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Elevational Gradient Resembles Latitudinal Gradient of Global Language Distributions

Permanent Link: http://ufdc.ufl.edu/UFE0042242/00001

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Title: Elevational Gradient Resembles Latitudinal Gradient of Global Language Distributions
Physical Description: 1 online resource (83 p.)
Language: english
Creator: Gleason, Kelly
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: biogeography, diversity, elevation, gradients, language, latitude
Interdisciplinary Ecology -- Dissertations, Academic -- UF
Genre: Interdisciplinary Ecology thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: This research investigated biogeographic parallels of species and language distributions and determined whether human language distributions conform to the elevational gradient hypothesis at global and regional scales. Language distribution patterns along elevation gradients were comprehensively addressed through three primary research questions which investigated (1) language richness along the elevational gradient, (2) Rapoport s rule along the elevational gradient, and (3) global mountains areas and their importance for language diversity. The distribution of extant languages from all latitudes were included in a global analysis, and those located on the major landmasses of Africa, Asia, Australia, Europe, North America, and South America were included in regional analyses. Significant regression models strongly support the elevational gradient hypothesis in global and regional language distributions. Globally, language richness steadily decreased with increasing elevation, but when language richness was standardized per 1,000 km2 of land area, a uni-modal hump of greatest language richness existed at intermediate elevations. Regional analyses of Africa, Asia, and South American continents showed similar patterns as the global analysis. Australia, Europe and North American regions which represent almost half the global land surface area, but a quarter of the global languages demonstrated different patterns than expected. Language diversity increased in along the elevational gradient in Australia, Europe, and North America, but were highly variable at the regional scale. Rapoport s rule was also tested along the global elevational gradient; mean language range size increased with increasing elevation. However, the predicted global pattern showed more variability at the regional scale. Finally, mountains did harbor greater language diversity than non-mountain areas at the global scale when accounting for sampling intensity in analysis. Mountain regions are important hotspots for both language and biological diversity. Globally, language diversity distributions along the elevational gradient resemble the latitudinal gradient in human language richness and mean language range size. Cultural/language diversity may be subject to fundamental biogeographic processes and evolutionary drivers of distribution and diversification. Elevation is clearly an important explanatory variable in the pattern of language diversity distributions, and likely serves as a proxy for numerous underlying and interacting mechanistic environmental variables.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Kelly Gleason.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Binford, Michael W.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-02-28

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042242:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042242/00001

Material Information

Title: Elevational Gradient Resembles Latitudinal Gradient of Global Language Distributions
Physical Description: 1 online resource (83 p.)
Language: english
Creator: Gleason, Kelly
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: biogeography, diversity, elevation, gradients, language, latitude
Interdisciplinary Ecology -- Dissertations, Academic -- UF
Genre: Interdisciplinary Ecology thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: This research investigated biogeographic parallels of species and language distributions and determined whether human language distributions conform to the elevational gradient hypothesis at global and regional scales. Language distribution patterns along elevation gradients were comprehensively addressed through three primary research questions which investigated (1) language richness along the elevational gradient, (2) Rapoport s rule along the elevational gradient, and (3) global mountains areas and their importance for language diversity. The distribution of extant languages from all latitudes were included in a global analysis, and those located on the major landmasses of Africa, Asia, Australia, Europe, North America, and South America were included in regional analyses. Significant regression models strongly support the elevational gradient hypothesis in global and regional language distributions. Globally, language richness steadily decreased with increasing elevation, but when language richness was standardized per 1,000 km2 of land area, a uni-modal hump of greatest language richness existed at intermediate elevations. Regional analyses of Africa, Asia, and South American continents showed similar patterns as the global analysis. Australia, Europe and North American regions which represent almost half the global land surface area, but a quarter of the global languages demonstrated different patterns than expected. Language diversity increased in along the elevational gradient in Australia, Europe, and North America, but were highly variable at the regional scale. Rapoport s rule was also tested along the global elevational gradient; mean language range size increased with increasing elevation. However, the predicted global pattern showed more variability at the regional scale. Finally, mountains did harbor greater language diversity than non-mountain areas at the global scale when accounting for sampling intensity in analysis. Mountain regions are important hotspots for both language and biological diversity. Globally, language diversity distributions along the elevational gradient resemble the latitudinal gradient in human language richness and mean language range size. Cultural/language diversity may be subject to fundamental biogeographic processes and evolutionary drivers of distribution and diversification. Elevation is clearly an important explanatory variable in the pattern of language diversity distributions, and likely serves as a proxy for numerous underlying and interacting mechanistic environmental variables.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Kelly Gleason.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Binford, Michael W.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2011-02-28

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042242:00001


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ELEVATIONAL GRADIENT RESEMBLES LATITUDINAL GRADIENT OF GLOBAL
LANGUAGE DISTRIBUTIONS



















By

KELLY ERIKA GLEASON


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2010

































2010 Kelly Erika Gleason





























To all who nurtured my intellectual curiosity, academic interests, and sense of
scholarship throughout my lifetime, making this milestone possible









ACKNOWLEDGMENTS

I thank the chair and members of my supervisory committee, Dr. Michael Binford,

Dr. Tim Fik, and Dr. Jane Southworth, for their mentoring and support, the staff for

always getting my paperwork in order, and to the director of Biology labs, Dr. Kent Vliet,

for supporting my teaching fellowship through the past two years. I thank Dr. Rick

Stepp for his theoretical contributions and fundamental data resources and to all those

who have helped me methodologically including Dr. Youliang Qiu, Dr. John Krigbaum,

and Dr. Rob Fletcher. I thank my parents, John Emory Gleason and Dayle Hosek, and

my husband, Nathaniel Brodie, for their patience and encouragement, which motivated

me to complete my study.









TABLE OF CONTENTS

page

A C K N O W LE D G M E N T S ................................................................ ...................... 4

L IS T O F T A B L E S ................................................... ........................ 6

L IS T O F F IG U R E S ................................................... ........................ 7

LIS T O F A B B R EV IA T IO N S ........................................................................... 9

A B S T R A C T ......................... 10.................10..........................

CHAPTER

1 INTRODUCTION..................... ........................ ......... 12

2 D A TA A N D M ET H O D S ........................................................... ............. 22

D a ta ................................................... ............................................... 2 2
E elevation D ata ................................................................ 22
L a n g u a g e D a ta ............................................................................... ...... ........ 2 2
M e th o d s .................................. ... ......... .. .............. ......... .........2 5
Language Richness along the Elevational Gradient ................ ....... ....25
Rapoport's Rule along the Elevational Gradient .............................................. 26
Global Mountain Areas and Their Importance for Language Diversity ...........26
S o ftw a re ....................... ........................................... ............... 2 7

3 R E S U L T S .............. ..... ............ ................. .............................................. 3 2

Language Richness along the Elevational Gradient............. ....... ..... ...........32
Rapoport's Rule along the Elevational Gradient........................... 33
Global Mountain Areas and Their Importance for Language Diversity.....................34

4 DISCUSSION..................... .. ................................. 48

APPENDIX: ANTHROPOGENIC LAND-USE / LAND-COVER CHANGE AND THE
GLOBAL DISTRIBUTION OF THREATENED AND ENDANGERED
LANGUAGES........................ ......... 56

In tro d u c tio n ................................................................................................ 5 8
M methods ........................ ...................... ..................... 62
R e s u lts .................. ... ......... ...................................... 6 3
D iscussio n .................................................... 6 5

LIST O F R E FE R E N C E S .......................... ...................................74

BIOGRAPHICAL SKETCH ......................... ................. 83









LIST OF TABLES


Table page

3-1 Spearman's rank correlation coefficients of elevation zones with language
richness and standardized language richness per elevation zone ................ 35

3-2 Truncation ranges which represent at least 99 % of the land surface-area per
region, used to remove the highly leveraging elevations with
disproportionately small land surface areas.................................................... 37

3-3 Multiple regression results with non-standardized languages truncated to
include 99% of the land surface-area per region.-Variables include elevation
zone (EZ), square root of EZ (SQRT_EZ), dummy of 2500 m and above
(D_2500m) and d_2500*sqrt_ez. n= number of languages in analysis, R2
indicates the adjusted R2 of the model, regression coefficient of elevation
variable, and F-ratio of m odel ...................................................................... 40

3-4 Multiple regression results with standardized languages. Variables include
elevation zone (EZ) and square root of elevation zone (SQRT_EZ)................... 41

A-1 Comparison of mean anthropogenic landcover change within threatened
language categories. ............... ... ............. ......... ... ............... 71

A-2 T-test results comparing number of cells of anthropogenic landcover change
in extinct versus extant and threatened status languages' 100 km buffer
around centroid location ....................... ............. ........... 73









LIST OF FIGURES


Figure page

2-1 Global languages and 100 m elevation zones ................................. ..............28

2-2 Cumulative percent of land surface area within each 100 meter elevation
zone. Arrows indicate the truncation points of the global dataset (100 m -
5000 m)...... ................... ............................... 28

2-3 Continental regions isolated for regional analyses (Africa yellow, Asia -
purple, Australia red, Europe orange, North America green, South
America blue, excluded areas from regional analysis brown) Only the
primary continental landmass was included in the regional analysis, all island
areas within each continent were excluded from analysis to mitigate the
effects of island biogeography on language diversity distribution patterns.........29

2-4 Global contour map of elevation gradient as used by Mace and Pagel 1995
to investigate Rapoport's Rule ..................................................................... 30

2-5 Global mountain and non-mountain areas as classified by Stepp et a/.2005...... 31

3-1 Global language richness of 100 m elevation zones................................. .... 36

3-2 Global language richness standardized per 1,000 km2 within the 100 m
elevation zones. Arrows indicate truncation points, data were analyzed from
100 m to 5000 m to exclude the highly leveraging tails in the distribution........... 36

3-3 Language richness along the African elevational gradient (n=1349) ..................37

3-4 Language richness along the Asian elevational gradient (n=1000).....................38

3-5 Language richness along the South American elevational gradient (n=316)......38

3-6 Language richness along the European elevational gradient (n=273)................39

3-7 Language richness along the North American elevational gradient (n=487).......39

3-8 Language richness along the Australian elevational gradient (n=135) ...............40

3-9 Language range size distribution is extremely positively skewed. Of the 5725
language range sizes included here, 1953 languages are not shown in the
tail of this distribution. .................................... ............................... 42

3-10 Language range size frequency distribution for log10 transformed global
geographic range sizes ............................................ ................. 42

3-11 Rapoport's Rule of un-standardized language richness along the global
elevational gradient. This figure demonstrates familiar steady decline in









language richness, and increasing area/range size of languages with
increasing elevation, as well as the increase in variability of area/range size
with increasing elevation ........... ............................... .... .... .......... 43

3-12 Rapoport's Rule of standardized language diversity along the global
elevational gradient. Standardized languages demonstrate the highest
richness uni-modal hump at intermediate elevations. Mean language area
(range size) increases consistently with increasing elevations. .......................... 43

3-13 Language richness (per km of contour) and mean language area (range size)
(10,000 km2) along the African elevational gradient......................... ............. 44

3-14 Language richness (per km of contour) and mean language area (range size)
(10,000 km2) along the Asian elevational gradient.................. ..................44

3-15 Language richness (per km of contour) and mean language area (range size)
(10,000 km2) along the South American elevational gradient. ............................45

3-16 Language richness (per km of contour) and mean language area (range size)
(10,000 km2) along the Australian elevational gradient.................... .......... 45

3-17 Language richness (per km of contour) and mean language area (range size)
(10,000 km2) along the European elevational gradient. .................... .......... 46

3-18 Language richness (per km of contour) and mean language area (range size)
(10,000 km2) along the North American elevational gradient..............................46

3-19 Global language richness in mountain vs. non-mountain areas (Stepp et al.
2 0 0 5 c rite ria ) ................ ........................................................... 4 7

A-1 Flowchart of analysis procedures. ............. ..... ........... ............ .......... 67

A-2 Cumulative frequency of global languages (Krause's endangered language
levels) ............... .......................................... ........ ........ 68

A-3 MODIS Global IGBP landcover 2001 mosaic ....... ....................................... 69

A-4 Global extent of critically endangered languages and anthropogenic LULCC
2 0 0 1-2 0 0 7 ............. .......... .. .. ......... .. .. ... ....... .............................. 7 0

A-5 Total global landcover change from 2001-2007......... ............................... 71

A-7 Mean number of change and stable anthropogenic landcover cells (500 or
less, n=1302 & 500 or more, n=5702). .......... ... ....... ...... ..... ......... 72

A-8 Mean change in anthropogenic landcover w/in 100 km of critical languages......72

A-9 Mean increase in anthropogenic landcover by threatened status....................73









LIST OF ABBREVIATIONS

DEM Digital elevation model

EZ Elevation zones

LULCC Land-use and land-cover change









Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master in Science

THE ELEVATIONAL GRADIENT RESEMBLES LATITUDINAL GRADIENT OF
GLOBAL LANGUAGE DISTRIBUTIONS


By

Kelly Erika Gleason

August 2010

Chair: Michael Binford
Major: Interdisciplinary Ecology

This research investigated biogeographic parallels of species and language

distributions and determined whether human language distributions conform to the

'elevational gradient hypothesis' at global and regional scales. Language distribution

patterns along elevation gradients were comprehensively addressed through three

primary research questions which investigated (1) language richness along the

elevational gradient, (2) 'Rapoport's rule' along the elevational gradient, and (3) global

mountains areas and their importance for language diversity.

The distribution of extant languages from all latitudes were included in a global

analysis, and those located on the major landmasses of Africa, Asia, Australia, Europe,

North America, and South America were included in regional analyses. Significant

regression models strongly support the elevational gradient hypothesis in global and

regional language distributions.

Globally, language richness steadily decreased with increasing elevation, but

when language richness was standardized per 1,000 km2 of land area, a uni-modal

hump of greatest language richness existed at intermediate elevations. Regional









analyses of Africa, Asia, and South American continents showed similar patterns as the

global analysis. Australia, Europe and North American regions which represent almost

half the global land surface area, but a quarter of the global languages demonstrated

different patterns than expected. Language diversity increased in along the elevational

gradient in Australia, Europe, and North America, but were highly variable at the

regional scale. "Rapoport's rule" was also tested along the global elevational gradient;

mean language range size increased with increasing elevation. However, the predicted

global pattern showed more variability at the regional scale. Finally, mountains did

harbor greater language diversity than non-mountain areas at the global scale when

accounting for sampling intensity in analysis. Mountain regions are important hotspots

for both language and biological diversity.

Globally, language diversity distributions along the elevational gradient resemble

the latitudinal gradient in human language richness and mean language range size.

Cultural/language diversity may be subject to fundamental biogeographic processes

and evolutionary drivers of distribution and diversification. Elevation is clearly an

important explanatory variable in the pattern of language diversity distributions, and

likely serves as a proxy for numerous underlying and interacting mechanistic

environmental variables.









CHAPTER 1
INTRODUCTION



At least since the time of Alfred Wallace, biologists have recognized the latitudinal

species richness gradient-whereby species richness decreases with increasing

latitudes- as one of the fundamental patterns of life on earth (Brown and Lomolino,

1998; Gaston, 1996; Rosenzweig, 1995; Willig, 2000; MacArthur, 1972; Whittaker,

1960; Pianka, 1966; von Humboldt, 1849), albeit with few exceptions in specific taxa

(Willig et al., 2003). Variation in species diversity along gradients of elevation is almost

as general and striking as the latitudinal variation in terrestrial species. Just as the

number of species decreases from the tropics to the poles, probably due at least in part

to broad-scale climatic controls (Pianka, 1966; Wallace 1876), species richness also

decreases from lowland to upland elevations (Korner, 2002; Lomolino, 2001; Kapos et

al., 2000). The equilibrium theory of island biogeography (MacArthur, 1972) postulates

that island size and distance from land determine the extinction and immigration rates of

species, respectively, and predicts that islands of certain sizes and isolation should

have a certain number of species. As both area decreases and the degree of isolation

increases while ascending mountains, this theory may help explain the reduction in the

number of species at higher elevations, just as it predicts lower diversity on smaller

more isolated islands. Gradients in elevation, like latitude, act as a surrogate for an

almost infinite number of underlying environmental gradients including temperature,

moisture availability, and climatic variability, that interact and possibly counteract

making distinct hypothesis testing of causal mechanisms difficult (Schemske, 2002).









Mountain areas as a whole are highly diverse biologically, due to the compression

of 'life zones' into relatively narrow geographic ranges. A 5000 m high equatorial

mountain comprises nearly all the climatic zones of the world over a relatively short

distance, whereas a similar series of thermal climates on flat land is spread across

thousands of square kilometers (Holdridge, 1967; Korner, 2002). This compression of

life zones explains why, on a 100 km grid scale, no landscape surpasses the biological

richness of mountains (Barthlott et al., 1996).

Though this pattern has long been recognized by ecologists and biogeographers,

it has not been as well documented quantitatively as the latitudinal gradient of diversity.

This is due in part to the interdependence along the elevational gradient of numerous

bio-physical variables, particularly temperature and moisture availability. Albeit difficult

to quantify and with some notable exceptions (Willig et al., 2003; Rhode, 1992), the

general decline in species diversity with decreasing temperatures and moisture

availability at higher elevations is a generally accepted rule applying across a multitude

of regions including the Andes (Terbough, 1977), Himalayas (Yoda, 1967), mesic

tropical regions (Erwin, 1988), New Guinea (Diamond, 1972; Kikkawa and Williams,

1971), and North America (Whittaker, 1960, 1977; Sfenthouraskis, 1992; Brown, 1971).

Within mountains, species richness generally decreases with increasing elevation in

proportion to the decrease in available land area (Korner, 2000), while endemism often

increases due at least in part to geographic isolation (Gentry, 1988; Peterson et al.,

1993).

The negative effect of altitude on biological diversity has been broadly

documented, but the pattern of decline in diversity richness has been more recently









debated, igniting much controversy as to the underlying mechanisms. Two patterns of

diversity distributions along the elevational gradient have been observed in a variety of

taxa (1) a linear/logarithmic decline in richness with increasing altitude, or (2) a uni-

modal 'hump' shaped relationship where intermediate elevations have the highest

diversity richness. This second pattern of highest diversity richness occurring at

intermediate elevations has been particularly evident when analyses were standardized

by area to account for the species-area relationship (Chatzaki et al., 2005; Lomolino,

2001; Rahbek, 1995). As the species-area relationship is a well established principle in

ecology (Gleason, 1922; MacArthur, 1972), surprisingly few studies have accounted for

area in their analysis.

Ecologists, and their predecessors, have argued for centuries about the underlying

mechanisms driving these patterns along the elevational gradient. Lawton et al. (1987)

summarized the influence of elevation on species richness being due to the following

environmental trends at higher elevations (1) reduction in productivity, (2) reduction in

the total area, (3) reduction in the resource diversity, and (4) the harshness and

unpredictability of the environmental conditions common at higher elevations. The 'mid-

domain effect' (Colwell & Lees, 2000) or the 'ecotone effect' (Lomolino, 2001) may

explain the peak in species richness at mid-elevations as the increasing overlap of

species ranges towards the centre of a domain, or that the transition zone between

communities is likely to have the greatest species richness.

Many studies have investigated biodiversity distribution patterns in relation to

environmental gradients; however few have considered fundamental biogeographic

patterns in intraspecific analyses of our own human species. Although the relationship









between biological and cultural diversity has been recognized for some time (Kroeber,

1963, first published in 1939), Harmon (1996), may have opened the door to biocultural

diversity research through his comprehensive review of the geographic overlaps

between linguistic and biological diversity. Languages are commonly used as a reliable

proxy for cultural diversity to represent the intraspecific geographic variation of the

human species (Harmon and Maffi, 2002). According to Maffi (1996 pp.3)" evidence is

emerging of remarkable overlaps between areas of greatest biological and greatest

linguistic/cultural diversity around the world. These striking correlations require close

examination and must be accounted for." A number of authors (Chapin, 1992; Toldedo

1994; McNeely and Keeton, 1995; Poole, 1995; Wilcox and Duin, 1995; Harmon, 1996;

Stepp et al., 2004) have shown that cultural and biological diversity tends to be

correlated. Controversially, a large number of these authors propose that indigenous

social systems, which tend to concentrate in areas of high cultural and linguistic

diversity, are more likely to conserve or coexist with biodiversity. Only a few possible

explanations can clarify these correlations (1) either small scale societies conserve or

enhance biological diversity; (2) biological diversity directly enhances cultural diversity;

or (3) large-scale, politically-complex, social systems (characteristic of unified political

states) reduce both cultural and biological diversity (Smith, 2001). These hypotheses

are not mutually exclusive, particularly if cultural diversification may be a product of co-

evolution between social and natural environments.

Considerable evidence has accumulated since Harmon's (1996) publication,

demonstrating that languages do in fact follow similar biogeographic distribution

patterns as biological diversity distributions, (Global, (Sutherland, 2003), Africa (Moore









et al., 2002), Americas (Smith, 2001, Mace and Pagel, 1995; Manne, 2003; Nettle,

1999), New Guinea (Stepp et al., 2004), Review (Maffi, 2005)). Linguistic cultural

diversity broadly follows the latitudinal gradient, with an increasing density of languages

from the poles toward the equator (Mace and Pagel, 1995; Stepp et al., 2004;

Sutherland, 2003; Currie and Mace, 2009; Manne, 2003; Cashdan, 2001; Collard and

Foley, 2002). There is also evidence that language diversity distributions may follow

'Rapoport's rule' (Currie and Mace, 2009; Mace and Pagel, 1995): a hypothesis

observed in species distributions where species' range size increases with increasing

latitude (Ruggiero & Werenkraut, 2007; Gaston, 2005; Stevens, 1989; Rapoport, 1982;

Brown, 1996). Studies have considered an abundance of biophysical and socio-

economic variables to explain geographic language distributions, from net primary

productivity (Currie and Mace, 2009) to mean number of televisions per 1000 km2

(Sutherland, 2003) with varying results as to their significance and explanatory power

(see Maffi, 2005 for review). Many studies have incorporated elevation-related

explanatory variables of habitat complexity or topographical heterogeneity with widely

varying results (Currie and Mace, 2009; Moore et al., 2002; Cashdan, 2001; Mace and

Pagel, 1995; Nichols, 1990). However few studies include elevation specifically (Moore

et al., 2002; Sutherland, 2003), and no study has exclusively addressed whether the

elevational gradient in language diversity resembles latitudinal distribution patterns in

the same way biological species distributions do.

Stepp et al. (2005), using point-location data in New Guinea, note that mountain

areas have higher linguistic and biological richness than non-mountain areas. Even

though area was not accounted for in their analysis, their observations are concurrent









with biological species phenomenon: mountain areas often support higher diversity than

lowland areas even though they cover smaller geographic areas (Holdridge, 1967).

Nevertheless, global distribution patterns cannot be inferred from one small regional-

scale investigation, nor can any underlying mechanisms be considered.

Currie and Mace (2009), the only study to utilize the language polygon data

recently made available by Ethnologue (Grimes, 2005), built a convincing model

explaining language range size distributions, and determined 'political complexity', or

the degree to which multiple cultures are integrated into one political structure, to be the

most important explanatory mechanism behind latitudinal patterns concurrent with

'Rapoport's Rule'. Currie and Mace consider numerous biophysical variables, but

exclude elevation as an explanatory variable in the final regression model due to

"opposite correlations than expected", based on the 'topography hypothesis' as

proposed by Stepp et al. (2005). They found language range size to be positively

correlated with greater topographical heterogeneity. As an extension of 'Rapoport's rule'

(based on Mace and Pagel, 1995) they reasoned that if language range size was

positively correlated with topographical heterogeneity, then language richness should

be negatively correlated. These correlations should not be surprising, in that they

corroborate many species distribution studies (Stevens, 1992). However, Currie and

Mace (2009), still discard topographical heterogeneity as an important explanatory

variable because it opposed the conclusion by Stepp et al. (2005), in their 'topography

hypothesis'; that mountain areas harbor greater biological and cultural diversity than

non-mountain areas. They expected language range size to be negatively correlated

and language richness to be positively correlated with topographical heterogeneity,









even though it contradicts a long history of ecological literature. As Currie and Mace

(2009), rejected elevation as an important variable strictly on theoretical grounds, the

elevational gradient hypothesis in language diversity distributions deserves to be

adequately tested. This confusion also demonstrates the importance of independently

evaluating hypotheses which address the elevational gradient in diversity from those

which address the importance of mountain areas (topographical heterogeneity) known

for heightened diversity and endemism.

These many conceptual confusions as to whether elevation is truly important in

explaining language diversity distributions may be due, at least in part, to the numerous

methodological concerns commonplace in the literature. Very few publications

explaining language diversity distributions have addressed the species-area relationship

and accounted for sampling intensity in their analyses (Sutherland, 2003; Manne, 2003).

Currie and Mace (2009), used standard deviation of altitude within language polygons to

represent topographical heterogeneity without standardizing by area of the polygon. It

is well known that increasing local extent also increases local heterogeneity of the

underlying landscape being sampled (Fortin and Dale, 2005). Their result that language

range size was positively correlated with topographical heterogeneity should not be

surprising; for larger language polygons are likely to have greater heterogeneity and

higher standard deviations of altitude; but simply as a function of the sampling method

employed and regardless of the actual relative topography. Stepp et al. (2005),

describe the number of languages on New Guinea without standardizing for the area

represented by mountain areas and non-mountain areas, and assume global parallels

to the local pattern based on visual interpretation of global maps showing language









diversity being concentrated in mountain regions. Manne (2003), investigated language

distributions at a 2 spatial resolution, where as others utilize a 30 arc-second spatial

resolution (Currie and Mace, 2009), and still others evaluate distribution patterns based

on political boundaries, using countries as the unit of analysis (Sutherland, 2003). Of

the research which has addressed the geographic distributions of languages, few have

founded their questions in biogeographical theory, properly addressed/acknowledged

the assumptions of their methods, or controlled for sampling intensity in their analyses.

Not surprisingly, the current literature on the distribution of language richness is

inconsistent, and in some cases, in conflict, particularly about the importance of

elevation as an explanatory variable.

Species distribution patterns along environmental gradients have been well-

documented, and even causal mechanisms of numerous confounded biological and

physical factors including moisture, temperature, and energy have been proposed

(Whittaker, 1953; Lawton et al., 1987; Currie, 1991). If humans have coevolved with the

other biota along these underlying environmental gradients, I hypothesize that the

intraspecific variation in human language distribution patterns would follow general

biogeographic principles observed in species distributions. This research may

demonstrate that the human species and our distributions may be correlated with similar

evolutionary drivers and biogeographic principles as species distributions. As

correlation is not causation, the deeper examination of the underlying mechanisms

behind these patterns will be left to future investigations.

The purpose of this research and its three primary questions was to establish

whether global language diversity distributions are consistent with just one of









biogeography's fundamental principles: the elevation gradient. In order to specifically

address the influence of elevation on language diversity distribution patterns, this study

intentionally ignores all other potential explanatory variables.

Research questions. Language distribution patterns along elevation gradients

were comprehensively addressed through three primary research questions:

1. Does language richness follow the elevational gradient known from species
distributions; does richness decrease at higher elevations?

2. Do languages follow Rapoport's rule; does range size increase at higher
elevations?

3. Do global mountain areas harbor greater language diversity than lowland areas?

The first question will determine, is there an elevational gradient of decreasing

language diversity parallel to species diversity distribution patterns? As human

populations generally decline with increasing elevation, I expect that their intraspecific

language richness may also decrease along the elevational gradient (CIESIN et al.,

2004; Huddleston et al., 2003). I expect that language diversity patterns along

gradients in elevation will resemble the latitudinall gradient' in diversity (Stepp et al.,

2004; Currie and Mace, 2009; Manne, 2003; Cashdan, 2001; Moore et al., 2002); as

elevation increases language richness is expected to decrease at least in part due to

the species-area relationship. As has been demonstrated in studies quantifying the

elevational gradient in species diversity (Rahbek, 1995, 1997; Lomolino, 2001), I expect

that when diversity data are standardized by land area, there will be a uni-modal 'hump'

in language richness at the intermediate elevations due to the 'mid-domain effect' in the

underlying resource gradient. At the global scale of analysis, I anticipate these broad

biogeographical patterns to be fairly evident in language diversity distributions; however









at finer regional scales of analysis, there will probably be more variability in the

conformity to the general biogeographic patterns. Particularly in Australia and North

America, as appears through visual interpretation (Figure 2-1), there may be a

divergence in the regional patterns from the general global rule.

The second question will investigate, do language range size distributions follow

Rapoport's rule' along the elevational gradient? (Ruggiero & Werenkraut, 2007; Gaston,

1996, 2005; Stevens, 1992; Rapoport, 1982). This research will determine if global

language range sizes do in fact increase with increasing elevations as has been shown

in relation to the latitudinal gradient (Mace and Pagel, 1995; Currie and Mace, 2009).

Again, I expect the regional analyses to express greater variability than global analysis

of this broad-scale pattern.

Finally, the third question will test inferences made by Stepp et al. (2005) and

determine, do global mountain areas harbor greater language diversity than non-

mountains areas? Overall, I expect mountain areas will have higher language richness

than lowland areas when data are standardized by area.

These three questions comprehensively evaluate the pattern of language diversity

distributions relative to an important biogeographic pattern of species distributions; the

elevation gradient hypothesis. This work is a step in resolving the inconsistencies

throughout the literature, as well as identifying parallels between species and cultural

diversity distributions which may share underlying historical mechanisms of

diversification and dispersal.









CHAPTER 2
DATA AND METHODS



Data

Elevation Data

A Digital Elevation Model (DEM) at a 10-minute spatial resolution was obtained

from www.worldclim.org where global mosaics of The Shuttle Radar Topography

Mission (SRTM) derived DEMs are made freely available (Hijmans et al., 2005). DEMs

were reclassified into 100- m elevation zones for analysis, as the smallest unit

commonly used in the literature to analyze elevation gradients (Gentry, 1988), and was

then used to create global contours per 100 meter relief in elevation in the second

question (Mace and Pagel, 1995). The DEM was further transformed to create % slope

and latitude variables for the third question's global analysis.

Language Data

Language data were obtained from the Ethnologue 15th edition (Grimes, 2005).

There are currently 6,909 extant languages documented in the database. These

languages were compiled from numerous published sources, and represent languages

which have been known to exist in the past 50 years as a person's 'first language.' As

there are hundreds of languages with fewer than 50 speakers, some of these may have

already gone extinct, but would still be included in the database until a peer-reviewed

publication could verify that language has gone extinct. Until recently, language data

were only available as point locations, which is problematic particularly at finer spatial

resolutions. Point data do not always best represent geographic locations of languages,

especially in wide-spread and/or discontinuous language groups whose point locations









were sometimes arbitrarily placed in areas where there were few or no actual speakers.

Polygon data, albeit not perfect, are a more reliable dataset for considering language

distribution patterns, as their ranges have been drawn by linguists to represent the

approximate boundaries of the actual geographic range where the language is spoken

(Maffi, 2005). No claim is made by Ethnologue as to the precision of these boundaries,

however these data are widely recognized as the most comprehensive and accurate

global data available on geographic distributions of languages (Currie and Mace, 2009;

Stepp et al., 2004; Maffi, 2005). In many cases these polygons overlap, just as they will

at broad scales. Although in reality, at fine-scales (i.e. household to community level)

languages usually do not overlap; therefore these data may be more appropriate for

broad scale analyses. Where the language range is not known, there was no polygon

drawn representing its range, and thus these languages were excluded from analysis.

Within regional analyses all languages on the major landmass were included but island

languages were excluded to mitigate the influence of island biogeography. In the final

global analysis, 5725 language polygons were evaluated of the 7719 known extant

languages.

These polygon data depict the traditional linguistic homelands of each language,

and do not attempt to map immigrant languages or those populations of speakers away

from their homelands. Macrolanguages, the 55 languages which are defined as

"multiple, closely related individual languages that are deemed in some usage contexts

to be a single language" (Grimes, 2005), are usually considered "colonial languages,"

and span over many political boundaries, have not been included in these analyses (i.e.

English in the USA, Portuguese in Brazil, and Swahili in Tanzania). Polygons were not









provided for languages which are considered widespread in a country, and would be

essentially identical to the country boundary, with few exceptions. There is certainly a

debatable conceptual argument in determining the languages which "count" versus

those that don't "count." However, these languages have been historically excluded by

linguists in analyses of the patterns and processes underlying language distribution, and

it can be argued that these macrolanguages and widespread languages may not

represent the long historical process driving language diversification (Lewis, 2009).

Furthermore, these data represent languages known in the past 50 years, and certainly

hundreds of indigenous languages were lost in South America alone during the colonial

period (Kaufman, 1994:47). Certainly these data are not complete representations of

the historical evolutionary patterns and processes of language distributions, but

represent our current understanding, and may call into question continental regions

being analyzed with a strong history of colonization such as the Americas and Australia

(Nettle, 1998). However these data are the best available, and even considering their

limitations, are constructive in helping us understand the patterns of intraspecific

diversification. The complete picture of pre-colonial language distributions may never

be fully understood, however colonial imperialism greatly influenced biotic species

distributions as well, and headway has been made in understanding the evolutionary

processes driving these patterns.

As the meaning of 'diversity' differs dramatically in the biological (Mayr, 1963;

Abruzzi, 1982) and linguistic literature (Nettle, 1998), it should be made clear that I am

investigating solely the intraspecific variation of human language richness and their

distributions. As Nettle (1998) notes, linguists tend to agree on the definition of









languages for the purpose of continental scale comparisons (the focus of this study),

however at finer scale resolutions the comparability of languages becomes much more

complicated (Romaine, 1994), and will therefore be avoided in this investigation.

Language diversity, like biodiversity, can be measured in a variety of different ways; in

this study, language diversity will be measured simply by the number of languages in a

given area.

Methods

Language Richness along the Elevational Gradient

The global 10-minute DEM was reclassified into100 m elevation zones (Figure 2-

1). As stated above, the total number of language polygons analyzed (n = 5725),

included only those found on continental landmasses, and excluded the 55

macrolanguages and those whose physical location has not been recorded. Within

each 100-m elevation zone, the number of languages was counted at global and sub-

continental regional scales. Regional analyses included major non-island landmasses:

Australia, Asia, Africa, Europe, North America, and South America (Figure 2-3).

Language richness within each 100-m elevation zone was standardized per 1,000 km2.

Language richness was plotted globally and by region; extreme values (at the very low

and very high elevations) were removed and analyses were performed on these

truncated data. As the lowest and highest elevations in the 'tails of the distribution'

tended to represent less than 1% of the total land surface area, truncation points were

utilized to include only the elevation zones which represented as close as possible to,

but not less than, 99% of the total land surface area (Figure 2-2, Table 3-2).

Spearman's rank correlations were calculated for language richness and

standardized language richness at global and regional scales. Data were evaluated









using ordinary least squares (OLS) multiple regressions at the global and regional

scales. After utilizing stepwise selection techniques the final variables in the non-

standardized model included elevation zone (ez), square root of elevation zone

(sqrt_ez), dummy variable for elevation above 2500 m (d_2500), and square root of

elevation zone multiplied by the dummy variable (sqrt_ez*d_2500). The final model of

standardized language richness per 1,000 km2 included elevation zone (ez) and square

root of elevation zone (sqrt_ez).

Rapoport's Rule along the Elevational Gradient

Global range sizes of language polygons, as well as the log-transformed range

sizes, were plotted with frequency histograms to evaluate the overall probability

distribution patterns. 'Rapoport's rule' was evaluated using methods employed by Mace

and Pagel (1995) in relation to the latitudinal gradient hypothesis in North America. The

elevation zone data described above were used to create contours of each 100 meter

change in elevation (Figure 2-4). Only language polygons which intersected these

global contours were counted to overcome issues of spatial autocorrelation. Language

richness values were then standardized by length of each contour to account for

sampling intensity, and then were plotted for each 100-m contour along the global

elevational gradient. Identical analyses were conducted at the regional scale using the

continental divisions used through the first question's analyses (Figure 2-3) to observe

regional scale patterns of language range size along the regional elevation gradients.

Global Mountain Areas and Their Importance for Language Diversity

To evaluate the Stepp et al. (2005) 'topography hypothesis' at a global scale, the

global DEM raster was reclassified into mountain and non-mountain zones based on the

Stepp et al. (2005) criteria: areas with mean slope > 5%, areas above 1000 m and









slopes > 2%, and areas above 2500 m on a 100 km2 grid (Figure 2-5). I do not mean to

imply these criteria are the most appropriate for a global analysis as there are numerous

ways to define mountains. These criteria were utilized strictly to assess the observation

posed by Stepp et al. 2005 that mountain diversity patterns in New Guinea also apply to

mountains globally. Stepp et al. (2005) criteria are a conservative estimate of

mountainous land areas and may be tropically biased. Language polygons within

mountain areas and non-mountain areas were counted, standardized by 10,000 km2

and graphed.

Software

All data preparation was conducting using Arclnfo 9.3 (ESRI, Redlands, CA, 2008)

and Hawth's Tools, Version 3.27 (Beyer, 2007) analysis pack. All statistical analyses

were conducted using NCSS 2007 software (Hintze, 2007).


















































Figure 2-1. Global languages and 100 m elevation zones


1.00
0.90
U
m 0.80
S0.70
-o 0.60
m 0.50
o 0.40
C 0.30
U 0.20
0 cumulative percent of land
C. 0.10
S0.10 surface area
.> 0.00 0 0 0 0 0 0 0 0
m 0 00 0 00 0 0 0 0 0A 0 0 0 0 0 0
E -I -I r-j r'j r' m m m m Ln Ln Ln A D kW
Elevation (100 meter Zones)
U fI


Figure 2-2. Cumulative percent of land surface area within each 100 meter elevation
zone. Arrows indicate the truncation points of the global dataset (100 m 5000 m)





































Figure 2-3. Continental regions isolated for regional analyses (Africa yellow, Asia -
purple, Australia red, Europe orange, North America green, South America blue,
excluded areas from regional analysis brown) Only the primary continental landmass
was included in the regional analysis, all island areas within each continent were
excluded from analysis to mitigate the effects of island biogeography on language
diversity distribution patterns.













Global Elevation Gradient Represented as Contours -
(Mace and Pagel 1995) Sampling Method


Legend
High:~M3 m

SI Low: -300 m
Equal Area Cylindrical Projedion "S 84
EM (10newi Ceaed by Kel.2yGeason
DEM (10 rin) worddlim.arg 0 1.2502500 5,000 7.500 10,000 Apri 2010


Figure 2-4. Global contour map of elevation gradient method used
1995 to investigate Rapoport's Rule in North America.


by Mace and Pagel













Mountains and Non-Mountain Areas
as Classified by Stepp et aL 2005 Criteria


A k






Sepp el 3 200 cmeria=
Sareas .25 m
- aleasw mean siopes 5%
- aweasabote 1000 m and slope 2%
- a'eas wlm 100 kmn o above areas
Equal Aea Cylidrica Projedion
DEM (10 in)- worldin-org


0 1 0 50 7 eters
0 1.2502500 5.000 7.500 10,000


Cealed by Keily G ason
Apn 2010


Figure 2-5. Global mountain and non-mountain areas as
criteria.


classified by Stepp et a/.2005









CHAPTER 3
RESULTS



Language Richness along the Elevational Gradient

As predicted, language distribution does conform to the elevational gradient

hypothesis at the global scale; language richness within 100-meter zones was

negatively correlated with elevation, and demonstrated the expected pattern of declining

diversity with increasing elevation (Figure 3-1, Table 3-1). When data were

standardized by area, linear correlations were neutral at the global scale (Figure 3-2 &

Table 3-1), probably due to the uni-modal 'hump' in language richness at intermediate

elevations (Figure 3-2), which adheres to a second-order polynomial function (Table 3-

4). As seen in species distribution analyses (Stevens, 1989), when these data were

standardized by area or sampling effort, the very low and very high elevation areas had

extremely high peaks in language richness (Figure 3-2). Stevens (1989) explained that

these tails in the distribution pattern may not be truly representative of the overall

diversity distributions due to the extreme differences in area represented at the very

lowest and very highest elevations and are therefore 'unreliable' data. Therefore, a

truncated curve, from 100 m-5000 m was utilized for global regression analyses, which

represented at least 99.5 % of land surface-area per region (Table 3-2). Regression

analyses emphasized that elevation was in fact an important factor in explaining the

global geographic distribution of languages (Tables 3-3 and 3-4). When data were

transformed to meet normality assumptions; extremely high R2 values and F-ratio

values demonstrated that in fact elevation has strong explanatory power in the

geographic pattern of language distributions (Table 3-3 & 3-4).









At the regional scale, similar patterns emerged as at the global scale; language

richness within the 100-m elevation zones was negatively correlated with elevation

(Tables 3-1 & 3-2) in all regions using non-standardized data (Figures 3-3 to 3-8).

However when data were standardized by area, linear correlations of Spearman Rank

values were near zero due to the uni-modal hump in diversity at intermediate elevations

in Africa, Asia, and South America (Table 3-1, Figure 3-3, 3-4, & 3-5). These regional

patterns generally conformed to the predicted global patterns of language distribution in

these three sub-continental regions which contain 75% of the languages included in

these analyses. However the language diversity distribution patterns in Australia,

Europe, and North America did not parallel the expected global pattern. These regions

demonstrated a positive correlation in standardized language diversity along the

elevational gradient (Table 3-1, Figures 3-6, 3-7, & 3-8); opposite than expected based

on the 'elevational gradient hypothesis'. The regression coefficients for Australia,

Europe, and North America all demonstrated an opposite directional relationship from

the global rule in all regressions performed (Table 3-3 & 3-4). Australian, European,

and North American language richness, which comprises just 25% of all languages

included in analyses, increases along the elevational gradient, with continuously

increasing diversity at higher elevations.



Rapoport's Rule along the Elevational Gradient

The analysis of "Rapoport's rule" in relation to the elevational gradient, revealed

another consistency between language and species distributions; global language

range size distributions are highly positively skewed (Figure 3-9), but generally log-

normal (Figure 3-10). At the global scale, mean language range size increased steadily









with increasing elevation (Figure 3-11 & 3-12) in support of 'Rapoport's rule' in global

language distributions. These data sampled using the Mace and Pagel (1995) contour

method validated global results from the first question, which generally demonstrated a

consistent decline in language richness with increasing elevation (Figure 3-11), and

standardized data demonstrated highest richness at intermediate elevations (Figure 3-

12).

At the regional scales of analyses there was, as expected, more variability in these

patterns (Figures 3-11 to 3-16). 'Rapoport's rule' was not clearly apparent in any

regional analysis, except perhaps along the Australian elevational gradient. Unlike the

results from the first question's analyses, this regional investigation of 'Rapoport's rule'

demonstrated the regions which hold the majority of the world's language diversity,

namely Africa and Asia, do not adhere to the globally distinct rule. 'Rapoport's rule' in

language distributions may be a variable phenomenon at the regional scale, but with

apparent broad scale patterns.



Global Mountain Areas and Their Importance for Language Diversity

At the global scale, over three times the language diversity can be found in non-

mountain land areas, which was expected, as non-mountain areas represent 89.67% of

all land surface area, as defined by the Stepp et al. 2005 criteria (Figure 3-19). While

accounting for sampling intensity represented by the smaller land surface-areas

covered by mountains; these mountain regions did, as expected, house almost three

times greater language richness per unit area than non-mountain areas (Figure 3-19).

The 10.33 % of the earth's land surface area classified as mountain regions in this

analysis using the Stepp et al. 2005 criteria may be a conservative estimate of the









global extent of mountain areas, as objective estimates of land surface area covered by

mountains tend to hover around 20% of the earth's land surface area (Kapos et al.,

2000; Korner, 2004; UNEP-WCMC, 2003). These results confirm the observation

posed by Stepp et al. (2005), that mountains do harbor higher cultural diversity than

non-mountain areas using planimetric criteria for area.. Only 10% of the global land

surface area contains almost one-third of all extant languages (Figure 3-19). These

results particularly emphasize the importance of accounting for sampling effort in

analyses.



Table 3-1. Spearman's rank correlation coefficients of elevation zones with language
richness and standardized language richness per elevation zone.

Language Global Africa Asia Australia Europe North South
richness America America

Non-truncated -0.9998 -0.7736 0.7441 -0.8544 -0.6722 -0.4197 -0.0765

Truncated -0.7441 0.9936 0.9998 -0.9958 -0.9958 -0.8056 -0.8695

Richness Global Africa Asia Australia Europe North South
standardized America America
per 1000 km2

Non-truncated 0.0484 -0.7373 0.2025 -0.5529 0.5014 0.2128 0.3437
Truncated 0.2025 0.1548 0.0411 0.6 0.7091 0.7881 0.3334
Negative correlation means that higher elevations have fewer languages. All correlation
coefficients are statistically different from zero at p < 0.05 except the standardized,
truncated relationships for Global, Africa, and Asia.




































Figure 3-1


Global language richness of 100 m elevation zones.


Figure 3-2. Global language richness standardized per 1,000 km2 within the 100 m
elevation zones. Arrows indicate truncation points, data were analyzed from 100 m to
5000 m to exclude the highly leveraging tails in the distribution.


2000
1800
1600
1400
1200
1000
In
t 800
t 600
C
400


00
200

0 0 0 0
0 0 0 0
100 m Elevation Zones
100 m Elevation Zones


70

60


S50
E
8 40
o

a
r-In



S20
C
. 10
U


l.11111


EU IE.LE.LE.LE.LE.LE.LE.LE.11LLE.I.


100 m Elevation Zones


IIII


I III


G...IIIIIIIII 111111 11111111 11111111 1111111~1











Table 3-2. Truncation ranges which represent at least 99 % of the land surface-area
per region, used to remove the highly leveraging elevations with
disproportionately small land surface areas..

Global Africa Asia Australia Europe North South
America America
Truncated 400- 100- 100-
ra) 100-5000 0-5000 00-3500 00100-5000
range (meters) 3500 1000 3500






350 1

-0.9
300
0.8
250 0.7 0

0.6 m
200 -
0.5
150 "
0.4
tW~
100 1 0.3
C:
_U .0.2o
S50 -# of langas 0
S-0.1 3
-# of langas per zone per 1,000 km2
0 ... 0
400 900 1400 1900 2400 2900 3400

Elevation (100 m Zones)


Figure 3-3. Language richness along the African elevational gradient (n=1349)































# of languages

# of languages per 1,000 km2

0 500 1000 1500 2000 2500 3000 3500 4000

Elevation Zone (100 meter bands)


0.000025



0.00002

a
-h


-0.00001



o
O
o
0.000005 ,
O0
0.000005
3


0
4500 5000


Figure 3-4. Language richness along the Asian elevational gradient (n=1000)





160 -- 20

140 -18
16 -O
120 -
14
100 1
12

a80 10
CUeai (D
38 1
060
-6 0
0
0 40 o

20 # of languages 2 2

langas per 1000km^2

100 600 1100 1600 2100 2600 3100 3600 4100 4600
Elevation Zones (100 meter bands)


Figure 3-5. Language richness along the South American elevational gradient (n=316)











90 16

80 14

70
12 0h
60 0
Ea 10a
S50 5
-: -8 (D
S40

4 30
4 0
20 o

10 # of languages 2 3
# of langas per 1,000km^2
0 p 1 .1 O0
0 500 1000 1500 2000 2500 3000 3500
Elevation Zones (100 meter bands)


Figure 3-6. Language richness along the European elevational gradient (n=273)




90 0.00,

80

70 0000

60 0000

150 -0
4--




# of angas per zoneo
10 langas per 1000 km2 000o

0 0
100 600 1100 1600 2100 2600 3100
Elevation Zone (100 meter bands)

Figure 3-7. Language richness along the North American elevational gradient (n=487)












0.000003


70 0.000003
o
-h
60
0.000002
50 -
w 0.000002 (
S40
(D
0.000001 -
CU
30 o
20 0
0.000001
20 3

10 # of languages

0 # of langas per 1,000 km^2
100 200 300 400 500 600 700 800 900 1000
Elevation Zone (100 meter bands)


Figure 3-8. Language richness along the Australian elevational gradient (n=135)




Table 3-3. Multiple regression results with non-standardized languages truncated to
include 99% of the land surface-area per region.-Variables include elevation
zone (EZ), square root of EZ (SQRTEZ), dummy of 2500 m and above


(D_2500m) and d_2500*sqrt_ez.
indicates the adjusted R2 of the
variable, and F-ratio of model.


n= number of languages in analysis, R2
model, regression coefficient of elevation


Global Africa Asia Australia Europe North South
America America
n= 5725 1349 1000 135 271 487 316

R2 0.9700* 0.9853* 0.9690* 0.9527* 0.9505* 0.9645* 0.9213*

EZ
regression
coefficient 0.2734* 0.1762* 0.3079* -0.0817 -0.0671* -0.1102* 0.1189*

F-ratio 495.307 453.412 351.814 43.533 148.798 203.791 131.610
* indicates p-value <0.001









Table 3-4. Multiple regression results with standardized languages. Variables include
elevation zone (EZ) and square root of elevation zone (SQRT_EZ)

Global Africa Asia Australia Europe North South
America America
n= 5725 1349 1000 135 271 487 316

Truncated
100- 400- 100- 100- 100-
range 5000 35-5000 1000 -35003500 5000
(meters)
%of
languages 99.49% 85.99% 99.49% 99.41% 99.3% 100% 99.67%
after
truncating
R2 0.5363* 0.7870* 0.5360* ---------- 0.8016* --------- 0.5350*

EZ
regression -0.0086* -0.0018* -0.0086* --------- 0.0036* --------- -0.0138*
coefficient
SQRT_EZ
regression 0.9076* 0.1447* 0.9076* --------- -0.0008 ---------- 1.3218*
coefficient
F-ratio 27.152 53.852 27.152 ---------- 137.404 --------- 27.036
indicates p-value <0.001

Australian and North American standardized language data had no variance in
association with elevation and the models would not run.











1400


1200

1000






z 400

200
2- 600 -


0 500000 1000000 1500000 2000000 2500000 3000000
Language range-size (km 2)



Figure 3-9. Language range size distribution is extremely positively skewed. Of the
5725 language range sizes included here, 1953 languages are not shown in the tail of
this distribution.


3000


2500


2000

M
= 1500
C

o 1000


500



2 3 4 5 6 7 8 9 10
Log geographic range-size (km 2)



Figure 3-10. Language range size frequency distribution for log10 transformed global
geographic range sizes











50 Mean Language Area/Range 2500 o
F-

E -- Standard deviation of Mean Language 0
S40 Area/Range | 2000
o # of Languages at Each 100 m Contour / |
o 3Band 0 S
S30 1500
"U 25 I
r 0
20 000 3




0
15 I

1 55000
coo

0 0 0
100 600 1100 1600 2100 2600 3100 3600 4100 4600 5100 5600 6100 -
Elevation zones (100 meter contours)


Figure 3-11. Rapoport's Rule of un-standardized language richness along the global
elevational gradient. This figure demonstrates familiar steady decline in language
richness, and increasing area/range size of languages with increasing elevation, as well
as the increase in variability of area/range size with increasing elevation.




S4.50E-03 9000
CD
0 M
E 4.00E-03 8000
o
S3.50E-03 7000 00
E 3.00E-03 6000

2.50E-03 5000
.(0
a 2.00E-03 4000 a
1.50E-03 3000 *
0 1.00E-03 2000 o
1E-03 # of langas per km of contour 2000
0 5.00E-04 1000 o
o Mean language area (10,000 km2) ;-
Z 0.00E+00 .. 0 3
100 600 1100 1600 2100 2600 3100 3600 4100 4600
Elevation zones (100 meter contour)


Figure 3-12. Rapoport's Rule of standardized language diversity along the global
elevational gradient. Standardized languages demonstrate the highest richness uni-
modal hump at intermediate elevations. Mean language area (range size) increases
consistently with increasing elevations.










0.035 30000


0.03 25000 K
CD

o 0.025
S20000 ,

0 0.02 (
E cD
15000 f
S0.015 Language richness per km of contour
0-0.015 D i
g --Mean language area (10,000 km2) -
10000 D
0.01 *

0.005 5000 g
z 3
0 0 .
-100 400 900 1400 1900 2400 2900 3400 3900
Elevation zone (100 meter contour)


Figure 3-13. Language richness (per km of contour) and mean language area (range
size) (10,000 km2) along the African elevational gradient.



0.016 90000

2 0.014 80000 |
0 0
o 70000
4- 0.012 -
E 60000
E 0.01
L. CD
S- language richness per km of contour 50000
0.008
W mean language area (10,000 km2) 40000 D
m: 0.006 N

S0.004 -
3 20000 a
o o
6 0.002 D10000
Z
3
0 0. 0
-100 400 900 1400 1900 2400 2900 3400 3900 4400 4900 5400 5900
Elevation zone (100 m contour)


Figure 3-14. Language richness (per km of contour) and mean language
size) (10,000 km2) along the Asian elevational gradient.


area (range


0.035


30000










0.012 6000


S0.01 5000 D
o
o

o5 0.008 4000 0
E

6 0.006 3000


S0.004 2000
C
co o
o o
S0.002 1000 o
z Language richness per km of contour ;
3
Mean language area (10,000 km2)
0 0
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

Elevation zone (100 m contour)

Figure 3-15. Language richness (per km of contour) and mean language area (range
size) (10,000 km2) along the South American elevational gradient.



0.004 9000

0.0035 8000

S7000
0.003 70
0
6000 I
o 0.0025 CD
0 55000
E (0
E 0.002 -C
4000 s.
.. N
U 0.0015 CD
S3000 0
S0.001 -
a 2000 o
CO Language richness per km of contour o
S0.0005 Mean language area (10,000 km2) 1000 3
o NJ
z 0.. 0

0 200 400 600 800 1000
Elevation zone (100 meter contour)


Figure 3-16. Language richness (per km of contour) and mean
size) (10,000 km2) along the Australian elevational gradient.


language area (range


0.012


6000











0.1 35000

0.09
S- 30000 M
E 0.08 -
O-
0 0.07 25000 |
(0
E CD
S0.06 Language richness per km of contour 20000 I

S0.05 Mean language area (10,000 km2) (D
Lcn
S0.04 15000 N
0.04 CD

c 0.03 10000 P
-- DO
o
0.02 o
5000 3
0.01

0 0
-300 200 700 1200 1700 2200 2700 3200 3700 4200
Elevation zone (100 meter contour)


Figure 3-17. Language richness (per km of contour) and mean language area (range
size) (10,000 km2) along the European elevational gradient.

0.0035 4500

4000
0.003 4000
(CD
8 3500 |
c 0.0025
0 3000 ,
4--
0
E 0.002 2500 CD

a 0.0015 2000 'C
) cn.
n 1500 N
UC0.001
S0.001 Language richness per km of contour
1000
Mean language area (10,000 km2) o

6O
5 0.0005 o500

z 3
0 0 VO
0 500 1000 1500 2000 2500 3000
Elevation zone (100 m contour)


Figure 3-18. Language
size) (10,000 km2) along


richness (per km of contour) and mean language area (range
the North American elevational gradient.










7000 6666

6000

5000

4000 U language richness

3 1 languages per 100,000km2 1000
3000
tw 12160
2000 1482.82

0 1000 527.04


Non-Mountain Mountain
Global Land Area as Classified by Stepp et al. 2005


Figure 3-19. Global language richness in mountain vs. non-mountain areas (Stepp et
al. 2005 criteria).









CHAPTER 4
DISCUSSION



As Lomolino notes (2001, pp. 3), "Spatial variation in natural resources may have

been a key impetus for dispersal, migration, and colonization of early humans across

the continents and to distant archipelagoes" (Howells, 1973; Clark, 1992; Flannery,

1994; Gamble, 1994). At least since the time of Linneaus, but probably since Aristotle,

it has been understood that mountain slopes possess a compressed and orderly

succession of climate, vegetation zones, and animals which mimic at a local scale the

broader latitudinal gradient in diversity (Linneaus, 1743). Particularly as humans have a

long evolutionary history coevolving with the species on which we depend along these

resource gradients, it should not be too surprising that the human species may also

align its distribution to some of these long established biogeographical principles. The

intraspecific variation in human cultural/language distributions may or may not be

comparable to inter-species variation in biodiversity distributions traditionally assessed

in relation to environmental gradients. The ambiguity in the hierarchical classifications

of species has many parallels in the classifications of language groups, as the structure

of how groups are related is heavily debated within the fields of both biology and

linguistics. The human species is clearly unique, but it is at least worth considering our

own intraspecific cultural variability in the context of these long observed biogeographic

patterns.

Consistent with established patterns of numerous taxa along elevational gradients

throughout the globe (Rahbek, 1995), global languages demonstrate a steady decline in

richness with higher elevation (Figure 3-1). Much of this pattern demonstrated by the









results from question one may be explained through the species-area relationship

(Gleason, 1922; MacArthur and Wilson, 1967); due to the reduction in land surface area

while ascending the elevation gradient, this monotonic decline in language richness was

expected. Without accounting for this species-area relationship, language richness

does decrease monotonically from sea-level to the highest mountain-tops, akin to

geographic variations in species distributions (Whittaker, 1960, 1977; Yoda, 1967;

Kikkawa and Williams, 1971; Terborgh, 1977; Heaney, 1991; Daniels, 1992;

Sfenthouraskis, 1992; Fernandes and Lara, 1993; Patterson et al., 1996, 1998).

When data were standardized to account for sampling intensity, language density

like species density was greatest at intermediate elevations (Whittaker, 1960, 1977;

Whittaker and Niering, 1975; Brown, 1988; Rosenzweig, 1992, 1995; Rahbek, 1995,

1997; Fleishman et al., 1998; Heaney, 2001; Rickart, 2001; Sanders, 2002; Sanders et

al., 2003; Bhattarai et al., 2004; Lee et al., 2004) and demonstrated a familiar uni-modal

hump shaped density distribution. Language richness increases gradually from sea

level, and ascending more rapidly towards the foothills or mid-elevations of mountains,

and then declining again as elevations approach mountains summits (Figure 3-2 to 3-9).

The counter-effects through opposite directional patterns of biotic factors, such as

isolation dependent immigration rates and heightened endemicity, as well as opposing

abiotic interactions of climate along this underlying environmental resource gradient

may contribute to the hump in language richness at intermediate elevations

(Rosenzweig, 1995, 1997; Lomolino, 2001; Lomolino et al., 2006). Surely there are

numerous other environmental and socio-political variables which contributed to the

distribution of global languages; but as hypothesized, there is a distinct elevational









gradient in language richness that resembles trends in the latitudinal gradient of

language richness (Stepp et al., 2004; Sutherland, 2003; Mace and Pagel, 1995) as well

as fundamental biogeographical principles of global species distributions (Humboldt,

1849; Darwin, 1859; Wallace, 1876; Merriam, 1890; Whittaker, 1960; Brown, 1978).

Overall, sub-continental regional-scale analyses revealed results similar to

predicted global scale patterns, and demonstrated that trends in language diversity

distributions along the elevational gradient may in fact be a global phenomenon at the

regional scale as well. Africa, Asia, and South America all demonstrated negative

correlations of regional language diversity with increasing elevation. However when

data were standardized by area, these correlations were neutral (Table 3-1); probably

linear correlations represented by the Spearman's statistics were balanced by the uni-

modal 'hump' in language richness which occurred at intermediate elevations (Figures

3-3 to 3-5). As Africa, Asia and South America contain 75% of languages being

analyzed on just over half the earth's land surface area, it is not surprising that these

dominant sub-continental regions reflect the global pattern. However the non-

conformity in the regional differences of the remaining 25% of the languages analyzed

from over 40% of the earth's land surface area may be more reflective of the non-

biogeographic driving mechanisms of language distributions. In contrast to the patterns

of predicted conformity seen thus far, the regional diversity distributions in Australia,

Europe and North America, did not follow predicted patterns posed by the elevational

gradient hypothesis. Interestingly, two regions with recent histories of colonization,

Australia and North America, as well as the region of their colonizers, Europe, did not

follow predicted patterns based on solely biogeographic principles. Most continents









have endured their own unique history of brutality and colonization for millennia

(Krauss, 1992), which is not unique to just Australia, Europe and North America.

Nevertheless, these three continents demonstrate distinctly different patterns of extant

language distributions relative to elevation gradients in the rest of the world. When data

were standardized by area, Australian, European, and North American language

distribution patterns show an unexpected positive correlation (Table 3-1) of increasing

language diversity richness with increasing elevation (Figures 3-6 to 3-8). The regional

variability in distinct biogeographic patterns may be explained through the complex

evolutionary and socio-economic histories which distinguish us as humans. Further

analysis of these patterns from a historical perspective would provide the additional

layer of explanatory detail behind the sub-continental regional variability.

As these regional results are incongruent with the global pattern, it is likely that

these variable patterns represent more than general biogeographic drivers of biotic-

abiotic interactions, but reflect the socio-political-economic histories of human cultural

historical processes or driving mechanisms of extant post-colonial language

distributions. These data utilized in this investigation capture extant languages known

only since the beginning of Ethnologue's efforts to record language data in the 1950's.

It is unfortunate, but likely that these data are not complete, and that knowledge of

native language locations and ranges may not represent the full history of diversification

processes. I expect the incongruous regional patterns between Australia, Europe and

North America, and the rest of the world, to be a combination of both compromised data

based on lost linguistic history in these regions, as well as the influence of non-









environmental drivers like colonization which affected the present-day North American

and Australian language distributions.

The parallels of ecological and cultural diversity distributions along the latitudinal

and elevational gradients are further emphasized by the results from my second

question which support 'Rapoport's rule' in languages along the global elevational

gradient (Figure 3-11 and 3-12). The global language range size distribution is highly

skewed (Figure 3-9), however generally log-normal (Figure 3-10), just as are range size

distributions in numerous other taxa (Gaston, 1996). Using the contour method to

assess the pattern of language distribution patterns, the broad scale global results

support those from the first analysis. At the global scale, language richness consistently

decreases with increasing elevation, or when contours are standardized by area the

expected uni-modal hump of greatest diversity at intermediate elevations parallels the

results from the previous analysis. The mean range size of languages intersecting the

100 m elevation contours increases with increasing elevations (Figure 3-11 & 3-12),

demonstrating a negative relationship between language richness and language range

size and that at the global scale languages generally do follow 'Rapoport's Rule'.

Biological species distributions generally follow 'Rapoport's rule' at the broad global

scale (Rapoport, 1989; Gaston, 2005) but demonstrate much greater variability at finer

regional and local scales which has driven much of the controversy as to the

universality of the rule (Ruggiero and Werenkraut, 2007).

As expected, regional analyses of 'Rapoport's rule' in relation to language richness

and range size reveal much more variable results and do not adhere to any general

global pattern (Figures 3-13 to 3-18). In the question one results, the regions with the









greatest proportion of the world's languages demonstrated the most parallel

biogeographic pattern to the global rule. However the results of 'Rapoport's Rule' which

utilized the Mace and Pagel (1995) contour approach, are much more unpredictable at

the regional scale; congruent with the non-conformity of biological species distributions

to 'Rapoport's rule' at regional and local scales.

The results from the third question demonstrated that global 'mountain regions' do

have higher language richness than 'non-mountain regions', as predicted by Stepp et al.

2005 from their analyses of language richness on New Guinea, but only when data

were standardized by land area. As the species-area relationship is well understood

(Gleason, 1922), it would be inappropriate to consider these results without accounting

for area, as it has been in all previous work of language diversity distributions in relation

to mountains. For only 10.33 % of the earth's land surface-area is defined as 'mountain

region' by the Stepp et al., (2005) criteria. If sampling intensity or land surface-area

covered by mountain regions was ignored, the results then reflected an opposite pattern

to that predicted by Stepp et al.'s (2005) 'topography hypothesis' (Figure 3-19). Currie

and Mace (2009), used these non-standardized local predictions in selecting important

explanatory variables of 'Rapoport's rule' in global language range distributions. Finally,

these results emphasize the critical importance of standardizing data by sampling area

in analyses. This fundamental principle of the species-area relationship (MacArthur and

Wilson, 1967; Gleason, 1922) has commonly been ignored in most previous

publications of language distribution patterns, and is one methodological concern that is

likely responsible for much of the inconsistency in the literature.









Globally, mountains do hold a greater number of languages per unit area than

lowland areas. The underlying mechanisms of this heightened and concentrated

language diversity probably depend on the compression of numerous life zones in the

underlying resource gradient utilized by human cultures along a relatively small

geographic area (Holdridge, 1967; Lomolino, 2001; Korner, 2004). Globally mountain

regions known for high endemism and biodiversity must also be acknowledged as

important hotspots for language and cultural diversity. Particularly if mountains are both

isolated and large enough to allow population persistence and divergence over

evolutionary time, mountain environments may promote heightened language

diversification as well as the parallel increased species diversification through co-

evolution.

Coevolution of humans and our natural resources: The human species is

clearly unique in our ability to overcome geographic constraints unlike most other

species; however the intraspecific variation of human language diversity does in fact

follow the elevational gradient pattern. As was also previously demonstrated along the

latitudinal gradient (Mace and Pagel, 1995; Cashdan, 2001; Moore et al., 2002; Manne,

2003; Sutherland, 2003; Stepp et al., 2004), language diversity distribution patterns do

mimic biogeographical patterns shown in numerous biological taxa. Patterns revealed

in these analyses indicate that language diversification patterns may have been driven,

at least in part, by similar evolutionary mechanisms which promote species

diversification and distribution patterns. As was eloquently stated by Theodosius

Dobzhansky (1937) "Nothing in biology makes sense except in the light of evolution".









Trends in language diversity along latitudinal and elevational gradients likely

represent human evolutionary adaptation to the myriad of interacting underlying

environmental variables and overarching climatic variables captured in resource

gradients (Harmon, 1996; Smith, 2001). This research demonstrates that

biogeographic factors which characterize environmental gradients may play an

important role in explaining these patterns. I expect evolutionary adaptations to the

biophysical environment, in addition to the interactions of social, cultural, economic and

political histories within that environmental context to be the key in finding these

explanations at a regional scale. As shown in Australia, North America and Europe,

there are clearly other important non-biophysical geographic variables influencing the

distribution patterns of languages at the regional scale (Crosby, 1986). The regional

socio-economic historical variables which were not included in this analysis will likely fill

the gap in understanding the finer scale variability in distribution patterns.

Further analyses need to expand the explanatory models of language distributions

at multiple scales to incorporate the myriad of social and environmental variables, but

should still be founded in biogeographic and evolutionary principles, and inspired by the

deep personal local histories which have promoted such exceptional diversity in our

own human species. As we continue to seek general principles in ecology to explain

patterns of species distributions, we should not fail to consider Homo sapiens as a

species dependent on resource gradients and vulnerable to driving mechanisms of

diversification in our own intraspecific distributions.









APPENDIX A
ANTHROPOGENIC LAND-USE / LAND-COVER CHANGE AND THE GLOBAL
DISTRIBUTION OF THREATENED AND ENDANGERED LANGUAGES


This additional appendix has been included because it was a worthwhile

graduate-level class research project that investigated a potential causal factor behind

the correlations of global biological and cultural diversity distributions. However there

were no significant results indicating global anthropogenic land-use/land-cover change

(LULCC) from 2001-2007 had any influence on the distributions of threatened and

endangered languages. There is more research to be done on this interesting topic;

however there are fundamental issues in assessing a long-term process of language

extinction within the context of just a 6-year window of LULCC in a general analysis at

the global scale. This appendix should be considered a seed for future research

directions. Without incorporating a much broader historical context or refining the

analysis to very specific and fine-scale spatial extent where longer temporal scales of

data are available, this analysis has little meaning. Therefore, it was kept separate from

the thesis body.









Abstract

There have been numerous studies identifying Land Use Landcover Change

(LULCC) as an influential driver affecting biodiversity richness and its distribution

(Diggelen et al., 2005). Correlations between distribution patterns of biological and

cultural diversity have also been recently highlighted (Maffi, 2005; Sutherland 2003).

However little is known about the influence of LULCC to cultural diversity distributions

and persistence. Using MODIS IGBP Classified Global Mosaic Data from 2001 and

2007, Landcover Change Trajectory maps were used to quantify rates and types of

landcover change within 100 km of 6659 extant languages, classified into critical,

endangered and vulnerable threatened categories (with fewer than 500, 1000, and

10,000 speakers respectively). Change in any landcover classification to cropland,

urban and built, and cropland/vegetation mosaic categories, where merged in analysis

of anthropogenic disturbance. Results indicate over twice the mean anthropogenic

landcover change to cropland, urban and built, and cropland/vegetation mosaic

classifications occurred in non-threatened language buffers, than in threatened

language buffers. Buffer zones of extinct language locations had magnitude of

landcover change significantly different from endangered languages, however not

significantly different from non-threatened language buffers. This result indicates that

historical processes driving language extinction patterns may differ from current threats.

Potential correlations between magnitude of LULCC and threatened status of languages

may guide inferential understanding of causal mechanisms influencing the persistence

and health of earth's cultural heritage.









Introduction

Numerous biogeographical principles have been studied in relation to the

distribution of biodiversity on a global scale (MacArthur and Wilson, 1967; MacArthur,

1972; Hubbell, 2001; Willig et al., 2003; Hillebrand, 2004). Although significant

correlations in the global distribution patterns of biological and cultural diversity (Mace

and Pagel, 1995; Nettle, 1999; Maffi, 2001b; Sutherland, 2003; Stepp et al., 2004; Maffi,

2005; Curry and Mace, 2009) have been noted in the literature over the past two

decades, still little is known of 'bio-geography' of language diversity, and even less of

the geographic relationship of threatened languages and endangered species.

Mace and Pagel (1995), recognize latitudinal patterns of language diversity

decreasing and language range increasing away from the equator towards the poles.

Stepp et al. (2004), demonstrate the convincing relationship in the diversity of

languages and plants worldwide (R2 value of 0.9675). Nettle (1998), argues that the

distribution patterns of language diversity and home range are mostly driven by

'ecological risk', where the lesser the climate variability in an area, the more insular a

culture can remain, restricting the spread and overall range size of a particular

language. Currie and Mace (2009), recently found little bio-physical evidence for

language diversification and attribute global distribution patterns to 'political complexity'

of the geographic region.

There are well known global threats to biodiversity with current extinction rates

well above historical levels (Chapin, 1999). Although less studied, numerous human

languages have also become extinct, while over 50% are considered threatened with

extinction (Krause, 1992; Crystal, 2000). Sutherland (2003) demonstrated that by

applying internationally agreed criteria for classifying species extinction risk, that









languages are more threatened than birds or mammals. Rare languages are more

likely to show evidence of decline than commoner ones. Sutherland showed that areas

with high language diversity also have high bird and mammal diversity and all three

show similar relationships to area, latitude, area of forest and for languages and birds,

maximum altitude. However the time of settlement has had little effect on current

distribution of language diversity. Although similar factors explain the diversity of

languages and biodiversity, the factors explaining the extinction risk for birds and

mammals (high altitude, high human densities and insularity) do not explain the

distributions of endangered languages.

Linguists who had taken up the study of indigenous minority languages have

expressed concern for the future prospects of these threatened languages and their

speakers in light of ever increasing social, political, and economic change. It was only

during the 1990s that this concern came to a head with a rapid and exponential rise of

interest in the issue (Dorian, 1989; Hale et al., 1992; Dixon, 1997; Grenoble and

Whaley, 1998; Crystal, 2000). To a large extent, this rise of interest was due to the

accumulation of a growing mass of data not only on the grammatical and lexical

features of the world's languages but also on the state of vitality of languages (Grimes,

2005). Throughout the world, the indigenous and minority languages of the world have

been disappearing at an alarming and accelerating rate, replaced by a small number of

ever-expanding, majority languages (referred to as 'killer' languages in Skutnabb-

Kangas, 2000). 'Clarion calls' about this language endangerment crisis have been

issued by linguists projected to threaten the survival of 50-90% of the world's

languages in the next 100 years (Krauss, 1992). While linguists and practitioners









worldwide rose to the call to increase awareness and resource availability to protect the

world's endangered languages. At the same time, other reasons for concern emerged

during the 1990s centered on what might be lost to the speakers themselves.

Links were suggested between linguistic (and cultural) diversity and biodiversity as

distinct but mutually supporting manifestations of diversity of life on earth. (Harmon,

1996; Krauss, 1996; Muhlhausler; 1996; Maffi et al., 1999; Maffi, 2001a). Crystal (2000)

argues that links between biological and linguistic ecologies are not just metaphorical

but mutually related through human knowledge, use and management of the

environment by diversity of cultures. Maffi (2005) calls for this relationship to be brought

out and studied in depth, to give substance to the international effort for the protection of

linguistic human rights. Analogous with the efforts to understand and protect

biodiversity and the state of the world's ecosystems, monitoring global trends in

linguistic diversity would benefit the protection. A goal of assessing and monitoring the

state of biological and linguistic diversity in an integrated fashion could concentrate

efforts efficiently and provide a new perspective and global diversity patterns and

persistence.

Despite the recent recognition of the threat to global languages as well as the

relationship of cultural and biological diversification patterns have drawn in the literature,

very few researchers have investigated spatial patterns in endangerment or extinction of

language and biological entities (Sutherland, 2003). Sutherland's (2003) investigation

analyzed extinction risk to languages, birds and mammals within political country

boundaries, when it is well known that biophysical and often cultural values and

linguistic expressions are not confined by these political boundaries. Hence, this study









investigated the threat of extinction to languages within local context of landcover

change, shown to clearly influence threats to biodiversity. Although recent studies have

identified Land Cover Change (LCC) as an influential driver affecting biodiversity

richness and its distribution (Diggelen et al., 2005), little is known about the influence of

LCC to cultural diversity distributions and persistence.

Using MODIS IGBP Classified Global Mosaic Data from 2001 and 2007,

Landcover Change Trajectory maps were used to quantify rates and types of landcover

change within 100 km of 6337 extant languages, classified into critical, endangered and

vulnerable threatened status categories (with fewer than 500, 1000, and 10,000

speakers respectively). Potential correlations between magnitude of LULCC and

threatened status of languages may guide inferential understanding of causal

mechanisms influencing the persistence and health of earth's cultural heritage.

Endangerment and extinction events may be historically related to environmental

changes, fragmentation, loss of their habitat, whether they be due to 'anthropogenic'

disturbance due to development or 'natural' disturbance due to 'climate change'. As

Sutherland (2003) states, the threatened status of a language is highly related to its

population size, as is evidence from biological diversity patterns. If language diversity

patterns adhere to biological diversity patterns; could the threatened status of a

language have a relationship with magnitude of landcover change across the

landscape? Do spatial patterns of threatened or extinct languages adhere to spatial

patterns of threatened species? Based on this relationship or spatial correlation of the

distribution of biological and cultural diversity, one cannot assume that similar

processes underlie these correlated distributions.









Methods

Language data were obtained from Grimes (2005), Ethnologue 17th edition. Of the

7719 classified languages in the database only 6337 extant languages where used in

the analysis due to data limitations. Of these 6337 languages, over half of them are

considered Vulnerable with fewer than 10,000 speakers, about a quarter of them are

considered Endangered with fewer than 1000 speakers, and about a fifth of them are

considered Critically Endangered with fewer than 500 living speakers. Also included in

the analysis were 270 extinct languages known to have disappeared since 1600 A.D.

(Figure A-3).

Landcover Change information was derived from a Change Analysis of Modis

IGBP (International Global Bio-sphere Project) Classified Global Mosaic from 2001 and

2007 (MCD12C1 NASA) 1 km spatial resolution (Figure A-4) conducted in IDRISI

(Eastman 2009). The IGBP has 16 landcover classifications including Water,

Evergreen Needleleaf Forest, Evergreen Broadleaf Forest, Deciduous Needleleaf

Forest, Deciduous Broadleaf Forest, Mixed Forest, Closed Shrubland, Open Shrubland,

Woody Savannah, Savannah, Grassland, Cropland, Urban and Built, Cropland/Natural

Vegetation Mosaic, Snow and Ice and Barren. Cropland, Urban and Built, and

Cropland/Natural Vegetation Mosaic LandUse categories were merged to investigate

overall Anthropogenic Landcover Change within 100 km buffer of the language centroid.

A 100-km buffer was selected based on Maffi's (2001b) publication noting that language

diversification increases outside a 100-km buffer of development.

Landcover Change was quantified using a trajectory analysis, where total and

mean change in cells' landcover classifications between 2001 and 2007 images was

determined. Of the total Landcover Change shown in the trajectory analysis the three









'anthropogenic' land use classifications (Cropland, Urban and Built, and

Cropland/Natural Vegetation Mosaic) were isolated and the total and mean change to

anthropogenic land use categories was determined within 100km buffer of languages

(Figure A-5). Overlaying global spatial data of extinct, endangered and extant language

distributions with the model's output, spatial analysis techniques (Rosenzweig, 1995)

were utilized to investigate a possible correlation between LULCC to the status and

survival of languages worldwide.

Using NCSS (Hinze, 2007), mean, overall and anthropogenic LCLUC, was

compared using a regression analysis of magnitude of LULCC and size of population of

a language's remaining speakers. Then each threatened status group was isolated and

statistics gathered for each group. Overall change and 'anthropogenic' LULCC were

compared for each status group with statistics of change in non-threatened languages.

Overall and strictly anthropogenic patterns of LULCC where analyzed using a Chi-

square test.

Results

There was huge variability in the extent of landcover change within all status

groups of languages, which likely contributes to no overall pattern in change and

number of remaining speakers within a language. The regression analysis of total

population of speakers of all languages with the number of overall landcover change

cells was not at all related (R2 = 0.0009, p > 0.05). Nor were there any correlations in

the regression analysis of total population of speakers of all languages with the number

of strictly anthropogenic landcover changes (R2 = 0.015, p > 0.05).









Chi-squared analysis of change detection demonstrates no difference in the

overall pattern of landcover change, or anthropogenic landcover change classifications

between threatened and non-threatened languages (all change: X2 = 0.03, p > 0.05,

anthropogenic change: X2 = 0.015, p > 0.05).

However, comparisons of LULCC that has occurred between 2001 and 2007

within 100 km buffer areas of languages show significantly greater overall as well as

anthropogenic LULCC in non-threatened language buffers than in all status groups of

threatened languages (Table A-1). Shown in Figure A-7, stable cells with no landcover

change dominate the landscape. Of the fraction of change cells demonstrating

landcover change, there is less anthropogenic change (to cropland, urban and built, and

cropland/vegetation mosaic) than overall change in classifications of non-anthropogenic

landcover change.

All threatened classifications (Critical, Endangered, and Vulnerable) show this

significant pattern of less than half the mean change of non-threatened languages per

100 km language buffer (p > 0.0001). Critical, endangered, and vulnerable languages

have less overall change and less anthropogenic change than non-threatened

languages.

Comparisons of landcover change within 100 km buffer of extinct language

locations show perhaps the most interesting result of this study. Areas of extinct

languages have greater change than areas of critical and endangered languages

(critical: mean- 21.74, p < 0.001, endangered: mean- 23.98, p < 0.001). However there

are no differences in the extent of change in extinct language areas and non-threatened









extant languages, demonstrating almost twice the amplitude of change in non-

threatened and extinct language locations than in threatened language locations.

Discussion

As increasing attention is drawn to the spatial correlations of linguistic and

biological diversity worldwide, still there is little consensus on the processes which

underlie these patterns. This investigation demonstrates significantly greater landcover

change as well as anthropogenic landcover change within 100 km of extant non-

threatened languages (10,000 < speakers) than within 100 km buffer of critically

endangered (500 > speakers), endangered (1,000 < speakers), and vulnerable (10,000

> speakers) languages. Although the distributions of threatened languages and

threatened species may be spatially correlated, LULCC may not influence threatened

language in the same way as threatened species' distribution. Concurrent with

Sutherland's (2003) finding that threatened languages are not related to human density

variable unlike threatened birds and mammals, this study demonstrates anthropogenic

disturbance may not be a significant driver in language endangerment. Threats of

extinction may be more relative to overall diversity as proposed by McArthur and Wilson

(1963) in relation to language as well as biological diversity.

Probably the most interesting finding in this research, that extinct languages as

well as non-threatened extant languages demonstrated over twice the overall and

anthropogenic LULCC than threatened extant languages which reflects temporal bias

within this study. Historical language extinction events may have been driven by

different underlying processes than those driving current threats of language extinction.

However, this study only analyzed the LULCC since 2001, and probably does not

capture the long term change that has lead to current and observable distribution









patterns. In order to fully assess the relationship of landcover change as a driver in

language extinction or threatened status, it is imperative that future analyses utilize

historical records to expand the temporal scale of analysis.

Regardless of spatial correlations in cultural and biological diversity, there are

clearly differing processes underlying their overall distributions. Regional variability in

both environmental variables, as well as in the complex history of interactions between

people, species and resources, surely reflect much inner variability regardless of the

scale of the analysis. This global analysis of threatened languages and anthropogenic

landcover change may allude to broad scale patterns in a globalized world, however

there are certainly many different individual stories to be told in the history of language

and culture. Much variability in needed future analysis at a more regional scale is to be

expected, and it will be important to include more socio-economic historical and bio-

physical variables to understand the full picture of factors influencing language

diversification and threats of extinction. In future analyses LULCC-potential maps to

investigate areas and languages that will most likely to endure future change and use

will be useful. These current trajectories could calibrate historical models to better

understand the influence of LULCC in relation to language diversification and extinction.





































Figure A-1. Flowchart of analysis procedures














7000


1000 I


Extinct

Extinct after 1600AD


<500 speakers

Critical


< 1,000 speakers < 10,000 speakers All Extant

Endangered Vulnerable 6337 Analyzed of 7750


Figure A-2. Cumulative frequency of global languages (Krause's endangered language
levels)














































Figure A-3. MODIS Global IGBP landcover 2001 mosaic































69


Modis Global IGBP Land Cover (2001 & 2007)

15BOVVW 1230'W 110'W 90'0'W 7 'O''W 5Or'W 3vO'W 10''W 10 0'E Mc3'E 5rTO'E 7-M'0'E 9m0O'E 110W'E 130'O"E 15OUTE 170:OVE


Am N 70O0'



0 0MO'N

















Modis Global Mosaic IGBP Land Cover 2001(1 km spatial resolution)
Created by Kelly Gleason
Created by Kelly Gleason













Global Extent of Endangered Languages and
Anthropogenic LandCover Change 2001-2007















0 375 750 1,500 ,250 3,000




Red 100km buffer of endangered languages wl <500 speakers drawn on...
Areas of LCLUC (Classified (IGBP) Landcover Change)
To Cropland (Orange)
To Urban and Built (Red)
To CroplandNegetation (Green) N
Modis Mosaic Compiled for 2001 and 2007 Images A
IGBP Classification by USGS Created by Kelly Gleason


Figure A-4. Global extent of critically endangered languages and anthropogenic LULCC
2001-2007










300000

250000

200000

150000

100000

50000

0
*# Cells
4 ..


TTI


Figure A-5. Total global landcover change from 2001-2007


Table A-1. Comparison of mean anthropogenic landcover change within threatened
language categories



Mean # of change cells P-value T-stat


< 500 speakers 21.75* < 0.001 -27.0112

< 1,000 speakers 23.98* < 0.001 -18.6867

< 10,000 speakers 36.28* < 0.001 -13.9802
All extant languages 104.77 > 0.1 ----------
* indicates significant change (p < 0.05)


I











120


S100 -
CO)

> 80
0 5001ess Change
-o
500less Stable
60
6o 500more Change

0) 500more Stable
o 40
a
0-

20
4 -
0

Z 0

Cropland Urban Crop/Veg Mosaic


Figure A-7. Mean number of change and stable anthropogenic landcover cells (500 or
less, n=1302 & 500 or more, n=5702)

4.5

4

3.5

3

2.5 500more Change

2
U 500less Change
1.5



0.5


Cropland Urban Crop/Veg Mosaic


Figure A-8. Mean change in anthropogenic landcover within 100 km of critical
languages












100.00

80.00

60.00

40.00

20.00

0.00


> 500
speaker
n=4965


1 /
All Extant
s (n=6539)
)


Figure A-9. Mean increase in anthropogenic landcover by threatened status


Table A-2. T-test results comparing number of cells of anthropogenic landcover change
in extinct versus extant and threatened status languages' 100 km buffer
around centroid location


Language status Mean change P-value T-stat

Extinct (n=270) 39.27

< 500 Speakers (n=1574) 21.75* < 0.001 6.4651

< 1,000 speakers (n=1819) 23.98* < 0.001 4.1337

< 10,000 speakers (n=3718) 36.28 > 0.1 0.7846

> 500 speakers (n=4965) 98.43 > 0.1 0.6075

All extant (n=6539) 104.77* < 0.05 1.8150
* indicates significant change (p < 0.05)


120.00 -i


< 1,000 < 10,000
speakers speakers s
(n=1819) (n=3718) (i


Extinct
(n=270)


<500
Speakers
(n=1574)


/









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BIOGRAPHICAL SKETCH

Kelly Erika Gleason grew up outside Seattle, in Mercer Island, Washington. She

attended college at The Evergreen State College, Olympia, Washington, and graduated

with full honors with a Bachelor of Science in plant ecology in the spring of 2002. Kelly

immediately began working as a canopy field ecologist for the United States Forest

Service and Bureau of Land Management. She continued her work as a field ecologist

taking part in numerous research projects for universities and government agencies for

the next 6 years besides a 2-year hiatus as a Peace Corps Volunteer in Paraguay.

Kelly returned to graduate school, in the fall of 2008, at the University of Florida in the

School of Natural Resources and Environment to focus on geography and GIS

applications. She received her Master of Science from the University of Florida in the

summer of 2010. Kelly will continue on for her doctorate degree at Oregon State

University's Department of Geography to focus on alpine forest-snow-water dynamics

through remote sensing and mountain hydro-morphological research.





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