4. Nettle 1998 (Unidad 2)

8/12/2019 4. Nettle 1998 (Unidad 2)

1/21

Explaining Global Patterns of Language Diversity

Daniel N ettle

M erton Coll ege, Oxf ord OX 1 4JD , U ni ted Ki ngdom

Received Jul y 2, 1997; revision received M arch 23, 1998; accept ed A pr il 8, 1998

The six and a half thousand languages spoken by humankin d are very unevenly distribut ed

across the globe. Language diversity generall y increases as one moves from the poles toward the

equator and is very low in arid environments. Two belts of extremely high language diversity

can be id entifi ed. One runs through W est and Central Af ri ca, whil e the other covers South and

South-East Asia and the Pacifi c. M ost of the worl ds languages are found in these two areas. Thispaper attempts to explain aspects of the global distribut ion of language diversity. It is proposed

that a key factor influencing it has been climatic variability. Where the climate allows contin-

uous food production throughout the year, small groups of people can be reliably self- suffi cient

and so popul ations fragment i nto m any small languages. Where the variabili ty of th e cli mate is

greater, the size of social network necessary for reliable subsistence is larger, and so languages

tend to be more widespread. A regression analysis relating the number of languages spoken in

the major tropical countries to the variability of their climates is performed and the results

support the hypothesis. The geographical patterning of languages has, however, begun to be

destroyed by the spread of Eurasian diseases, Eurasian people, and the world economy. 1998

Academic Press

INTRODUCTION

The diversity of human language is oneof its most intriguing features, and overthe last decade scholars from several dis-ciplines have become interested in whatpatterns of linguistic diversity might tellus about the human past (Cavalli-Sforza

et al 1988; N ichols 1990, 1992; Renfrew1991; M ace and Pagel 1995; Dixon 1997). Atleast thr ee senses of the term li nguisticdi versity need to be di sti nguished. Fir stl y,there are regions of the world, such asCameroon or Papua New Guinea, wherethere are very large numbers of differentlanguages, and others where there arevery few. Such diversity i s the topic of this

paper, and I will refer to regions withmany languages as being high in languagediversity,just as biologists refer to regionswith many species as high in species di-versity.

Language diversity needs to be clearlydistinguished from phylogeneti c diversityof

languages, whi ch i s a matter of how manydifferent language fami lies or branches oflanguage fami li es are present.1,* Some-times the two types of diversity go to-gether, as in Papua New Guinea and thePacifi c. H owever, there is no necessaryconnection between them, and there aremany discrepancies. Centr al Afr ica, for

exampl e, has hun dreds of di fferent lan-guages and so is high in language diver-sity. H owever, almost all of those lan-guages are very closely related, belongingto the Bantu family, and so the region islow in phylogenetic diversity. I will arguein thi s paper that the language diversity ofLatin A merica is quite low relative to com-parable parts of A fri ca and A sia. H owever,

it is generally agreed (the claims of Green-berg [1987] notwithstanding) that the lan-guages of Lati n America belong to dozensof different families, and so the phyloge-

* See Notes section at end of paper for all foot-

notes.

jo u r n a l o f a n t h r o po l o gi c a l a r c h a eo l o gy 17, 354374 (1998)

a r t i c l e n o . AA980328

354

0278-4165/98 $25.00Copyright 1998 by Academic PressAll rights of reproduction in any form reserved.

8/12/2019 4. Nettle 1998 (Unidad 2)

2/21

netic diversity of the continent is high,somethi ng w hich has implications for ourunderstanding of the Latin American past(Nichols 1990).

The thi rd sense of li nguistic diversity

which must be distinguished is that ofstructural diversi ty on some linguistic pa-rameter. For exampl e, the languages ofthe world order their basic consti tuents ina number of different ways. There areSOV l anguages (Subj ect- Verb- Obj ect),SVO l anguages (Subj ect- Obj ect- Verb),VSO languages and so on. A set of lan-guages exhibi ts high structur al diversity

in word order if many dif ferent orders arerepresented among them. Structur al di-versity wi ll tend to be correlated wi th phy-logenetic diversity, since where there aremany different fami li es there wil l often bemany different structural types of lan-guage. However, the connection is not anecessary one, since there can be severalstructural types within one fami ly, or, ob-

viously, several fami li es wi th the samestr uctur al type. N onetheless N ichols(1992:250) found that high str uctural di-versity on the basic syntactic and morpho-logical parameters she looked at was in-deed coincident wi th high phylogeneticdiversity, both being typi cal of the Pacifi cand New World.

This paper, then, is an investigation of

language diversity, whi ch is not closelyrelated to the other two types of li nguisticdiversity. Structural diversity and particu-larly phylogenetic diversity give us infor-mation about the evolution of humanpopulations along the axis of tim e. Forexampl e, i t is th e lack of phylogenetic di-versity amongst the languages of Centraland Southern Africa which tells us that

the spread of Bantu-speaking farmers totheir present locati ons was comparati velyrecent.

I will argue that language diversity alsocarries information of interest to the an-thr opologist, archaeologist or histori an.H owever, this information is not prim aril y

about events in deep time but about theorganisati on of people in space. That is,where a region i s dotted w it h many smalllanguages, we can infer that the popula-tion has arrayed itself , socially and eco-

nomi call y, in many small (though not nec-essari ly isolated) communi ties. Where asingle language is spread over the wholeregion, we can infer that powerful mech-anisms of regional integration have beenat work, and we may wish to ask whatthose are. This paper, then, asks the q ues-tion of, what, in general, determines thesize of language communities found in a

human population. In Section 1, I discussthe main geographical pattern s in the di s-tr ibut ion of languages across the globe. InSection 2, I discuss the principal vectorswhich spread languages, and suggest thatthe theory developed in Nettle (1996) toexplain patterns of language diversity inWest Africa might explain patterns inother parts of the world too. That theory

li nked the size of language groups, viasubsistence pattern s, to cli mate. In Sec-tions 3 and 4, I test the theory for all themajor tropical countries by means of aregression analysis relating the num ber oflanguages spoken in each country to cli-matic vari ables. The data pr esentedstrongly support the theory.

Before turning to Section 1 and the

global distribution of languages, however,a bri ef discussion of how languages areidentifi ed i s in order. Ascertaini ng the sizeof community speaking a particular lan-guage presupposes being able to delin-eate it, and throughout this paper I willrefer to counts of languages in particularcountries. I have been able to do this be-cause linguists routinely list and refer to

enti ti es call ed languages. H owever, theconcept is not unproblematic.

There is vari ation in speech norm s bothwithin and between all known communi-ties. The problem for an analysis of lan-guage diversit y i s distinguishin g languageboundari es from dialectal or idi olectal

355LANGUAGE DIVERSITY

8/12/2019 4. Nettle 1998 (Unidad 2)

3/21

variation. The criterion usually proposedto do this is that of mutual intelligibility,and formal techni ques for measuri ng t hishave been devised (Casad 1974). H ow-ever, not only are such techniques rarely

used in the field, but their very basis isproblematic. Fir st, intell igibili ty i s a gradi-ent phenomenon, and there can be vari-ous degrees of part ial understandi ng. Onemay find chains of dialects in which adja-cent vari eties are perfectl y intelli gible, butthose at opposite ends of the chain aredefini tely not. It is unclear how best todraw boundaries in such cases. Second,

mut ual i ntelli gibil ity can be asymmetr ical.Thir d, intelli gibil it y vari es a great deal ac-cording to the context and the particularspeakers involved, and fourth , intelli gibil -ity depends upon much the parties i n-volved want to understand each other,which is a product not of linguistic struc-tur e but of social factors (Wolff 1959; H ud-son 1996:35-36).

Such arguments have lead many au-thors to stress the problematic nature ofthe term language (Hudson 1996:36; Ro-maine 1994: Chapter 1). Whilst acceptingits dif fi culties, I w ould argue that we neednot disregard as worthless data from fieldli nguists on the number of languages spo-ken in different areas. To see why, theparall el w ith the problem of indivi duating

biological species can be considered.The science of ecology makes and tests

quantitative predictions about the num-ber of species which will be found in dif-ferent envi ronm ents (see, e.g., H uston1994), yet th e concept of species is scarcelyless problematic than that of language.For sexual organisms, the cri terion cit ed isthat of (in)ability to interbreed, yet this

criterion has all the diffi culties of the(un)intell igibi li ty cri teri on for languages.There are chain species, exampl es of par-tial ability to interbreed, and even asym-metri cal interferti li ty (M ayr 1963; Sokaland Crovello 1970; Abruzzi 1982). The jus-tification for holding on to the concept of

species is that, although there are prob-lem cases, it is a convenient way of cap-tur in g real facts about biological di versity.I would make the same case for linguisticdiversity and thu s justi fy holding on to the

language concept in the following ways.First, though there are cases of blurredboundaries, there are many others wherethe communi cative discontinui ties arequite obvious, and indeed highly salientto the people concerned. Indeed, Dixon(1997:8) argues that the clear cases are typ-ical and the blurred ones rather rare. Sec-ond, the linguistic arbitrariness of the lan-

guage/ dialect di sti ncti on is relati velyunimportant, if linguists employ it ap-proximately self-consistently. As long asthis is the case, then counts of what fieldli nguists have deemed to be languageswi ll give a r easonable relative measure oflanguage di versity. For exampl e, Romain e(1994: Chapter 1) points out the arbitrari-ness of the language boundaries usually

identified in Melanesia, which do not cap-tur e the r ange of comm uni cati ve practi cesof this highly multil i ngual population.However, she is quite clear that there ismore di versity of something to do with vari-eti es of l anguagein M elanesia than in otherareas, and the conventional count of lan-guages d oes captur e thi s f act, even if itfail s to capture other aspects of the li n-

guistic geography of the area. Linguistsseem to agree often enough in practi ceabout what languages are for the assum p-tion to be made that their results fromdif ferent continents are r oughly compara-ble. Third, although error is introducedint o the data by the i ndeterm inacy of l an-guage boundaries, t his err or is effecti velyrandom. It is thus more likely to obscure

real tr ends than create apparent ones, andany clear patterns which are still evidentare likely to represent genuine effects. Fi-nally, the magnitude of variation in lan-guage diversity between different regionsis very large. If there was a difference inthe average number of square kilometers

356 DAN IEL NETTLE

8/12/2019 4. Nettle 1998 (Unidad 2)

4/21

per language in, say, Papua New Guineaand Namibia, of two- or threefold, i twould be tempting to conclude that therewas nothing more going on than noise inthe data. However, the real difference is

80-fold (about 540 km2

for Papua N ewGuinea, and about 40,000 km2 for Nami-bia), and so it seems reasonable to look forunderlying causes.

In what follows, then, I shall proceed touse counts of languages without furthercomment, though it should of course beborne in mind that these carry a largemargin of error around them.

1. GLOBAL PATTERNS OF

LANGUAGE DIVERSITY

There are many more languages spokenin the world than is commonly realised.The best available catalogue is the Ethno-logue, compiled by the Summer Instituteof Linguists, of which Grimes (1993) is a

recent edit ion. Gri mes (1993) li sts 6528 li v-ing languages, as well as a number of ex-tinct ones.

What is even more striking than thesheer number of languages is the uneven-ness of their distribut ion. Gri mes (1993)li sts languages by country, so, althoughthe country is not an ideal uni t of compar-ison, I w il l use it here. Eight hundr ed and

sixty-two languages are spoken in PapuaNew Guinea. This is 13.2% of the globaltotal, yet Papua N ew Guin ea representsonly 0.4% of the worlds land area, andonly 0.1% of the worlds population livethere.2 At the other extreme, Russia has1.5% of the languages and 15.3% of theland area. We can gain an idea of theglobal distribution by plotting the relative

language diversity of each country, as inMap 1.

The sim plest w ay to do thi s would be todivide the area of countries by the numberof languages spoken and shade them ac-cording to this value. However, doing soartificially inflates the apparent diversity

of small er countries, for the followingreason. M any languages cross nati onalboundaries, and the smaller the country,the more of its languages tend to do so.For example, 21 languages are spoken in

the Gambia, which is a very narrow stripof land, but all of them are also spoken inSenegal or elsewh ere. Di viding the area ofthe Gambia by 21 would thus give an ex-tremely misleading figure for the averagearea occupied by a Gambian language. Ihave taken two measures to mini mizethe small country effect. Fir st, countri essmaller than 50,000 km2 have been ex-

clu ded fr om the analysis. Second, ratherthan divi din g the number of languages bythe area of the country, I have performeda regression of t he logari thm of t he num-ber of languages on the logarithm of thearea of the country.3 The number of lan-guages does in crease wit h in creasin g area(ln[LANGS] 0.49 ln[AREA] 0.53; r.45,df 124,p .001), but with consider-

able scatter and hence a relatively low rvalue. The relative language diversity ofeach country has thus been determ in edby the value of the standardized residualfrom this regression line.

The relative language diversity of all thecountries of the world larger than 50,000km 2 is shown on Map 1. The darker theshading, the more relatively diverse the

country. Note that the resolution of themap is only at the level of the countrycountries like India and M exico whichhave very diverse and less diverse regionsare shaded uni forml y using the m ean.

Several clear patterns can be observedin M ap 1. First, language diversity tends tobe greatest near the equator and decreaseas one moves North or South away from

it, as several authors have noted (Breton1991; N ichols 1990, 1992; M ace and Pagel1995). Species di versity decreases in asimilar way as one moves away from theequator (Stevens 1989; M ace and Pagel1995).

Second, there are more specific associ-


8/12/2019 4. Nettle 1998 (Unidad 2)

5/21

ations between language and biologicaldiversity. I n the Old World , there are twogreat belts of very high language diver-

sity, which correspond almost exactly tothe two great belts of equatorial forestw h i ch h ar b ou r so m an y of t h e O l dWorlds species. One runs from IvoryCoast across West Africa into Zaire. Theother r uns from South In dia and peninsu-lar Southeast Asia down through the In-donesian islands into N ew Guinea andthe islands of the Western Pacific. Total-

ling up the l anguages l isted in Grimes(1993) for the 17 main countries in thesetwo belts4 gives 3929, which is 60% of allthose in the worl d. A s well as being plen-tif ul where species are numerous, lan-guages are few where species are few.Large areas of whi te on M ap 1 corr espond

to the Sahara and Arabian desert s, w hi chthough in the tropics are arid and poor inspecies.

Third, the New World distribution isslightly different from that of the OldWorld. There is a latitudinal pattern, but thehighest diversity is not in Amazonia, as wemight expect, but in Mexico. Furthermore,the overall level of diversity is lower than inthe Old World. I will discuss possible rea-sons for this in Section 4 below.

N one of these three patterns is a sim ple

product of th ere being more people in themore di verse areas, as I shall show i n Sec-tion 4. The orders of magnitude of thedifferences are also very large. We have,therefore, some strong trends to explain:language diversity is inversely related tolatitude, is low in deserts and arid places,

MAP 1. M ap of t he world showing t he relative language diversity of the major countri es. This iscalculated by regressing the logarithm of the number of languages spoken in the country (Source:

Gri mes 1993) against the logari thm of th e area of the count ry, and shading each count ry accordi ng

to the standardised residual. The shading sheme is as follows: White (least diversity),zres 0.5;

Light dooting, 0.5 zres 0; H eavy hathcing: 0 zres 0.75: Black (most diversity):zres 0.75.

358 DAN IEL NETTLE

8/12/2019 4. Nettle 1998 (Unidad 2)

6/21

is high in the environment that producesequatorial forest, and i s relatively low andan omalo u sly d istr i b u ted in th e N ewWorld. In the next section, I bri efl y discussthe vectors by which languages are spread

and outli ne the ecological theory fromNettle (1996) which, I will argue, may ac-count for the observed patterns.

2. VECTORS OF LANGUAGE

SPREAD

As we have seen, the vast m ajority of alllanguages are spoken by small communi-

ties in the tropics. Most of these commu-nities are primaril y dependent on non-industrial ways of making their living, orhave been until very recently. Few lan-guages are r outinely wr itten down, stil lless used in television or radio. Very feware the official vehicle of any nation-stateor other large-scale poli ti cal el it e. I n thi ssection, then, I wi ll suggest and briefl y

justify a nu mber of generalizations whi chcan be drawn about the typi cal case ofhow a language develops. These are, fi rst,that it is transmitted orally; second, that itpasses through pri mary social bonds;thir d, that state policies are not usuall yrelevant; and fourth, that it is encapsu-lated in the wi der system of economi c li fe.I will now explain each of these general-

isations in turn.First, language transmission is basically

oral and informal. This means that lin-guistic norms pass between indi vidualswho interact face-to-face. It seems to bepart of the nature of linguistic transmis-sion that, in the absence of positive inter-acti on, p eoples l anguages di verge. It fol-lows that people who maintain the same

speech norm s mu st be connected by socialor economic ties, and that lack of face-to-face interaction between groups will tendto mean that their languages diverge.

Second, however, the m ere p resence ofinteraction between people is no guaran-tee of their li nguisti c homogeneity. Socio-

li nguistic dif ferences can persist despit eroutine interaction between the people in-volved. Trade between groups can takeplace via lingua francas, or pidgins, or bi-lingual individuals, while the underlying

differences between their first languagesare undiminished.What determi nes w hether groups wi ll

converge or remain uni form li nguisticall yis a matter of social identification (LePage1968). Where ind ivi duals are closely in-volved enough to want to be identifi edwith each other, they wil l mutually ac-commodate their speech behavior. How-

ever, t hey may also interact whil e acti velymaintaining ethnolinguistic distance, andthe question of what determines whetherthey do so or not is an important one.

The answer seems to li e in the strengthand nature of the social bonds i nvolved(Milroy 1980). It is convenient in this con-text to distinguish between primary andsecondary social bonds. Pri mary social

bonds are relatively enduring, are oftenformed early in life, and are multivalent.This means that they are not formed forany one specific purpose. Rather they aregeneralized social linkages (Sahlins 1972)which may bring the actors together inmany dif ferent contexts and at dif ferenttimes. Social bonds within ethnolinguisticgroups in non-industrial societies typi-

cally appear to have this character; theyform a dense web of relationships, whichare often r ein forced by biological and cul-tural kinship, and which may form thebasis for common ri tual acti viti es and cel-ebrations, common defence, commonfarm work or hunting, gifts, and foodshari ng, as well as trade narr owl y defi ned.In most societies, these pri mary bonds

have also been those on whi ch peoplehave depended for their basic livelihood.

Secondary social bonds, by contrast, arebased on specifi c functions, such as atrade in a speciali zed good li ke salt ormetal, or a speciali zed servi ce. Pur elycommercial trade creates only a second-


8/12/2019 4. Nettle 1998 (Unidad 2)

7/21

ary bond, as recip rocati on t ends to be im -mediate, and no future moral responsibil-ity for the other party is entailed (this isSahl in s [1972] balanced recip rocit y ).Secondary bonds are associated wi th

greater social distance than primary onesand are more typical of the relationshipsbetween ethnoli nguisti c groups thanthose wi thin them. I w ould argue that sec-ondary bonds are not themselves suffi -cient for sociolinguistic identifi cation.Thus, whether social interaction will leadto the adoption of a common mothertongue is determined whether the social

bonds created are primary or secondary.Li ke alm ost all di chotomi es, the di sti nc-

ti on between pri mary and secondarybonds simpl ifi es an underl ying contin-uum. Pairings of farming and hunting orherding peoples give a good example ofthis. The pygmies of central Afr ica, forexamp le, are speciali zed hun ters, wh ononetheless consum e a great deal of th eir

diet in cereals grown by neighbori ngfarmers. It is unlikely that they could sur-vive r eliably by hunting and gatheringalone (Bailey et al. 1989), and so this rela-tionship is of vital importance to them,although it is in origin a single-purposerather a multi valent one. Each pygmygroup is paired with a particular group offarmers. The in terdependence is so great

that, i n each case, the pygmies no haveaccommodated to the farmers and nolonger have a disti nctive language.

Furt her south i n Afr ica, in the Kalahari ,San hunter-gatherers also d epend uponexchange w it h neighboring farm ers, yethave r emained l i nguisti call y distinct(Headland and Reid 1989). In the WestA fri can savanna, one fi nds wi despread

symbi osis betw een Ful ani pastoralists andcereal farm ers, part icularly the H ausa.Even the most purely pastoral Fulani con-sume a high proport ion of their calori es ascereals whi ch they have tr aded for mil kand other products (Swift 1986:180). How-ever, the distinction between Hausa and

Fulani remains, and a relatively small pro-portion of those Fulani who are sti ll herd-ers have adopted H ausa as a mothertongue (though most speak it as a linguafranca). The difference i n outcome f rom

the pygmy case may reflect a difference inthe extent of investment in the particularexchange relationship in the dif ferentcases. Fulani pastoral networks are ex-tremely extensive, and go well beyondH ausaland, and the San seem to haveboth a wi de choice of trading partners andgreat fl exibilit y in their own subsistencearr angements. N eit her San nor Fulani

households are, then, as ir reversibl y at-tached to their parti cular agri cultur ali stpartners than the pygmies, and so theyh av e r et ai n ed t h ei r d i st i n ct m o t h ertongues.

Accepting that the distinction is some-wh at sim pl istic, then, w e can nonethelessassert that, in general, people come tohave the same first language as those to

whom they are linked by primary bondsin the sense I have described.

The thir d generali zation about lan-guage transmission i s that the infl uence ofspeciali zed poli ti cal and govern mentalstructures is of li mited importance, atleast wi th regard to mother tongues.There are obvi ously some cases of t he do-main of a language being determined by

the extent of a particular political forma-tion, such as the spread of vernacularLatin through Europe as part of Romanexpansion. H owever, there are manymore cases where great empir es have con-trolled areas for long peri ods of tim e wi th-out ordinary country people coming tospeak the language of the el it e at all , oronly learning it as a secondary tongue for

use in dealing with government. Theoverw helmi ng majorit y of languages eventoday are unoffi cial, mi norit y tongues, andmost persist despite the incorporation oftheir speakers in wi der state, religious andregional systems. Even in densely popu-lated, early industri ali zed Europe, the

360 DAN IEL NETTLE

8/12/2019 4. Nettle 1998 (Unidad 2)

8/21

virtual identify of language, state and na-tion was approximated only in the nine-teenth century (Coulmas 1992:33), and inmany European countri es min ori ty lan-guages are still spoken. Overall, it is rea-

sonable to conclude that the determ inantsof whi ch fir st language one learns are usu-ally based in very quotidian and local so-cial situations, rather than in courts orparliaments.

The final generalization about languagetransmission is that the spread of a lan-guage is rooted in an economic system. Itmight appear that the choice of primary

social associates is a purely social or cul-tural matter. However, in non-industrialsocieties where relatively little of the cir-culation of food and labor passes throughthe wages and money, there is no realdistinction between economy and society.An individuals social associates also tendto be those with whom he labors in thefields; or from whom he borrows land,

livestock, or seeds; or with whom he com-bines to appropriate and defend land.Bonds which are social in character arecemented by real exchanges of food andservices, and it is to social associates thatpeople turn in time of shortage (Colson1979; for ethnographic examples, see e.g.Watts 1983; Cashdan 1985; Legge 1989). Itseems, in general, that languages are

rooted in networks of social bonds whichhave a real economic importance. This is akey assumpt ion of the ecological theorywhich I will advance below.

We have now discussed in generalterms t he vectors by wh ich languages arespread. Cl earl y, there are a small num berof languages which present a very differ-ent profile to that outlined above. For ex-

ample, the last 500 years has seen a smallnum ber of Eur asian languages spr eadwi dely, fi rst in Eurasia, and then acrossthe globe, by el ite poli ti cal, technological,and demographic dominance. I would ar-gue that these events are rather untypicalof language evolution as a whole. This is

not because there have not been local l an-guage spreads at other times and otherplaces; there alw ays have, as polit ieswaxed and waned. The difference is thescale of the Eur opean spr eads, whi ch

was qui te unpr ecedented. These recentspreads have until very recently affectedon l y a f ai r l y sm al l p r op or t i on of t h eworl ds languages, sin ce in most areas,in di genous languages have persi stedalongsid e the colonizers. The Americasare an excepti on, whi ch we discuss fur therin Sections 4 and 5. Perhaps a few dozenof the six and a half thousand languages

were spread to their present range by re-cent el ite dominance, wi th a few hundredalr eady obliterated by their expansion.For the great bulk of the remainder, myearl ier asserti on that more local factorsshould be considered seems justified.

Thus we can return to our basic task ofexplaining the global distribution shownin Map 1. It is clear from the foregoing

discussion that the formation of any par-ticular ethnolinguistic group will be acomplex interplay of many locally specificfactors; formation of social bonds will de-pend upon precise topographical, mi li -tary, epidemiological, demographic, andcultural situations, as well as more nebu-lous contingencies such as the ri se and fallof local pr estige and in fl uence. H owever, I

beli eve general explanati ons are appro-priate for the global trends. The mainpatternsthe latitudi nal gradient, theparallel wi th species di versit ypatternstrongly with biological or ecological phe-nomena, which suggests that the appro-pri ate theory wi ll be one that li nks humanagents to their ecological setting.

Individuals in any non-industrial soci-

ety have to sim ultaneously solve manydif ferent ecological problems, from cop-ing wi th disease, to providing fuel, ferti l-izer, and drinking water, to optimizingintergroup relations and population pres-sure. All of these may influence the evo-lution of their social systems. In my West


8/12/2019 4. Nettle 1998 (Unidad 2)

9/21

African study (Nettle 1996), I made out acase that one environmental factor couldbe identified which had a preeminent in-fluence on many aspects of life, especiallythe formation of linguistic groups. I called

that factor ecological risk.Ecological risk is the amount of varia-tion which people face in their food sup-ply over tim e. Vari ation can occur bothseasonall y (withi n a year) and inter-annu-ally, with periodic years of shortage.H ouseholds cannot operate for more thana few days without food and so must de-velop mechanisms to deal w it h temporary

shortfalls in its supply.There is a l arge li terature on the m ech-

anisms by which people cope with risk(e.g., Cashdan 1985; M innis 1985; H uss-Ashmore, Curry and Hitchcock 1988; De-Garine and Harrison 1988; Halstead andO Shea 1989; Cashd an 1990). Four keystr ategies may be id enti fi ed ([Halsteadand O Shea 1989:3]; Colson [1979:21] di -

vides them into five rather than four buther schema is basically the same). Theseare diversification of productive and in-come-generati ng acti vi ti es, storage offood, mobil it y, and exchange.

In the current context, I will discussonly the mechanism most relevant to lan-guage diversity, that of exchange. In Net-tle (1996), I argued that patterns of social

exchange, wh ich determ i ned l anguagespread, were related to patterns of rain-fall. Where rainfall is continuous throughthe year, households can reliably producefood all year round and so are little de-pendent on exchange outside the imme-diate vicini ty for subsistence pur poses.W here small groups are basicall y self -sufficient, there is no incentive for them to

form networks of primary bonds outsidethe im mediate vicini ty, and so many smalllan g u ag es ev olv e. Wh e re th ere is amarked dry season or probability of adrought year, on the other hand, house-holds form social ties over a wide area, togain access to resources elsewh ere in ti me

of local shortage. Here, the languages aremuch more widespread, as the extensivesocial networks spread li nguistic normsover a wider area. In the extreme case ofthe northern savanna of West Africa, pas-

toralists cope with between the brief anderratic rainy seasons by moving, but alsoby exchange of stock and services through wide networks of kinsmen throughoutWest Africa (Berg 1976:24; cited in Legge1989). Correspondi ngly, languages li keFu lfu ld e are sp read ov er mil l io n s ofsquare kilometers.

The theory which emerged from the

discussion in Nettle (1996) was, then, thefollowing: the greater the ecological risk,the larger the social networks people mustform to ensure a reliable supply of basicsubsistence pr oducts. Sin ce li nguisti cnorms are spread through those same so-cial networks, it follows that the averagesize of a language group should also in-crease as ecological risk increases. Thi s

prediction was tested using a regressionanalysis and was found to hold true bothwhen the size of a language was estimatedin term s of area occupi ed and w hen i t wasesti mated i n term s of number of speakers.

In thi s paper, I test whether the predic-tions of the ecological risk theory hold forother parts of the world as well as WestAfri ca. I use the countr y as the basic u nit

of analysis. This is convenient because thebest available dataset on global languagediversity (Grim es 1993) is organized bycountry. However, in many ways, coun-tri es are troublesome units of compari son.First, they are not all the same size. Sec-ond, they are not ecologicall y homoge-neous, and, third, languages overlap theirboundaries. All of these problems have to

be taken into consideration in the statisti-cal analysis, as I will describe in Section 3below.

In Nettle (1996), the magnitude of eco-logical ri sk for each location was mea-su r ed b y cal cu l at i n g t h e n u m b er ofmonths in the year in which enough rain

362 DAN IEL NETTLE

8/12/2019 4. Nettle 1998 (Unidad 2)

10/21

falls for useful plant growt h to occur usin gthe Gr owi ng Season formul a (LeHouer ou1989), which is described in Section 3 be-low. The formula captures the extent towhich biological production is variable

within a year. It may also be a reasonableproxy for how much it varies betweenyears, since l ocations w ith shorter rainyseasons tend to have m ore errati c r ain fallthan t hose wi th longer ones. The formulais also used here, and an average value ofthe Growing Season (the Mean GrowingSeason or M GS) is obtained for eachcountry using climatological records. The

M GS can vary from 0 (no months in whi chthere is adequate rain fall for food produc-tion, hence no possibili ty of self-suffi-ciency and extremely high risk) to 12months (continuous rainfall and hencefood production all year round, very lowrisk). The Growing Season formula takesaccount of rainfall and temperature. Theassumption implicit in its use is thus thatthese are the main li mit ing factors on foodproduction. This is certainly a simplifica-ti on. It may, however, be reasonably accu-rate at least for the tropics. In the temper-ate latitudes, variation in day length andthe presence of frost become more impor-tant, and for this reason as w ell as forsome others given in Secti on 3, only tr op-

ical countri es are considered in the re-gression analysis.Extensive social networks are effecti ve

in mitigating risk for two slightly differentreasons, and these give r ise to two differ-ent hyp otheses to be tested. First, increas-ing the spatial extent of a social networkgives individuals in it access to more dif-ferent mi croenvironments and types of

land, whi ch may have food products avail -able at different times. Thisspatial averag-ing of ecological risk leads to the predic-tion that the spatial extent of languagegroups should in crease as the degree ofecological ri sk they are exposed to in-creases. Converting this into the number

of languages per country gives the fi rsthypothesis to be tested:

Hypothesis 1. The l onger the M ean Growing

Season, the more languages there wil l be spoken

in a country of a given area.

Second, increasing the number of pro-ductive households in a social networkdecreases the variation experienced by allof them as a simple consequence of thelaw of large numbers. This i s true as longas there is some degree of statisti cal inde-pendence between them. This numericalaveraging leads to the prediction that then u mber of in d iv id u als in a lan gu age

group should increase as the degree ofecological risk increases. Converting thisin to th e rela t ion sh ip b etwe en M eanGrowi ng Season and l anguages per coun-try gives the second hypothesis.

Hypothesis 2. The l onger the M ean Growing


in a country of a given population.

The tw o hypotheses would only beequivalent in a world where populationdensity was the same everywhere. I willtherefore test them separately. In Section3, I give the details of the methods used todo this. In Section 4, I give the results.

3. TESTING THE THEORY:

METHODS

Ecological and linguistic data were col-lected for all the countries of the worldover 50,000 k m2. Smaller countri es w ereexclud ed because the problem of lan-guages crossin g national boundaries i n-fl ates their apparent diversity, as de-scri bed in Secti on 1 above. Excepti onswere made for the Solomon Islands and

Vanuatu, since, though both these coun-tries are small, they are rich in languagesnone of whi ch crosses their borders asthey are islands.

The analysis was restr icted to countr iesfalling wholly or mainly within the tropi-cal zone. This is str ictly defi ned as the area


8/12/2019 4. Nettle 1998 (Unidad 2)

11/21

between the tropics of Capri corn andCancer, but for the present purposes, thebroader defi niti on of the area between30N and 30S is preferabl e, as it capt ur esmore of the area of the climatic regimes

we know as equatorial and tropical.The analysis has not been extended to thetemperate lati tud es, for several reasons.First, the available measure of ecologicalrisk (the M ean Growi ng Season) t akes ac-count only of rainfall and average temper-atur e, and thus cease to reali sti call y reflectthe possibilities of food production as onemoves out of the tropical zone. Second,

the economi es of the tropical countri es aremore rural and more dominated by sub-sistence activit ies th an those of most ofthe temperate countries. The linguisticmap of, say, European countries, reflectsto a considerable extent the rise of stan-dard national languages since the indus-tri al revoluti on, rather than longer termecological processes.

Th e Gro win g Season fo rmu la (L e-H ouerou 1989) was empl oyed as a m ea-sure of ecological risk. A month is in-cl u d ed i n t h e gr ow i n g season i f t h eaverage d ail y temperature is more than6C and the total precip it ati on in mi ll im e-ters is more than twice the average tem-perature in centigrade. The Growing Sea-son is an in verse measure of ecological

risk: the more growing months there are,the less the ecological risk.

The M ean Growi ng Season (henceforthMGS) was found for each country usingmeteorological records in W ernstadt(1972). The number of weather stationsper countr y vari es fr om 100 to over 200,and the tim e peri od for whi ch informationis avail able vari es from a few years to 50 or

more. The stations are d esigned t o be di s-tri buted evenly around each country.Since countri es are not ecological units,and are often large enough to span manyecological regim es, t here is in some casesa danger of pr oducin g a meani ngless av-erage cli mate wh ich does not correspond

to that experienced by any of its commu-nities. As a safeguard against this, coun-tries w here the standard deviation ofthe growi ng seasons from the differentweather stati ons w as greater than 2

months were exclud ed. This affected 19countries, which are marked with an as-teri sk in the App endix.

The num ber of li vin g l anguages spokenby a resident community in each country(LAN GS) was obtained from the Ethno-logue12th edition (Grimes 1993). For thereasons given in Section 1, simply divid-in g the area or popul ati on of each countr y

by the number of languages exaggeratesthe diversity of smaller countries. I haveinstead taken the area of the country (forHypothesis 1) or its population (for Hy-pothesis 2) as one in dependent vari able inthe mul tipl e regression analysis, the M eanGrowing Season being the other.

A number of countries were included inthe sample used in Nettle (1996). As this

study is intended to independently testthe applicability of the theory to other ar-eas, those countr ies were also exclu dedfr om most of t he analyses. The 13 affectedcountries are marked with a dagger () inthe Appendix.

There are two different linguistic vari-ables of i nterest, corresponding t o the twodifferent hypotheses. One is the number

of languages spoken relative to the coun-trys area, the other the number of lan-guages relati ve to the nu mber of in habit-ants. To calculate these variables, figuresfor both the area (AREA) and the popula-tion (POP) of each country were obtainedfrom the UN Demographic Yearbook. Thepopulation fi gures are m id- year estimatesfor 1991.

To reduce skewness and kurtosis. loga-rithms were taken of the LANGS, AREAand POP vari ables. The skewness andkurtosis of the MGS did not differ signif-icantly from 0, and so it was left untrans-formed.

Obviously, both the ecological and lin-

364 DAN IEL NETTLE

8/12/2019 4. Nettle 1998 (Unidad 2)

12/21

guistic variables are highly approximate,the former because of the simpl icity of theformula and the unevenness of the data,the latter because of the inherent difficul-ties involved in counting languages. We

shoul d th us expect at best an approximaterelationship.

4. TESTING THE THEORY: RESULTS

The appendix displays the number ofli ving languages (LAN GS), population(POP), area (AREA), number of completeweather station records, and Mean Grow-

in g Season (M GS) for 74 countr ies, in clu d-in g, for the sake of completeness, the WestAfr ican countri es and those wi th vari ableclimates, which are excluded from the re-gressions unless otherw ise stated.

H ypothesis 1. The longer the M ean Growing


in a country of a given area.

Multiple regression shows that this is in-deed the case. The equation produced is:

ln[LANGS]

0.58 ln[A REA] 0.23 M GS 5.32

43 countr ies: r .66, df 40, p .001

The relationship between Mean Growing

Season and t he number of l anguages oncethe area of the country has been con-trolled for is shown in Fig. 1. If the WestAfrican countries are included, it is obvi-ous that they sit in the same distribution.In deed, the regression equation in clu di ngthem is virtually the same:

lnLANGS

0.53 lnAREA 0.24 M GS 4.77

55 countr ies:r .65, df 52,p .001

It is also immediately apparent that all theLatin American countries (including the Ca-

ribbean) fall well below the rest of the dis-tribution. There are a number of possiblereasons for this. One is simply sparsity ofpre-colonial population. Even now only asmall fraction of the land in tropical South

America is populated (less than 5% accord-ing to Partridge 1989:5). I t is therefore nosurpri se that there are fewer groups thanmight be expected. M exico, where diversityis the highest in the Americas, was alsomore thoroughly peopled at the time of Eu-ropean expansion than areas fur ther South.Perhaps a stronger reason for low diversityin the Americas is that so many i ndigenous

languages have been lost. There are a num-ber whose demise is known about, butmany more who must have disappearedwithout record, principally through the in-fectious diseases which ravaged the conti-nent in the decades following Europeancontact (Crosby 1986). In Cuba, the mostextreme outlier in Fig. 1, the indigenouspopulation was decimated within a very few

years of European contact (Niddrie 1971:82),and the precolonial linguistic situation islargely unknown.

If the Latin American countries are ex-cluded, the regression relationship ismuch improved (Fig. 2):

lnLANGS

0.42 ln[A REA] 0.30 M GS 3.48

36 countries: r .82, df 33, p .001

The relationship is not just a product ofcompari ng dif ferent continents. Signifi -cant relationships are also found withinthe Asia/Pacific and African groups. Thedata fr om Latin America are too few toobtain an independent mult ipl e regres-

sion.Asia/Pacific:

lnLANGS

0.49 lnAREA 0.32 M GS 4.22

18 countr ies:r .87, df 15, p .001


8/12/2019 4. Nettle 1998 (Unidad 2)

13/21

FIG.

1.

Thenu

mberoflanguagesineachcountryregressedagainstthem

eangrowingseasoninmonth

soncetheeffectsofthecountryssizehave

beencontrolledfor.

366 DAN IEL NETTLE

8/12/2019 4. Nettle 1998 (Unidad 2)

14/21

8/12/2019 4. Nettle 1998 (Unidad 2)

15/21

Africa: (West African countries ex-cluded)

lnLANGS

0.50 lnAREA 0.23 M GS 4.37

18 countr ies:r .77, df 15,p .01

The remarkable sim il ari ty of t he in depen-dent equations from the two continentalregions show what a powerful and univer-sal determinant ecological risk is.

H ypothesis 2. The longer the M ean Growing

Season, the more languages there wil l be spokenin a country of a given population.

M ulti ple regression again confi rms thatthi s is the case. The equati on is:

lnLANGS

0.33 lnPOP 0.18 M GS 0.62

43 countries:r .63,d f 40,p .001.

Again, the WestA fri can countries sit i n thesame distri bution. I ncludi ng them givesthe equation:

lnLANGS

0.34 lnPOP 0.18 M GS 0.65

55 countries:r .64, df 52,p .001

Once again , exclud ing t he Latin A mericancountries improves the relationship.

lnLANGS

0.25 lnPOP 0.22M GS 0.32

36 countries:r .78,d f 33,p .001

Once again, the relationship holds inde-pendently in the two major continents:

Asia/Pacific:

lnLANGS

0.24 lnPOP 0.26 M GS 0.02

18 countries:r .82,d f 15,p .001

Africa: W est A fri can countri es ex-cluded

lnLANGS

0.10 lnPOP 0.20 M GS 1.55

18 countr ies:r .64,d f 15,p .05

The predictions of both ecological riskhypotheses are clearly met for the wholeof th e tropical worl d. If the previously ex-cluded countries with very variable grow-ing seasons are included, the significanceof the relationships is not changed, the rvalues being slightly lowered (Hypothesis1:r 0.61,df 58,p .001; H ypothesis 2:r .54, df 58, p .001; Latin America

included, West African countries ex-cluded in both cases).

The r esults of this analysis show a con-sistently strong relationship between lan-guage diversity and climatic patterns.They suggest that ecological ri sk has beenan extremely important factorprobablythe most important single factorin peo-ples strategies of group formation and

communication in the tropical world as awhole. The r values using the t wo diff er-ent hypotheses do not differ significantly,so it is not p ossible to conclu de that eitherthe spatial or the numerical process ismore im portant. U suall y, they go hand inhand.

5. CONCLUSIONS

It seems, then, that a key determ inant oflinguistic diversity is to be found in thebasic facts of ecology and subsistence.There are many other factors, too, whichwe have not considered here and nodoubt contribute greatly to the observed

368 DAN IEL NETTLE

8/12/2019 4. Nettle 1998 (Unidad 2)

16/21

vari ance. A more sophisticated analysiswould incorporate such factors as relief,epidemiology and population density. Itwould also consider the influence of dif-ferent subsistence systems on the rela-

tionship of social network and ecology.Access to marine resources, for example,allows communities to diversify whenfaced with seasonal shortages. Li vestockherdi ng also functions as a form of proteinstorage, and the available species of stockvari ed wi dely betw een areas in t radit ionalsocieties (Diamond 1997a: Chapter 9).N onetheless, given the unavoidably ap-

proximate nature of the data used, thecorrelations are im pressive and testi fy to astrong influence of ecology on the historyof human communities.

The assumption of this paper has beenthat the curr ent pattern of language diver-sity represents some kind of ecologicaleq u i l ib r iu m . Th is assu mp ti on seemslargely justifi ed by the corr elations ob-

tained. H owever, it should be remem-bered that the equilibrium observed is aproduct of a p arti cular historical peri od,since almost all of the societies on whichthe data set is based are, or were untilrecentl y, subsistence farmers and herd ers.The pattern of language diversity was nodoubt very different when the continentswere covered with hunter-gatherers and

wi ll again be very di fferent as the changesassociated with the modern world econ-omy become m ore w id espread.

Before the transition from hunting andgathering to farming, the amount of lan-guage diversity may have been evengreater than it is now, sin ce hunt er-gath-erer communities tend to be smaller thanfarming communities living in similar

habitats. In the Kalahari , San hunter-gath-erer groups number a few thousand whi letheir Tswana farmer neighbors are twomi ll ion (Grim es 1993). In aboriginal Au s-tralia, communities never exceeded about500 individuals, though their territorysizes vari ed accordin g to the ecological

producti vity of the area (Bir dsell 1953).Thus even in arid areas, very large com-muni ties did not evolve. This diff erence ofoutcome may reflect the different subsis-tence str ategies of farmers and foragers;

foragers respond to ecological variabilityprimarily by moving to alternative re-sources. Farmers, with their heavy invest-ment in land, cannot so easily move andso must use large social netw orks to br in gthe resources to them.

As agriculture spread out from its cen-ters of development, it pushed waves ofli nguistic and demographic homogeneity

before it (Renfrew 1991). This process wasparti cularl y clear in Afr ica, wi th the Bantuexpansion, and Eurasia, wi th the Ind o-Eu-ropean, Sino-Tibetan and other spreads(Diamond 1997a, 1997b). The languages ofthe spreading farmers eventuall y brokeup in to d aughters whose number was de-termined by the ecological regime of thearea, but it is likely that the number of

new languages in many areas was l essthan the number of hunter-gatherer lan-guages which had been subsumed or dis-placed. The number of languages in theworld may thus have been lowered at theonset of the N eolithic, w hen the globalpattern we have detected in this paperbecame establ ished.

The global pattern is currently under-

going another transformation. This iscaused by the economi c, technological,and demographic take-off of Eurasianpopul ati ons and the consequent spread ofEur asian peopl es, crops, di seases, andlanguages to the other continents. Thecauses and d ynamics of thi s expansion arewell beyond the scope of th is paper, but itis clear that their net effect will be to

greatly reduce the worlds language diver-sity; one recent projection suggested that90% of li ving languages are thr eatenedwi th exti nction in the next centur y (Pink er1994:259).

In some conti nents, such as A ustrali aand the Americas, the Eurasian expansion


8/12/2019 4. Nettle 1998 (Unidad 2)

17/21

has involved a demographic and li nguisticreplacement of indigenous peoples, whomainl y succumbed to in fecti ous di sease,wi th Eu ra sia n d au g h ter p o p u lat io n s(Crosby 1986). In South America, the in-

digenous diversity has already been lost,which is why South America fails to pat-tern wi th the other continents in Fig. 1. InNorth America and Australia, most of thelanguages had, as of 1993, a few eld erl yspeakers, so they still appear in the dataset of this paper, but they are not beingtransmi tted to new generations and sowil l disappear rapidly over the next 20

years (Krauss 1992; Dixon 1997).In other continents where Eurasians

never settled in numbers, such as tropicalAfr ica, language diversity is still beinglost, though in this case it is due to lin-guistic replacement wi thout correspond-ing demographic replacement. Englishand French, along with the larger and

more dominant in digenous l anguagessuch as Swahili, are spreading at the ex-pense of local languages as more peopleare sucked i nto the l arger social networksassociated with the modern world econ-

omy. Sim il ar processes can be observed inthe Pacific, with the spread of Tok Pisin,Tagalog, and Bahasa alongsid e Engli sh.What the ult im ate result s of these changesin human ecology on the worl ds languagediversity will be it is as yet impossibleto say.

ACKNOWLEDGMENTS

This research was carr ied out in t he departm ent of

Anthr opology at University College London, and

generously fun ded by th e M edical Research Council .

I am grateful to Leslie Aiello, Robin Dunbar, Ruth

Mace, Daisy Williamson and many others for advice

and assistance. A ll remaini ng err ors are of course

my own.

APPENDIX

Country Languages Areaa Populationb Stationsc M GSd SD(GS)e

Algeria* 18 2381741 25660 102 6.60 2.29

Angola 42 1246700 10303 50 6.22 1.87

Australia* 234 7713364 17336 134 6.00 4.17

Bangladesh 37 143998 118745 20 7.40 0.73

Benin 52 112622 4889 7 7.14 0.99

Bolivia* 38 1098581 7612 48 6.92 2.50

Botswana 27 581730 1348 10 4.60 1.69

Brazil* 209 8511965 153322 245 9.71 5.87

Burkina Faso 75 274000 9242 6 5.17 1.07

C.A.R. 94 622984 3127 13 8.08 1.21

Cambodia 18 181035 8442 9 8.44 0.50

Cameroon 275 475422 12239 35 9.17 1.75

Chad 126 1284000 5819 11 4.00 1.81

Colombia 79 1138914 33613 35 11.37 1.37

Congo 60 342000 2346 10 9.60 1.69

Costa Rica 10 51100 3064 38 8.92 1.78

Cote dIvoire 75 322463 12464 9 8.67 1.25

Cuba 1 110861 10736 13 7.46 1.55

Ecuador* 22 283561 10851 44 8.14 3.47Egypt 11 1001449 54688 50 0.89 0.89

Ethiopia* 112 1221900 53383 36 7.28 3.10

French

Guiana 11 90000 102 5 10.40 0.80

Gabon 40 267667 1212 14 8.79 0.77

Ghana 73 238553 15509 28 8.79 1.68

370 DAN IEL NETTLE

8/12/2019 4. Nettle 1998 (Unidad 2)

18/21

APPENDIXContinued


Guatemala* 52 108889 9467 59 9.31 2.23

Guinea 29 245857 5931 8 7.38 1.22

Guyana 14 214969 800 5 12.00 0.00H onduras* 9 112088 5265 13 8.54 2.53

India 405 3287590 849638 218 5.32 1.92

Indonesia 701 1904569 187765 58 10.67 1.82

Kenya* 58 580367 25905 34 7.26 3.61

Laos 93 236800 4262 7 7.14 0.35

Liberia 34 111369 2705 21 10.62 0.84

Libya 13 1759540 4712 54 2.43 1.60

M adagascar* 4 587041 11493 81 7.33 2.96

M alawi 14 118484 8556 20 5.80 1.50

M alaysia 140 329749 18333 63 11.92 0.37

M ali 31 1240192 9507 17 3.59 1.97M auritania 8 1025520 2036 8 0.75 0.83

M exico* 243 1958201 87836 272 5.84 2.69

M ozambique 36 801590 16084 90 6.07 1.39

M yanmar 105 676578 42561 30 6.93 0.81

N amibia 21 824292 1837 6 2.50 1.89

N epal 102 140797 19605 16 6.39 1.98

N icaragua* 7 130000 3999 8 8.13 2.15

N iger 21 1267000 7984 10 2.40 1.28

N igeria* 427 923768 112163 24 7.00 2.16

Oman 8 212457 1559 2 0.00 0.00

Panama 13 75517 2466 5 9.20 0.75Papua N .G. 862 462840 3772 8 10.88 1.96

Paraguay* 21 406752 4397 16 10.25 2.51

Peru* 91 1285216 21998 40 2.65 4.22

Phillipines 168 300000 62868 64 10.34 1.92

Saudi Arabia 8 2149690 14691 10 0.40 0.92

Senegal 42 196722 7533 12 3.58 1.11

Sierra Leone 23 71740 4260 23 8.22 0.59

Solomon Is. 66 28896 3301 1 12.00 0.00

Somalia 14 637657 7691 28 3.00 1.69

South Africa* 32 1221037 36070 114 6.05 3.50

Sri Lanka* 7 65610 17240 17 9.59 2.59

Sudan* 134 2505813 25941 43 4.02 2.82

Suriname 17 163265 429 2 12.00 0.00

Tanzania 131 945087 28359 45 7.02 1.90

Thailand 82 513115 56293 54 8.04 1.57

Togo 43 56785 3643 11 7.91 1.78

UAE 9 83600 1629 6 0.83 0.69

Uganda 43 235880 19517 21 10.14 1.17

Vanuatu 111 12189 163 4 12.00 0.00

Venezuela* 40 912050 20226 44 7.98 2.73

Vietnam 88 331689 68183 40 8.80 1.59Yemen 6 527968 12302 2 0.00 0.00

Zaire 219 2344858 36672 16 9.44 1.90

Zambia 38 752618 8780 30 5.43 0.67

Zimbabwe 18 390759 10019 52 5.29 1.43

N icaragua* 7 130000 3999 8 8.13 2.15

N iger 21 1267000 7984 10 2.40 1.28


8/12/2019 4. Nettle 1998 (Unidad 2)

19/21

APPENDIXContinued


N igeria* 427 923768 112163 24 7.00 2.16

Oman 8 212457 1559 2 0.00 0.00

Panama 13 75517 2466 5 9.20 0.75Papua N .G. 862 462840 3772 8 10.88 1.96

Paraguay* 21 406752 4397 16 10.25 2.51

Peru* 91 1285216 21998 40 2.65 4.22

Phillipines 168 300000 62868 64 10.34 1.92

* Denotes a country with variable Growing Season, excluded from most of the analyses.

Denotes a count ry used in N ettle (1996) and excluded f rom some of the analyses.a km2.b Thousands.c The number of weather stations used in calculating MGS.d

The Mean Growing Season (months).eThe standard deviation of the Growi ng Season valu es from the diff erent weather stations in t hat countr y.

REFERENCES CITED

Abr uzzi, W . S.

1982 Ecological theory and ethnic di fferentiation

among human populations. Current Anthro-

pology 23: 1321.

Bail ey, R. C., G. H ead, M . Jenik e, B. Ow en, R. Recht -

man, and E. Zechenter1989 Hunting and gathering in a tropical rainforest:

Is it possible?American Anthropologist 91:59

82.

Barbujani, G.

1991 Wh at do languages tell us about human mi-

croevolution? Trends in Ecology and Evolution

6: 151155.

Barbujani, G., and R. R. Sokal

1990 Zones of sharp genetic change in Europe are

also linguistic boundaries. Proceedings of the

National Academy of Sciences of the U .S.A. 87:18161819.

Berg, E.

1976 The economi c impact of drought and i nflati on in

the Sahel. Research for Economi c Develop-

ment, Ann Arbor.

Birdsell, J. B.

1953 Some envir onmental and cultu ral factors in-

fluencing the structuring of Australian ab-

original populations. American Naturalist 87:

171207.Breton, R. J-L.

1991 Geol inguisti cs: Language dynamics and ethn o-

li nguisti c geography. University of Ottawa

Press, Ottawa.

Casad, E. H.

1974 Dialect intelligibility testing. Summer Institute

of Linguistics, Norman, OK.

Cashdan, E.

1985 Coping wi th ri sk: Recipr ocity amongst t he

Basarw a of N orthern Botswana. M an (n.s.)

20: 454474.

1990 (Editor) Risk and uncertainty in tribal and peas-

ant economies.Westview, Boulder.

Cavall i-Sforza, L. L., A . Piazza, L. M enozzi, and J.

Mountain

1988 Reconstruction of human evolution: Bringing

together genetic, archaeological, and li nguistic

data. Proceedings of the National Academy of Sci-

ences of the U.S.A.85: 60026006.

Colson, E.

1979 In good years and bad: Food strategies of

self-reliant societies.Journal of A nthr opologi-

cal Research 35: 1829.

Coulmas, F.

1992 Language and Economy. Blackwell, Oxford.

Crosby, A. W.1986 Ecological im peri ali sm: The biological expan-

sion of Eur ope, 9001900.Cambri dge Univer-

sity Press, Cambridge.

DeGari ne, I., and G. A . H arr ison (Editors)

1988 Coping wi th uncertainty in the food supply. Ox-

ford University Press, Oxford.

Di amond, J.

1997a Gun s, germs and steel : The fates of human soci -

eties.Jonathan Cape, London.

1997b The language steamrol lers.Nature389:544546.

Dixon, R. M . W.

1997 The ri se and fall of languages.Cambridge Uni-

versity Press, Cambridge.

Greenberg, J. H.

1987 Languagein theAmericas.Stanford University

Press, Stanford.

372 DAN IEL NETTLE

8/12/2019 4. Nettle 1998 (Unidad 2)

20/21

Grimes, B. F.

1993 Ethnologue: The worl ds languages. 12th editi on.

Summer Institute of Linguistics, Dallas.

Halstead, P., and J. OShea

1989 Intr oduction: cultural responses to ri sk and

uncertainty. In Bad y ear economics: Cul tur al

responses to risk and uncertainty, edited by P.Halstead and J. OShea, pp. 17. Cambridge

University Press, Cambridge.

Headland, T. N. and L. A. Reid

1989 Hunter-gatherers and their neighbours

from prehistory to the present. Current An-

thropology 30: 4366.

H udson, R. A.

1996 Sociolinguistics. 2nd Edition. Cambridge Uni-

versity Press, Cambridge.

Huss-Ashmore, R., J. J. Curry, and R. K. Hitchcock

(Editors)

1988 Copin g wi th seasonal constrai nts. University

Museum, University of Pennsylvania, Phil-

adelphia.

Huston, M. A.

1994 Biological diversity : The coexistence of species

on changing landscapes. Cambridge Univer-

sity Press, Cambridge.

Krauss, M.1992 The worl ds languages in cri sis.Language68:

410.

Legge, K.

1989 Changing responses to drought amongst the

Wodaabe of Ni ger. In Bad year economics:

Cultural responses to risk and uncertainty, ed-

ited by P. Halstead and J. OShea, pp. 8186.

Cambridge University Press, Cambridge.

LeH ouer ou, H . N .

1989 The grazing l and ecosystems of the Af ri can Sa-hel. Ecological Studies 75, Springer, Berlin.

LePage, R.

1968 Problems of description in multilingual

communities. Tr ansacti ons of the Phi lological

Society 1968: 189212.

M ace, R., and M . Pagel

1995 A latitudinal gradient in the density of hu-

man languages in North America. Proceed-

ings of the Royal Society of London, B 261:

117121.

Mayr, E.

1963 Animal species and evolution. Belknap Press,

Cambridge.

Milroy, L.

1980 Language and social networks.Blackwell, Ox-

ford.

M inni s, P. E.

1985 Social adaptation to f ood stress: A prehi storic

south-western example.University of Chicago

Press, Chicago.

Nettle, D.

1996 Language diversity in West Afr ica: An eco-

logical approach. Journal of An thr opologicalArchaeology 15: 403438.

Nichols, J.

1990 Lin guistic di versity and the fi rst settlement

of the New World. Language66: 475521.

1992 Li nguisti c div ersity i n space and tim e.Chicago

University Press, Chicago.

Niddri e, D. L.

1971 The Carr ibean. In Latin America: Geographi-

cal perspectives,edited by H. Blakemore and

C. T. Smith, pp. 73120. Methuen, London.Partr idge, W . L.

1989 The human ecology of t ropical land settle-

ment in Latin America: Overview. In The

Human ecology of tropical land settlement in

Latin Ameri ca, edited by D. A. Schumann

and W. L. Partr idge, pp . 319. Westview,

Boulder.

Pinker, S.

1994 The language instinct. Penguin, H arm ond-

sworth.Renfrew, C.

1991 Before Babel: Speculations on t he ori gins of

linguistic diversity. Cambridge Archeological

Journal 1:323.

Romaine, S.

1994 Language in society: A n in troduction to Socio-

linguistics.Oxford University Press, Oxford.

Sokal, R. R., and T. J. Crovello

1970 The biological species concept: A cri tical

evaluation. American Naturalist 104: 127153.Stevens, G. C.

1989 The l atitudinal gradient in species range:

how so many species coexist in the tropics.

American Naturalist 133: 240256.

Swift, J.

1986 The economi cs of pr oduction and exchange

in West African pastoral societies. In Pasto-

ralists of the West African Savanna, edited by

M . J. Adamu and A. H . M . Kir k-Gr eene, pp..

M anchester University Press, M anchester.Watts, M.

1983 Sil ent vi olence: Food, famine and the peasantry

in northern Ni geri a. Berkeley: U ni versity of Cal-

ifornia Press.

Wernstadt, F. L.

1972 W orld cl imatic data. Clim atic Data Press,

Lemont, PA.


8/12/2019 4. Nettle 1998 (Unidad 2)

21/21

Wolff, H.

1959 Intelli gibil ity and inter-ethnic attit udes.A n-

thr opological Li nguistics1: 3441.

NOTES

1

Nichols (1992:232237) calls this kind of diversitygenetic diversity. This designation is confusing given

the recent interest in comparing the distribution of

languages w ith that of human DN A (e.g., Cavalli-

Sforza et al 1988; Barbujani and Sokal 1990; Barbujani

1991), and so I avoid it here.

2 These percentages are calculated from the Worl d

banksW orld D evelopment Report 1993.3 The reason for taking logarithms of these vari-

ables can be found in Section 3.4 Ivory Coast, Ghana, Togo, Benin, Nigeria, Cam-

eroon, Zaire, Tanzania, India, Vietnam, Laos, Phill-

ipines, M alaysia, Indonesia, Papua New Guinea,Vanuatu, and the Solomon Islands. The number

given will be very slightly inaccurate, as languages

spoken in two neighbori ng countr ies will have been

counted twi ce. This p robably involves no more than

30 or 40 languages in a total of nearly 4000.

374 DAN IEL NETTLE

Documents

4. Nettle 1998 (Unidad 2)