetheless, ourmeasure of skill (age with body size con-trolled) is strong and significant for bothdecades.

Methodological differences are capturedby comparing interview against the baselineof focal data in Table 2. The strong negativeeffect of interview methodology in the 1980sresults from the end-of-day interviews being

much less rigorous than those used morerecently. Interview data in the 1990s/2000are more comparable with focal data due tomore rigorous interview techniques. Thestriking difference in the latter decade isbetween the young hunter’s notebook dataand the focal follow data. Controlling forage and grip strength, these young huntersfind all ten prey at only 48% of the focalfollow rate (P<0·0001; Table 2). Thisstrong effect is due to inexperience as

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opposed to methodological differencesbecause interview, focal and notebook datain the sample from the latter decade all showhigh validity as demonstrated above.

The problem of self-selection makes itdifficult to determine whether some huntersare better than others because they havemore experience or if better hunters aresimply spending more time hunting. Toavoid this problem we can examine thenotebook data for improvements from abaseline and enter number of days huntedinto the regression. No increase in huntingability (measured as return rate, findingrate, and probability of kill upon encounter)among the 11 young men is detectable overthe 13·5 months since the beginning of theexperiment despite the fact that the meannumber of hunting days per individual is 54.The 95% confidence interval of the par-ameter estimate of days spent hunting onreturn rate while controlling for age andindividual is unfortunately quite wide(�0·00485, 0·000948) due to high day-to-day variance in hunting returns. The upper95% level would translate into an increase of0·3 kg/hr/year, an order of magnitude abovethe rate of increase seen in Figure 2(a).Nonetheless, the lack of improvement of the11 individuals each with an average of 54days of experience is consistent with the factthat it takes over two decades for huntingreturn rates to peak at around age 40,though more data are clearly needed torefine our estimate of the rate of increasewith experience. The age effect on huntingreturn rate in the same young hunter regres-sion is only 0·013 kg/year (P=0·0022),which is only half the rate of increase fromage 14 to 40 in Figure 2(a) (about 0·027 kg/year) for the entire hunting example. Thisfurther demonstrates how previous lack ofexperience adversely affects hunting ability.

Probabilities of successful pursuitsThe probability of killing prey uponencounter is modeled in Table 3 using

logistic regression (failed attempt=0 orkill=1). Each data entry used in the prob-ability of kill-upon encounter analysis con-sists of a man-pursuit, so each pursuitrecorded has an entry for each personinvolved. Direct effects in the modelsinclude age, age-squared, body size anddecade (1980s vs. 1990s/2000). The firstmodel in Table 3 controls for individualskill differences and different numbers ofpursuits with the ten main prey items byentering individual and prey type as randomeffects.

Probability of a kill after an encounterincreases and then decreases with age. Theage effect on the probability of making a killis positive, and the age-squared term isnegative. Both are significant. Figure 4 givesthe probability of kill upon encounter foreach of the ten important prey species acrossthe lifespan. Most prey demonstrate shallowability peaks in the mid to late 30s with theexception of monkeys. Monkey pursuit suc-cess demonstrates a sharp rise in ability intothe 40s. This is due to complex maneuver-ing amidst dense undergrowth necessary tomake a clear shot at mobile monkeys in theforest canopy without shooting or being shotby other hunters. The strong age effect onthe probability of a kill upon a monkeyencounter is also due to young men whomay not even carry a bow but, nonetheless,often assist in monkey hunts by climbingtrees to flush out monkeys. This is oneexample of the allocation of hunting tasks onthe basis of differential skill. Better monkeyhunters also scored better in the arrowshooting contests as demonstrated by thecorrelation between individual randomeffect coefficients (measures of intrinsicability) for monkey pursuit success andarrow shooting contests (r=0·46, P=0·006,n=31).

We could find no significant effect of bodysize on probability of kill upon encounterand conclude that strength is relativelyunimportant in comparison to skill. This is

650 . ET AL.

surprising because strength was found to bean important determinant of archery ability.However, approximately 60% of harvestanimal biomass comes from hand huntedanimals, and once an animal is grabbed orsubdued most Ache of hunting age are prob-ably capable of making a kill. It is likely asubtle combination of pursuit stealth andknow-how learned through many repetitionsthat make the difference between a kill and afailed opportunity.

The probability of kill upon a monkeyencounter is modeled separately in Table 3as Ache hunters inform us that monkeys area very difficult prey to kill. Indeed, the age,decade, and individual random effect arestronger for monkeys than in the all-ten preymodel. Using the logistic regression resultsin Table 3, we can solve for the probabilityof kill upon encounter for all ten prey andmonkeys at each age. At the peak age in

ability (age 45), men in the 1980s made akill on 24% of encounters but men in the1990s/2000 only made kills on 10% ofencounters. The probability of a successfulkill upon a monkey encounter in the 1980sis about 50% higher than in 1990s/2000.Perhaps this decade effect results from lostpractice. No significant age by decade inter-action could be found on the probability of asuccessful kill; thus it appears that men of allages are less efficient from lack of practice.

GLIMMIX logistic models of the probability of kill upon encounter

Model Effect Estimate Pr>�t�

Pursuits with all ten prey (n=2642) Intercept �4·0300 0·0003Age 0·0853 0·0010Age�age �0·0010 0·0019Body size 0·0077 0·4515Decade 80 1·0398 <0·0001Individual (n=91) 0·0577 0·0536Coati 1·1859 0·0025Armadillo 1·1226 0·0015Tegu lizard 1·0365 0·0226Monkey 0·7543 0·0328Paca �0·0005 0·9988C peccary �0·0878 0·8498Agouti �0·3140 0·4939Deer �0·9235 0·0310W-L Peccary �1·1562 0·0036Tapir �1·6192 0·0038

Monkey pursuits (n=588) Intercept �4·4932 0·0068Age 0·2071 0·0015Age�age �0·0023 0·0034Body size �0·0214 0·4517Individual (n=71) 0·4446 0·0132Decade 80 1·3929 <0·0001

The all-ten-pursuits model has prey type as a random effect with prey listed indescending order of kill probability. Decade 80 is a dummy variable indicating if thepursuit occurred in the 1990s/2000 (=0) or in the 1980s (=1).

Table 3

Discussion

Ache informants provide anecdotal evidencesuggesting that successful hunting requiresintelligence and a period of learning. Forexample, informants state that teenageboys learn hunting by first specializing onarmadillos and pacas, which are burrowinganimals that require only a little practice to

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b (k

ill/e

nco

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20 25 30 35 45 55 60 65

Figure 4. LOWESS curves of the probability of kill upon encounter across the lifespan for all ten preyspecies. Those with no discernible age effect are given an average success rate in the legend. Species: coati(— - —); monkey t. lizard 65/140=.46 (— —); armadillo (······); collared peccary (– – – –); paca (— - —);agouti 25/200=.13; white-lipped peccary deer 69/663=.10; tapir 2/202=.01 ( ).

kill. Later, boys begin using bows andarrows (that are made for them by oldermen) and start to hunt terrestrial game,deer, peccaries, tapir, etc. But if they woundthese animals, older hunters are usuallycalled to help track them. Only in their late20s do hunters begin to make their owntools and participate in monkey hunts asarchers. This is partially because predictingwhere monkeys will flee, and getting intoposition for a good shot through thick forestundergrowth is complicated. In addition,hunters surround monkeys and shoot fromall angles and older hunters are afraid theyounger inexperienced hunters will eithershoot somebody by accident (not correctlycalculating the trajectory of their arrow) orget shot themselves. Such accidents arecommon (KH was shot in the back in 1982during one of these events). Finally, calcu-lating where an arrow will fall requires inte-grating information about angles, velocity,wind speed and intervening vegetation.Young hunters frequently lose severalarrows in each hunt, while older huntersusually find most of the arrows they shoot.

Consistent with this description of theontogeny of hunting skills are the obser-vations of two men who suffer from mentaldisabilities. The first, who was mostafflicted, was able to kill armadillos, pacas,tegu lizards and coatis, but no other game.The first three of these species are found inburrows and killed by hand, and coatis arefound in trees, and caught by hand andslammed to death when they jump toescape. The other concentrated primarily onthe same hand-killed animals although hesometimes killed monkeys as well. Between1994 and 2000 his total game harvest con-sisted of 79% armadillos and only 7%monkeys whereas the average for all otherhunters was only 60% armadillos and 14%monkeys.

We have found significant age effects onimportant measures of hunting ability (withbody size controlled). Most notable of theseare total number of prey found per hour andthe probability of kill upon encounter withimportant prey species, especially monkeys.Peaks in these measures occur much later inlife than age of peak physical performance.

652 . ET AL.

We could find no significant effect of bodysize on the probability of a successful pur-suit. While strength is certainly an importantfactor in some foraging activities such asbow-and-arrow shooting, our results indi-cate that skill is more important in attainingproficiency in finding and killing prey.

Additionally, analysis of longitudinalnotebook data from the inexperiencedhunters shows no detectable improvementafter 13·5 months. It remains to be seen ifreservation-born Ache will ever perform atthe same elevated level as their forest-borncounterparts. The low rates of finding preyfor the hunters collecting data in notebooks(Table 2), the lack of improvement bythe young hunters over the initial 13·5months of the study, and the strong negativeeffect of decade in pursuit success (Table 3)suggest not.

Interesting comparisons can be madebetween our results and data from Olympicathletes as peak ages in ability in varioussports appear to map on to the skill-intensityof the activity. Despite the drastic improve-ments over the last century in Olympictraining methods and equipment, Schulz &Curnow (1988) have found that the meanage of peak performers in many events hasremained rather constant from 1896 to1980. In men’s short distance running, peakperformance is around age 23, medium dis-tance running age 24, long distance runningage 27, and swimming age 20. Sport requir-ing more precise motor control takes longerto reach proficiency. For example, baseballplayers reach their prime at age 27 andgolfers age 31 (Schulz & Curnow, 1988;Horn, 1988).

In comparison to these Olympic sports,traditional hunting takes even longer toreach proficiency. As mentioned in theintroduction, hunting return rate curvespeak around 35 for the Hiwi (Kaplan et al.,2000), 40 for the Machiguenga and Piro(Gurven & Kaplan, n.d.), early or mid-40sto mid-50s for the Etolo (Dwyer, 1983),

45–50 for the Hadza (Marlowe, 2000), and37–42 for the Ache [Figures 2(a) and 5].Ohtsuka (1989) found Gidra huntersaround the ages of 35–45 to have returnrates four times higher than hunters in theirlate teens, though both age groups had equalaverage grip strength. A dataset collected byan Ache informant of all game killed atArroyo Bandera from 1995–99 that partiallyoverlaps with the one used here, also showsan age peak at age 40 in units of gameweight killed per day. Indeed, from age athighest strength (24) to age at peak returns(40), the average return rate for an Acheman more than doubles from 0·3 to 0·7 kgof raw meat per hour [Figures 2(a) and5]. This 16-year lag between peak strengthand peak hunting ability strongly implicatesa significant skill component in hunting abil-ity, a conclusion also reached by Marlowe(2000) for Hadza hunters.

However, when interpreting age specificencounter and return rates, it is importantto consider the use of total time spent outof camp as the denominator in these ratemeasures. We believe that post-peakdeclines in these curves with age result atleast in part from older men adopting asearch strategy that exploits more honey,fruits, larvae and palm products. Youngmen may also adopt a similar strategy, andthis may then contribute to the peakednessin the return rate curve and the power ofthe age-squared terms in the finding ratemodels. This measure may underestimatethe true finding rate per unit of huntingsearch time. Nonetheless, this strategy makessense in that younger men with undevelopedhunting skills, like older men with decliningstrength and endurance, have loweropportunity costs per unit of hunting timeforegone for time spent exploiting easier-to-acquire nongame forest products.

Given that Ache start hunting around theages of 12–15, it takes nearly 30 years forhunters to reach their prime. Given currentdemographic trends, a 12-year-old Ache

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male on the reservation can expect to live toage 64 and would have expected to liveto age 51 in the pre-contact period (HillHurtado, 1996). In both cases, lifeexpectancy is beyond the age of reachingproficiency as a hunter, a requirement forreaping the returns on investment in skill.Extant apes do not meet this requirement.For example, a 12-year-old free-living malechimpanzee expects to live to about age 25(Hill et al., 2001). Even if chimpanzeesdeveloped hunting skills at the same rate asAche hunters, they would not reap returnson their investment because of the differ-ence in the mortality profile (Kaplan et al.,2000; Kaplan & Robson, n.d.). This sug-gests that the human foraging niche and theelongation of the human lifespan are linkedand that selection is likely to favor concomi-tant increases in skill investment (e.g., brainsize) with decreases in mortality (e.g., tooluse and food sharing).

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Figure 5. Comparison between hunting return rate and strength, an equally weighted composite measureof weight, grip strength, arm diameter, 50-m dash speed, push-ups, pull-ups, and chin-ups for 71 men.Both return rate and strength are on the scale of percent of adult maximum. Recall that sample sizes forhunting return rate at later ages are small.

Chimpanzee huntingInteresting contrasts can be drawn betweenthe predatory behavior of modern hunter-gatherers and that of chimpanzees. Perhapsthe most important difference is thathumans hunt at a rate several orders ofmagnitude above chimpanzees (Hill, 1982;Stanford, 1999) and hunted meat consti-tutes only 1–3% of the calories in chimpan-zee diets but an average of 59% for tentropical hunter-gatherer societies (Kaplanet al., 2000). This contrast stems from dif-ferences between the human and chimpan-zees in both search and pursuit modes ofhunting. Stanford (1999) states that theGombe chimpanzees he has observed do notsearch for meat but rather fortuitouslyencounter prey on foraging forays. However,Mitani & Watts (1999), who work with theNgogo chimpanzees, document that 41%(20/49) of hunts appear intentional inthat pre-encounter hunting parties were

654 . ET AL.

unusually silent and walking in single file.Moreover, in a description that sounds strik-ingly similar to Ache hunting behavior,Mitani & Watts (1999:445) discuss how‘‘the chimpanzees would stop, look up intothe trees, scan, and change directions severaltimes without calling’’ and ‘‘were extremelyattentive to any arboreal movements andwould stop and search whenever motionwas detected’’. While chimpanzees mayintentionally search for prey on occasion,Ache hunters always deliberately search forprey while moving through the forest. Weattribute this behavioral difference to theenhanced ability of humans to locate preythrough tracking and identifying spoor andfeeding signs. This ability allows humanhunters to locate and pursue prey that chim-panzees do not encounter. The ability toevaluate cryptic signs and then choosewhen to follow up and when to abandonthe pursuit requires human levels ofintelligence.

Human foragers also kill a variety ofarboreal, terrestrial, aquatic, and burrowingprey with a myriad of often amazinglycreative tactics (see Kaplan et al., 2000).Chimpanzees focus their hunts on redcolobus monkeys—82% of all huntsrecorded at Gombe (Stanford et al., 1994),55% at Mahale (Uehara et al., 1992), 78%at Taı (Boesch & Boesch, 1989), and 91%at Ngogo (Mitani & Watts, 1999). Thispattern clearly indicates nonrandom preyselection for a smaller primate species thatcan be trapped or overtaken. In contrast,Ache hunters appear to harvest prey innumbers much closer to their environ-mental density. For eight of the majorprey species, mean environmental densitypredicts 80% of the variation in annualharvest (Hill & Padwe, 2000). Humanhunters are capable of taking the primeindividuals of even the largest of species inany habitat, whereas chimpanzee huntingappears to focus on immature red colobusmonkeys [84% of colobus kills at Gombe,

70% at Mahale, 78% at Taı, 66% atNgogo (Mitani & Watts, 1999)]. Piro andMachiguenga hunters focus on prime-aged individuals (Alvard & Kaplan, 1991).Moreover, cooperating Ache hunters oftensystematically exterminate all or most mem-bers of social groups of brown capuchinmonkeys or coatis upon encounter.

Adult chimpanzee males make themajority of kills (Uehara, 1997). Forexample, adult males made 86% of the killsat Ngogo (Mitani & Watts, 1999) and somemales are documented as being much betterhunters (e.g., Frodo in the Gombe popu-lation who killed 10% of the colobus popu-lation in his home range, Stanford, 1995).Boesch & Boesch-Achermann (2000) showthat important Taı chimpanzee huntingtechniques, such as ambush and blockmovements and anticipation of both preyand fellow chimpanzee hunters, are pro-gressively learned over a 20-year process,beginning at age nine or ten. The learningbehavior is inferred because young chim-panzees observe hunting tactics employedby their older counterparts and adapt theirmovements accordingly (Boesch & Boesch-Achermann, 2000). Humans have takenskill and learning to a new level, and this ispresumably reflected in increased brain size,longer juvenile dependence, and an elevatedability to harvest prey.

Human life history evolutionResults presented in this paper haveimportant implications for understandingthe evolution of the life history of ourspecies. If hunting was an important econ-omic activity of early hominids, the learningcurve for hunting success may partiallyexplain why humans have big brains, longlearning periods, and long lifespans (Kaplanet al., 2000). The same forces would applyto any type of skill-intensive foraging inhominid history (ibid.). For example, rootdigging, as described for Hadza foragers,often involves difficult rock excavation

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and the solving of ‘‘intricate engineeringproblems’’ (Hawkes et al., 1989:344).

The hominid shift into a skill-intensiveforaging niche where elevated returns arerealized later in life should select forincreases in brain size, provided largerbrains facilitate increased capacity for learn-ing and mortality rates are sufficiently low toallow for returns on skill investment (seeKaplan & Robson, n.d. for a theoreticalmodel demonstrating such effects). Ourresults in this paper are consistent with amodel that sees humans moving into a skill-intensive foraging niche after a reduction inmortality or one in which the length of thelifespan, large brain, and foraging niche aretightly bound in a co-evolutionary process.Moreover, the learning of social skills mayalso produce parallel selective pressures withthe acquisition of goods and services cominglater in life through social manipulation andcoalition building, an hypothesis worthy offuture tests.

Regardless of the prime mover in thisevolutionary process, we suggest that cur-rent human adaptations allow post-reproductive females and adult males toproduce large food surpluses in theform of hunting, fishing, tuber digging, andother forms of extractive foraging. Thisfacilitates social systems characterized bypair-bonding, a sexual division of labor,increased food sharing, and provisioningof dependent juveniles and reproductivefemales (Lancaster & Lancaster, 1983).These characters, combined with mortalityreduction from sociality and food sharing(Kaplan et al., 2000) perhaps favor anextended juvenile period (see Stearns, 1992;Charnov, 1993) that in turn facilitates evengreater investments in skill and learning.Conceivably, this process created run-awaypositive feedback selection for rapidencephalization and lengthened juvenileperiod and adult lifespan, stabilizing at lifehistory parameters exhibited by modernpeoples.

Conclusions� Age-specific hunting performance meas-

ures—finding rates and probabilities ofkill upon encounter for important preyand archery ability—look very differentfrom strength schedules. Proficiencypeaks in hunting ability tend to occurlater in life. Hunters at the peak returnrate age of 40 harvest on average 0·7 kgper hour while hunters at peak strengthharvest at Y that rate (Figures 2 and 5).

� Inexperienced hunters show no signifi-cant increases in hunting return rates afteran average of 54 days of practice.

� Our results suggest that skill acquisition(for almost 30 years) is an important com-ponent of Ache hunting. These results havepotentially significant implications for thehuman life history—the co-evolution oflarge brains, food sharing, provisioning,decreased mortality, increased fertility,increased period of juvenile dependence,sexual division of labor, and pair-bonding—in response to the occupationof a human-like foraging niche focusingon difficult-to-acquire and nutrient-densefood resources acquired in large packages.

AcknowledgementsWe want to thank the Ache for all theirhospitality and to the kbuchuvegis for theircareful effort and enthusiasm in self-data collection. Three ‘‘anonymous’’ JHEreviewers, JHE editor Terry Harrison, OzziePearson, John Bock, Mike Gurven, andKeely Baca were very helpful for commentsand criticism. Kim Hill thanks MagdalenaHurtado for insights into foraging pat-terns of teenage boys and older men.The following University of New Mexicosources funded this project: a Research andAllocations Grant to Kim Hill and a LewisR. Binford Graduate Fellowship; StudentResearch and Allocations Committee Grant;Research, Projects, and Travel Grant; anda Latin American and Iberian InstituteResearch Grant to Robert Walker.

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