Ethnographic and Modeled Costs of Long-Distance, Big-Game Hunting

Society for American Archaeology

Ethnographic and Modeled Costs of Long-Distance, Big-Game HuntingAuthor(s): Deanna N. GrimsteadSource: American Antiquity, Vol. 75, No. 1 (Jan., 2010), pp. 61-80Published by: Society for American ArchaeologyStable URL: http://www.jstor.org/stable/20622482 .

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ETHNOGRAPHIC AND MODELED COSTS OF LONG-DISTANCE, BIG-GAME HUNTING

Deanna N. Grimstead

Evolutionary ecology provides a rich pool of models from which archaeologists derive expectations about prehistoric human

behavior. Signaling Theory (ST) has been applied successfully in ethnographic and certain archaeological contexts. Other

applications have fallen prey to post-hoc explanation of aberrant archaeological patterns. This paper evaluates the claim

that big-game hunting was a costly foraging behavior when traveling great distances, and therefore was undertaken as a

form of costly signaling. The central place foraging model is used in conjunction with caloric expenditure formulae, derived

from human energetics and locomotion research, to evaluate the cost of travel and transport versus the returns for large and small prey items. It is shown that big game continues to yield significant energetic returns even in situations where

travel costs are comparatively high (i.e., 100-200 km round-trip). Small game hunting becomes energetically costly when a forager makes a procurement round-trip of more than ca.10 km. Large game animals are the highest return prey items

even when procurement distances are comparatively great because humans are physiologically well-adapted for carrying

objects over long distances. While the capture of big game animals may have bestowed prestige upon prehistoric hunters or served as some other signal of individual quality, these prey animals were not overly costly in terms of energetic

efficiency?even under increased travel costs. These results emphasize the difficulty of separating social prestige from opti mal foraging as the basis for big-game hunting in archaeological contexts.

La ecologia evolucionista proporciona una fuente abundante de modelos utilizados por los arque?logos al formular expecta tivas sobre el comportamiento humano prehist?rico. La Teoria de Senales (ST, por sus siglas en ingles) se ha podido aplicar con exito en ciertos contextos arqueol?gicos y etnogrdficos. En otros casos, su aplicaci?n ha sufrido el invento de explica ciones post hoc de patrones arqueol?gicos aberrantes. Este trabajo eval?a la afirmacion de que la caceria de presas grandes era un comportamiento de forraje costoso en viajes de larga distancia y que por eso se emprendia como forma costosa de

senalar. Se emplea el modelo de forraje de sitio central, junto con formulas de gasto cal?rico que se derivan de la investi

gaci?n de la energetica y locomocion humana, para asi evaluar el costo del viaje y del transporte versus los rendimientos de items de presa de la caza mayor y menor. Se demuestra que la caza de presas grandes produce rendimientos energeticos impor tantes aun en situaciones donde los costos de viaje sean relativamente altos (i.e., 100-200 km ida y vuelta). La caza de pre sas pequenas se vuelve energeticamente costosa cuando unforrajero teorico complete un viaje de obtenci?n de mds de ca. 10

km. Los animales de caceria mayor representan el rendimiento mds alto aun cuando las distancias de obtenci?n sean relati vamente grandes porque los humanos estdn bien adaptados fisiol?gicamente a cargar objetos a troves de distancias largas.

Mientras que la captura de animales de caceria mayor puede haber concedido prestigio a cazadores prehist?ricos, o puede haber servido para senalar alguna otra cualidad personal, estas presas de caceria no eran demasiado costosas en terminos

de la eficacia energetica, aun con mayores costos de viaje. Estos resultados enfatizan la dificultad de separar del prestigio social el forraje optirno como base de la caceria mayor en contextos arqueol?gicos.

Signaling theory is a neo-Darwinist perspec tive on communication among and between individuals. This broad theory may be used

to explain any situation where a signaler and receiver engage in communication that is mutually beneficial (e.g., Andersson 1976; Greenfield 2002). Signalers and receivers communicate mutually beneficial honest signals via visual, olfactory, and tactile cues. Most anthropological applications of

signaling theory have utilized a subset dealing with communication that is costly to produce. The basic

premise of costly signaling theory is that a costly signal results in an honest communication of qual ity or capability because only high-quality indi viduals can "afford" the costs and/or risks involved in producing the signal (Zahavi and Zahavi 1997). Several classic nonhuman examples of this com

munication can be found within the plumage of sev

Deanna N. Grimstead Department of Anthropology, University of Arizona, Tucson, AZ 85711 ([email protected])

American Antiquity 75(1), 2010, pp. 61-80

Copyright ?2010 by the Society for American Archaeology

61



62 AMERICAN ANTIQUITY [Vol. 75, No. 1,2010

eral avian taxa (Andersson 1976;Andersson 1992),

stotting behavior amongst ungulates (Byers 1984;

Fitzgibbon and Fanshawe 1988), and male roars

within the red deer (Cervus elaphus; Clutton-Brock

1979). The prevalence of honest signals within eco

logical contexts suggests honest signals should be detectable within human societies, but identifying how these signals manifest across human cultural variations has proven quite difficult (e.g., Tovee et

al. 2006). Archaeological identification of this vari

ability is confounded by several factors that are not

major constraints on analyses in the present (cf.

Codding and Jones 2007). Applications of costly signaling theory to archaeological data sets is dif ficult methodologically because direct observation of the signal, the signaler, and the directional pref erence of the receiver may not necessarily be vis ible within archaeological datasets (Grimstead and

Bayham 2007, 2009). Variables that are visible either directly or via direct proxies in modern ethno

graphic research, such as mating opportunities, number of offspring, and the cost of the behavior

(e.g., Bliege Bird et al. 2001; Smith 2004), are at

best fuzzy and at worst not available for archaeo

logical inquiry. These difficulties have not prevented archaeol

ogists from applying costly signaling theory to their research. Unfortunately, some archaeological appli cations have ignored these complications, which have ultimately caused a theoretical conflict between optimal foraging theory and costly sig naling theory. In these applications prey taxa that were once thought to be high return (i.e., large game) are now thought to be of low economic value and evidence of costly male show-off behavior

(Hildebrandt and McGuire 2002, 2003; McGuire

and Hildebrandt 2005; McGuire et al. 2007). Sev

eral rebuttals to this argument have previously been

published (Broughton and Bayham 2003; Codding and Jones 2007), resulting in a debate over whether

large game is costly or high return relative to other

options. This paper seeks to answer if big game is

costly, and under what conditions would we expect it to become costly. In answering these questions special attention is paid to travel and transport costs

associated with long-distance procurement strate

gies, given the assumption of prehistoric foragers

acting as central place foragers (Cannon 2003; Ori ans and Pearson 1976).

Signals and Optimality: A Theoretical Background

The question addressed in this paper draws its the oretical base from evolutionary ecology (Pianka 1994), and more specifically from signaling the

ory (Hasson 1994,1997; Zahavi andZahavi 1997) and the central place foraging model (Cannon 2003; Orians and Pearson 1976). The central place for

aging model comes from a subset of evolutionary ecology termed "optimal foraging theory" (Schoener 1979). This model assumes the forager's optimal foraging goal is to identify the patch that will maximize the net returns to the central place, given a landscape with internally homogenous resource patches located at varying distances from the central place (Figure 1; Cannon 2003; Orians and Pearson 1976). Thus in Figure 1, we see resource patch a?infinity (x), at increasing dis tances from the central place. If each patch pro duces the same gross caloric returns, then each

patch's rank will decrease with increasing distance from the central place. Similarly, the net return on

the same sized and ranked prey item from these resource patches will decrease with distance trav eled from the central place. This model of optimal foraging more accurately predicts the type of behavior displayed in prehistoric human popula tions than the prey choice model, which has been used extensively in archaeological applications of

optimal foraging theory (e.g., Bayham 1979;

Broughton 1999; Grayson 2001), because human

foragers will tend to travel some distance to a

known patch of prey or targeted foraging area, rather than stochastically forage across a con strained area.

Signaling theory provides a neo-Darwinian per

spective on animal communications (Hasson 1994,

1997). Under this theory, several forms of com

munication are believed to be the result of natural

selection processes that produced a population of

efficient communicators, relative to the informa

tion being conveyed and received. A subset of sig

naling theory, termed "costly signaling theory" (e.g., Bliege Bird and Smith 2005), may be more

familiar to some under several other names, includ

ing: the show-off hypothesis (Hawkes 1991); the

handicap principle (Zahavi and Zahavi 1997); hon est advertisement (Johnstone 1995); and honest

signaling (Randier 2006). Under this model indi viduals from vertebrate and invertebrate classes



Grimstead] ETHNOGRAPHIC AND MODELED COSTS OF LONG-DISTANCE, BIG-GAME HUNTING 63

Cri/Cwi

Central Place

Cro/Cwo

Figure 1. The central place foraging model. A simple sketch of the central place foraging model with the central place, patch type a, b, c.oo, and the outbound and inbound travel displayed.

may use visual, audible, or olfactory cues to eval uate potential mates and/or allies, and these cues

generally reveal the condition, health, or stress load of the individual being evaluated.

There are two primary players in the game of honest signaling: the signaler and receiver. The sig naler communicates information to the receiver about its quality through physical displays, traits, vocalization, or olfactory cues (Bradbury and

Vehrencamp 1998; Zahavi and Zahavi 1997). The information being transmitted or signaled has a

high degree of variability through time, across

space, among taxa, and between individuals (Arak

andEnquist 1993). Generally, these signals provide information about an individual's quality. Signals can be used by receivers as a proxy measure for an individual's ability to compensate for environ

mental stress (Buchanon 2000; Hill 1995), para site load or resistance (Getty 2002; Peters et al.

2003), general health (Getty 2002; Thornhill and

Gangestad 1993; Thornhill and M0ller 1997), abil

ity to comply with social norms (Wenegrat et al.

1996), degree of heterozygosity (Hutchison and Cheverud 1995; Norris 1993), and ability or

propensity to defend offspring (Randier 2006).

Only the highest quality individuals can "afford" to consistently produce or maintain signals that are

significantly affected by the above factors, which make these signals a reliable indicator of quality. As a result, receivers have a vested interest in cor

rectly identifying honest signals and making the best choice given the environmental and?in some cases?social situation.

Kin selection, sexual selection, and predator prey relationships are all evolutionary ecological venues where one can observe signaling behavior.

An example of the kin selection variant of signal ing involves parent-offspring interactions. Chicks of many avian taxa provide an honest signal of their

deteriorating physical condition by chirping more

loudly and frequently (Mock and Parker 1997; Quillfeldt et al. 2006; Roulin 2001). Predator-prey relationships generally involve optimizing strate

gies on the part of the predator, while prey gener ally invest in avoidance or defense (Pianka 1994). A classic example of predator-prey signaling is

ungulate stotting behavior (Byers 1984; FitzGib bonandFanshawe 1988; Zahavi and Zahavi 1997), where high stotting rates and heights are negatively correlated with the frequency of predator pursuits and capture rates (FitzGibbon and Fanshawe 1988).

Anthropologists generally apply the third version of signaling theory, reproductive signaling (e.g., signals that come into play in the arena of sexual

selection), which assumes that costly behavior or

physical characteristics tend to communicate the

potential quality of a mate. Among many bird

species, plumage and bill color (Norris 1993; Peters et al. 2003), the size and symmetry of tail feathers

(Andersson 1992), and song repertoires and sound

(Searcy and Yasukawa 1996) serve as proxy mea sures of a bird's quality. In the mammalian class,

only healthy and large-bodied red deer (Cervus ela

phus) can produce lengthy and low-sounding roars, used by females to pick reproductive partners (Clutton-Brock andAlbon 1979).



64 AMERICAN ANTIQUITY [Vol. 75, No. 1, 2010

Anthropological Applications

Most anthropological applications of signaling the

ory have addressed honest signaling in sexual selec tion (i.e., Aldenderfer 2006; Bliege Bird and Smith

2005; Bliege Bird et al. 2001; Boone 1998; Gur ven and Hill 2006; Hawkes 1991; Hildebrandt and McGuire 2003; Smith 2004). Anthropologists have

applied signaling theory in studies of the Merriam of Melanesia (Bliege Bird et al. 2001; Smith and

Bliege Bird 2000; Smith et al. 2003), the Ache of South America (Hill and Hurtado 1996; Kaplan and Hill 1985), the Hadza of east Africa (Hawkes et al. 2001), the !Kung of southern Africa (Wiess ner 2002), horticultural foragers located within the

Ecuadorian Amazon (Patton 2005), and the inhab

itants of Ifaluk atoll in Micronesia (Sosis 2000).

Many of these applications suggest that risky or

costly male hunting behavior may have been used as a signal of hunting ability, physical quality, and/or social and ecological knowledge.

Archaeological applications, in part motivated

by the success of the socioecological applications listed above, have applied this theoretical frame

work in a variety of locations: the Andean high lands (Aldenderfer 2006), Central America

(Neiman 1997), and the western United States

(Hildebrandt and McGuire 2002, 2003; McGuire and Hildebrandt 2005; McGuire et al. 2007). Some of these applications assume that the transport costs

associated with subsistence related pastoralism and

prey procurement were more costly than other sub sistence items or choices (Aldenderfer 2006; Hilde brandt and McGuire 2002, 2003; McGuire and Hildebrandt 2005; McGuire et al. 2007). Hilde

brandt and McGuire (2003) and McGuire and Hildebrandt (2005) have argued that increasing rel

ative abundance of artiodactyls during the Mid

Holocene is evidence of the adoption or

intensification of costly signaling by male hunters, because the encounter rates were lower than

expected from smaller mammals and the transport of large mammals was costly when compared to

other smaller-bodied prey (see Broughton and Bay ham 2003; Codding and Jones 2007; Hockett 2005 for useful criticisms of this proposition). Both

attempts at applying costly signaling theory assume

that large-bodied prey items are costly because of

transport costs and/or encounter rates.

Hildebrandt and McGuire (2003) and McGuire

and Hildebrandt (2005) invoke the idea that high return prey items are costly based upon ethno

graphic examples, which suggest that these items

produce very low returns rates, generally as a result of transport costs or pursuit costs (e.g., Smith et al.

2003). Under these applications, male big-game hunting is argued to be costly because males could return higher day-to-day caloric yields by foraging or hunting smaller, more abundant, or more local

prey items. This theoretical basis is contrary to most

optimal foraging models and measured return rates in ethnographic contexts (see summaries in Kelly [1995] and Kuhn and Stiner [2006]). Indeed, re

analyses of the data from several of the ethno

graphic contexts used to establish the "big game is

costly" premise has cast doubt on the inordinately high costs of large game as a costly signal (Gurven and Hill 2006; Hockett 2005; Wood 2006).

Previous research has shown that large-bodied prey are simply the most economical choice when available within the local habitat (Ugan 2005), but this research did not address the potentially unique effects of high travel costs, specifically how these costs might reduce the returns of large prey rela tive to other foods in the environment. Thus this

paper seeks to answer the extent to which big game is costly relative to other smaller prey items as a

function of distance traveled to procure those items. This paper generalizes the scenarios of costs, returns, and environment to make a simple evalu ation of the cost-return gradient, and emphasizes that using the model derived here will require unique adaptations to the specific contexts observed

archaeologically.

The Costs of Central Place Foraging

Orians and Pearson (1976) originally defined the central place forager as one that does "not consume

their prey where they are captured but return with them to some fixed central place where they are

eaten, stored, or fed to dependent offspring" (1976:156). Archaeologists have since applied the

central place foraging model in order to understand field processing decisions (Metcalfe and Barlow

1992; O'Connell and Hawkes 1988; O'Connell et

al. 1990), nonclimatically driven increases in

archaeological proxies of foraging efficiency (e.g.,

Broughton 1999; Cannon 2003), and estimations of human behavior (Bettinger and Baumhoff 1997; Zeanah 2004). There are three periods during which




Distance Traveled

Figure 2. Net returns and distance. A cartoon of the rela

tionship between net returns and distance traveled for

prey type /.

energy is expended under central place foraging: there is a trip away from the central place, a period of time spent foraging, and a return trip (Orians and Pearson 1976:156). As the distance traveled away from the central place to a distant patch (travel time) increases, the net returns from a prey item of size x will decrease (Figure 2; Cannon 2003). Round-trip travel time, the weight of the trans

portable prey item, and the body mass of the indi vidual forager are key variables when attempting to calculate the cost of prey items given distance y, and calculating the cost can be done relatively accu

rately because of extensive anthropological and

biophysical research aimed at understanding the caloric costs of locomotion and movement.

In terms of caloric expenditures, it is difficult to show that large game are more costly than small

game, assuming zero transport costs, even if mass

capture is the foraging strategy (Bayham 1979; Ugan 2005). Ugan (2005) found that the only sit uation in which small items will produce greater returns than large prey items is if the small prey items have very low or zero processing costs. Ugan (2005) cited small fishes or mass-collected

grasshoppers (e.g., Madsen and Schmitt 1998) as

examples of small prey with little or zero process ing costs. This paper focuses on the variable of dis tance traveled from the central place, and on a

quantitative evaluation of the procurement cost of four prey ranks obtained 0-200 km from the cen tral place.

The Cost of Transport (COT)

The cost of transport (COT) here refers to the amount of energy consumed in order to move a unit of body mass per unit of distance (Taylor 1994). For example, the cost of transport for running is .21 liters 02/kg/km (Cavagna and Kaneko 1977;

Margaria et al. 1963), or if we convert into kcal

(-4.8 kcal/liter 02), then this equation would be 1.008 kcal/kg/km. The energetics literature often uses ml or liters of oxygen consumed when refer

ring to the costs of transport, which takes into account the imperfect conversions of oxygen con sumed by muscles into energy measurements, such as calories or Joules (Blaxter 1989). Throughout the paper I will assume running as the primary form of locomotion during an outbound and return for

aging trip. This assumption is aimed at placing highest cost behavior into the calculations, so that a minimum distance of costly hunting behavior can be identified. Modeling walking, the most efficient form of human locomotion, would only minimize the cost of foraging and make it difficult to iden

tify costly behavior. There are two general rules for calculating the

energetic cost of running. First, within humans the

relationship between speed and oxygen consump tion for the cost of running is linear, which leads to the rather counterintuitive conclusion that the amount of energy consumed during transport at a

given distance is independent of speed (Margaria et al. 1963; Taylor 1994; Taylor et al. 1970). That

is, an individual running 5 km/h uses the same amount of energy running 1 km as s/he would if

running 10 km/h (i.e., twice that speed) for the same 1 km distance. This is different than walking, where the COT is U-shaped (Figure 3). Walking is

exponentially more costly after the distal apex of the U or approximately 3.3 km/h (Alexander 1980, 1991; Bramble and Lieberman 2004). The second

general rule is that the COT for running is linearly related to body size, such that an individual with less body weight will consume less 02/km than a

larger individual. This second rule may be

expressed as

^^ = io.7m:?-316-v (l) Mb

b K)

where Emetah is the energy metabolized measured




I-1-1-1-1-1-1-r 0 1 2 3 4 5 6 7

Speed (ms-1)

Figure 3. The cost of transport (COT). This figure shows the cost of transport for walking (U-shaped) and running (linear). The y axis displays the COT measured in milli liters of oxygen per kilogram per kilometer. The x axis dis

plays the speed measured in meters per second. Reprinted by permission from Macmillan Publishers Ltd: Nature

432:345-354, and by authors' permission.

in watts, Mb is the body mass measured in kilo

grams, V is the running speed measured in meters/second (Taylor 1994). Interestingly, this

equation applies to a range of body sizes, from mice to elephants (Taylor 1994). Research sup

ports the conclusion that the single most important variable in evaluating the cost of running is body mass?regardless of age, sex, or stature?and it

appears that external loads can be treated as a sim

ple increase in an individual's body mass (Bergh et al. 1991; Bourdin et al. 1993; Davies and Thomp son 1979; Padilla et al. 1992). This is a valuable

observation for the current application as parame ters for the weights of the individual forager, as well as their increased load weight from transporting our

imagined prey items, can be set without having to

consider other variables such as sex, age, and speed of transport.

The Cost of Transporting a Load

How much does any addition in carried weight increase the cost of transport? Research in adults

suggests that increases in external loading during

walking, running, and stair climbing (Caiozzo and

Kyle 1980; Davies 1980; Maloiy et al. 1986; Malville et al. 2001; Myers and Steudel 1985; Pan

o o

120

80

dolf et al. 1977; Taylor et al. 1980) indicate a lin ear relationship, wherein increases in total weight (body weight + external loading) is proportional to

the percentage of increased work, oxygen con

sumption, or caloric consumption. A few studies, however, have shown that when running with an

external load equivalent to five percent of an indi vidual's body weight, the cost of transport mea

sured via V02 (ml/kg1/min1) actually decreases

(Bourdin et al. 1995; Cooke et al. 1991; Davies

1980; Griffin et al. 2003; Thorstensson 1986). In a case study conducted on adolescents (Davies 1980), the opposite appeared to be true. Davies concluded

that, because adolescents are not completely bal anced in their weight distribution, the external load

ing actually forces a more evenly distributed

weighting, thereby increasing the efficiency of run

ning with weight. The others (Bourdin et al. 1995; Cooke et al. 199; Griffin et al. 2003; Thorstensson

1986) suggest that the increase in efficiency is related to a decrease in stride length and increase in step length.

The discrepancies between these sources appear at first glance to be problematic. A critical evalua tion of Figure 4 allows the author to make an argu

ment for a proportional increase in the gross metabolic cost of additional loads during locomo tion. Figure 4 shows the ratio of total body mass,

MtotaI (original body mass + the external load), to

the original body mass without the load, Mbody, on

the x axis. The y axis portrays the ratio of loaded metabolic rates, EmetabL (metabolism measured dur

ing walking with a load), compared to the unloaded metabolic rate, ?metabU (metabolism recorded dur

ing standing). Other sub- or supra-optimal walk

ing speeds (Graphs a, b, and d in Figure 4) show that the relationship between external loading and

metabolic costs is not proportional, but the optimal walking speed (Figure 4c) does show the gross metabolic cost to be linear. Thus, despite the dis

agreement in the above literature, for the purposes of this paper it seems reasonable to assume that the

relationship displayed in Figure 4c generally reflects the relationship between added load and metabolism at optimal walking and running speeds.

Furthermore, this paper need only show a weak

average trend of linearity within modern data, since the archaeological record produces an averaged collection of individual behavior, and the contrary

examples listed above tend to be from unique cases,




Figure 4. Averaged COT for eight loaded body masses. The x axis shows the ratio of total body mass, MtotaI (original body mass + the external load), to the original body mass without the load, M^, averaged for eight adults. The y axis por

trays the ratio of loaded metabolic rates, EmetjlhL (metabolism measured during walking with a load), compared to the

unloaded metabolic rate, ?metab(J (metabolism recorded during unloaded walking), and averaged for eight adults. Figure 4a, 4b, 4c, and 4d all represent the same cost of transport calculations and averages for four different speeds: .5

meters/second, 1.0 meters/second, 1.5 meters/second, and 2.0 meters/second, respectively. Figure is reprinted with per mission from Griffin et al. (2003).

such as in adolescents or specific load-weight classes. Additionally, it will be assumed that because body weight has been determined to be the

single most important variable in predicting the cost of running in humans and nonhumans (Bergh 1991; Bourdin et al. 1995; Bourdin et al. 1993;

Taylor et al. 1980), increases in external loading during running will produce an increase in the COT

roughly equivalent to what would be expected for

an increase in body mass. This conclusion is sup

ported by experimental research conducted recently by McGowan et al. (2006).

Reasonable Costs and Distances

The above discussion shows that the single most

important variable when calculating the energetic COT is distance traveled. Well-traveled routes are

potentially visible from the archaeological record,




Table 1. A Non-exhaustive Survey of Ethnographic and Experimental Data on Travel and Transport

Group Distance

Time of Average Weight Purpose of

travel_Speed_of Load_Transport Citation

Tarahumara 177 km

>8.0km

150-250 km

70 hrs

N/R

N/R

2.53 km/hr 45.36 kg N/R 27.2 kg

N/R N/R

64 km 378 min and

412 min

805 km 5 days

10.16 km/hr 0 kg

161 km/day N/R

N/R

Carry supplies out of

a steep canyon To visit family or

surrounding cities

Kickball competition

Letter Delivery_

Lumholtz (1902)

Sports Illustrated (1963)

Balkeand Snow (1965)

Balke and Snow (1965)

Lumholtz (1902)_ San of Central 29.075 km 275 min 6.175 km/hr Hunting Persistence

Kalahari_equipment hunting

Western Mono

of California

N/R N/R N/R 36 kg Transportation of

acorns from

gathering site to

residential base

Liebenberg (2006)

McCarthy (1993)

California

Basket

Experiment

1.5-1,065 km* N/R 5 km/hr 18.5-36 kg Used Californian

burden baskets to

estimate total load size

Bettinger et al. (1997)

Luo and Kikuyu N/R

of East Africa

N/R N/R 34 kg Various loads Moloiy etal. (1986)

Great Basin and N/R

California

baskets

N/R N/R 1.4-59 kg Conical carrying basket volume

estimates

Barlow et al. (1993)

Eastern Napalis 95 km 9-12 days N/R 73 ? 15 kg Food, exchange,

_and wage labor

Malville (1999) and

Malville et al. (2001)

Northestern

Nepalis

Not Given Not Given N/R 53.05 ? 9.06 kg Food, exchange,

_and wage labor

Panter-Brick (1992)

N/R = Not Recorded.

*Range reflects variation in the utility of a transported load, given the degree of field processing.

given current work associated with geochemical sourcing (Eerkens et al. 2007; Feranec et al. 2007; Grimstead 2005,2009; Hoppe et al. 1999; Reynolds et al. 2005) and GIS modeling (Anderson and Gillam 2000; Hare 2004). Archaeologists know far less about travel speeds, travel times, load weights, and of course the rationales for carrying resources over significant distances. A non-exhaustive survey of ethnographic and experimental research shows a great deal of variety in all of these categories (Table 1).

Experimental research has shown that a load

ranging from 18 to 59 kg is physically and eco

nomically feasible for an individual to transport over significant distances (Barlow et al. 1993; Bet

tinger et al. 1997). These values are consistent with

ethnographic observation coming from the Tarahu mara (Balke and Snow 1965; Lumholtz 1902;

Sports Illustrated 1963), the Western Mono of Cal

ifornia (McCarthy 1993), and the Luo and Kikuyu

of East Africa (Maloiy et al. 1986), all shown in Table 1. The Tarahumara of Northern Mexico are a particularly well-documented example. It is not unusual for a Tarahumaran to travel 177 km carry

ing a 45-36 kg load, at an average speed of 2.53 km per hour (Lumholtz 1902). This distance far sur

passes the 20-50 km maximum distance and 25 kg carrying load, the proposed upper boundary for

regional exchange networks (Lightfoot 1979). Another example of traveling moderate dis

tances at a near-running pace comes from docu

mentation of persistence hunting among the San of Kalahari (Liebenberg 2006). The average distance traveled in eight observed pursuits was 29.08 km, at an average rate of travel of 6.175 km/hr. Unfor

tunately, only the pursuit expenditure was recorded, not the expenditures during search or meat trans

port. Including these variables would tend to

increase the round-trip distances traveled, as well as provide information about the weight of food




transported. Traversing great distances with a sig nificant load is reasonable, however, as exempli fied by the Nepalese. For the purposes of food

procurement, exchange, and wage labor the

Nepalese were documented to transport loads

weighing 40-90 kg across nearly 100 km (Table 1; Malville 1999; Malville et al. 2001; Panter-Brick

1992). These sources of hunting and transport costs

suggest, in aggregate, that significant distances can

be traveled efficiently, both with and without sig nificant loads. In addition, the parameters for the distance traveled, the weight transported, and the rate of travel for prehistoric peoples should be at least broad enough to account for the variability observed ethnographically. Though imperfect, the data provide useful parameters for the calculation of the costs of food transport. The conditions for this analysis include a 200 km limit for outbound

trips?close to the observed maximum distances traveled amongst the Tarahumara and Nepalese? and a maximum load weight of 74.73 kg. Because distance is the most important variable when cal

culating the cost of transport, here speed is ignored.

Methods

The cost of procurement is an important consider ation when calculating the relative returns of one

prey item over another (Pyke et al. 1977). At least one previous study modeled these costs for human

foragers based upon ethnographic data (Ugan 2005). In the present paper only a portion of the total procurement cost stemming from food trans

port will be calculated. Ugan's (2005) research has

already shown the relative and specific costs of

procurement within the contexts of a prey choice

model, which excludes travel and transport vari ables. This paper builds upon Ugan's (2005) work

by evaluating the cost of procurement within four

prey body sizes (i.e., "Prey Rank"; Table 2) in an

attempt to understand how distance affects the prey ranks of an optimal diet.

Our theoretical prey ecosystem is modeled after the North American Southwest desert ecosystems.

Within any given resource patch our prey ranks include: a large and small black-tailed deer

(Odocoileus hemionus), Prey Rank 1 and Prey Rank 2, respectively; a large antelope jackrabbit (Lepus allem), Prey Rank 3; and a large desert cot

tontail (Sylvilagus auduboni), Prey Rank 4. Prey ranks are based upon: the gross body mass (in kg) of the largest (141 kg) and smallest (33 kg) black tailed deer documented in the southwest (Hef felfmger 2006); the largest documented antelope jackrabbit (4.3 kg; Whitaker 1996); and the largest documented desert cottontail (1.2 kg; Whitaker

1996). Maximum weights are used here under the

assumption that humans are foraging optimally, meaning they will always take the highest ranked or largest prey item when available (Pyke et al.

1977). This analysis requires the conversion of gross

weights into edible caloric returns, with the under

lying assumption that field processing would decrease the amount transported to a weight closely aligned with the edible caloric returns. This was

accomplished by using analogous taxa: Artiodactyl (Caribou, Rangifer tarandus) and Lagomorph (Arctic hare, Lepus arcticus). These helped to derive estimates of the edible mass produced, the kcal returns per kg of edible body mass, and the total kcal returns for the prey ranks used in this

analysis (Table 2). Based upon processing data derived by Smith (1991) among the Inujjuamiut, approximately 50 percent of the total mass is ined

ible, and for this paper assumed to be discarded dur

ing field processing. Body masses were modeled for proposed human

foragers at masses of 50 kg, 75 kg, and 100 kg. As discussed above, body mass is the most important variable for calculating transportation costs (02 or

kcal). This study therefore models each of these individuals (i.e., at 50,75, and 100 kg) as androg ynous predators.

Equations for Deriving the Cost of Procurement

The point at which a specific prey rank becomes

costly to transport is represented by the following relationship: when the energetic return (Er) is less than the cost of procurement (C), the prey item is

costly, or Er < Cp (see Table 3 for acronyms and

definitions). While this is a rather intuitive rela

tionship, Er and Cp have their own unique sets of variables that must also be determined. Energetic return (?r), for example, requires knowing the live

mass (Mlp) of

a prey type, the edible mass (Mep) for

any given prey species, and converting the edible mass into kilocalories (kcal). Smith (1991) uses 2 coefficients to convert live mass

(Mlp) into edible



70 AMERICAN ANTIQUITY

Table 2. Body Mass, Edible Mass, and kcals for Four Prey Items.

[Vol. 75, No. 1,2010

Prey Body Edible Kcal of edible

Taxa_Rank_Mass (kg)_Mass (kg)*_portion** Black-tailed deer -

Large (Odocoileus hemionus) 1 141 74.73 204,162.36 Black-tailed deer - Small (Odocoileus hemionus) 2 33 18.48 50,587.36

Antelope Jackrabbit (Lepus allem) 3 4.3 2.41 4,820 Desert Cottontail (Sylvilagus auduboni)_4_L2_167_1,340 *Edible mass estimates derived from Smith (1991:181): Artiodactyl

= 53% of total mass; Lagomorph = 56% of total mass

**Kcal of edible portion derived using 1kg of edible weight = 2,732 Kcal for Artiodactyl and 2,000 Kcal for Lagomorph (Smith 1991:181)

mass (M ), coefficient q, and converting edible mass

(Mep) into kcal. In this paper coefficient q is derived from the edible portion data provided in Smith (1991), as outlined in Table 2 of this paper. Coefficient q is a species-specific coefficient; i.e.,

Artiodactyla q = .53, and Lagomorpha q = .56. The coefficient q must be determined experimentally within modern animal communities. Thus Er of

prey type / (En) may be calculated by Equation 2:

Eri=(Mlp*q)j =

(Mep*j) (2) Deriving the cost of procurement (Cp) in this

application of the central place foraging model

requires calculation, in kilograms, of the outbound

trip (Walking = Cwo\ running = Cm) plus the inbound

trip (Walking = Cwi; running = Cri), the first of which is load-free and the second load-bearing. Thus the cost of procurement (Cp) during running is derived from Equation 3(a), while Cp during

walking is derived from equation 3(b) (walking):1

Cpr=C? + Cn.+ # + (3a)

cPw = Cwo + cw/ + A + A/ (3b)

To calculate the cost of running or walking dur

ing an unloaded trip (i.e., outbound), Equations 4(a) and 4(b) were used, respectively.

Cro=(a*k(Mbh)d) (4a)

CW0=(b*k(Mbh)d) (4b) Cw I Cwo is the cost of running/walking on the out bound trip, a is the coefficient of the cost of trans

port for running measured in 02 liters/kg/km (also equivalent to .21 liters 02/kg/km; Lieberman et al.

2009), b is the coefficient of the cost of transport for walking measured in 02 liters/kg/km (also equivalent to .16 liters 02/kg/km; Lieberman et al.

Table 3. List of Acronyms Used in This Article.

Acronym Definition

a The coefficient of the cost of running measured in 02 liter/kg/km (equivalent to .21 liters 02/kg/km) b The coefficient of the cost of walking measured in 02 liter/kg/km (equivalent to .16 liters 02/kg/km)

Cp Cost of procurement

Cri Cost of running (inbound)

Cm Cost of running (outbound)

Cn Cost of running (total)

Cwi Cost of walking (inbound)

Cwo Cost of walking (outbound)

Cwt Cost of walking (total) d Distance (km)

Er Energetic returns (Kcal)

^ Handling costs of prey type / (Kcal) j The coefficient that when multiplied by the number of kilograms edible mass yields the total number

of kilocalories from the edible mass of prey type i

Mbh Body mass of the human subject (kg)

Mep Edible body mass of prey type i

Mlp Live body mass of prey type i

pt Pursuit cost of prey type i (Kcal)

q_The coefficient for prey type i that when multiplied by live body mass yields the edible mass_



Grimstead] ETHNOGRAPHIC AND MODELED COSTS OF LONG-DISTANCE, BIG-GAME HUNTING

25,000 -\

Distance (km)

>r-Kcal cost-wafc {100kg ind3) m-Kcal cobt-rurr {100kg ind3)

Figure 5. Linear plot of Crt and / kcal. The cost of walking is 76.2 percent of the cost of running.

2009), k is an average conversion of factor of kcal to liters 02 (4.8 kcal = 1 liter 02/kg/km; Lieberman et al. 2009), Mbh is the body mass of the human, and d is the distance traveled. Distance traveled (d) was approached incrementally starting with a 2-km

round-trip and ending with a 400-km round-trip as

follows: 2, 10, 20,40, 60, 80, 100...400. To calculate the cost of running or walking dur

ing an in-bound or load-bearing trip, Equations 5(a) and 5(b) were used, respectively:

Cri=(a*k(Mhh+Mep)d) (5a)

Cwi=(b*k(Mhh + Mep)d) (5b)

Where Cri I Cw{ is the cost of running/walking on

the inbound trip, M is the edible mass of prey type /, and the others are the same as in previous equa tions.

Results

Does the cost of long-distance procurement make

big game less efficient when compared to locally available small game animals, as recent applications of costly signaling theory claim? If so, then at what distances does the trade-off become important? To

answer these questions the costs of procurement were calculated using the above equations for three different predator sizes (Individual 1 = 50 kg, Indi vidual 2 = 75 kg, and Individual 3 = 100 kg), four

prey rankings, and distances ranging from zero to 400 km (see note 2). Figure 5 displays the linear

equations, derived with the above equations given the set parameters, of the cost of running (Cri or Cm) and the cost of walking (Cwi or Cwo) during an

unloaded trip at x distance from the central place, or the outbound trip to the chosen patch for the indi vidual who carries 100 kg. The cost of walking is 76.2 percent of the cost of running, a deduction that is consistent with the kinetic literature. Because here

we are most interested in "costly" procurement behavior, further analysis will be conducted only for the cost of running. The reasoning behind this exclu sion has two parts. First, we are interested in evalu

ating at what point big game become costly relative to smaller, more locally procured items. Thus, mod

eling non-optimal foraging behavior?that is, run

ning rather than walking at optimal speeds?allows us to model costly and perhaps risky behavior. Sec

ond, it is reasonable to assume that in a scenario of

optimal foraging, where the time spent foraging detracts from other fitness pursuits, we would expect that reducing hunt expedition time would be a good



72 AMERICAN ANTIQUITY [Vol. 75, No. 1,2010

Figure 6. Linear plot of the total cost of running (Crt) for all 3 individuals given Prey Rank 1.

fitness enhancing strategy, as well as signal to poten tial mates physical condition, hunting prowess, sta

mina, etc. Furthermore, modeling running versus

walking may be irrelevant given the small caloric

expenditure increase relative to the amount of meat, or amount of kcal, that can be moved and the amount

of prestige that may be obtained under either trans

port speed. Similar arguments have been made by Lieberman et al. (2009).

In terms of the total cost of transport, it is clear that Individual 3 consumes more energy than Indi viduals 2 and 1 (Figure 6). Furthermore, the graph ical representation shows that Individuals 1 and 3 create a type of upper and lower limit for assess

ing the cost of transport. Because a simple linear

equation exists in the calculation of the cost of

transport, Individual 2 will be removed from the

remaining analyses for the sake of graphical clar

ity. The net returns (Er - Crt) for Individuals 1 and

3 for all prey ranks are presented in Figure 7. It is clear that the net returns decrease linearly for all

prey types as the cost of procurement increases

(Figure 7). These linear equations of the net returns are used to calculate the "point of no returns" for Individual 3 (Table 4). Table 4 shows that: (1) the

energetic returns derived from large prey items

have the capacity to absorb exceptionally large travel costs due to the size of the meat package, and

(2) the cost of procuring small mammals rises very

rapidly with distance from the central place. For

example, the net returns from Prey Rank 4 reach zero at a round-trip distance of 6.6 km.

A critical observation for this study is observed in Figure 8. Figure 8 shows that Individual 3 would

have to make a round-trip in excess of 440 km before the returns from Prey Rank 1 become less than the returns from 31 antelope jackrabbits (Prey Rank 3) procured from within one kilometer of the central place. This figure also shows the relation

ship of Prey Rank 2 (the modeled small artio

dactyls, with 33 kg of dressed weight) to the 31

individually captured antelope jackrabbits. It is more costly to obtain this small (33 kg) artiodactyl within one kilometer of the central place than it is to obtain 31 antelope jack rabbits. In fact, it becomes more costly to procure the smaller artio

dactyls (Prey Rank 2) when 11 or more Prey Rank 3 individuals can be sequentially captured, as mea

sured in the currency of calories without the inclu sion of processing costs (see Ugan 2005 for a

discussion of the importance of processing costs

when considering caloric returns).




250,000

200,000

150,000-1

_ 100,000 ?S 3 *

50,000

100 150 Distance (km)

200

- Return (indl/preyl) - Retur n {i nd 1/preyS) " Retur n (ind a^preyl) Retur n {i nd 3'prey3)

9 ? Return {ind1^prey2) = ? Return {ind1Jjprey4} *-Return {inri3fcrey2) u.Return {ind3/prey4}

Figure 7. The total net returns for Individuals 1 and 3 within all prey ranks.

Discussion

This paper evaluated the distances over which prey of a variety of sizes becomes costly to pursue. The

study expands upon previous research (e.g.,

Broughton and Bayham 2003; Cannon 2003; McGuire and Hildebrandt 2005; Ugan 2005) by

asking if transport costs can sufficiently reduce the net returns for large, high-return prey items to the

point at which procuring locally available, small, low-return prey items is more energy-efficient. The interest in this question is in part derived from the

desire of many archaeologists to identify costly male foraging behavior in an attempt to identify are

nas of male signaling behavior. The foraging scenarios modeled here show that

a 141 kg artiodactyl yielding 74.73 kg of edible mass continues to produce significant net returns even when procuring the game includes a round

trip distance of as much as 400 kilometers. It would

take 31 large jackrabbits, captured within one kilo meter of a central place, to match an artiodactyl of

this size procured at a distance of 200 km. Ethno

graphic data suggests that an outbound trip of 200 km is feasible, but it seems likely that any food

transportation over this distance would require meat

drying and curing. Drying or curing may further increase efficiency through lightening of the load, but it would also require additional time away from the central place, thereby minimally reducing effi

ciency. Based upon this modeled data it seems clear that large game remains the most economically efficient choice available to hunters, even when

significant distances are required to procure and

transport such an item. This conclusion is signifi cant when considering the tendency of some

archaeologists to assume that long-distance hunt

ing is very costly. In fact, if it is abnormal costs we are interested in identifying, then we should be most surprised by, and searching for, long-distance transport of small prey.

The above calculations have excluded the costs

of handling and pursuit in an attempt to explore the

importance of distance. Simms (1987) shows that

the processing costs for a jackrabbit are approxi mately .05 hr/kg. The jackrabbit used in this study

weighs approximately four kilograms and yields a

processing time of approximately 12 minutes. To convert this into kcal expenditures the following formula was used: V02 ml/kg/min

=

3.966*V(km/hr) + 7.736 (Davies and Thompson 1979). Assuming that processing energetic expen



74 AMERICAN ANTIQUITY

Table 4. The Point of No Returns.

[Vol. 75, No. 1,2010

Roundtrip distance

at which the

Prey Rank_returns equal zero_Linear equations used*

1 737.2 km Return (ind3/preyl) = 204,162

- 276.92784 x Distance (km) 2 229.7 km Return (ind3/prey2)

= 50,587 - 220.22784 x Distance (km)

3 23.6 km Return (ind3/prey3) = 4,820

- 204.02928 x Distance (km)

_4_6.6 km_Return (ind3/prey4) = 1,340 - 202.21488 x Distance (km)

*Equations in y = b + mx format, where y = Kcal returned given the round trip distance and x = travel distance.

ditures are equivalent to the most efficient walking speed (4.68 km/hr), an unloaded individual weigh ing 100 kg (i.e., Individual 3) would consume

151.47 kcals during the 12 minute processing ven

ture. This cost reduces the returns from 4,820 kcal

jackrabbit to 4,668.5 kcal, which is approximately 50 kcals greater than the net returns from a jackrab bit procured at a distance of one kilometer. Else where it has been shown that even if small prey items are captured collectively (Ugan 2005), they still have low returns relative to large game because of processing costs. That generality is extended here by saying that the additional cost of transport reduces the value of the small game even further,

regardless of whether it was procured collectively or individually. In sum, even if processing costs are

added to transport costs, a medium to large black tailed deer will almost always give greater returns

than small mammals regardless of the distance traveled. This is true except when small prey have

very low processing costs (cf. Madsen and Schmidt

1998; Ugan 2005). Does this mean that large numbers of jackrab

bits in an assemblage should be considered a costly signal? In terms of energetic efficiency, most small

game are more costly in the prey choice model and

the central place foraging model relative to larger prey options. While small game is more costly even

if the large mammal is procured at a significant dis

tance, there are a number of reasons why small

prey items are less likely to be used in a hunter's

efforts to "show off." Harvesting en masse is sel

dom the accomplishment of one or a few individ

uals, thereby reducing the clarity or strength of

signal from any one of the individuals. Moreover, the volume of edible tissue to protective skin sur

face is less in small animals, meaning that consid

erably more work lies ahead for the recipients of

small prey animals, making them not only more

work but a lower quality signal than a higher qual ity meat package. In signaling, display of the catch and the distribution of it often serve as the forum for the signaling (e.g., Smith and Bliege Bird 2000). Small game is not ideal for broader community distribution, nor does it serve as a fantastic display, relative to a large meat package. It should be noted, however, that in an arid environment or in a habi tat suffering from resource depression any meat

may serve as a strong signal to the broader com

munity or social group (Grimstead and Bayham 2010).

These results are consistent with expectations of distant patch utilization in the context of local resource depression. Other studies have argued that

high-ranking prey items should dominate the diet

early in a temporal sequence, because human pop ulations tends to be smaller and the encounter rates

with preferred taxa are high (see Gray son [2001] for a review). As the availability of preferred prey ranks decreases, foragers or farmers should engage in resource intensification. This may include an

increase in the use of small, less-preferred prey. Most important for this analysis, significant trans

port costs of large game would also be tolerated to a large degree. The rate of increase in travel costs ensures that only higher-ranked prey types will be

pursued in distant hunting grounds, in order to off set the price of meat transport, as was shown in the caloric returns differences between the small and

large artiodactyls in this analysis. An increase in artiodactyl relative abundances

viewed in archaeofaunal data sets should not be assumed to represent increases in male signaling behavior. The data presented here supports the

premise that increases in artiodactyl relative abun

dances, where declining abundances were predicted due to hypothesized resource depression (e.g.,

Broughton 1999; Cannon 2003), are expected




250,000 -

200,000 -

150,000 -

100,000

50,000

0

0 50

i i

100 150

Distance (km)

200 ?r?

250

* ? Return (ind?/preyl) n ?Return {ind3/prey2) - Return for individual 3 from 31 Jackrabbite colected

mMn 1 k?ameter of the central place.

Figure 8. The net returns for Individual 3 within Prey Ranks 1 and 2.

because of the high economic value associated with

large game regardless of the distance traveled to

procure it. The presence of large game should only be assumed to represent a costly signal when it can be shown that the prey came from significant dis tances: well over 150 kilometers, for most prey ranks. Large game may have in fact served as a pres tige item or signal not because it was consistently costly to procure, but rather because of its consis

tently high economic value. High economic value in a prey item may indeed come to be synonymous

with high signaling value in a prey, especially in

ecologically stressed environments, but not because of the cost of procurement.

These results suggest that food is likely a poor way to distinguish costly signaling behavior in the

archaeological record. Some extraordinary exam

ples from the present may be conducive for devel

oping a costly signaling model to derive

predictions, but simply using the presence or absence of a specific taxon is ill-advised until such a model has been built. Bliege Bird et al. (2001) and Smith et al. (2003) show some ethnographic evidence of high economic costs and great risk associated with the procurement of sea turtles. The model and tests employed by Bliege Bird et al.

(2001) and Smith et al. (2003) necessitate a clear identification of certain costly signaling criteria, such as: a specific costly behavior, the signaler, the

receivers, and a directional preference by the

receivers, as well as a clear understanding of why and how the signal is costly. While these ethno

graphic examples are tantalizing, meeting these cri teria archaeologically is difficult in the best of circumstances and impossible at worst because our data may not have the ability to re-create the cir cumstances enabling us to identify the agents. Crit ical attention to temporal, spatial, and cultural contexts may increase the likelihood of decipher ing prehistoric signals, but archaeologists are far

more likely to reach post hoc conclusions that big game is costly with no real test of the premise.

Conclusion

Several archaeological applications of costly sig naling theory have employed ethnographic analogy to claim that big game is costly to procure. These

applications have been the basis for the proposition that increases in the relative abundance of large game is a sign of the ascendance of male costly sig naling behavior, as underwritten by female labor




(McGuire and Hildebrandt 2005). According to the

prey choice model, large game generally produce higher net returns than small prey items, even when

transport costs are figured into the equation. The results of this analysis show that within a theoreti

cally constructed ecosystem, the procurement of a

large prey item is almost never costly. The excep tions to this are when the large prey item is very small for the prey rank (i.e., ~10-20 percent of adult

male size), and when a large quantity of small prey can be obtained expediently from the central place with very low processing costs per individual.

In archaeological applications of costly signal ing it must be shown that the net returns are below that which may be obtained from other sources, or that the returns are less than zero. Zooarchaeolo

gists may be able to provide pertinent data, such as ordinal level prey size classes (i.e., prey ranks) and relative distances of procurement. If archaeologists insist on looking to food for information on costly signaling behavior, then they must look to ecolog ically rare items that are overrepresented in the diet, or to items that have extraordinary costs or very high personal risks associated with their procure ment. This is not to say that signals are not being transmitted when an individual returns to the cen tral place with a large artiodactyl carcass. Indeed, other currencies of signaling are likely at work. For

example, the individual may be signaling hunting prowess, habitat knowledge, foraging efficiency, or social rank. These variables are even more difficult to quantify archaeologically than distance and eco nomic efficiency, making the archaeological iden tification of costly signaling difficult.

The most important conclusion of this paper is that big game retains its value as a high-return prey item for humans even when transport costs are com

paratively high. This observation lends some degree of support to research into the evolution of human

locomotion, which suggests that foraging behav ior was a strong factor in the natural history of human bipedality (e.g., Lieberman et al. 2009). Human bipedality is a very efficient locomotion

system that tolerates the addition of in-line sym metrical weight without significantly affecting the total cost of transportation. Further research into how to identify costly signaling behavior within

prehistoric societies should be pursued because of its importance as a conveyor of prestige and power in human society.

Acknowledgments. I would like to thank Steve Kuhn, Mary Stiner, and Dave Raichlen for comments on earlier versions of this manuscript. Bruce Winterhaider, Frank Bayham, Jack

Broughton, Kelly McGuire, and Bill Hildebrandt all pro vided stimulating personal dialogue, which precipitated this research. All errors both in reason and language are my own.

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Notes

1. It is customary for optimal foraging models to include

the cost of pursuit (p,) and processing costs (Af) when calcu

lating the cost of procurement, but these variables will be set

to zero in this analysis for the sake of isolating the costs of

transportation. 2. A detailed spreadsheet of the data used in the present

analysis may be obtained by contacting the author. These data

matrices provide detailed calculations of running and walking

during the inbound and outbound trips, as well as the total

cost of transport.

Submitted May 22, 2009; Revised June 26, 2009; Accepted

September 22, 2009.

ARTICLES

507 The Terminal Formative to Classic Period Obsidian Assemblage at Palo Errado, Veracruz,

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Charles L. F. Knight and Michael D. Glascock

525 Settlement, Environment, and Politics in the

San Bartolo-Xultun Territory, El Peten,

Guatemala

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628 Annual Index

^ LATIN AM K RICAN

ANTIQUITY Vou;\u 20 M'Miu-.R * Dm \u;i ;< 2009



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