Modern Language Range Mapping for the Study of Language Diversity

Hannah J. Haynie1,2 Michael C. Gavin2

1University of Colorado

2Colorado State University

Corresponding Author:

Hannah J. Haynie email: [email protected] tel: +1-303-735-8219

August 29, 2019

Abstract

Research regarding the ecology and evolution of languages has advanced rapidly in recent years, increasing demand for digital spatial location data for individual languages. However, language mapping and geographic information system (GIS) data creation has not kept pace with this demand. To date, language range information is typically contained in small, private regional datasets, paper maps and published illustrations, or in a very small number of global digital datasets. Each of these sources has advantages and disadvantages, and though these considerations may not be apparent to all data users, they can have significant impacts on research results. Mapping of language ranges is a particularly complex problem in regions where colonial histories have had dramatic effects on language diversity, language locations, and the relative representation of colonial and indigenous perspectives. Here we consider how the creation of digital language range data can better meet the needs of researchers interested in language diversity, discuss how better standards for data quality and transparency can be implemented for digital language range maps, and present a map of North American language ranges that has resulted from the use of these procedures and standards.

Introduction

Language maps have been published for centuries in paper books and atlases (e.g. Franquelin 1684; Wenker et al. 1926; Goddard 1996; Asher & Moseley 2007). Recently, demand has grown for digital map products representing the locations and ranges of languages. For continent-scale or global-scale research, many scholars are dependent either on point representations indexed by databases like , or the polygon representations of languages in licensed products like the World Language Mapping System (Hammarström, Forkel & Haspelmath 2019; Global Mapping International (Firm) 2012). Although there has been interest among linguists in better utilizing digital tools to improve the state of language mapping for over a decade, linguist-driven attempts at improving the coverage and accessibility of language location GIS data have centered on map database projects that take advantage of the flexibility of GIS, but ultimately leave unsolved many important problems that complicate digital mapping of languages (LINGUIST List & Indiana University 2019; Dahl & Veselinova 2006). Access to high quality language maps and responsible use of spatial language data is still hindered by numerous obstacles.

Because language mapping is a multifaceted and understudied topic (Luebbering 2013), we focus specifically on the use of maps in the study of language diversity and the current problems posed by the use of language maps in this area of research. We propose that for this field, the problems of accessibility, quality, and transparency in language mapping are best addressed by the development of new standards which can be used to create well documented digital datasets that can be updated and cited.

While mapping the locations of ethnolinguistic groups is not a new practice, accessibility varies for the many language maps that have been created over time. Representations of language ranges are often found in the pages of other publications (e.g. Goddard 2005), limiting their accessibility to researchers who may not have the manpower, tools, or skills to digitize them. Digitized versions of some of these maps can be accessed online through individual researcher websites and collaborative web platforms (e.g. Wikimedia Commons 2019), however many of the regional datasets that are widely available in digital format have been separated from the citation information, temporal data, digitization history, and other details that might allow a researcher to assess whether the data is suitable for their research.

For global studies, digitizing entire atlases is prohibitively time consuming, driving scholars toward a small number of digital datasets (e.g. Global Mapping International (Firm) 2012; University of Groningen & CIA World Factbook 2015). These convenient digital datasets are not appropriate for all uses. World Language Mapping System (WLMS) documentation, for example, describes the data in its primary language area layer as, “polygons delineating the linguistic homelands of most of the language-in- country entries in the .” However, this source represents the spatial extent of many languages in terms of their contemporary ranges, not what most linguists consider to be their “homelands”. Inferences from this spatial data about historical processes that may have affected these languages and their configuration in space may therefore reflect language loss, resettlement of ethnolinguistic groups, and other recent sociohistorical dynamics, rather than the longer-term language change and diversification processes of interest to certain researchers. This is particularly problematic in regions like , where the contemporary spatial distribution of indigenous languages may reflect European settlement and subsequent history more than the processes that shaped these languages’ ranges prior to colonial contact. The magnitude of this problem is amplified by the fact that many users of this data may not be familiar enough with the homelands of various languages to identify error in this dataset with regards to consistent representation of language homelands.

Consistency and accuracy of language range representations can have consequences for many uses of language range data in studying language diversity from an ecological perspective. For example, the general global diversity patterns found in datasets with poor resolution or inaccurate boundaries may be qualitatively similar to the patterns of diversity that can be observed in detailed, accurate language range data, but using the less accurate ranges to extract climate or environmental data can introduce significant error. In other cases, missing information about the source or temporal focus of a language range map can make it difficult to assess the suitability of that spatial information for a particular research purpose.

Because language range maps have typically been produced by linguists, primarily for the purpose of illustrating individual grammars and comparative research publications, transparency has not been among the values embodied by the existing language map tradition. Maps are often drawn and published without any mention of sources referenced, acknowledgement of speaker communities whose knowledge informed those maps, or information about the context of map creation or principles and procedures followed in the process. Much of this information is not recoverable for published maps. However, the use of metadata and detailed source citation is important for tracing the information and influences that shape future map products. It can be hoped that enhancing the transparency surrounding sources will also help map users to understand the contexts and perspectives reflected in map data, including the extent to which missionary and colonial viewpoints have shaped the scholarly understanding of historical language geography.

Methods

We implement a set of standards for metadata, as well as a set of procedures for map digitization and polygon drawing, to create a maximally suitable map of North American languages for planned studies of language diversity patterns.

Because this dataset was created for the primary purpose of researching spatial patterns of language diversity, several of the representational decisions that were made in the process of its creation reflect priorities associated with specific intended uses. Languages are represented as occupying non- overlapping ranges. Polygons are an abstraction that, for language ranges, conflates various types of settlement and land use, and cannot capture the complexities of language contact, bilingualism, and shared land use. We follow the majority tradition in North American language range mapping by treating language ranges as non-overlapping. However, we believe that work to investigate the nature of language boundaries and develop a more nuanced representation of the types of boundaries that exist could improve future versions of this dataset.

The research for which this dataset is intended focuses on processes of language diversification that occurred deeper in history than the European colonization of North America. Therefore, we have chosen to represent languages in the best approximation of their locations immediately prior to European colonial incursion. In practice, this means privileging information that represents the earliest documented locations of languages. However, the date represented by individual polygons varies across the extent of the dataset, and for some languages no particularly early documentation is available. We also encountered a significant body of language range data that provides little or no explicit information about time of reference. We considered this information only when it was clear that the approximate time of reference was at or immediately before European settlement. The result of this complex temporal reference problem is that the North American language range dataset created in this project considered data representing a range of dates from the late 16th century to the early 20th century, with a bulk in the 19th century. We use the abstract “Time of European Contact” time reference as a shorthand for this spatial variability, and include references to source information for each polygon so that map users may recover more detailed information about temporal reference on a polygon-by-polygon basis.

Finally, results of research on language diversity can be influenced by missing data. In order to avoid artifacts of missing language range data in future research, to best of our ability, the mapping procedures followed in the creation of the North American language range map were designed to maximize spatial coverage. Although poorly attested languages may not be understood well enough to work out their relationships to other languages or to analyze their grammars, their existence matters to the spatial patterns of language diversity that we are interested in. Therefore, we opted to include languages mapped by prior scholars even when poor documentation has caused them to be excluded from the language database we use for reference (Hammarström, Forkel & Haspelmath 2019).

Metadata

We include metadata in the vector map data file that describes the purpose, recommended and prohibited uses of the data, descriptions of assumptions and generalizations, time range of reference, user notes, date of map creation, and map creators. A credits field is also included, which contains more detailed citation information and acknowledgment of funding sources. An additional documentation file is distributed with the map dataset that provides a lengthier description of the data, information about features and attributes, and a list of sources for each polygon in the dataset. This metadata is intended to help users evaluate whether the dataset is appropriate for their intended uses, identify further information about the languages and language range representations included in the dataset, and avoid use of the data for purposes it is incompatible with (e.g. for legal claims or political purposes).

Feature Attributes

Each polygon in the dataset contains information for three attributes: Language Name, Glottolog ID, and .

Language Name contains the name of the language, as commonly used in scholarly literature. Though this may appear to be a straightforward piece of information, languages are often known by multiple names, or by multiple different spellings of a name. Selecting a single name for use in labeling a language map requires decision making regarding which names and orthographic conventions are widely recognized and, ideally, preferred by language speaker communities. At present, not all language names preferred by communities are in common usage in the scholarly community; we welcome feedback from communities on language name preferences and plan to make periodic updates to this data accordingly.

Glottolog ID is an identifier used to standardize language reference to entities cataloged in the Glottolog database. Not only are language names sometimes applied differently by different sources, but scholars do not always agree about what constitute separate languages, as opposed to dialects or entire branches of families. To avoid introducing our own biases into this dataset, we include a language-level Glottolog identifier for nearly all language polygons. This allows clear identification of the languages associated with each map polygon, as well as a means of directing users to information about language classification and bibliographic references. The inclusion of a standardized identifier also makes it possible to match data from other resources (e.g. Kirby et al. 2016) to the languages represented in this spatial dataset. There are several conditions under which languages may not match directly to a language-level entity in Glottolog. These are resolved as follows:

1. Poorly attested languages of unknown genealogical affiliation with no identifiable corresponding Glottolog entity are retained in the dataset, with “NA” for Glottolog ID. 2. Poorly attested languages from a known family with no exact match among language-level entities in the Glottolog database but an approximate match to a higher level grouping in Glottolog are matched to the Glottolog entry for that higher level grouping (e.g. the Laguna language in source maps would be matched to Glottolog’s Eastern Keres). 3. Poorly attested languages from a known language family with a very closely related sister in Glottolog but no language-level entity in Glottolog that is an exact match are merged with the sister (e.g. Tecual is merged with Huichol). 4. Poorly attested languages from a known family with no corresponding language-level Glottolog entity and no sisters known to be especially closely related nor any immediately higher level groupings are retained, with “NA” in the Glottolog ID attribute and the appropriate family in the Language Family attribute (e.g. Nicola is kept in the map, with “NA” in the Glottolog ID field and “Athabaskan” in the Language Family field). 5. Entities included on all source maps as languages, with no finer detail available, and that correspond to a subgroup entity in Glottolog are retained and matched to the Glottolog ID of the corresponding subgroup entity. 6. Entities included on source maps as languages that correspond to a dialect entry in Glottolog are matched to the language-level parent entity in Glottolog (e.g. Mutsun and Rumsen are merged in a single polygon, with the Glottolog ID corresponding to Southern Ohlone).

The Language Family attribute includes the name of the highest level family entity to which the language can be positively identified as belonging to. This data is imported from Glottolog (Hammarström, Forkel & Haspelmath 2019), purely for ease of use in situations where genealogical affiliation is a consideration. Minimally documented languages for which no relationships to other languages or families have been positively identified all have the value “Poorly Attested” for their language family; map users must be aware of this to avoid accidental treatment of these languages as related entities, and are cautioned about this in the dataset documentation.

Boundary Drawing

The boundaries drawn in the North American language range map are based on information contained in a large set of source maps. These maps were scanned in high resolution (minimum 300 dpi), georeferenced, rectified, and vectorized by project personnel, edited to include basic metadata, and collected as separate layers in a local file geodatabase. The continent-scale final dataset includes boundaries drawn by project personnel by considering the information in all digitized language range representations for any given language. In considering the information in alternative sources, a set of ranked priorities were used to evaluate sources:

1. Priority is given to maps that can be clearly determined to represent a time period at or immediately before the time of European contact for any language. 2. Priority is given to maps created by scholars known to have been in the field, interacting directly with language speaker communities. 3. Priority is given to maps with higher spatial resolution (i.e. more detailed maps). 4. Priority is given to maps that focus on a single language, subgroup, or family over region- or continent-scale maps. 5. Priority is given to maps that were created soon after the time period they document (i.e. an early map might be given preference over a map created many decades later to represent the same time period). 6. Priority is given to maps that were georectified with minimal error during the digitization process. Where error remains after georeferencing and rectification, both the location of boundaries and the locations of other nearby landmarks are considered in drawing boundaries.

While we find good overall agreement in the locations of languages, conflicting boundary placement across sources are not uncommon. We resolved language range boundary conflicts according to the following criteria:

1. Where a single, clearly preferred source can be identified based on priority criteria, that map is used as the primary source for a language’s range in the aggregate map. 2. Where multiple sources are equally preferred, information from all preferred sources is considered in creating aggregate range boundaries for that language. 3. Where possible, aggregate boundaries drawn from multiple sources follow the generalized path of the boundary “bundle” that corresponds to all preferred sources. 4. Where boundaries do not align across preferred sources, natural landmarks are used to identify information that is consistent across sources, which is given priority in determining boundary locations relative to these landmarks. 5. Where no landmarks are identifiable in conflicting preferred sources, information from less preferred sources is also considered. 6. Where landmarks and lower priority sources are insufficient to resolve boundary conflicts, boundaries are drawn approximately midway between boundaries in all preferred sources.

The output of these procedures is a language range dataset that represents a best approximation of language locations for a maximal set of languages at approximately the time of European colonial contact. While this set of procedures is unable to erase the colonial legacy of prior language mapping projects or to perfectly correct the error in existing language range representations, it provides a single, well-documented continent-scale language range dataset with sufficient information for users to evaluate the appropriateness of the dataset as a whole for their intended uses, as well as to recover relevant details associated with each individual polygon.

North American Language Range Map

This procedure was followed using a non-exhaustive set of source maps for the languages of North America. The resulting digital language range dataset (visualized in Fig. 1) includes spatial boundaries for 449 languages of North America representing their approximate distribution at the time of European contact.

Currently, the vector dataset associated with this map is available by contacting the corresponding author. The authors are currently in the process of making it available via a web portal for non- commercial purposes with appropriate attribution. Map revision will occur periodically based on feedback solicited from scholars and communities, and the availability of additional published sources. Revisions will be released in numbered versions to prevent confusion and enable stability of data and accurate citation by users.

Eastern Canadian east2534

North Alaskan Inupiatun nort2943

North Slavey Gwich'in nort2942 gwic1235 Western Canadian Inuktitut west2618 Seward Peninsula Koyukon Inupiatun nort2945 koyu1237 Lower Tanana Han lowe1425 hann1241 Tanacross Dogrib tana1290 dogr1252 Holikachuk Northern holi1241 Upper Tanaina Tutchone Degexit'an Kuskokwim tana1289 Upper Tanana nort2941 dege1248 uppe1438 uppe1437 Ahtna ahte1237 Chipewyan Central Southern chip1261 Alaskan Yupik Eyak Tutchone cent2127 eyak1241 sout2957 Tagish Kaska tagi1240 kask1239 Slavey sout2959

Tlingit Swampy tlin1245 Sekani Plains Cree swam1239 seka1250 plai1258 Pacific Gulf Yupik Tahltan paci1278 tahl1239

Beaver beav1236 1 nask1242 Nass-Gitksan wood1236 2 gitx1241

Babine 3 babi1235 4 Stoney 5 Carrier ston1242 Severn carr1249 Ojibwa seve1240 Sarcee Bella Coola Central 1 Tsetsaut tset1236 sars1236 bell1243 Southern Ojibwa 2 Aleut aleu1260 Chilcotin Shuswap nort1552 3 Coast coas1300 chip1241 Montagnais 6 chil1280 shus1248 4 Haida haid1248 mont1268 5 Haisla hais1244 6 - heil1246 7 Siksika 7 Kwak'wala kwak1269 siks1238 8 Comox como1259 8 Lillooet Gros Ventre Salteaux 9 Sechelt sech1246 lill1248 gros1243 west1510 10 Squamish squa1248 27 Nuu-chah-nulth nuuc1236 9 Kootenai 11 Pentlach pent1242 28 Ditidaht diti1235 10 Nicola kute1249 Assiniboine 12 Nooksack nook1247 11 Okanagan 29 Makah maka1318 27 87 NA assi1247 13 Twana twan1247 30 Chemakum chim1310 okan1243 31 Clallam clal1241 28 12 Micmac 32 Quileute quil1240 mikm1235 33 Quinault quin1251 88 34 Lower Chehalis lowe1427 29 Columbia-Wenatchi 35 Upper Chehalis uppe1439 31 30 32 colu1241 Atikamekw 36 chin1286 13 Spokane Coeur Kalispel Malicite-Passamaquoddy 33 Hidatsa atik1240 37 Cowlitz cowl1242 spok1245 d' Alene kali1309 38 Clatskanie-Kwalhioqua NA lush1252 34 Lushootseed coeu1236 hida1246 Algonquin 39 Tillamook till1254 35 40 Kathlamet kath1253 89 Crow Mandan Eastern algo1255 male1292 41 Alsea alse1251 36 37 crow1244 mand1446 Ojibwa 42 Siuslaw sius1254 yaki1237 43 Coos coos1249 38 Lakota east2542 Cayuse 44 Upper Umpqua uppe1436 39 40 cayu1241 nezp1238 lako1247 Arikara Menominee 45 Galice gali1261 90 Eastern arik1262 Dakota meno1252 Abenaki 46 Shasta shas1239 Tenino dako1258 91 Laurentian east2544 teni1238 Potawatomi 41 laur1250

pota1247 Ottawa Huron Western kala1400 14 Rogue River Athabaskan tolo1259 42 Kalapuya otta1242 wyan1247 Abenaki 15 Karuk karo1304 Molala Cheyenne

mola1238 Ho-Chunk west2630 16 Yurok yuro1248 chey1247 Omaha-Ponca 43 44 hoch1243 Neutral 17 Konomihu kono1241 omah1247 Fox 18 Hupa hupa1239 Iroquoian 226 Kickapoo mesk1242 225 Mohawk 19 Wiyot wiyo1248 73 nort2947 227 Oneida moha1258 kick1244 228 onei1249 20 Yana yana1271 45 74 96 Mahican 21 Mattole matt1238 14 Shoshoni Erie mahi1248 237 22 Eel River Athabaskan wail1244 Northern nort2947 Munsee 238 23 Kato kato1244 46 Paiute shos1248 15 Delaware 239 24 Northern Yukian yuki1243 nort2954 240 25 Northern Pomo nort2966 16 17 Achumawi muns1251 Pawnee Susquehannock 26 Central Pomo cent2138 18 achu1247 Arapaho Iowa-Otoe wint1259Wintu pawn1254 susq1241 Unami 19 75 arap1274 iowa1245 Delaware 21 22 20 Miami 87 halk1245 92 nucl1653 225 Wenro wenr1236 unam1242 88 Northern Straits stra1244 226 Onondaga onon1246 23 24 47 Shawnee 89 -Walla Walla wall1238 76 227 Seneca sene1264 25 48 Kansa shaw1249 Ofo 228 Cayuga cayu1261 90 Wasco wasc1239 patw1250Patwin 91 Umatilla umat1237 49 Nisenan kans1243 ofo1242 229 Nanticoke-Conoy nant1249 92 nort2952 26 nise1244 230 Powhatan powh1243 229 93 Taos nort1550 50 77 Nawathinehena 231 Nottoway nott1246 Rio Grande Tewa tewa1260 78 Ute-Southern nawa1259 94 51 79 237 Loup loup1243 95 -Walapai- hava1248 52 Paiute 238 Wampanoag wamp1249 Mojave moha1256 80 Tutelo-Saponi 47 Konkow nort2951 Mono utee1238 239 Quiripi wamp1250 81 tute1247 230 240 Mohegan-Pequot-Narragansett mohe1244 48 Northeastern Pomo nort2967 53 mono1275 49 Eastern Pomo east2545 241 Meherrin mehe1242 Northern Kiowa Osage Cherokee 231 50 Southern Pomo sout2984 59 Panamint 242 Carolina Algonquian caro1243 51 Coast coas1301 yoku1256Yokuts pana1305 Navajo 93 kiow1266 osag1243 cher1273 243 Woccon wocc1242 Jicarilla 241 242 52 Karkin kark1259 55 54 nava1243 Yuchi 232 Hopi 94 jica1244 58 82 83 160 yuch1247 84 hopi1249 60 Quapaw Tuscarora 56 61 Kawaiisu Zuni 85 tusc1257243 53 Northern Ohlone nort2969 62 kawa1283 95 zuni1245 86 160 Kiowa kiow1264 quap1242 Catawba 97 54 Chalon chal1270 Serrano 96 161 Tawasa timu1245 Chickasaw cata1286 55 Esselen esse1238 64 63 162 Pakana NA Koasati 67 65 66 serr1255 Piro chic1270 56 Obispeño obis1242 Western piro1248 163 NA koas1236 57 Island Chumash cruz1243 57 68 Apache 164 Houma NA 58 Salinan sali1253 Cahuilla Wichita 165 Biloxi bilo1248 west2615 Creek 59 Southern Ohlone sout2986 69 cahu1264 wich1260 161 98 coma1245 cree1270 60 Tule-Kaweah Yokuts tule1245 70 Tipai 71 61 Buena Vista Yokuts buen1244 cadd1256 Tunica 162 233 62 Interior Chumash cent2139 tipa1240 Alabama Tohono tuni1252 63 Ineseño ines1240 99 Mescalero-Chiricahua alab1237 234 64 Purismeño puri1259 O'odham 72 Akimel mesc1238 Mikasuki 65 Ventureño vent1242 Kitsai adai1235 toho1245 O'odham mika1239 232 Chisca NA Adai Natchez 66 serr1254 Jumano kits1249 163 choc1276 235 67 Barbareño barb1263 akim1239 natc1249 233 Cusabo NA juma1248 234 Yamasee yama1265 68 tong1329 Kiliwa Pensacola 69 Luiseño luis1253 189 164 235 Guale gual1239 kili1268 190 165 NA 236 Guazoco-Tocaste NA 70 Ipai ipai1239 Concho 191 192 Chacato 71 Quechan quec1382 Timucua Opata conc1237 NA 193 72 Paipai paip1241 97 Acoma west2632 tonk1249 timu1245 seri1257Seri 100 atak1252 98 Maricopa mari1440 101 opat1246 Lipan chit1248 99 Cocopa coco1261 lipa1241 236 102 100 Eudeve eude1234 189 Eyeish NA 101 Pima Bajo pima1248 194 Toboso Solano 195 190 bida1238 191 Opelousa NA NA sanf1266 213 192 Bayogoula NA Ais 73 Takelma take1257 Cochimi Tarahumara 193 Apalachee apal1237 212 Alafay NA 212 coch1272 103 tara1322 NA 74 Klamath-Modoc klam1254 104 105 106 194 Karankawa kara1289 213 Tocobaga NA 75 Atsugewi atsu1245 Coahuilteco Carizo 195 Aranama aran1265 214 Jaega NA Cotoname 76 Washo wash1253 Mayo Lagunero coah1252 196 Quinigua quin1252 215 Tequesta NA mayo1264 107 NA Calusa 77 Wappo wapp1239 Southern NA coto1248 216 Mískito misk1235 78 Northern Sierra Miwok nort2968 217 Dorasque dora1242 calu1239 214 79 Plains Miwok plai1259 Tepehuan 218 Ngäbere ngab1239 sout2977 80 cent2140 108 219 Buglere bugl1243 81 Southern Sierra Miwok sout2985 215 82 Palewyami Yokuts pale1254 Comecrudan 109 come1251 83 Towa jeme1245 Guaicura Acaxee 196 84 Tubatulabal tuba1278 guai1237 cahi1243 85 Eastern Keres east1472 102 Jova jova1234 86 Southern Tiwa sout2961 Guachichil 103 Guarijio huar1255 110 104 Yaqui yaqu1251 NA 105 Northern Tepehuan nort2959 Xixime Zacateco 155 Maratino mara1266 106 Temori NA NA 156 Naolan naol1234 107 Huite opat1247 111 NA 157 Northern Puebla nort2957 108 Comanito NA 112 155 158 Totonac toto1252 109 Guasave NA 156 159 Eastern Highland Otomi east2556

110 Tahue NA 113 Huastec 166 Tepehua tepe1243 111 Pericú peri1250 huas1242 167 Mezquital Otomi mezq1235 112 Totorame NA 116 168 Ixtenco Otomi ixte1237 113 Cora cora1260 117 Pame 169 Northern Popolocan popo1294 114 Eastern Durango Nahuatl east2695 118 nort2984 157 170 Northern Alta Mixtec nort3199 115 Western Durango Nahuatl west2778 171 Highland Zoque zoqu1261 119 114 Tekwexe 166 NA 172 Sayula sayu1241 120 158 173 Chinantecan chin1484 167 159 115 122 121 174 olut1240 123 175 Texistepec Popoluca texi1237 Yucatec Maya Purepecha 125 124 168 Eastern Nahuatl 176 Tzeltal tzel1254 yuca1254 126 pure1242 127 177 Tzotzil tzot1259 128 east2720 178 Zoque chia1261 129 Central 116 Kaskan NA 169 117 Tepecano tepe1278 Nahuatl 135 136 118 Huichol huic1243 130 cent2258 119 Tecoxquin NA 131 170 171 Tabasco Chontal 132 172 120 Coca NA 137 138 139 173 taba1266 121 Querétaro Otomi quer1236 134 133 140 174 122 Pinome NA 148 Itzá 141 175 Chol 123 Central Mazahua cent2144 143 142 itza1241 124 Tilapa Otomi tila1239 145 144 151 176 chol1282 125 San Francisco Matlatzinca sanf1262 178 177 146 154 126 Jalisco Otomi NA 147 149 153 180 179 127 Otomian otom1297 152 200 Fig. 1: Languages of North America 128 Michoacán Nahuatl mich1245 150 181 182 183 129 Atzingo Matlatzinca atzi1235 135 Western Popolocan popo1294 184 201 197 216 130 Chumbia NA 136 Eastern Popolocan popo1294 185 202 Tol 131 Cacaopera caca1247 203 198 137 Northern Baja Mixtec nort3200 186 204 199 tolo1259 Pech Ulwa 132 Tolimeco NA 138 Chochotec choc1279 187 pech1241 179 Tojolabal tojo1241 ulwa1239 133 Coatepec Nahuatl coat1240 139 Cuicatec cuic1234 180 Chiapanec chia1262 188 205 206 134 Cuitlatec cuit1236 140 Central Baja Mixtec cent2266 181 Chuj chuj1250 207 Pipil 208 141 Trique triq1251 182 Q'anjob'al qanj1241 pipi1250 142 Central Core Zapotec cent2146 183 Chicomuceltec chic1271 143 Mephaa meph1234 184 Ixil ixil1251 Matagalpa 144 Western Zapotec west2645 185 Tektiteko tekt1235 mata1288 145 Amuzgoan amuz1254 186 Mam mamm1241 197 Cholti chol1283 Time of European Contact 198 Chortí chor1273 209 146 Southern Baja Mixtec sout3179 187 Tapachultec tapa1260 147 Chatino chat1268 188 Tz'utujil tzut1248 199 Poqom poco1241 210 148 Northern Core Zapotec nort2987 200 Mopán Maya mopa1243 149 Southern Core Zapotec sout3003 201 Kekchí kekc1242 150 Pochutec poch1244 202 Sipacapense sipa1247 203 Uspanteco uspa1245 151 Oaxaca Mixe oaxa1241 220 Maléku Jaíka male1297 152 Lowland Oaxaca Chontal lowl1260 204 K'iche' kich1262 205 Kaqchikel kaqc1270 Rama 221 Cabécar cabe1245 153 Zapotec zapo1437 222 Bribri brib1243 154 Huavean huav1256 206 Lenca Salvador lenc1243 rama1270 207 Xinca xinc1247 223 Kuna sanb1242 224 Boruca boru1252 208 Lenca Honduras lenc1242 220 209 Subtiaba subt1250 221 210 Mangue moni1237 222 211 Teribe teri1250 211 223 217 224 218 219 Bibliography

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