The Use of General Ecological Principles in Archaeology

The Use of General Ecological Principles in ArchaeologyAuthor(s): Donald L. HardestySource: Advances in Archaeological Method and Theory, Vol. 3 (1980), pp. 157-187Published by: SpringerStable URL: http://www.jstor.org/stable/20170156 .

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The Use of General

Ecological Principles in Archaeology

DONALD L HARDESTY

Ecology and archaeology have been acquaintances for a long time, and

the personality of archaeological methods and theory has been strongly in

fluenced by the contact. The relationship is, however, distant. Watson,

LeBlanc, and Redman (1971:107) summarize it best as a perspective, a way of looking at the world that may be useful in answering some questions; that is, ecology is used heuristically, not as a set of formal explanatory prin

ciples that can be brought to bear upon archaeological problems. Not

withstanding the large contribution that the heuristic approach has made to

archaeological research, it has its disadvantages. Explanatory models are ad

hoc, constructed for archaeological data from a particular site or region; because they are tied to specific environments and to specific human

groups, the models are unlikely to have any relevance elsewhere. The reason

is that predictions drawn from such models are most likely to be confirmed

under ecological and cultural settings that are identical to the model. That

is, of course, never the case; because of historical factors, no two settings are the same. The probable outcome is the failure to confirm most of the

predictions, giving the impression that there are no general ecological prin

ciples, that the human ecology of each site or region is unique. But the possibility of that impression being an illusion cannot be over

looked. Simon (1969) points out, for example, that a few simple princi

ples operating in complex environments can produce an almost infinite

variety of human behavior; the same may be true of the principles of

ecology. If so, a new research strategy is needed that recognizes a closer

4

ADVANCES IN ARCHAEOLOGICAL METHOD Copyright ? 1980 by Academic Press, Inc. AND THEORY, VOL. 3 All rights of reproduction in any form reserved.

ISBN 0-12-003103-5



158 DONALD L. HARDESTY

camaraderie between ecology and archaeology and that provides an alter

native to the self-fulfilling prophesy of the ad hoc approach. That is exactly the direction proposed by a few archaeologists. Thus, Ford (1977:183) com

ments that

Anthropology has produced few, if any, lawlike statements; but the physical and

biological sciences have. If we make our problems operational in terms appropriate to

analysis of a more general theory such as synthetic evolutionary biology, we can employ those principles and free ourselves from induction and tautology.

The purpose of this chapter is to look closely at the new research strategy, to

consider its strengths and weaknesses, and to make some suggestions about

how it may be used to further our understanding of the past. First is an

illustration of how general ecological principles have been used in ar

chaeological research. The second part of this contribution turns to a crit

icism of the strategy and what future direction is needed; that direction is

likely to demand much more interaction between ecology and archaeology than is suggested by the preceding quote. Finally, the chapter suggests the

way in which general ecological principles can be used to explain the pro cesses of cultural diversification, a key problem in archaeological inter

pretation (Binford 1962).

HOW GENERAL PRINCIPLES ARE USED: AN EXAMPLE

Yellen (1977) gives an interesting example of how general ecological prin

ciples can be applied to archaeological data by using the stability-time

hypothesis to explain and predict observations of the behavior of hunters

and gatherers living in deserts. Ethnographers and archaeologists alike have

been perplexed with the social organization of aboriginal Australia, the

Kalahari desert of southern Africa, and the American Great Basin. On the

one hand, there is abundant evidence that looseness and fluidity should be

considered as principles of social organization; thus Julian Steward as

signed these principles to the Great Basin Shoshoni (1938). But on the other

hand, there is equally abundant evidence for rather rigid organizing prin

ciples, and that has impressed another group of researchers. Service (1962)

reconstructs, for example, patrilineal, exogamous bands for the precontact

Shoshoni, and Jennings and Norbeck (1955) see resistance to rapid change, and a general conservatism, as the key to understanding Great Basin pre

history. Why does the contradiction exist? The easy way out would be to

dismiss the evidence of one side or the other as being erroneous, nonrep

resentative, or as having some other methodological or interpretative weakness. Yellen, however, accepts the existence of both kinds of evidence

and argues that both flexibility and persistence are organizing principles consistent with the most advantageous strategies of desert life. His argu

ment follows from the predictions of the stability-time hypothesis.



THE USE OF GENERAL ECOLOGICAL PRINCIPLES IN ARCHAEOLOGY 159

The most general use of the hypothesis is as an explanation for bio

geographical differences in species diversity and behavior; it gives an

answer to the question of why so many kinds of species live in some places and so few in others. Sanders (1969) and others who have developed the

hypothesis argue that the question is best answered by the ecological

strategies that are made advantageous, or not, by environmental stability, on the one hand, and stress, on the other. These conditions are observed as

endpoints on the continua of physical severity, such as extreme tempera tures and the magnitude of fluctuations in resources or other life oppor tunities. Yellen makes a good case for replacing the latter continuum with

the predictability of resources; that is, a stressful place to live is one in

which it is very difficult to predict when, where, and what resources will be

available, and the opposite is true for stability. Deserts are considered by Yellen to be severe and unpredictable to hunters and gatherers, and he ac

cordingly turns to that part of the stability-time hypothesis that explains and predicts response to stress.

What kind of strategy is most advantageous in an unpredictable environ

ment? Resources (or other life opportunities) change suddenly and unex

pectedly, acting against the exclusive use of one or a few of them. The use of

many resources is, however, advantageous. Such a strategy demands be

havior sufficiently flexible to rapidly shift from one to another, and

Yellen cites studies of bird communities in various parts of the world to sup

port the general proposition that behavioral plasticity and flexibility of

organisms is greater in stressful than in stable environments. The behavior

of hunters and gatherers in deserts is, then, expected to be flexible, a predic tion that is confirmed by ethnographic and archaeological data on the social

organization of such groups. But what about the contradiction? Here Yellen introduces us to Holling's

(1973) argument that resiliency is advantageous in unpredictable envi

ronments; that is, the organism is expected to roll with the punch, chang

ing enough to cope with perturbations, such as shifting behavior to make up for a suddenly failed resource but still keeping the same fundamental struc

ture and behavior. In other words, persistence is predicted, and that is

precisely how the social organization of desert-dwelling hunters and gath erers has been interpreted by some. Thus, Yellen is able to use the stability time hypothesis to shed new light on an old archaeological and anthropo

logical problem. And what more can be asked of a research strategy?

SOME LOGICAL PROBLEMS

Attempts such as Yellen's have not always met with sweetness and light, however. Schiffer (1978) argues, for example, that the use of general eco

logical principles in archaeology and anthropology is plagued with log ical weaknesses. Analogy is one use whereby parallels are drawn between a




principle and human behavior; Gall and Saxe's (1977) application of the

principles of succession to explain the evolution of complex states is given as

an example. Here, parallels are drawn between the increasing diversity of

ecological communities undergoing succession, and their correspondingly

greater ability to "buffer" environmental stress, and the increasing diver

sity of human societies undergoing evolutionary change. Gall and Saxe con

tend that if a social system becomes better buffered against environmental

stress as it becomes more complex and diversified, then complex societies

have a selective advantage over simple societies and are bound to replace them over time. The weakness of the succession analogy, and one that can

be extended to similar analogies according to Schiffer, is that "superficially

appealing" correspondences are opposed by numerous areas of complete

dissimilarity that are either not recognized or are ignored. Thus, Gall and

Saxe pay no attention to other predictions deduced from the principles of

succession that seemingly do not apply to the evolution of the state: these

include the greater stability of climax communities, although states are

usually less stable, and the greater energy efficiency of climax communities, in apparent contrast to states.

Another use is homology. Here, general ecological principles and human

behavior are assumed to be identical in important ways, as illustrated by Osborne's (1977) use of r-selection and k-selection to explain the shift from

land-based to marine resources in Peru. The homology comes from the

assumption that the growth curves of all animals, including Homo sapiens, have the same sigmoid shape and are controlled by the same processes.

Rapid growth in the beginning selects for ecological strategies that, among other things, maximize resource use. The process is called r-selection, and

Osborne argues that the rapid growth stage of hunting and gathering

populations will be associated with r-selection strategies, typically the

specialized exploitation of a few highly productive resources such as big

game. After growth-inhibiting processes level off the growth curve, the ad

vantage shifts to ecological strategies that are more intensive and energy ef

ficient. Such k-selection strategies allow individuals to cope more effectively with a limited resource base. Osborne proposes that hunters and gatherers

began to use marine resources as part of a shift to k-selection strategies tied

to the stabilization of population size at carrying capacity. Schiffer sees Osborne's approach as a "born-again population pressure

model, with all of its theoretical and operational weaknesses, that no

amount of ecological baptism can conceal" (1978:14). In other words, the

homology, if it does exist, is trivial (we already know about population

pressure), and it obscures the real problem of making such models workable

for archaeological data. Homologues may exist, according to Schiffer, but

they must be nontrivial and workable before they will advance archaeolog ical knowledge.




If analogy and homology are poor logical arguments, then, what is the

proper role of general ecological principles in archaeological explanation? One possibility that Schiffer conceives is reduction, "effected when one

discipline's principles can be deduced from the theoretical premises of

another" (1978:15-16). That implies two sets of principles that are indepen dent: one making up general ecological theory, the other making up a

theory of human behavior. Unfortunately, neither one exists, at least in

Kuhn's (1970) sense of a paradigm. Schiffer argues that logical reduction is

not yet possible because of the absence of agreed upon principles of cultural

processes, and it can also be argued that general ecological principles are in

the same state. Despite its rigorous methodology, mathematical precision, and experimentation, ecology is not much further along, if at all, than

the social sciences in discovering general laws, and competing schools of

thought vie with each other for general acceptability. The hazards of crossing disciplinary boundaries on search and seizure

missions are clearly stated in Schiffer's critique. General ecological prin

ciples are unlikely to become an important part of explanation in ar

chaeology unless more attention is^iven to the problems of making them

workable for archaeological data and to logical relationships between ecol

ogy and archaeology. The latter problem arises from two characteristics

of ecological theory. First, the theory has developed largely independent of

data on human ecology; that is, the explanatory principles and hypotheses

making up the theory have been mostly tested with data on the ecology of

species other than Homo sapiens. The reason for this is mostly historical, but the implications are crucial to the use of ecological principles in the

human sciences: it is likely that modifications in the theory will have to be

made, including the discovery of new principles, to accommodate people. That would also be true for the theory to take into account new data on the

ecology of any species; it has nothing to do with human "exceptionalism."

Accordingly, there is no reason to assume that the theory, as it now stands, is sufficiently general to cover human ecology.

Second, and already mentioned, ecological theory is in a state of flux, with many competing principles and hypotheses. Only the most foolhardy, therefore, would use one principle or another, without showing why it is a

better explanation than the others. The transfer of general ecological prin

ciples across disciplinary boundaries is, then, dependent upon the results of

multiple-hypothesis testing. Those principles that are the most general, and

probably that have been tested with the greatest variety of species, have the

best chance of being applicable. All of this suggests that it is wrong to relate ecology and archaeology as

the donor and recipient of explanatory principles; rather, the most produc tive relationship is one of interaction between a general theory and a new

data base. The relationship is new to human ecology, but it is not new to



162 DONALD L HARDESTY

the palaeosciences. Palaeontology, our sibling discipline, is using its dia

chronic, evolutionary data on species ecology to test and modify general

ecological models based upon synchronie and experimental data (e.g., Gould 1977; Raup 1977; Raup et al 1973; Van Valen 1965, 1973). The ad

vantage of diachronic data is also held by archaeology. Notwithstanding the

incompleteness of their data, it appears that the palaeosciences have the best

potential for serving as the "evolutionary" arm of general ecology. Such a

role is consistent with Plog's (1974) argument that the goals of archaeology should be limited to evolutionary problems.

The purpose of the last part of this paper is to illustrate what is involved

in using general ecological principles and archaeological data interactively.

Competing models of species diversity are tested with archaeological data as

possible explanations for evolutionary patterns of cultural diversity. The

first section discusses how such models can be made workable for ar

chaeological data; the second outlines the test implications of the various

models considered and discusses how the test results can be used to modify

general ecological theory.

THE ECOLOGICAL EXPLANATION OF CULTURAL DIVERSITY

The explication of the processes of cultural diversification is one of the

most important general problems in archaeology and anthropology (Bin ford 1962). Why are there more kinds of cultures in some places and in

some periods of time than in others? The same question is asked by

ecologists but is aimed more generally?thus the title of Hutchinson's

(1959) classic paper "Homage to Santa Rosalia, or Why are There so Many Kinds of Animals?" Models of species diversity have been devised to

answer Hutchinson's question, and here we are interested in whether these

models can shed light on cultural diversity and in whether data on cultural

diversity can be used to improve the generality of general ecological theory. The approach is opposite, and is an alternative, to the argument that

cultural "diversity and (the) processes of diversification are not explicable in terms of biological process" (Binford 1962:218).

Why should explanations of species diversity have anything at all to do

with the principles of cultural diversity? Such a question follows from the

larger problem of why explanations of variability among species should

have any meaning for our understanding of variability within a single

species. At least part of the answer lies in what ecological theory seeks to ex

plain: differences among organisms that have to do with how they cope with

environmental problems. Biological differences, such as those defining




species, are important only in that they provide a way of keeping genetic in

structions for environmental problem-solving reasonably intact. But if

problem solving of that kind mostly originates from learned behavior, and

only incidentally from genetic instructions, differences in cultural and other

rules of behavior gain priority in ecological explanation; the biological

species concept is no longer appropriate. Cultural rules are propagated in

social groups, and social groups thus become equivalent to the species for

purposes of ecological analysis. Because of the flexibility of learned

behavior, social groups with essentially the same genetic instructions can

devise very different rules for solving environmental problems. For that

reason, the ecological contrasts among neighboring social groups of the same species are often as great, if not greater, than the contrasts among

coexisting species. That is true not only for Homo sapiens but also for other

higher mammals (e.g., Kummer 1971). Therefore, because it is the ecolog ical differences that are important to ecological theory, there is no reason

why models of species diversity should not be applicable to intraspecific

diversity, provided that significant differences do exist in the ecology of

subspecific social groups.

Making the Models Workable for Archaeological Data

Before turning to the models of species diversity, we must consider how

they can be tested with archaeological data. What kind of information is

needed? First of all, the intraspecific equivalent of the species must be de

fined, and the means by which it can be recognized must be identified;

otherwise, there is no way of measuring "diversity." The ecological mean

ing of the species concept is a significant difference in lifestyle, as discussed

in the preceding paragraph. That implies that intraspecific differences in

lifestyle about the same as that separating species can be used to define the

species equivalent. How much difference is that? The answer lies, perhaps, in the same methods that some evolutionary taxonomists have used to

decide whether a local population belongs to one subspecies or another

(namely Mayr 1963); that is, rules have been devised to do the separation, such as two populations belong to the same subspecies if 95% of their

members hold 95% of their biological features in common. For our pur

poses, the separation rule can be stated as the allowable difference in the

behavior of the social groups being compared. Archaeological manifesta

tions of that behavior will be in the form of artifacts and other material re

mains; therefore, the differences among human groups that define species

equivalents are measured by the similarity in artifact assemblages. That is, of course, a method that is familiar to most archaeologists. The question




that has to be answered is whether the 95% rule should be used or whether some other level of similarity is best. Once that question has been answered, the recognition of "cultural species" with archaeological data is straightfor

ward.

The measurement of cultural diversity can then be made comparable to

that of species diversity. Ecologists use the concept of diversity in three

ways (Pielou 1975; Whittaker 1977):

1. Species richness?the number of species present 2. Species evenness or equitability?the relative importance of species

present 3. Total species diversity?measures that combine species richness and

evenness

Species richness is the most simple measure, requiring only that the number

of species in a standard sample be counted, and seems to be the most effec

tive measure of diversity (Whittaker 1977:2-4). "Standard sample" is, of

course, a key variable. The number of cultural species is partly a function of

the size of the geographical area and the length of the time period in which

the count was made; accordingly, scale must be considered. But what is the

appropriate scale to study cultural diversity? The answer to that question is

tied to the kind of explanatory model being used, among other things. MacArthur's (1972) theory of island biogeography, for example, argues

that scale in itself is an important cause of differences in species diversity. The other kind of information needed to test most models of species

diversity is suggested by their similar underlying logic, using the following

key assumptions (Hutchinson 1965; Diamond 1978; Schoener 1974; Whit

taker and Levin 1975):

1. Resources or other life opportunities in the environment are finite. 2. These finite resources subpartitioned among species sharing the en

vironment; the ecological niche defines what share a species re

ceives.

3. The ecological niche is occupied more or less exclusively.

Therefore, if the environment is partitioned into large shares, and the share

of each species is exclusive, it is reasonable to argue that species diversity is

less than if smaller shares were available. Questions such as how wide

(large) are the niches of single species and how far apart are the niches of

two species, or how much do they overlap, are, for that reason, especially

important to the understanding of species diversity. That is agreed upon. The models disagree, however, over the cause of partitioning and niche dif

ferentiation.

Because the niche concept is an important part of most models of species




diversity, it plays an important role in making the models useful for the

explanation of cultural diversity. The niche is, in effect, the distinctive

ecological behavior of the species. As with the species, "distinctive" is the

key to the meaning of the niche concept and implies that differences or con

trasts in the ecological behavior of species are necessary for its definition.

What kind of differences? In a classic study, MacArthur (1958) was able to

document that five congeneric warblers in New England forests coexist in

the same trees because they use, on the average, different size branches, dif

ferent vertical zones, and different strategies to search for the same food.

Thus, "niche differences are not simply a matter of differences in habitat or

food, but may also consist of differences in technique for finding the same

food in the same habitat" (Diamond 1978:324). MacArthur's study showed the importance of quantifying niche differ

ences, as well; the niches of the warblers could not otherwise be distin

guished, nor could their coexistence be explained (Diamond 1978). The

method of quantification is to measure species use of one or more re

sources. Which resources are used depends on the species being compared; that is, because niches are defined by ecological contrasts, the resources se

lected are those that are used differently. Niche width, niche distance, and

niche overlap can then be measured by some characteristic of the utilization

function. Thus, standard deviation is often used as a measure of niche

width (e.g., May and MacArthur 1972), and niche separation or distance

can be measured as the difference between the statistical means of the utili

zation curves for the compared species (ibid.); niche overlap can be

measured in a similar way (e.g., Pianka 1974). Figure 4.1 illustrates niche

MEAN OF SPECIES B

i

VARIATION IN RESOURCE X

Figure 4.1. Illustration of niche separation along a single resource axis (niche

dimension), showing terminology used in text.




definition with resource utilization curves. In practice, of course, a single en

vironmental variable (niche dimension or axis) is unlikely to give complete information about the niche differences that exist among species; several

niche dimensions are better indicators. Together, they define what Hutchin

son (1965) calls a niche hypervolume or hyperspace, the total ecological life

style. The measurement of the niches of cultural species can, then, be based

upon two kinds of data:

1. Data that show differences in the way that resources or other life op

portunities limited by the environment are utilized.

2. Data that show differences in how resources are acquired. Hardesty

(1975) illustrates how the niche width of cultural species can be measured with data on resource use, taking advantage of a method devised by Levins

(1968). The method uses an index of resource diversity n

niche width = 1 /L? (Pi)2 i where p? is the proportion of the total subsistence of a species contributed

by resource /, and n is the total number of resources used for subsistence.

Table 4.1 illustrates the method with subsistence data on the Mistassini Cree

TABLE 4.1

Niche Width Calculations from Total Resource Variety for Mistassini Cree, Canada3

Resource Biomass(lb) p? (p?)2

Moose 4000 .448 .202 Caribou 1500 .168 .029

Bear 210 .024 .000 Beaver 2120 .237 .058

Hare 114 .013 .000 Muskrat 240 .027 .001

Porcupine 60 .007 .000 Mink 33 .004 .000

Squirrel 8 .000 .000 Marten 5 .000 .000

Otter 110 .012 .000 Loon 44 .005 .000

Geese 67 .008 .000 Ducks 231 .026 .001

Ptarmigan 150 .017 .000 Spruce grouse 38 .004 .000 Ruffled grouse 1 .000 .000

Owl 1 .000 .000

?(p,)2 = .291

Niche width = 1/0.291 = 3.436

Data from Rogers (1972:104, Table 3.1). Source: Donald L. Hardesty, "The Niche Concept: Some Suggestions for Its Use in Human

Ecology." Human Ecology 3, 1975. Copyright ? 1975 by Plenum Publishing Corporation.

Reprinted by permission of the publisher.




of Canada given by Rogers (1972:104, Table 3.1). The method assumes that

resource diversity is a fair measure of how the environment of neighboring

groups is being partitioned. Niche overlap among cultural species can be

measured in a similar way, using a formula also given by Levins (1968). But diversity is only one of the ways in which resources may be used dif

ferently. Size is another. A shift from large animals to small animals has

been observed by many archaeologists, for example, to accompany the so

called Mesolithic transition (e.g., Binford 1968). Some have inferred such a

shift to mean an increase in population pressure (e.g., Cohen 1977:80). Whatever the explanation of why the shift took place, it is clear that re

source size can be used as an archaeological indicator of niche differences.

Figure 4.2 illustrates how these differences might appear. Tool size is

another archaeological indicator of niche differences if variability in size can

be related to differences in how resources are used.

Other niche dimensions are defined to show differences in how resources

or other life opportunities are acquired. Cook (1973) and other economic

anthropologists have shown that tools, time, and the organization of work

MEAN AT TIME T0

SMALL VARIATION IN SIZE OF ANIMAL USED FOR SUBSISTENCE

LARGE

Figure 4.2. Example of what a niche shift along an animal-size axis would look like.

The vertical scale gives the number of individuals of a given size that has been iden

tified in the archaeological record.




are the key variables in production systems; these are, therefore, the most

suspect as niche dimensions. Consider the hypothetical example illustrated

in Figure 4.3. Here, two human populations are using the same resources, but at different times of the year. Exactly that situation has been docu

mented by Bates (1971) for Turkish farmers and nomads, if land is defined as

the limited resource. Farmers use the land for domestic cultigens during their

growing season, and the nomads use the same land for grazing their herds

during the remainder of the year. Barth (1956) has documented a simi

lar situation in Pakistan. The cause of the niche differences observed is

debated, but there is agreement that time is the key niche dimension.

Seasonality has, of course, been documented with archaeological data for

single human populations (e.g., Clark 1954). What is needed are studies of

seasonality patterns among neighboring populations. Is the seasonality pat tern tied to the life cycle of the resources being used, as is usually inferred, or is it a way of allowing coexisting populations to use the same resources?

Another hypothetical example is illustrated in Figure 4.4. In this case,

tools, time, and the organization of work are combined to define a range of

production methods for a given set of domestic crops. The range follows the

sequence outlined by Boserup (1965), from extensive farming to intensive

farming. Two coexisting populations produce the same resources but use

MEAN USE TIME OF SPECIES A

MEAN USE TIME OF SPECIES B

Jan. 1 July 1

TIME OF THE YEAR WHEN RESOURCE X USED

Dec. 31

Figure 4.3. Example of what a niche difference along a time axis would look like.

The vertical scale gives the yield (production) of resource X for a given time of the

year.




MEAN FARMING INTENSITY OF SPECIES A

MEAN FARMING INTENSITY OF SPECIES B

0 Years 25 Years

LENGTH OF FALLOW PERIOD FOR METHOD OF CROP PRODUCTION

50 Years

Figure 4.4. Example of what a niche difference along a total production-system axis would look like. The production system is approximated by the length of time that a farm field must be kept out of production to rejuvenate. Following the classification proposed by Boserup (1965), the most extensive method of farming has the longest fallow period; the field is farmed continuously if no fallow period is indicated. Because a particular cultural species may use different production

methods for different fields, the total production system is represented as a distribu tion curve. The vertical scale gives the yield (production) of each production method used.

somewhat different, although overlapping, methods. The different distribu

tion of production methods defines two distinct niches.

Models of Species Diversity

Having now considered the ways in which models of species diversity can

be made workable for archaeological data, we are ready to turn to the

models themselves. In general the explanations are either historical or func tional. Historical models portray species diversity increasing by some func

tion of time or by unique historical events; that is, a particular pattern of

diversity exists for reasons that cannot be specified by a few general prin

ciples. The functional model, on the other hand, invokes general principles of necessity. Perhaps the most common form of such models predicts a

limit to diversification, a limit that is controlled by principles of inter




specific competition, pr?dation, or physical stress. Some models have, of

course, elements of both historical and functional explanation; the stability time hypothesis is an example.

The Stability-Time Hypothesis Yellen's paper (1977), discussed in an earlier section, clearly shows how

the stability-time hypothesis can be used to explain and predict some aspects of the behavior of hunters and gatherers; it is also an explanation for species

diversity. The hypothesis states that species diversity increases continuously over time but that environmental stress counteracts the trend (Bretsky and

Lorenz 1970; Sanders 1969). There are at least two test implications. The

first is that older ecosystems and communities should be more diverse than

younger ones, given the same amount of stress; the second is that, given

ecosystems and communities of the same age, those under the least stress

will be the most diverse. Unfortunately, neither of the implications is easily observed: the age of ecosystems and communities is difficult to ascertain, and environmental stress cannot be easily measured. Indeed, Sanders uses

physiological stress as a measure because its effects can be observed on the

organism; but, because cause and effect are thereby not separated, a cir

cular argument ensues. These problems with testing the stability-time hy

pothesis have led to the accusation that it is a tautology (Peters 1976:9-10).

Why, then, should it be used as an explanation of cultural diversity? The reasons are twofold. In the first place, the archaeological evidence of hu

man populations making up an ecological community is often dateable,

making it possible to compare communities of more or less the same age.

Second, if environmental stress is equated with the predictability of re

sources or other life opportunities, as Yellen does, then circular reasoning is avoided; that is, stress, as environmental predictability, can be observed

and measured independently of its supposed effects. Here is a good example of how archaeological data on human ecology can be used to test a model of

species diversity that is otherwise plagued with logical weaknesses.

A few additional comments about the predictability of the environment

are in order before going further; it cannot be measured separately from the

organism being studied. The same environment may be very predictable for

some and completely unpredictable for others, depending upon how re

sources or other life opportunities in the environment are being used.

Technology and social organization are, therefore, essential parts of any at

tempt to measure environmental predictability for human populations. Both have an impact upon the meaning of an environment for a population, not only because they define the means of production but also because

they can modify the predictability of the environment. Technology makes

changes by controlling and redistributing water (irrigation technology), by




weather modification, by artificial fertilization, and the like. Social or

ganization makes changes through economic networks that involve the

transportation of resources from one place to another and from one time to

another. Thus, the desert habitat that Yellen found to be so unpredictable for hunters and gatherers is considerably more predictable for modern

irrigation farmers. There is, therefore, reason to doubt that "external"

measures of environmental predictability, such as rainfall variability (e.g.,

Baerreis, Bryson, and Kutzbach 1976; Jorder 1977; Reher 1977), will be

of much value to general explanations. The best measure is probably

something that considers the simultaneous effects of climate, technology, social organization, and the like. Perhaps something equivalent to the an

nual yield of production systems would work: a highly variable yield sug

gests an unpredictable environment, and a sustained yield suggests a pre dictable one. That measure is, of course, difficult to make workable for

archaeological data, but suitable material indicators of variability in yield are possible.

Now let us return to the test implications of the stability-time hypothesis for cultural diversity. There are two kinds of predictions: comparative

predictions and evolutionary predictions. Comparative predictions are the

same as those stated in the first paragraph of this section; that is, they hold

constant the age and the stress of ecological communities made up of

cultural species and compare cultural diversity. But it is an evolutionary

prediction that may be of most interest. The stability-time hypothesis leads to the prediction that evolutionary patterns of cultural diversity in

an ecological community will reflect environmental predictability and its

changes over time. Consider an environment that is initially highly predict able to a cultural species. Diversification continues over time and produces a curve of continually increasing cultural diversity. But if episodes of en

vironmental stress shock the evolving community of species, diversity would either fall drastically or level off, depending upon the severity of the

shock. Holling (1973) argues, for example, that the diverse, equilibrium communities usually found in stable environments are most susceptible to an unexpected perturbation; the result is often a catastrophic change in the

community, causing a drastic reduction in species diversity. Because of this, Gould (1977:222) suggests that the "ultimate control (of diversity in equilib rium communities) is exerted by the very rare major fluctuation (of the envi

ronment)." If so, the expected shape of the diversification curve shows

rapid growth interspersed with periods of sharp declines or plateaus. The stability-time hypothesis predicts, on the other hand, that in un

predictable environments, cultural diversity is kept at or below a limit set by the necessity of having wide niches. That is, a reasonably large number of resources or other life opportunities must be available to each species to




minimize the risk of an unexpected loss of one resource or another; there

fore, wide niches are demanded and, because of that, few species are possi ble. The limiting similarity models, to be discussed later, give some explana tions of exactly what the functional limits on diversity are. Notwithstanding the theoretical limit, however, the evolutionary pattern of cultural diversifi

cation may show peaks or episodes of rapid proliferation corresponding to

periods of increased predictability of the environment; such peaks will, of

course, be squashed by a return to normal conditions.

Limiting Similarity Models

Perhaps the most provocative of the functional explanations propose that

species diversity is limited by niche similarity; that is, the ecological lifestyle of coexisting species can become only so much alike before the relationship is disrupted. Why? Two explanations are currently popular. One argues that niche similarity is partly limited by environmental variability but that a

limit exists independently of moderate to small fluctuations; the other sees

niche similarity limited by competition among species.

Variability in the Environment The first kind of explanation is il

lustrated by a model proposed by Robert May and Robert MacArthur (May and MacArthur 1972; May 1974, 1976; see also Nisbet et al. 1978). Based

upon mathematical studies, the May-Mac Arthur model predicts that under

the conditions of a constant, deterministic environment, there is no limit to

how closely together neighboring niches may be packed; therefore, there is

no theoretical limit on species diversity. But if the environment is allowed to

fluctuate randomly, a different situation prevails. May and MacArthur

(1972:1109) initially conjectured that "in the real world, environmental

fluctuations will put a limit to the closeness of species packing compatible with an enduring community, and that species will be packed closer or wider

as the environmental variations are smaller or larger." That was found to

be true if the fluctuations were severe. Species packing is about propor tional to the amount of variance, as would also be expected by the stability time hypothesis. But if the random fluctuations are kept within the range from 30 to .01% of the mean, then "the closest species packing consistent

with stability falls only from 2 to 1 times the utilization function variance"

(ibid.); that is, the model predicts a definite limit on species diversity. May and MacArthur interpret such a limit to mean that environmental variabil

ity is not an important cause of species packing unless it is severe. If true, the model contradicts the stability-time hypothesis.

Species packing in the May-MacArthur model is measured by niche

separation. Using a single niche dimension, food size, the model portrays the distance between neighboring niches as the difference in the way that

food size is used. The actual measure is d/w, where d is the difference be




tween the means of the compared utilization curves and w is 1 standard

deviation. Thus, the predicted limit, given above, is d/w = 1 or 2.

The test implications of the May-MacArthur model for patterns of

cultural diversity are similar to those of the stability-time hypothesis, but

more precise predictions are possible. Once again environmental fluctu

ations are to be equated with the variability in an index of production yield or something similar; that is, predictability in effective environment is

measured. In very unpredictable environments, cultural diversity is ex

pected to be more or less proportional to the amount of uncertainty. And in

very predictable environments, cultural diversity is expected to have no

limit; that implies that it will increase continuously over time. Both of these

predictions are consistent with those of the stability-time hypothesis. But in

most environments, those with only moderate stochasticism, cultural diver

sity is expected to reach and maintain a limit. That suggests that any com

munity of cultural species with moderate to small fluctuations in production

yield, whether foragers, farmers, or industrialists, should have approxi

mately the same diversity if niche dimensions are equivalent. And in the

same way, the evolution of a community of cultural species in the same kind

of environment should increase to a limit and then level off; that is, diver

sification should not be continuous over time, as implied by the stability time hypothesis.

The May-MacArthur model further predicts that the limit is approx

imately d/w = 1 or 2, and archaeological data can be used to test this more

specific expectation. Niche dimensions that best separate cultural species must first be defined; then, utilization curves can be plotted and niche widths and distances measured. Again, recall that the best niche dimensions to use are limited resources or other life opportunities that are used or pro duced differently. The dimensions need not be natural resources: indeed, such "created" resources as art objects could probably be used to define the niches of social classes in complex societies; certainly there is a difference in use (see Hardesty 1978, 1979 for a further discussion of niche dimensions in human ecology).

Competition. The May-MacArthur model is supported extensively,

although not exclusively, by mathematical evidence, but by little observable

evidence (May 1976; Nisbet etal 1978). Furthermore, several of the assump tions used in the model, such as a saturated environment, resources equally available to interacting species, and no significant difference in species

abundance, may not be realistic in many situations (Whittaker and Levin

1975:176-177). Thus, Pianka (1974:2141) notes that the May-MacArthur model "assumes an equilibrium community in a fully saturated environ

ment with all resources being used fully; as such, variation in the intensity of competition is not modelled." What effect does competition have upon

diversity? Pianka argues that if environment is not limiting, for whatever




reason, there is absolutely no reason why the same niche should not be oc

cupied by different species. Thus, Connell (1975) documents that competi

tion, forcing niche exclusion, is virtually absent under heavy pr?dation or

environmental stress; population size is kept low so that the supply of resources doesn't exceed demand. Nor is the environment limiting under

conditions of superabundant resources, and Weins (1977:591-592) sees

competition as unimportant under such conditions. In general, if the supply of resources or other life opportunities exceeds demand, it can be assumed

that competition has little impact upon niche differentiation and, therefore,

species diversity.

But, as environment becomes more limiting, that is, as demand exceeds

supply, competition intensifies. What are the conditions under which that is

likely to happen? Environmental predictability seems to be important, for

two reasons: (1) competition models assume an equilibrium community, which are found in predictable environments, and (2) continuous competi tion is assumed by competition models, and that is most likely in predictable environments (Weins 1977:591). Whatever the cause, however, intensified

competition is likely to make the principle of competitive exclusion (Hardin

1960) more effective and, consequently, to limit niche similarity. A limit is

thereby placed upon species diversity.

Testing Pianka's "niche overlap hypothesis," as he calls it, depends upon some way of recognizing and measuring competition. Diamond (1978: 325-328) considers niche shifts to be the best evidence of competition, in

cluding the following:

1. Niche differences among neighboring species that are very similar in

other ways 2. Niche differences in otherwise identical species that occupy environ

ments with and without other similar species 3. Diachronie changes in the niche of the same species after it has

moved from an environment with potential competitors to one with

out, or vice versa

Notwithstanding arguments that such niche shifts could be explained by yet unknown limiting factors and the like (e.g., Connell 1975; Weins 1977), the

evidence seems to favor competition as the best explanation. But the obser

vation of niche shifts per se cannot be used to measure competition without

getting into the same kind of circular reasoning that weakened the stability time hypothesis; that is, the cause is not empirically separated from the ef

fect. One way of separating the two is to follow MacArthur's (1972) con

cept of diffuse competition, also used in Pianka's model. MacArthur and

Pianka propose that weak competition from many sources is additive and

has the same effect as more intense competition from a few sources; for that




reason, the number of competitors can be viewed as a measure of the inten

sity of competition. For our purposes, then, the intensity of competition is

measured by population size or population density, and that provides a

source of empirical data that is independent of data used to measure niche

shifts.

Now we can consider the implications of the niche overlap model for pat terns of cultural diversity. The limit on diversity is set by the intensity of

competition; therefore, a test of the model requires that population size or

density or another measure of competition be plotted against the average niche overlap of cultural species, either using comparative or evolutionary data. If the model is correct, the ensuing curve should have a definite upper

plateau. Care must be taken, however, to be sure that environmental vari

ability is controlled for the test; otherwise, the effects of competition and

variability cannot be separated. Probably the best method is to compare the

competition curves in environments with different degrees of predictability. The curves should be similar if the Pianka model is correct.

Equilibrium Theory of Island Biogeography

Perhaps the most influential functional model of species diversity to

emerge in recent years is the equilibrium theory of island biogeography pro

posed by Robert MacArthur (MacArthur and Wilson 1967; MacArthur

1972; Simberloff 1974). The model portrays islands or other isolated

patches being packed with species up to a predictable limit determined by the size of the island and its distance from a colonizing source?a mainland

in the case of islands. That limit is reached at the intersection of two

mathematical functions: the rate of immigration of new species from the

colonizing source, and the rate of extinction of species already on the

island. When a vacant island is first colonized, the rate of immigration is

relatively high. Almost all colonists are representatives of new species. With

continual migration, however, the chance that a new arrival is a new species decreases; the rate of immigration correspondingly decreases. The immigra tion rate reaches zero when all species from the colonizing source have ar

rived. Mathematically, then, the immigration rate is some inverse function

of species diversity. By contrast, the island extinction rate is an increasing function of species diversity because of the following: (1) there are more

species to become extinct as diversity increases, and (2) the intensity of com

petition, pr?dation, and the like increase with diversification. The falling

immigration rate curve eventually intersects with the rising extinction rate

curve: the intersection point is the predicted level of species diversity on the

island. Intersection points, the equilibria of species diversity, are not the same from one island to another because of the effect of distance and island size. Immigration rates are highest when an island is close to its colonizing




source and lowest when it is far away. Extinction rates are highest on a

small island, principally because random population fluctuations are more

pronounced, and lowest on a large island; accordingly, diversity levels, or

equilibria, are highest on large islands close to a source of colonization.

Conversely, the equilibria are lowest on small islands that are far away. Some promising applications of MacArthur's theory of island biogeog

raphy to archaeological data have been made in Oceania (see Clark and Ter

rell 1978). Kaplan (1976) has, for example, nicely illustrated its usefulness

for understanding cultural and biological processes on the small "stepping stone" islands of Nissan and Pinipel in Papua, New Guinea. Both are

situated, along with the neighboring Feni Islands, between New Ireland, a

large island in the Bismarck Archipelago, and Buka, in the northern Solo

mons. Without the stepping stones, New Ireland and Buka would be sepa rated by more than 100 miles of open water. Did Nissan and its neighbors

play a role in creating the ethnographic similarities and striking differences

in race, language, and culture between the two? MacArthur and Wilson

(1967) suggest that stepping stones are important to species dispersal in two

ways: (1) they shorten distances that must be traveled at any one time and,

therefore, are the most likely routes of dispersal, and (2) they regulate the

frequency and the rate of species movement between larger land masses.

Both of these hypotheses share a fundamental assumption with "gravity" models in geography (Chorley and Haggett 1967:559), namely that the like

lihood of attraction between two social groups or geographical areas is a

direct function of size or mass and an inverse function of distance. For

species dispersal, the attraction of a small stepping stone is greatly reduced

because of intensified competition among colonists. The result is that only a

few species from a source area successfully make the leap, making the step

ping stone an effective filter.

Using this perspective, Kaplan (p.78) defines two research goals, as

follows:

1. To what extent has the presence of Nissan as an island intermediate between New

Ireland and Buka served to increase the likelihood that trade goods, ideas, and people

might travel between these two regions?

2. In what ways has Nissan acted as a "filter," or regulator, influencing the transmis

sion, or "dispersal," of trade goods, ideas, and people between the Solomons and

New Ireland?

Theoretical, ethnographic, and archaeological approaches are used to

answer the questions. Terrell (1974) built an "interaction" model of the

region showing the most likely trade routes among the islands if the

distance-mass assumptions of the biogeography and gravity models are

realistic. Kaplan then compares that theoretical model with actual trade

routes reconstructed from ethnographic data; the fit is quite close. The

stepping-stone model is, then, a good predictor of human interaction in this




part of the Pacific, suggesting that short hops rather than long-distance movement best explains trading and communication routes.

Directly to the point of Kaplan's question, both the stepping-stone model

of island biogeography and ethnographic data support trade between Buka

and New Ireland via Nissan, Pinipel, and the Feni Islands. Furthermore, an

analysis of ceramics from archaeological sites on Nissan shows an unbroken

stylistic sequence from ad 500 to historic times, styles that are virtually the

same as those from Buka. One sherd is stylistically foreign and suggests trade with other islands. Kaplan's first question can, then, be answered:

"There is some indication that known historic patterns of interaction be

tween New Ireland and the Solomons via Nissan developed during the

prehistoric past over a period of more than a thousand years" (Kaplan

1976:85). The second question is partly answered with ethnographic data showing

that Nissan did indeed act as a filter. Kaplan (p. 82) notes, for example, that

Buka cookware, a breakable commodity, was not usually traded beyond

Nissan; most of it was consumed on that island. Archaeological data have

not yet been brought to bear upon the question, but the implications for ar

chaeological research in the future are clear.

That theories of island biogeography have been used to explain human

ecology on real islands is not surprising. We should not, however, overlook

the possibility of their usefulness for understanding the human ecology of

conceptual islands: land-bound geographical regions that are isolated from

other regions in much the same way that real islands are. Perhaps the

American Great Basin and the Mexican highlands, both topographically and otherwise divided into small basins, would be ideal. Each basin can be

modeled as an island. The entire region would then consist of a cluster of

islands of different sizes and distances from one another; race, language, and cultural diversity could then be predicted from the calculated equilibria for each island. For the purposes of this chapter, the diversity of cultural

species could also be predicted from the equilibria. How can archaeological data be used to test the accuracy of the equilib

rium model of island biogeography? May (1978: 163-164) lists three kinds

of data that are needed, and they can be made workable for data on the di

versity of cultural species. The first is data on changes in cultural diversity that take place over time on islands where an equilibrium is suspected to ex

ist; if the model is correct, the changes should be best explained as random

fluctuations. Second, the model can be tested with data on changes in

cultural diversity on islands that were vacant or depopulated and that subse

quently refilled. The evolutionary pattern expected over time is rapid diver

sification in the beginning, followed by a leveling off as the equilibrium is

reached. Archaeological data are, of course, most appropriate for this kind

of test. Finally, data on evolutionary changes in the diversity of cultural




species are needed on islands that are suspected to have diversities well

above the equilibrium because of unique invasions, forced colonization, and the like; the diversity level should drop back to the predicted

equilibrium.

Stochastic Models

The models that have been discussed so far are deterministic; that is, the

underlying cause of species diversity, cultural or otherwise, is mostly com

petition, environmental stress, or some other controlling process. That

assumption is not, however, shared by all. Some see causation as the result

of a very large number of interacting processes, no one of which has con

trol; the cause of diversity is, therefore, best understood as the sum total of

the unique histories of particular species (Simberloff 1978; see also Raup et

al. 1973:526). And because of its complexity, the cause of diversity from

one situation to another is unlikely to be the same.

Such an interpretation does not mean that the study of patterns of species

diversity is fruitless. The cause of each coin tossed landing heads or tails, for example, is too complex to specify, depending upon a multitude of fac

tors: wind velocity, wind direction, balance of the coin, direction of the

toss, and so forth (this example is given in Raup et al. 1973:526). Yet we

know that the pattern in the long run can be predicted to be 50% heads and

50% tails. Why? Events with complex, multivariate causes behave as if they were random variables, and random variables have statistically predictable

patterns of behavior. For example, we know from the "central limit theo

rem" of probability that the sum of many independent random variables

will have a very nearly normal distribution (MacArthur 1972:42). The ex

pected pattern of events with very complex causes can, then, be deduced

from a probabilistic model.

That is an approach presently being taken by some paleontologists and

biogeographers to see if observed patterns of species diversity can be ex

plained by the null hypothesis of randomness (e.g., Boucot 1976, 1978; Gould 1977; Kitts 1975; Levandowsky and White 1977; Osman and Whit

latch 1978; Raup 1977; Raup et al. 1973, 1975, 1976; Simberloff 1978). In a

classic study of the effects of randomness on diversity, for example, Raup et

al. (1973) set out to see if patterns of "phyletic diversification in the fossil

record" (p. 525) could be explained by a model based upon the following

assumptions: (1) every geographical region has a limit on the number of

taxa that can coexist, and (2) after that limit, or equilibrium, has been

reached, changes in diversity over time can be predicted by stochastic pro cesses. That is, the causes of the extinction or origins of each taxon are com

plex, in effect unknowable, but the overall pattern of extinctions and

originations can be simulated with a random walk (ibid., p. 526). And that

simulation is exactly what the authors do.




A computer program was written to see what would happen if a single

lineage were subjected to stochastic processes over time. At each successive

time period, the simulated lineage met one of three fates: it became extinct, it branched to produce a second lineage, or it was allowed to persist without

branching. Random numbers automatically generated by the computer chose the fate. The simulation was then compared to the actual fossil record

of several regions. Many similarities are apparent, so that stochasticism

could not be eliminated as a cause of evolutionary patterns of phyletic

diversity, but neither could the possibility of deterministic causes be dis

counted; some periods of rapid extinction are, for example, definitely not

best explained by random walk and suggest that some deterministic process was responsible.

The probabilistic model is not really an explanation for species diversity because it does not specify a cause; rather, it is a null hypothesis that must

be rejected before deterministic explanations can be retained. For that

reason, simulations of what evolutionary patterns of cultural diversity look

like under stochastic conditions are badly needed. The computer program

logic could be very similar to that used by Raup et al. Start with a single cultural species colonizing an uninhabited area, and assign to the species a

set of cultural rules for making an artifact in a particular style. Begin the

species on a journey through time, divided into periods of, say, 100 years, each of which commences with a fate that is randomly chosen by computer.

(Recall that the fate of the cultural species is assumed by the model to have

complex, specific causes; the actual distribution of fates over time can be,

therefore, best described as a random walk.) What fates are possible? The

species can become extinct without disseminating its unique artifact style; the species can persist without disseminating its artifact style; the species can fission into two, each of which keeps the same artifact style; the species can fission into two, the offspring creating a new artifact style; the spe cies can persist and adopt a new artifact style; and so forth. Several simula

tion runs will produce the evolutionary patterns of artifact style diversity to

be expected if the causes are stochastic. The predicted patterns can be then

compared to actual observations to ascertain whether the null hypothesis can be rejected. If so, more deterministic models of diversity, such as

limiting similarity, can be tested; if not, ad hoc explanations may be the

best.

Discussion

Models of species diversity can, then, be made workable as possible ex

planations of cultural diversity and can be tested with archaeological data, at least in theory. The models that have been discussed are not intended to

be exhaustive; other competing models of species diversity can be gleaned




from the ecological literature. Rather, they illustrate something of the range of explanations that exist and the kind of problems that must be overcome

to render them useful for archaeological interpretation. More importantly, the discussion shows the need for multiple hypothesis testing if general

ecological models are to be used effectively in archaeology and anthro

pology. What about the generality of now-existing models of species diversity?

Are they capable of accommodating human ecology data and principles? Or

must the models be modified? Human behavior is unique in its capacity to

change the amplitude and predictability of environmental fluctuations, and

it is here that many of the species diversity models are likely to be too nar

row or specialized. Those that use responses to environmental variability as

explanatory principles, such as the stability-time hypothesis and the May MacArthur model, assume that environment is controlled by external pro

cesses; there is no provision for a species that responds by changing its en

vironment, for a process of ert?ronmental change that is internal to the

model. What must be added to make the models more general is an interac

tive process that connects the community of cultural or biological species and its environment.

To illustrate what that means in practice, consider two models that use

environmental variation to explain the evolution of complex human so

cieties. The first is Gall and Saxe's (1977) argument, already discussed, that the evolutionary process is analogous to that of ecological succession.

In their model, increasing cultural diversity is assumed to have a selective

advantage because diversity buffers the impact of environmental change and makes the ecological community more stable. But diversification may not be the cause of community stability; rather, increased diversity and

community stability may be the consequence of environmental stability

(Goodman 1975; May 1975; Pielou 1975; Whittaker 1977). If so, it is the

cause of environmental stability that is critical to the evolution of complex human societies, and that is not considered by Gall and Saxe.

Isbell (1978), by contrast, develops a model to explain the emergence of

the state in the central Andes that does take environmental variability, and

its modification by human behavior, into account; Jorde (1977) suggests a

similar model for the American Southwest. The model gives the selective

advantage to behavior that reduces the amplitude and increases the predict

ability of environmental fluctuations. Economic exchange networks con

necting geographically separated human populations are examples of this

kind of behavior, as is the establishment of a storage complex to "even

out" fluctuations over time. Both of these are expected to be advantageous

and, therefore, to become more common over time. Isbell gives archae

ological evidence to show that the culture history of the central Andes is




marked by just that kind of behavior: increasingly elaborate storage and

distribution facilities, increasingly centralized decision-making apparatus, and political entities with increasingly larger geographical territories. The

last is added here because recent mathematical models have shown that

greater spatial heterogeneity can increase the stability of an ecological com

munity (May 1974; Murdoch and Oaten 1975). An interesting implication of Isbell's model is that ideology may play an

important role in making the environments of human ecological com

munities more stable. Willey (1962) argues, for example, that widespread

religious horizons, such as the Olmec and Chavin, served to integrate

geographically separated populations. The ecological significance of closer

ties is that it encourages and makes possible economic exchange networks, behavior that dampens local fluctuations in the environment. If so, it rein

forces Vayda and Rappaport's (1968) argument that ideology is important to ecological explanation and does not justify the separation of "spiritual" and ecological models, as does, for example, Adams (1977:94).

The "internalization" of the cause of environmental variability is, as far

as we know, absent in general ecological models. Yet Homo sapiens is not

the only species that uses behavior to change its effective environment in a

habitat, although Homo has capitalized upon that ability. Burrowing animals do exactly the same thing, for example, by using biological struc

tures and mostly genetically controlled behavior to create microclimates

that are considerably different from the surrounding habitat. What that im

plies, of course, is that animals have two kinds of responses to environmen

tal fluctuations: (1) they can change their behavior or biological structure, as general ecological models assume, and (2) they can change their environ

ment by using biological structures, technology, social organization, and

the like. The addition of the latter response to general ecological models

would render them much more capable of accommodating human be

havior.

If environmental change is the response chosen, an interactive process

connecting human behavior, or that of another species, and the environ

ment can be conceptualized. Environmental uncertainty is countered by some kind of behavior, such as intergroup exchange of uncertain resources, that lowers the level of the uncertainty. But a less risky environment is

suitable for finer niche partitioning and a finely tuned adaptation. And that

is associated with the diversification of biological or cultural species. Fine

tuning, however, makes the species, now occupying more specialized niches, susceptible to smaller, more localized fluctuations in the environ

ment. The expected consequence is even more elaborate "buffering" be

havior to make the environment even more predictable. And so the pro cess continues toward 100% predictability. The evolutionary pattern of




species diversification to be expected from the positive feedback process

depends upon which model is the best explanation. May and MacArthur's

limiting similarity model, for example, predicts that diversification will con

tinue as environmental fluctuations are reduced down to about 30% of the mean. At that point, no further change in diversity should take place until

the predictability of the environment has been increased to nearly 100%.

After that, diversification can continue without limit.

But as a community of biological or cultural species diversifies in an in

creasingly stable environment, the dilemma of all equilibrium communities

must be faced: unexpected major disturbances in the environment are likely to be disastrous (Bretsky and Lorenz 1970; Gould 1977; Holling 1973). The

disturbance may be external, such as a major climatic shift, or it may be in

ternal, such as the collapse of behavior that buffers unexpected fluctua

tions; the overthrow of a strong central government is an example if it

destroys exchange and storage systems. Whatever the reason for the shock, the finely tuned, narrow-niched species making up the community are likely to become extinct, either as a biological entity or as a behavioral entity. A

period of revolutionary cultural change is, for example, the likely archaeo

logical manifestation of the breakdown. The interactive process then begins anew.

CONCLUSION

The use of general ecological principles is a viable alternative to ad hoc

ecological explanation in archaeology. Changes must be made in their ap

plication to archaeological data, however, if logical problems are to be overcome. First of all, and perhaps most important, the principles cannot

be obtained by search and seizure and used without consideration of their

place in ecological theory. Few, if any, of the principles in general ecology are so generally accepted that they are paradigmatic; consequently, com

peting principles are likely to exist. And, furthermore, the principles may not be sufficiently general to accommodate human ecology. That suggests that the best use of general ecological principles is interactively with ar

chaeological data, testing competitors and making changes to improve their

generality. The second major change that must be made if models derived

from general ecology are to be useful for archaeological explanation is to

make them workable. Few applications have considered this problem; yet if

such models cannot be tested, the explanation is inadequate. More research

in these two directions may bring about a revolution in ecological explana tion.




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The Use of General Ecological Principles in Archaeology